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• Published: 21 September 2020

Increase in domestic electricity consumption from particulate air pollution

• Pan He   ORCID: orcid.org/0000-0003-1088-6290 1 , 2   na1 ,
• Jing Liang 3   na1 ,
• Yueming (Lucy) Qiu   ORCID: orcid.org/0000-0001-9233-4996 3   na1 ,
• Qingran Li 4 &
• Bo Xing 5  

Nature Energy volume  5 ,  pages 985–995 ( 2020 ) Cite this article

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• Energy and behaviour
• Environmental impact

Accurate assessment of environmental externalities of particulate air pollution is crucial to the design and evaluation of environmental policies. Current evaluations mainly focus on direct damages resulting from exposure, missing indirect co-damages that occur through interactions among the externalities, human behaviours and technologies. Our study provides an empirical assessment of such co-damages using customer-level daily and hourly electricity data of a large sample of residential and commercial consumers in Arizona, United States. We use an instrumental variable panel regression approach and find that particulate matter air pollution increases electricity consumption in residential buildings as well as in retail and recreation service industries. Air pollution also reduces the actual electricity generated by distributed-solar panels. Lower-income and minority ethnic groups are disproportionally impacted by air pollution and pay higher electricity bills associated with pollution avoidance, stressing the importance of incorporating the consideration of environmental justice in energy policy-making.

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Air pollution has resulted in many types of negative externalities, a situation that calls for policy intervention to address the associated damages. Policymakers and research are widely concerned with increases in mortality risk, which are direct damages induced by pollution, as well as co-damages in terms of other welfare losses. These damages are generated via different channels including the physical and mental health impact on human beings, decreases in labour productivity 1 , 2 , the decline in subjective well-being 3 , the harm of cognitive competence 4 , 5 , disturbances in ecosystem health 6 , a diminished value of local environmental amenities and properties 7 , increases in household medical expenditure and so on. Accurate assessment of such externalities is crucial to estimating the social cost of pollution for the design and evaluation of policies such as a Pigouvian tax imposed on polluters for such external costs or for pollution control, or a cap-and-trade programme that establishes a market issuing allowances to internalize such cost 8 . While direct pollution damages are often measured in existing studies, there are not many discussions in the literature about the magnitude of the co-damages. A key challenge to quantify these co-damages, however, is to understand the interactions among pollution, human behaviours 9 and technologies. People can mitigate exposure to environmental risks by taking various avoidance behaviours, such as adjusting outdoor activities 10 , 11 and purchasing face masks and air purification systems in the short term 9 , 12 , and migrating to new living locations in the longer term 13 . Avoidance behaviours alleviate the negative health impact of pollution 14 but come at a cost, for example, spending less time doing outdoor activities 10 , 15 , and may lead to further impacts such as increased energy consumption due to a shift from natural to mechanical ventilation 16 , and increased need for heating or air-conditioning or for other activities such as watching television 17 , 18 in residential buildings. Commercial buildings may also be affected via further complexities if individuals choose to work remotely due to air pollution to avoid exposure during commuting 19 . However, commercial buildings might have better indoor air quality due to better ventilation 20 so that people can stay in commercial buildings for longer period of time. These two effects can cancel out, and thus we hypothesize that air pollution does not have a statistically significant impact on commercial buildings as a whole. Such effects and the consequential extra environmental damage are, however, hardly addressed explicitly and quantitatively in current studies, which leads to biases in damage evaluation. Our paper fills in this gap in the literature.

While electricity demand is driven up by pollution-averting behaviours, air pollution can also reduce electricity supply. High concentrations of particulate matter reduce solar electricity generation due to the changed solar irradiance. The emission of aerosols can attenuate solar radiation by scattering and absorbing sunlight before it reaches the solar panel 21 , and thus reduces photovoltaic (PV) performance 22 , 23 . Large particles in particulate matter can also generate dust on top of solar panels. In areas with severe air pollution such as China, the potential of solar PV generation decreased on average by 11–15% between 1960 and 2015 (ref. 22 ); the decrease of point-of-array irradiance can even reach 35% in the most polluted areas 23 . Such interaction adds another dimension to the complexity of assessing pollution externalities. Existing studies take a predominantly engineering perspective that relies on computer simulations to calculate the change in solar irradiance due to air pollution or on field experiments to measure the changes in electricity generation of a few solar panels in response to air pollution. While providing critical estimation on the relationship between particulate pollution and solar electricity generation in certain refined meteorological and geographical conditions, these studies fall short in evaluating how much actual solar generation is affected at a large scale. Our paper contributes on empirical grounds and serves as a crucial reference for policy-making.

As pollution co-damages are closely related to both demand-side human behaviours and supply-side solar power generation, the distribution of these co-damages raises environmental justice concerns. Lower-income households or minority ethnic groups can be more vulnerable to the impact of air pollution. Individuals from these groups usually reside in locations with higher air pollution levels 24 . Moreover, they may live in affordable housing and buildings that are old, not well insulated and equipped with fewer energy-efficient appliances, all of which leads to higher energy-related expenditures 25 , 26 . Increased electricity bills due to more indoor hours, therefore, translates into a larger proportion of the household income for these groups, compared to their higher-income or non-minority counterparts. This constrains other essential expenditures such as on medical services by these lower-income and minority households, thus leading to further adverse health impacts 27 . Our analyses incorporate the equity aspects of pollution co-damages to provide necessary implications for policy design towards environmental justice.

This article demonstrates how the interactions among air pollution, human defensive behaviour and the energy supply system can influence the estimates of negative externalities caused by air pollution. Using consumer-level daily and hourly electricity consumption data and solar panel generation records in the city of Phoenix, Arizona, during the period 2013–2018, we show how particulate air pollution, indicated by concentrations of both PM10 and PM2.5 (that is, particulate matter 10 micrometres or less in diameter, and 2.5 micrometres or less, respectively), triggers consumer avoidance behaviours as well as lowers the generation of solar energy. Our sample covers 4,313 residential buildings and 17,422 commercial buildings. A variety of demographic and socio-economic characteristics are associated with the consumer dataset, based on which we further explore the heterogeneity of the co-damages associated with income and ethnicity. Estimates can be biased by endogeneity issues due to reverse causality (that is, air pollution induces changes in energy consumption as well as solar electricity generation, which in turn also affects the air quality) and missing variables (for example, unobservable characteristics of the local economy and physical environment can affect the air quality and energy consumption simultaneously). To address the endogenous biases, we use wind direction as an instrumental variable (IV) for pollution concentration. This IV has a direct impact on concentrations of pollutants but not on energy consumption, which creates variation in air quality that is exogenous to consumption, thus leading to an unbiased estimation of the pollutant coefficient. Our main results are based on daily average data. We also analyse hourly data to examine the intra-day heterogeneity in the impact on electricity usage. The study area of our analysis is the fifth-most populated city in the United States 28 and ranks among the top five most-polluted cities in the country 29 . This suggests that, even though they are based on a region of a developed country, our results can provide valuable insights and benchmark statistics when compared with studies in developing countries, with dense populations and low-ranked air quality.

Effect of air pollution on the demand sectors

Through an IV fixed-effects panel regression, we regress the individual household’s daily electricity consumption on air pollution level, while controlling for other confounding variables. Detailed modelling can be found in the Methods section. The validity of the IV estimation is also supported by the first-stage regression, which shows a significant positive correlation between the daily average cosine of the prevailing hourly wind direction angle and the concentration of air pollution, meaning that wind in the upwind direction of pollution sources would bring higher particulate concentration (columns 1 and 3 in Table 1 ). The considerable F statistics of far more than ten for testing the statistical significance of the excluded instrument indicate a strong IV in both the regressions for PM10 and PM2.5. We find that a higher concentration of particulate pollutant results in a statistically significant increase in residential electricity consumption. An increase of 1 µg m –3 in PM10 concentration raises the daily residential electricity consumption by 0.020 kW h (column 2 in Table 1 ). Residents are more sensitive to a change in PM2.5 concentration, as a 1 µg m –3 rise in PM2.5 concentration causes an 0.145 kW h (column 4 in Table 1 ) increase in daily electricity consumption. In this way, one more standard deviation of PM10 and PM2.5 would increase the daily residential electricity consumption by 0.85% and 1.74%, respectively, from the mean, based on the descriptive statistics in Supplementary Table 1 . Such effects are also seasonally heterogeneous (Supplementary Table 12 ) as the increased electricity consumption has a larger magnitude in the peak of summer (July and August), while the significance diminishes during the winter (November to April).

To validate our hypothesis that the increased electricity consumption is caused by averting behaviours that shift outdoor activities indoors, we next examine the pollution–kilowatt-hour relationship on an hourly basis. Results using hourly data confirm that air pollution increases residential electricity consumption and imply a possible reallocation of time due to air pollution. As shown in Fig. 1 , residential electricity consumption increases considerably during the daytime but decreases slightly during evenings when affected by air pollution. While both are statistically significant, the summed change (the area above the horizontal line of zero minus the area below) still shows an overall increase of daily electricity consumption aligning with the findings based on Table 1 . This possibly indicates a change of activities during the day: as air quality deteriorates, residents tend to participate in indoor energy-dependent activities such as watching television and turning on the heating or cooling system. They may also move activities usually conducted in the evenings, for example, doing the laundry, ahead to the daytime, so electricity consumption during the night-time drops. The drop in consumption during the evening might also be due to the effect of pre-cooling or pre-heating, from turning on the heating or cooling system during the daytime.

The coloured dots show the changes in hourly electricity consumption, obtained from panel regression at the hourly level. The coloured vertical lines show the 95% confidence intervals. As the information on hourly electricity price is available only for a small part of the residential and commercial samples, we conduct the analysis both with and without the regressor of price as a control variable.

Source data

To further support our findings, we test whether individuals tend to reduce outdoor trips, using a daily county-level dataset of mobility nationwide in the United States (details are included in the Methods ). As shown in Supplementary Table 13 , the number of trips per person decreases as the concentration of air pollution increases, implying that people are staying home for more hours due to air pollution.

We next discern the effects of air pollution among residential consumers with different socio-economic characteristics. The potential heterogeneous effects can be caused by different aspects of environmental injustice. On the one hand, as consumers of disadvantaged socio-economic status can be exposed to higher levels of pollution 8 and can live in houses that are less energy efficient 25 , 26 , their pollution-induced increase in electricity demand can be larger than that of their advantaged counterparts. On the other hand, their ability to self-protect against air pollution is likely to be restricted by their limited disposable income, or they may be simply less attentive to air pollution. If the effect of these constraints dominates their behavioural responses to pollution, then we may observe a smaller change in electricity demand for disadvantaged households. As a result, whether and how the effect of air pollution on electricity consumption differs across socio-economic status becomes an empirical question. Our summary statistics show that lower-income and non-white consumers are associated with higher particulate matter (PM) concentrations and lower baseline electricity consumption (Supplementary Table 2 ), implying a possible heterogeneous effect. Thus, we test such heterogeneity for different income and ethnic groups. Using the available data on household characteristics, the sample is divided into three levels of per capita income: low, medium and high (see Methods for details). The sample is also divided into four ethnic groups (White, Asian, Hispanic and other) to conduct the regression analysis separately.

The results show that lower-income and Hispanic consumers have a larger increase in electricity consumption in response to a unit increase in PM pollution. The IV estimates in Fig. 2 illustrate that the marginal effect of pollution on electricity demand is the highest for the low-income group. For ethnic groups, Hispanic consumers increase their electricity consumption more than white consumers. The empirical estimates for heterogeneous groups imply that the effect of low energy efficiency and high exposure possibly overrides the constraint of disposable income. By contrast, a previous study found that higher-income consumers need to use more energy in response to changing weather conditions in China 30 . Existing studies have found that lower-income consumers tend to live in homes that are not energy efficient 25 , 26 , which can lead to a higher increase in electricity consumption due to air pollution. Two studies 31 , 32 find that Hispanic households have higher energy use intensity due to residing in less energy-efficient homes. These findings of Hispanic households help justify our results because when air pollution increases and people need to spend more time indoors, inefficient homes (such as Hispanic homes) will increase their electricity consumption more compared to an efficient home. The medium-income group shows less of an electricity increase compared to both the low-income and high-income groups, which might result from low-income households having inefficient homes 25 , 26 and high-income households needing more energy in response to changing weather conditions 30 . The socio-economic heterogeneity embedded in air pollution issues requires more subtle investigations and tests given the multiple mechanisms that can balance the effects of each other. We also reran the model separately for each residential building to get the unique estimated impact for the individual consumer. The results show similar heterogeneity. As shown in Supplementary Fig. 1 , air pollution demonstrates a different marginal effect for each building, and the summary statistics in Supplementary Table 5 show a similar pattern as that observed in Fig. 2 .

Results are based on IV methods. The solid dots represent the values of the coefficients that measure the change in daily electricity consumption in response to a 1 µg m –3 increase in PM concentration. The vertical lines represent 95% confidence intervals.

Our results show that contrary to findings in the residential sector, electricity usage in commercial buildings as a whole sample is not significantly affected by air pollution in general, although the usage in individual industries shows statistically significant changes. The results in Table 2 show that although the IV is still valid and strong (the coefficients of Wind cosine are positively significant in the first-stage results in columns 1 and 3, and the F statistics are considerable), IV estimates indicate no statistically significant effects (columns 2 and 4 in Table 2 ). In this way, the hypothesis that particulate pollution has no effect on energy use in commercial buildings as a whole cannot be rejected. We then examine if the hourly estimates could imply any indoor–outdoor activity shifts. There is not sufficient evidence to show that air pollution affects electricity usage in commercial buildings (Fig. 3 ). Although the results show a similar pattern of electricity consumption in commercial buildings as in residential buildings, the coefficients of hourly pollution concentrations are barely statistically significant.

The coloured dots show the changes in hourly electricity consumption, obtained from panel regression at the hourly level. The coloured vertical lines show the 95% confidence intervals.

Such an insignificant effect on commercial buildings overall is likely a result of mixed effects by air pollution that cancel each other out. On one hand, when estimating the micro-environment exposure, incorporating work activities will induce higher exposure to air pollution compared to home-only activities, partially due to higher pollution exposure during transit or commuting 19 . This implies that workers have the incentive to stay at home or to work from home to avoid a higher average pollution exposure, which lowers the energy consumption of the commercial buildings. We further test this hypothesis by our analysis of the effect of air pollution on personal trips. With a daily county-level dataset of mobility nationwide in the United States, we test whether individuals tend to reduce outdoor trips (details are included in the Methods ). As shown in Supplementary Table 13 , the number of trips per person decreases as the concentration of air pollution increases. The same conclusion holds for both work trips (Supplementary Table 14 ) and non-work trips (Supplementary Table 15 ). On the other hand, commercial buildings on average might have a different building envelope or better building management system 20 that can lead to a better indoor environment 33 compared to residential buildings, so that when ambient air pollution increases, some people might want to stay inside commercial buildings for a longer period time, potentially increasing electricity in these buildings. Building occupants may also use less natural ventilation in polluted weather, and thus can increase the energy consumption of buildings due to increased mechanical ventilation 16 . These effects may cancel out so that we are not observing a statistically significant effect of air pollution on average for all commercial buildings in our sample.

The insignificant effect of air pollution on commercial buildings as a whole actually validates our residential electricity consumption result. There could be a concern that our regression model still fails to capture some physical relationship between electricity consumption and other unmeasured meteorological variables, which correlates with air pollution. Or there could be a concern about an incorrectly specified functional form (an incorrect description of the relationship between our independent and dependent variables). As a result, the positive impact of air pollution on residential electricity consumption could be purely due to these physical relationships, and not due to consumer behavioural change. The insignificant result in the commercial sector actually implies that our regression model can capture those physical relationships well, so that our estimated increase in residential electricity consumption is indeed due to consumers’ behavioural changes.

Such statistically insignificant results of commercial buildings can, however, conceal the sectoral heterogeneity as air pollution can substantially affect the commercial sectors that are closely related to indoor activities. Due to the nature of different industries, each commercial building serves a specific purpose, with some sectors more likely to be affected by air pollution. Sectors such as retail trade, recreation and service can have increased electricity consumption where more of their customers spend more time inside the buildings to avoid being exposed to outdoor pollution. Thus, we separate the effect by sector as shown in Fig. 4 . With a similar averaged pollution concentration across all sectors (Supplementary Table 4 ), the retail sector responds most intensely to an increase in air pollution concentration (0.086 kW h increase in electricity consumption per µg m –3 increase of PM10 concentration, and 0.560 kW h increase per µg m –3 increase of PM2.5 concentration), followed by the recreation and service sector (0.026 kW h per µg m –3 and 0.167 kW h per µg m –3 , respectively). By contrast, the other sectors reduce their electricity consumption, also as expected (0.028 kW h per µg m –3 and 0.178 kW h per µg m –3 for PM10 and PM2.5, respectively; both significant at a 90% confidence level). As a result, one standard deviation increase of PM10 and PM2.5 would lead to a 1.82% and 3.34% increase, respectively, in the retail trading sector; 1.13% and 2.00% increase, respectively, in the recreation and service sector; and 0.79% and 1.39% reduction, respectively, of electricity consumption in the other sectors. These effects with opposite directions in different sectors balance each other out when summed, and thus lead to an insignificant change in energy consumption for the whole sample. Taken together with our analysis above, these results show that individuals are more likely to reduce outdoor trips in general and particularly those related to work. However, the final destinations for the remaining trips may shift at least partially from open spaces to sheltered areas, and thus lead to more energy consumption in malls, recreation centres and so on. This distributional result stresses the importance of looking into sectoral nuance based on understandings of how consumer behaviours differ by industry as a response to varying air quality.

Effect of air pollution on the supply sector

We then used a similar panel IV regression to regress individual consumers’ daily solar electricity generation on air pollution level, while controlling for confounding variables ( Methods ). The IV of wind direction again proves powerful in explaining the variation of PM10 and PM2.5 with its positive significance in columns 1 and 3 in both Tables 3 and 4 , while the F statistics continue to verify it as strong. We find that particulate pollution also reduces the electricity generation of distributed-solar panels in both residential and commercial buildings. IV estimation shows that a 1 µg m –3 increase in PM10 concentration significantly reduces the electricity generated by solar panels by 0.435 kW h in residential buildings (column 2 in Table 3 ) and by 0.022 kW h in commercial buildings (column 2 in Table 4 ). PM2.5 has an even larger effect—a 1.888 kW h reduction per µg m –3 increase for residential buildings (column 4 in Table 3 ) and 0.093 kW h reduction per µg m –3 increase for commercial buildings (column 4 in Table 4 ). In terms of percentage change, one standard deviation increase of PM10 and PM2.5 would result in a 25.01% and 30.64% reduction, respectively, of solar electricity generation for residential buildings with solar panels from the mean solar electricity generation, and 0.13% and 0.15% reduction, respectively, for commercial buildings. The comparison also indicates that commercial buildings are much less affected if considering that the power of solar panels is on average larger in commercial buildings, referring to the descriptive statistics in Supplementary Tables 1 and 3 . A possible reason is that the solar panels in commercial buildings are better maintained, with dust cleaned off them in a timely manner.

This study explores the co-damage of air quality degradation via human defensive behaviour on the demand side and the performance of clean energy techniques on the supply side, respectively. Our results show that particulate pollution, while exposing individuals to health risks with direct emissions, can further add to their loss with regenerative feedback, which boosts energy consumption due to longer times spent indoors and the downgraded performance of solar panels. While previous studies predominantly focus on the positive consequences of the defensive behaviours in alleviating health impacts 10 , 12 , this research shows the possible pathways in which air pollution generates extra damage by interacting with such defensive behaviours 9 . Our analysis also shows that residents from low-income or Hispanic groups are more heavily affected, highlighting the vulnerability of those of specific socio-economic status in responding to environmental change and the potential environmental justice issues that should be addressed by policy design 24 , 27 .

Several limitations should be noted. First, our analysis addresses the situation in the city of Phoenix, Arizona. In spite of its top rank for air pollution levels in US cities, the concentration of PM is still far less than that in many developing countries such as Mexico or China 34 , 35 . Meanwhile, response levels can also differ due to cultural differences. Therefore, our results should be extrapolated with caution. In addition, our dataset lacks information on specific household end-use activities (for example, heating and cooling, or air purification). Thus, we are not able to pinpoint exactly what appliance or appliances are more intensively used against higher particulate concentrations, for further details on the mechanisms that we discuss. We leave these for future research that draws on high-resolution data in various geographical areas.

Several critical policy implications stem from the findings of this research. First, when calculating the marginal damage factors from air pollution, policymakers need to explicitly consider the co-damages generated from the feedback of consumer behaviours and clean technology performance, which is insufficiently discussed in the current literature, as well as policy analysis and evaluation. Lack of consideration of these pollution co-damages will lead to an under-estimation of the welfare gains from pollution control policies. Our results also stress the necessity to investigate comprehensively the consequences of air quality alerting systems, for example, alleviated health risks 36 , changed automobile traffic flows as individuals endeavour to escape from pollution as a response 37 , decreased outdoor recreation 10 , 38 and so on. Second, the fact that air pollution disproportionally affects those of low socio-economic status threatens energy and environmental justice, and again stresses that air pollution control can not only result in health benefits as a whole, but also contribute to an equitable distribution of such benefit. The disproportional impact also highlights the importance of energy policies that can improve the home energy efficiency of lower-income and ethnic minority groups to accelerate the achievement of fairness and equity. Third, our findings provide one more justification for the need to clean the electricity grid and improve the efficiency of renewable energy generation techniques. In addition, the expansion of solar power should consider the effect of air pollution when setting reasonable development targets. The results comparing the impacts on commercial PV units and residential PV units suggest that there should be clear messages or incentives to communicate the importance of the cleaning and maintenance of PV units to residential consumers.

The data were provided by Salt River Project, one of the two largest utility companies in Arizona. Hourly electricity consumption in kilowatt-hours was available for 4,313 residential units (spanning from May 2013 to April 2017) and 17,422 commercial units (spanning from May 2013 to April 2018). For the residential units in the sample, a Residential Equipment and Technology Survey was also conducted in 2014, which asked about detailed sociodemographic information, building characteristics, appliance and other energy technology attributes, and energy consumption behaviours. For the commercial units, a six-digit code in the North American Industry Classification System is available to identify the sector type of the building. We aggregate the electricity consumption to the daily level for analysis. The daily electricity price is constructed by taking the average of the hourly prices. For commercial consumers, both the electricity charge and demand charge are included as price variables. The zip code zone of each building is also available in the dataset, which enables a spatial match with the air quality and meteorological variables.

Salt River Project also has distributed-solar consumers in its service territory. These solar panels can be installed on the rooftop of buildings or can be ground-mounted. For each distributed-solar consumer, our dataset has information on the hourly electricity generated by the consumer’s solar panels, along with the installation dates of the solar panels. There are 260 residential distributed-solar consumers (6.03% of the residential sample) and 330 commercial distributed-solar consumers (1.89% of the commercial sample) in our dataset.

We combine meteorological observations from multiple sources. Records of air quality, including daily average concentrations of PM2.5 and PM10, are retrieved from pre-generated data files of the United States Environmental Protection Agency 39 . Climate factors including the daily average temperature, total precipitation and average wind speed are obtained from Global Surface Summary of the Day 40 . The hourly wind direction data come from the Environmental Protection Agency’s pre-generated data files. We obtain the solar irradiance data from the National Renewable Energy Laboratory’s National Solar Radiation Database 41 . For missing solar irradiance data for a given location in a given time period, we use the simulated solar irradiance by the National Renewable Energy Laboratory for a given day in that location in a typical meteorological year.

We adopt an inverse distance weighting interpolation that has commonly been used in previous literature 42 , 43 to match the air quality and meteorological records with the zip code zone of each building. First, the distance between each air quality monitoring station and the geometric centre of the zip code zone is calculated. Next, the daily records of all the stations less than 50 km away from the geometric centre are averaged together and weighted by their inversed distance to the centre. This weighted average is used as the matched air quality record for all the buildings within the zip code zone. The climate records are matched in a similar way. The inverse distance weighting is conducted in Stata 14.0 using the wtmean command with 34 meteorological stations and 67 air pollution monitoring stations. To test whether our analysis is sensitive to the radius of the inverse distance weighting procedure, we change the caliper to 10 km and 20 km and rerun the analysis, as shown in Supplementary Tables 16 – 19 (for 10 km) and Supplementary Tables 20 – 23 (for 20 km). The coefficients change only slightly in magnitude but their signs and statistical significance remain, indicating the robustness of our results.

Since datasets addressing individual travelling behaviour are rarely publicly available at the localized level for the study area, we resort to the COVID-19 Impact Analysis Platform by the University of Maryland 44 , 45 for a national-level exploration. Established for studies on COVID-19’s impact, this dataset includes the daily number of trips per person at the county level starting from 1 January 2020, which is further broken down into work and non-work trips. The information on trips comes from mobile device location data. Since the massive outbreak of COVID-19 in the United States took place no earlier than March, we adopt the records in January and February and match them with the air pollution and climate data from the above sources using a similar method.

Empirical strategies

We first estimate a generalized linear squared model on the panel dataset of residential and commercial units separately with the equation

where i indexes an individual residential or commercial consumer and t indexes the day of the sample. Elec_Con it refers to the daily electricity consumption of consumer i on day t . Pollution it is the daily average concentration of either PM10 or PM2.5. X it is a vector of control variables, including cooling degree days and heating degree days (estimated using daily average temperature), daily total precipitation, wind speed and electricity price (average daily electricity price for the residential consumers, and demand charge and energy charge for the commercial units). We also control for the concentration of ozone as another major pollutant that affects air quality and thus the outdoor activities of consumers. The variable α i is customer fixed effect, and it controls for the time-invariant attributes of the consumer such as square footage and the number of stories as well as environmental awareness of building occupants. The time fixed effects τ y and δ m include the year fixed effect and the month-of-year fixed effect. The time fixed effects capture the time-varying factors across years and seasons, such as economic development and change in local energy policies. Weekend and Holiday are dummy variables for holidays and weekends, respectively. The Holiday dummy is equal to one if the day belongs to the following US federal holidays: New Year’s Day, Martin Luther King Jr Day, Presidents’ Day, Memorial Day, Independence Day, Labor Day, Columbus Day, Veterans Day, Thanksgiving and Christmas. The ε i,t is the error term. Standard errors are clustered at the building level. We are interested in β 1 , which indicates the electricity use increase per µg m –3 increase of particulate concentration, ceteris paribus.

We analyse how the impact of air pollution differs by different income groups. Using the available data on household characteristics, the sample is divided into three levels of per capita income: low, medium and high. The division, provided by the Pew Research Center, is based on the minimum household income level of different household sizes varying from one to five (US$24,042/34,000/41,641/48,083/53,759 for middle income, and US$72,126/102,001/124,925/144,251/161,277 for upper income in 2014 (ref. 46 )). Since the household size is recorded as 1.5, 3.5 and 5 persons, we take an average of the two adjacent minimum household income levels for the 1.5- and 3.5-person households.

We test whether and by how much the particulate pollution affects solar energy generation with,

where Elec_Solar it refers to the daily electricity generated by solar for consumer i on day t , and the other terms are the same as in equation ( 1 ). The X it is modified to adapt to factors that can affect the power generation of solar panels, including climate factors that can affect the performance of solar power (temperature, precipitation, wind speed and surface albedo) and electricity prices, which can affect the motivation of consumers to actively maintain solar panels in good condition (consumers are encouraged to do so if the price is higher). The distributed-solar consumers in our sample were on a net-metering plan under which they could sell excessive solar electricity at retail electricity prices.

The naïve general least squares (GLS) estimation (results shown in Supplementary Tables 6 – 9 ) suffers from endogeneity issues due to reverse causality and missing variables 47 , 48 . As air pollution changes the behaviour patterns and increases the energy consumption of consumers, the latter can result in more electricity generation and thus pollution emissions. Meanwhile, if consumers spend more time indoors, the demand for vehicle travel may also decrease and lead to reduced emissions from transportation 48 . Omitting such pathways would lead to a biased estimation of the effect of air pollution. Besides, air quality and individual socio-economic activities can be jointly affected by the same factors, such as the local economy and physical environment 47 . Since all such factors cannot be observed in our datasets, these missing variables could bias the estimation.

To address these issues, we resort to using wind direction for an IV estimation. Its validity has been verified by multiple existing air pollution studies 47 , 49 , 50 . The idea is that wind direction affects regional air quality as it transports pollutants in specific directions. As the wind direction fluctuates on a daily or even hourly basis, it can convert the study area upwind or downwind of the pollution. Other than this pathway, wind direction (while controlling for wind speed) can hardly affect electricity consumption or solar electricity generation, and thus can meet the exclusive restriction for a valid IV.

We use the daily average cosine of the angle between the prevailing wind direction and the hourly wind direction as our IV following the previous studies 47 , 51 with modifications to adapt to our daily-level data. We first plot the distribution of the hourly wind direction of all the climate stations to obtain the prevailing wind direction, which turns out to be 180°. We then calculate the cosine of the angle between each hourly wind direction observation and this prevailing direction, and finally obtain the daily average for each climate station, which matches with different zip code zones. In this way, we can conduct the first-stage regression before running equation ( 1 ) or ( 2 ) as

where Wind_dir it indicates the daily wind direction variable, e it is the error term and other terms are the same as in equations ( 1 ) and ( 2 ). The coefficient γ 1 , after we run the first-stage model, is statistically significant with an F value larger than ten, implying that the IV is relevant and strong. We then use the predicted values of pollution from equation ( 3 ) in the second-stage model when we run equation ( 1 ) or ( 2 ).

It should be noted that the maximum value of the electricity consumption of the commercial buildings in our sample is extraordinarily large (Supplementary Table 3 ). However, there is no way for us to rule out the possibility that this value is reasonable given the decent variation of daily electricity consumption in the commercial buildings that this value belongs to. Therefore, we keep these potential outliers for the main analysis but also rerun the regressions, dropping commercial buildings with a maximum daily electricity consumption over 500 kW h and 1,000 kW h. The results provided in Supplementary Tables 10 and 11 show that our key results remain robust after the change. Also, about 10% of the buildings have a constant daily electricity consumption of zero in the raw data. We regard them as shut-down buildings and remove them from our sample.

We further test how air pollution affects residential and commercial electricity consumption at the hourly level. The identification is similar to equation ( 1 ) but using the matched hourly data of electricity use and air quality (lagged for one hour). The electricity consumption and solar electricity generation of one particular hour will not influence the air quality of the previous hour, and thus there is no reverse causality issue. In addition, such an immediate hourly reaction of building energy use will not lead to an immediate change (within the same hour) in local PM pollution levels for the following reason. The hourly change in building electricity consumption leads to an hourly change in electricity generated at power plants. The coal-fired power plants surrounding the Phoenix metropolitan area are all located at least 100 miles away. This implies that the transmission of the PM pollution from these power plants to Phoenix will take time (considering that the average wind speed in Arizona cities is less than 23 miles per hour and the average wind speed in our sample is 2.66 m s –1 or 6 miles per hour), and thus will not influence the local PM pollution within an hour. The notable hourly variation in local PM pollution (such as in morning hours and late afternoon hours) in Arizona mostly comes from other sources such as motor vehicles and road dust, instead of from power plants, based on the study by Clements et al. (ref. 52 ). As a result, the hourly change in building energy consumption will not alter local PM pollution in the Phoenix metropolitan area immediately.

To examine whether individuals stay at home instead of commuting to work on polluted days, we conduct a regression analysis on personal trips with,

where Trip jt indicates the trips per person in county j on day t ; π j and dow t denote the county and day-of-week fixed effects; and the other terms are similar to those in equations ( 1 ) and ( 2 ) but at the county level. On the basis of regressions using the total trips, we further test the effect of pollution concentration on the work and non-work trips. Due to a similar source of endogeneity, we are instrumenting the pollution using the wind direction with,

where Wind_dir jt indicates the daily wind direction variable for county j on day t , and the other terms are the same as in equation ( 4 ). We calculate the daily average cosine of the angle between the prevailing wind direction and the hourly wind direction as our IV in a similar way to that described above. The prevailing wind direction is retrieved from the median of the wind angle of each county during the study period.

Data availability

Records of air quality and hourly wind direction were retrieved from pre-generated data files of the United States Environmental Protection Agency at https://aqs.epa.gov/aqsweb/airdata/download_files.html . Climate factors were obtained from Global Surface Summary of the Day at ftp://ftp.ncdc.noaa.gov/pub/data/gsod/ . The solar irradiance data from the National Renewable Energy Laboratory’s National Solar Radiation Database is at https://maps.nrel.gov/nsrdb-viewer . The high-frequency electricity data are from the Salt River Project. As they are restricted by a non-disclosure agreement, they are available from the authors upon reasonable request and with permission from the SRP. The county-level trip data are available upon request from the COVID-19 Impact Analysis Platform of the University of Maryland at https://data.covid.umd.edu/about/index.html . Source data are provided with this paper.

Code availability

All data and models are processed in Stata 14.0. The figures are produced in R studio (based on R 3.6.1). All custom code is available on GitHub at https://github.com/hepannju/Increase-in-domestic-electricity-consumption-from-particulate-air-pollution .

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Acknowledgements

Funding for this research was provided by the National Science Foundation under grant no. 1757329. We thank J.H. Scofield, C. Canfield, Y. Li, H. Zhang and the seminar participants at the Center for Global Sustainability of University of Maryland, Division of Resource Economics and Management of University of West Virginia, and the Institute of Energy, Environment and Economy of Tsinghua University for their helpful comments during the preparation of this paper.

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These authors contributed equally: Pan He, Jing Liang, Yueming (Lucy) Qiu.

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Department of Earth System Science, Institute for Global Change Studies, Tsinghua University, Beijing, People’s Republic of China

School of Earth and Ocean Sciences, Cardiff University, Cardiff, UK

School of Public Policy, University of Maryland, College Park, MD, USA

Jing Liang & Yueming (Lucy) Qiu

Nicholas School of the Environment, Duke University, Durham, NC, USA

Department of Forecasting, Resource Planning and Development, Salt River Project, Tempe, AZ, USA

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All the authors conceived the paper and designed the research. P.H., J.L. and Y.Q. designed the analysis methods, performed the analyses and wrote and revised the paper. B.X. processed the data. Q.L. reviewed several draughts and made revisions.

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He, P., Liang, J., Qiu, Y.(. et al. Increase in domestic electricity consumption from particulate air pollution. Nat Energy 5 , 985–995 (2020). https://doi.org/10.1038/s41560-020-00699-0

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Electricity load forecasting: a systematic review

• Isaac Kofi Nti   ORCID: orcid.org/0000-0001-9257-4295 1 ,
• Moses Teimeh 2 ,
• Owusu Nyarko-Boateng   ORCID: orcid.org/0000-0003-0300-2469 3 &
• Adebayo Felix Adekoya   ORCID: orcid.org/0000-0002-5029-2393 3  

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The economic growth of every nation is highly related to its electricity infrastructure, network, and availability since electricity has become the central part of everyday life in this modern world. Hence, the global demand for electricity for residential and commercial purposes has seen an incredible increase. On the other side, electricity prices keep fluctuating over the past years and not mentioning the inadequacy in electricity generation to meet global demand. As a solution to this, numerous studies aimed at estimating future electrical energy demand for residential and commercial purposes to enable electricity generators, distributors, and suppliers to plan effectively ahead and promote energy conservation among the users. Notwithstanding, load forecasting is one of the major problems facing the power industry since the inception of electric power. The current study tried to undertake a systematic and critical review of about seventy-seven (77) relevant previous works reported in academic journals over nine years (2010–2020) in electricity demand forecasting. Specifically, attention was given to the following themes: (i) The forecasting algorithms used and their fitting ability in this field, (ii) the theories and factors affecting electricity consumption and the origin of research work, (iii) the relevant accuracy and error metrics applied in electricity load forecasting, and (iv) the forecasting period. The results revealed that 90% out of the top nine models used in electricity forecasting was artificial intelligence based, with artificial neural network (ANN) representing 28%. In this scope, ANN models were primarily used for short-term electricity forecasting where electrical energy consumption patterns are complicated. Concerning the accuracy metrics used, it was observed that root-mean-square error (RMSE) (38%) was the most used error metric among electricity forecasters, followed by mean absolute percentage error MAPE (35%). The study further revealed that 50% of electricity demand forecasting was based on weather and economic parameters, 8.33% on household lifestyle, 38.33% on historical energy consumption, and 3.33% on stock indices. Finally, we recap the challenges and opportunities for further research in electricity load forecasting locally and globally.

Electricity is the pivot in upholding highly technologically advanced industrialisation in every economy [ 1 , 2 , 3 ]. Almost every activity done in this modern era hinges on electricity. The demand and usage of electric energy increase globally as the years past [ 4 ]; however, the process of generating, transmitting, and distributing electrical energy remains complicated and costly. Hence, effective grid management is an essential role in reducing the cost of energy production and increased in generating the capacity to meet the growing demand in electric energy [ 5 ].

Accordingly, effective grid management involves proper load demand planning, adequate maintenance schedule for generating, transmission and distribution lines, and efficient load distribution through the supply lines. Therefore, an accurate load forecasting will go a long way to maximise the efficiency of the planning process in the power generation industries [ 5 , 6 ]. As a means to improve the accuracy of Electrical Energy Demand (EED) forecasting, several computational and statistical techniques have been applied to enhance forecast models [ 7 ].

EED forecasting techniques can be clustered into three (3), namely correlation, extrapolation, and a combination of both. The Extrapolation techniques (Trend analysis) involve fitting trend curves to primary historical data of electrical energy demand in a way to mirror the growth trend itself [ 7 , 8 ]. Here, the future value of electricity demand is obtained from estimating the trend curve function at the preferred future point. Despite its simplicity, its results are very realistic in some instances [ 8 ].

On the other hand, correlation techniques (End-use and Economic models) involve relating the system load to several economic and demographic factors [ 7 , 8 ]. Thus, the techniques ensure that the analysts capture the association existing between load increase patterns and other measurable factors. However, the disadvantage lies in the forecasting of economic and demographic factors, which is more complicated than the load forecast itself [ 7 , 8 ]. Usually, economic and demographic factors such as population, building permits, heating, employment, ventilation, air conditioning system information, weather data, building structure, and business are used in correlation techniques [ 7 , 8 , 9 ]. Nevertheless, some researchers group EED forecasting models into two, viz. data-driven (artificial intelligence) methods (same as the extrapolation techniques) and engineering methods (same as correlation the techniques) [ 9 ]. All the same, no single method is accepted scientifically superior in all situations.

Also, proper planning and useful applications of electric load forecasting require particular “forecasting intervals,” also referred to as “lead time”. Based on the lead time, load forecasting can be grouped into four (4), namely: very short-term load forecasting (VSTLF), short-term load forecasting (STLF), medium-term load forecasting (MTLF) and long-term load forecasting (LTLF) [ 6 , 7 , 10 ]. The VSTLF is applicable in real-time control, and its predicting period is within minutes to 1 h ahead. The STLF is for making forecasting within 1 h to 7 days or month ahead [ 11 ]. It is usually used for the day-to-day operations of the utility industry, such as scheduling the generation and transmission of electric energy. The MTLF is used for forecasting of fuel purchase, maintenance, utility assessments. Its forecasting period ranges from 1 week to 1 year. While the LTLF is for making forecasting beyond a year to 20 years ahead, it is suitable for forecasting the construction of new generations, strategic planning, and changes in the electric energy supply and delivery system [ 10 ].

Notwithstanding the above-mentioned techniques and approaches available, EED forecasting is seen to be complicated and cannot easily be solved with simple mathematical formulas [ 2 ]. Also, Hong and Fan [ 12 ] pointed out that electric load forecasting has been a primary problem for the electric power industries, since the inception of the electric power. Regardless of the difficulty in electric load forecasting, the optimal and proficient economic set-up of electric power systems has continually occupied a vital position in the electric power industries [ 13 ]. This exercise permits the utility industries to examine the dynamic growth in load demand patterns to facilitate continuity planning for a better and accurate power system expansion. Consequently, inaccurate prediction leads to power shortage, which can lead to “dumsor” and unneeded development in the power system leading to unwanted expenditure [ 7 , 14 ]. Besides, a robust EED forecasting is essential in developing countries having a low rate of electrification to facilitate a way for supporting the active development of the power systems [ 15 ].

Based on the sensitive nature of electricity demand forecasting in the power industries, there is a need for researchers and professionals to identify the challenges and opportunities in this area. Besides, as argued by Moher et al. [ 16 ], systematic reviews are the established reference for generating evidence in any research field for further studies. Our partial search of literature resulted in the following [ 10 , 12 , 17 , 18 , 19 , 20 , 21 ] papers that focused on comprehensive systematic review concerning the methods, models, and several methodologies used in electric load forecasting. Hammad et al. [ 10 ] compared forty-five (45) academic papers on electric load forecasting based on inputs, outputs, time frame, the scale of the project, and value. They revealed that despite the simplicity of regression models, they are mostly useful for long-term load forecasting compared with AI-based models such as ANN, Fuzzy logic, and SVM, which are appropriate for short-term forecasting.

Similarly, Hong and Fan [ 12 ] carried out a tutorial review of probabilistic EED forecasting. The paper focused on EED forecasting methodologies, special techniques, common misunderstandings and evaluation methods. Wang et al. [ 19 ] presented a comprehensive review of factors that affects EED forecasting, such as forecast model, evaluation metric, and input parameters. The paper reported that the commonly used evaluation metrics were the mean absolute error, MAPE, and RMSE. Likewise, Kuster et al. [ 22 ] presented a systematic review of 113 studies in electricity forecasting. The paper examined the timeframe, inputs, outputs, data sample size, scale, error type as criteria for comparing models aimed at identifying which model best suited for a case or scenario.

Also, Zhou et al. [ 17 ] presented a review of electric load classification in the smart-grid environment. The paper focused on the commonly used clustering techniques and well-known evaluation methods for EED forecasting. Another study in [ 21 ] presented a review of short-term EED forecasting based on artificial intelligence. Mele [ 20 ] presented an overview of the primary machine learning techniques used for furcating short-term EED. Gonzalez-Briones et al. [ 18 ] examined the critical machine learning models for EED forecasting using a 1-year dataset of a shoe store. Panda et al. [ 23 ] presented a comprehensive review of LTLF studies examining the various techniques and approaches adopted in LTLF.

The above-discussed works of literature show that two studies [ 20 , 21 ] address a comprehensive review on STLF, [ 23 ] addresses forecasting models based on LTLF. The study in [ 24 ] was entirely dedicated to STLF. Only a fraction (10%) of above systematic review studies included STLF, MTLF and LTLF papers in their review; however, as argued in [ 10 ], the lead time (forecasting interval) is a factor that positively influences the performance of a chosen model for EED forecasting studies. Again, a high percentage of these studies [ 10 , 12 , 17 , 18 , 19 , 20 , 21 , 22 , 24 ] concentrated on the methods (models), input parameter, and timeframe. Nevertheless, Wang et al. [ 19 ] revealed that the primary factors that influence EED forecasting models are property (characteristic) parameters of the building and weather parameters include. Besides, these parameters are territorial dependant and cultural bond. Thus, the weather pattern is not the same world-wide neither do we use the same building architecture and materials globally.

Notwithstanding, a higher percentage of previous systematic review studies overlooked the origin of studies and dataset of EED forecasting paper. Also, only a few studies [ 12 , 17 , 19 ] that examined the evaluation metrics used in EED forecasting models. However, as pointed out in [ 17 ], there is no single validity index that can correctly deal with any dataset and offer better performance always.

Despite all these review studies [ 10 , 12 , 17 , 18 , 19 , 20 , 21 , 22 , 24 ] on electricity load forecasting, a comprehensive systematic review of electricity load forecasting that takes into account all possible factors, such as the forecasting load (commercial, residential and combined), the forecast model (conventional and AI), model evaluation metrics and forecasting type (STLF, MTLF, and LTLF) that influences EED forecast models is still an open gate for research. Hence, to fill in the gap, this study presents an extensive systematic review of state-of-the-art literature based on electrical energy demand forecasting. The current review is classified according to the forecasting load (commercial, residential, and combined), the forecast model (conventional, AI and hybrids), model evaluation metrics, and forecasting type (STLF, MTLF, and LTLF). The Preferred-Reporting Items for Systematic-Review and Meta-Analysis (PRISMA) flow diagram was adopted for this study based on its ability to advance the value and quality of the systematic review as compared with other guidelines [ 16 , 25 , 26 ]. The current study contributes to knowledge as follows:

A comprehensive and detailed assessment of previous state-of-the-art studies on electricity demand forecasting; based on used methods, timeframe, the train and test split of data, error, and accuracy metrics applied to forecast.

We present a concise summary of the useful characteristics of compared techniques in electric load forecasting.

We identified the challenges and opportunities for further studies in electric load forecasting.

The remaining sections of the current paper are structured as follows. “ Methodology ” section presents the methods and materials used in the current study. “ Data collection ” section presents the results and a detailed discussion of the outcomes, and “ Study framework ” section presents the summary of findings and direction for future studies.

Methodology

The current study presents a systematic review of pertinent literature on electrical energy forecasting.

Data collection

A total of eighty-one (81) state-of-the-art research works published in journals, conferences, and magazines, and student’s thesis relevant to the scope of the current study were downloaded from the internet, thus using keywords and terms which included AI, Electricity Prediction (EP), Energy Forecasting (EF), Machine Learning (ML), and combination of AI and EP, AI and EF, ML and EP, ML and EF. Each downloaded literature was then carefully studied and categorised into the two methods of electrical load forecasting data-driven (artificial intelligence) methods and engineering methods.

Study framework

According to Moher et al. [ 16 ], the quality of every systematic review is based on building protocol, which outlines the justification, hypothesis, and planned methods of the investigation. However, only a few systematic review study reports of their framework. A detailed, well-described structure for systematic reviews facilitates the understanding and evaluation of the methods adopted. Hence, the PRISMA model [ 26 ] was adopted in this study (Fig.  1 ). As shown in Fig.  1 , the PRISMA presents the flow of information from one stage to another in a systematic review of the literature and gives the total number of the research identified, excluded, and included and the reasons for inclusion and exclusions.

Source : Moher et al. [ 26 ]

The adopted PRISMA flow diagram.

The PRISMA flow diagram involved five (5) phases, as shown in Fig.  1 . Phase 1 consists of outlining the review scope, developing questions, and inclusion or exclusion. Phase 2 searches the literature with keywords to identify potential studies. Phase 3 includes determining the addition of a paper by screening its abstracts if it meets inclusion criteria. While phase 4 includes characterisation of paper for mapping by keywords. This review aimed to document an overview of research in the field of electric load forecasting to make way for future studies. As a result, a fifth (5) step offers an in-depth quantitative synthesis (meta-analysis) of studies included in the review.

Our search of literature retrieved one hundred and one (109) papers from online journals and eleven (11) from under sources, making one hundred and twelve (120) records in all (see Fig.  1 ). Of the 120 records, 21 were duplicates, hence, removed leaving ninety-one (99) record shortlisted for the screening stage. At the screening stage, fifteen (15) records were removed; thus, studies that were not related to electrical energy, and those that the primary publication language was not in English. Leaving eighty-four (84) records, of the 84 records, we further remove five (5) more records that were published before the year 2010, two (2) record omitted due to overlapping, this reduced eligible papers for analysis to seventy-seven (77). The 77 papers were used for the qualitative analysis. Ten (10) records that presented a review of electric load forecasting were also removed, and the remaining (67) were used for the quantitative analysis, as shown in Fig.  1 .

Results and discussion

Electrical energy consumption can be classified as residential (domestic), commercial (non-residential), or industrial. Residential or domestic refers to the home or a dwelling where people globally live from day-to-day. At the same time, commercial consumers are the business and industries that require massive supply than residential users for their businesses [ 27 ]. Selected literature was on electric load forecasting was classified into two main categories AI methods and engineering methods. However, each category was further grouped into residential and commercial or combined (residential and commercial), and the outcomes are presented. A total of seventy-seven (77) papers were eligible for the qualitative analysis, while sixty-seven (67) were included in the quantitative analysis, as already discussed above in this study.

AI methods used in electrical energy demand forecasting

This section presents the studies that were based on AI techniques.

Combined (commercial and residential)

Most works on EED forecasting sought to forecast the total load (residential and commercial) demand on the supplying authority. This section presents the selected studies that fell in this category of electric load forecasting.

Hybrid models

In a way to harness the strength in different machine learning techniques, some researchers sought to hybrid two or more ML techniques to improve the forecasting accuracy of their models.

A short-term (next-day) EED forecasting model based on the historical meteorological parameter to forecast the future load on the Greek Electric Network Grid using Support Vector Machine (SVM), ensemble XGBoost, Random Forest (RF), k-Nearest Neighbours (KNN), Neural Networks (NN) and Decision Trees (DT) was proposed by [ 28 ]. The mean absolute percentage error (MAPE) was used as a performance metric for comparison among selected models by the author. The study achieved a reduction in prediction error or + 4.74% compared with the Operator of the Electricity Market in Greece predictions. In other studies [ 29 ], long-term (10 years) EED forecasting using NN and Autoregressive Integrated Moving Average (ARIMA) was proposed to forecast the EED of Kuwait. Weather temperature and humidity, average salary, gross domestic, oil price, population, residence, passengers, currency earning rate, and economic factors like (total import and export in USD) were used as independent variables. The study concluded that NN outperformed ARIMA and weather parameters were found to be more significant than average salary, gross domestic and oil price.

A Particle Swarm Optimisation (PSO) and Differential Evolution (DE) for forecasting the Andhra Pradesh Grid using weather parameters were presented [ 6 ]. The reported concluded that better prediction accuracy was achieved with PSO and DE than the conventional time series forecast model. In another study, a Curve Fitting Algorithm (CFA) was proposed for forecasting electricity power demand for an hour/day/week/month [ 30 ]. Their study shows that future electricity demand can be effectively forecasted based on past demand. In a process to increase forecasting accuracy, historical electricity data combined with Twitter data was used as an input variable to hybrid ANN and SVM forecast model to forecast the electricity consumption in Dutch [ 14 ]. The authors compared the performance of ANN to SVM and concluded that the ANN outperformed the SVM. On the other side, the SVM performance in accuracy increases in long-term forecasting. The authors again admit that inclusion of weather data as input could not increase model performance. Similarly, in Ghana, an attempt to predict the 30-day ahead EED demand of the Bono region using a hybrid ML (MLP, SVM, and DT) based on historical weather and electricity demand was made [ 7 ]. The authors achieved 95% prediction accuracy; however, it affirms that the inclusion of household lifestyle as an input variable will improve prediction accuracy.

The hybrid of ML (SVM and RF) and time-series models Generalized Linear Model (GLM) and ARIMA forecast model was proposed for predict electricity consumption in South Africa based on the historical electricity price, load demand, and weather parameter [ 31 ]. The outcome of the study showed that the ARIMA-GLM combination performs better for long-term forecasting. Similarly, a combination of quantum search with SVM (quantum computing and the chaotic mechanics) for forecasting yearly EED in Taiwan was presented [ 32 ]. The empirical analysis revealed that the proposed model exhibits considerably enhanced forecasting performance than other SVM-based forecasting models. A hybrid of mode decomposition (EMD), PSO, and SVM model was present for forecasting short-term EED demand of the Australian electricity market [ 33 ]. Sulandari et al. [ 34 ] proposed a hybrid of ANN and Fuzzy algorithm and a recurrent formula (LRF) to predict electricity demand in Indonesian. The study results showed that the hybrid model performed well with low values of RMSE. Likewise, in [ 35 ], a hybrid of clustering technique (K-means) and ARIMA forecasting model was presented to forecast university buildings electricity demand. Paper revealed that the hybrid model outperformed the ARIMA model alone as a forecast model.

An artificial neural network (ANN)-based forecast model for short-term forecasting of Chhattisgarh State electricity demand was proposed [ 36 ], using historical weather data as input variables. The results conclude that ANN can efficiently forecast electricity demand. Likewise, an ANN model was applied to forecast the short-term electricity demand of the Iraqi National Grid [ 37 ]. The authors achieved high accuracy and a reasonable error margin. In a similar study, a DT algorithm forecast model was proposed for forecasting future EED on the Yola/Jimeta power transmission company using weather parameters. Again, a short to medium term EED forecasting using deep machine learning (ML) algorithm long short-term memory (LSTM)-based neural network enhanced with genetic algorithm (GA) for feature selection was proposed [ 9 ], to forecast France metropolitan’s electricity consumption. The mean absolute error (MAE) and root-mean-square error (RMSE) were used as the performance metrics, and the weather parameter was used in the independent variable. Their results affirm that weather parament is very useful in forecasting future electricity demand.

An enhanced Convolutional Neural Network (CNN) and enhanced SVM-based forecast model was presented for forecasting electricity price and load forecasting using [ 38 ]. Despite the enhancement made by authors, they recommended additional enhancement of classifiers to improve prediction. In a similar study, an ANN model to forecast consumer demand in North Cyprus was proposed [ 39 ]. Their results affirm the ability for ANN effective automatic modelling of electricity; however, the study concluded this could be achieved when the training and testing datasets are meaningful. A forecast model using a deep belief network using historical EED of Macedonian (2008–2014) was proposed to forecast a short-term (1 day) EED. The outcome of the forecast shows a reduction in MAPE by 8.6% by the proposed model compared with traditional techniques [ 2 ]. In Young et al. [ 40 ], an ANN with Bayesian regularisation algorithm-based model for short-term load forecasting of commercial building electricity usage was carried out. An ANN and hybrid methods to forecast electricity consumption of Turkey were proposed Aydogdu et al. [ 41 ]. The proposed model gave an average absolute prediction error of 2.25%.

Khwaja et al. [ 11 ] presented an ensemble ANN predictive model to enhance short-term electricity load forecasting. Different from existing studies, the authors combined both bagging and boosting techniques to train bagged-boosted ANNs. The study results showed that the proposed ensemble technique offered a reduction of both variance and bias compared to a bagged ANN, single ANN, and boosted ANN. Also, Ahmad et al. [ 42 ] combined Extreme Learning Machine and an enhanced Support Vector Machine to forecast short-term electricity demand. The study outcome showed that the proposed hybrid model outperformed other state-of-the-art predictive models in terms of performance and accuracy. Atef and Eltawil [ 43 ] proposed a deep-stacked LSTM forecasting model to forecast electricity demand. The paper reported that bidirectional (Bi-LSTM) networks outperformed unidirectional (Uni-LSTM) in terms of forecasting accuracy. Also, a generalised regression Neural Network (GRNN) predictive model was proposed in [ 44 ] to predict short-term electricity demand. The study results showed that a GRNN of 30 neurons offered better prediction accuracy in terms of MAPE and MAE than a GRNN of 10 neurons.

Fuzzy logic models

A Fuzzy Logic (FL)-based forecasting model for the next-day electricity demand in Albania was presented [ 45 ]. The time, the historical and forecasting value of the temperature and the previous day load (L) served as the independent variables for the forecast of the next-day consumption. The outcome of the study yielded accurate forecasting by the FL model. Motepe et al. [ 46 ] proposed an adaptive neuro-fuzzy inference system (ANFIS) model for forecasting South African electricity demand. The author concluded that adding temperature as an input parameter to the proposed model did not enhance forecast accuracy, as typically expected.

A new economy (stock indices) reflecting the STLF model for electricity demand forecasting was proposed [ 47 ]. The authors attempted to forecast the future demand for electricity based on the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) data. The study revealed a significant relationship between stock market function and energy demand, which was helpful to financial analysis wanting to do reverse engineering.

Residential load forecasting

A survey in 2019 shows that EED forecasting, especially short-term load forecasting for individual (residential) electricity customers, plays an increasingly essential role in the future grid planning and operation Kong et al. [ 51 ]. Similarly, the outcome of Leahy and Lyons [ 48 ] affirms that water heating styled used by a household is even more essential than the number of electrical appliances when explaining domestic electrical energy usage. Furthermore, a study in Portugal shows that residential electricity consumption since 1990 has been increasing more rapidly than the Gross Domestic Product (GDP) per capita [ 49 ]. Therefore, studies that focused on forecasting residential EED is of necessity. However, the results revealed that eight (8) out of the sixty-seven (67) representing (11.94%) reviewed works focused on residential electricity forecasting, and a section of these studies is presented.

An hourly prediction of residential energy consumption using RF and SVM was proposed by Hedén [ 50 ], using 187 households in Austin. Error metrics are mean bias (MB), coefficient of variance (CV), and MAPE. The study achieved better forecasting accuracy compared with traditional time series models. Again, an LTSM for short-term residential load forecasting based on residential meter reading [ 51 ] and resident behaviour learning [ 52 ] was studied. The outcomes of these works showed the effectiveness of the LTSM in electricity demand forecasting.

In the same way, linear regression, RF, and SVM predictive model (data-driven) for estimation of city-scale energy use in buildings were proposed by Kontokosta and Tull [ 53 ]. The outcome of the study revealed that adequate electricity consumption in a building could be predicted using actual data from a moderately small subset of buildings. Likewise, unsupervised ML algorithms such as Self-Organising Maps (SOM), k-means, and k-medoid were used to cluster residential electricity based on their trend of electricity use within the day [ 54 ]. The study found that households and how they use electricity in the home can be categorised based on specific customer characteristics. Mcloughlin et al. [ 55 ] examined residential electricity consumption patterns in Irish based on occupant socio-economic and dwelling variables. The variable examined includes the number of bedrooms, dwelling type, age of household head, social class, household composition, and water heating.

The study outcome showed a positive association between maximum demand periods and several household appliances, especially dishwashers, electric cookers tumble dryers, with electric cook topping. The study further found that the time of use of electrical appliances was dependent on occupant characteristics, and younger occupants of a household tend to use more electricity than the older. Alike, an ARIMA, NN, and exponential smoothening forecast model were proposed for forecasting household electricity demand [ 56 ]. The authors concluded that forecasting accuracy varies considerably depending on the choice of forecasting techniques/tactic and configuration/selection of input parameters. Again, a neural network model for predicting residential building energy consumption was proposed by Biswas et al. [ 57 ]. The outcome of the study showed that models based on OWO-Newton algorithms and Levenberg–Marquardt outperformed previous literature.

Engineering and traditional electrical energy demand forecasting

This section presents the electrical energy demand forecasting study based on the traditional time-series and engineering models.

A predictive model for the prediction of medium-term (1-year) electricity consumption of general households based on the lifestyle of the household using Lasso and Group Lasso was proposed [ 58 ]. Their results showed that household lifestyle such as family composition, age, and house-type is good predictors of electricity consumption in a home. Similarly, in Nigeria, an attempt was made to estimate the electricity demand of residential users to support energy transitions using the engineering approach, such as calculating the total power consumed in a household based on the power rating of appliance and their duration of use [ 59 ]. The study concludes that the proposed system can serve policymaking in Nigeria to improve the financial sustainability of the energy supply structure. An extended Autoregressive Distributed Lag (ARDL) model was proposed to estimate residential electricity consumption per capita demand function, which depends on the GDP per capita in Algeria [ 60 ]. The study concluded that promoting financial growth among citizens of Algeria would reduce electricity consumption, since wealthier people (higher income earners) mighty use of more efficient appliances.

In Bogomolov et al. [ 61 ], authors used general public dynamics derived data from cellular network and energy consumption dataset to predict the next-day energy demand. The study could serve a model to enhance the energy meter to promote energy conservation. Likewise, the historical data of on–off times of residential appliances were used to predict the next-day electricity demand using Bayesian inference [ 62 ]. The study concluded that historical electricity consumption data only is not adequate for a decent eminence hourly forecast.

Also, an ARIMA and Holt-Winter model was proposed for forecasting the national electricity consumption of Pakistan from 1980 to 2011 [ 3 ]. The study revealed that the demand in household energy consumption would higher as compare with all other sectors. Jain et al. [ 63 ] proposed an ARIMA forecaster for forecasting electricity consumption. The proposed model achieved a MAPE of 6.63%. The authors concluded that the ARIMA model has the potential of computing in EED forecasting with other forecast techniques. Integration of three (3) forecasting model, long-range energy alternative planning (LEAP), ARIMA, and Holt-Winter, was proposed for forecasting long-term energy demand in Pakistan [ 64 ]. The study would be valid for energy supplies for accurate estimation of users’ demand in the future. The combination of Extreme Learning Machine and Multiple Regression for forecasting China’s electricity demand was proposed [ 65 ]. A quantile regression (QR) model for long-term electricity demand forecasting in South Africa within 2012 and 2015 was presented by Mokilane et al. [ 66 ]. The model was helpful to power distribution industries in the country.

An attempt was made to forecast the electricity consumption of Ghana by 2030 using ARIMA-based model. The study outcome projected that Ghana’s electricity consumption would grow from 8.5210 billion kWh in 2012 to 9.5597 billion kWh in 2030 [ 67 ]. However, in 2017, a report by the energy commission revealed that electricity consumption was 14,247 GWh [ 68 ]. A generalised additive model was adopted for forecasting medium-term electric energy demand in a South African power supply system [ 69 ]. The outcome of the study revealed a useful application of the proposed model in the power generation and distribution industries in the country. An investigation between the association of causal nexus and (environmental pollution, energy use, GDP per capita, and urbanisation) in an attempt to forecast Nigeria’s energy use by 2030 was carried out using the ARIMA and ETS models [ 70 ]. The study outcome showed better forecasting accuracy by both models, and a high rise in energy demand was observed.

Multiple regression analysis approaches for forecasting the yearly electricity demand in commercial sectors and electricity access rates in rural and urban households in some selected West African countries, which included Ghana, were carried out by Adeoye and Spataru [ 71 ]. The study showed that there is a very high variation in hourly electricity demand in the dry seasons. Their results affirm Nti et al. [ 7 ] report that the demand pattern of electricity in Ghana is highly dependent on the month of the year. In a way, one can say there is a partial agreement in these two studies. A time-series regression model for forecasting South African’s peak load demand was presented [ 72 ]. Experimental fallouts indicated that when the temperature is included as an input parameter, improvement in accuracy by the forecast model was realised. ARIMA-based predictive model for predicting both sectoral and total electrical energy consumption of Turkey for the next 15 years was proposed [ 73 ]. The study points out that the demand for electrical energy in agriculture sectors, transport, public service, residential, and commercial will keep increasing.

Similarly, partially linear additive quantile regression models for forecasting short-term electricity demand during the peak-demand periods (i.e., from 6:00 to 8:00 pm) were carried out in South African [ 74 ]. The authors found out that the use of the proposed system in power utility industries for the planning, scheduling, and dispatching of electricity activities will result in a minimal cost principally during the peak-period hours. Caro et al. [ 75 ] predicted the Spanish electricity demand using the ARIMA model. The study achieved an improvement in the short-term predictions of electricity demand with less computational time. A short-term electricity load forecasting model based on dynamic mode decomposition was proposed in [ 76 ], the proposed model showed better stability and accuracy compared with other predictive models.

Quantitative analysis of findings

The descriptive statistics of the study outcome is presented under this section with tables and charts.

Algorithms used for forecast models

As part of the aim of the review, we sought to find out the most used algorithms in electricity forecasting models. The most top nine (9) used algorithms found in the sixty-seven (67) article are presented (Fig.  2 ); this includes only algorithms that were used in more than a single paper. The study outcome revealed that 90% out of the top nine algorithms were AI-based, with ANN representing 28% of AI models used in electricity forecasting. Besides, the ANN models were primarily used for STLF where electrical energy consumption patterns are more intricate than LTLF. The traditional AFRIMA recorded 17.5% due to its efficiency in LTLF, where load fluctuations and periodicity are less critical.

Top nine (9) most used algorithms for electricity forecasting

Additionally, a high percentage of regression models is used for LTLF prediction. The study outcome shows how AI is applied in various sectors of the economy to improve efficiency and profitability. Also, we observed that the SVM, PSO and Fuzzy are gaining more popularity in recent studies, a sign of increasing attention of researchers on these algorithms for EED forecasting.

Study origin

Table  1 shows the studies and their origin (countries). The origin of surveyed studies was examined, in order to find the linkage between the power crisis in the continent and studies on electrical energy demand forecasting. Concerning geographical coverage, it was found that a high number of studies (31.34%) were carried out in Europe, 17.91% in Africa and 19.40% in Asia. Making Africa the third-highest, however, interestingly, most of the studies in Africa were carried in South Africa (five representing 41.67%), three (3) representing 25% in Ghana, with the rest from Nigeria and other African countries. The energy crisis that hit Europe in 2008, reported by [ 77 ], can be attributed to the numerous studies in electricity forecast, as shown in Table  1 . It was further observed that 2 out of 3 studies in Ghana were based on national or regional electricity demand forecasting (one long-term, one short-term (1 month)). While the third study aimed at identifying the relationship between electricity demand and economic growth. Thus, it suggests that the limited number of studies in electricity demand forecasting by both academicians and professionals in this field might have partially contributed to the power shortages facing the nation, which in 2015 resulted in “Domsur”.

Used evaluation metrics

The performance of every forecasting model is examined based on the difference in error between the actual value \(\left( y \right)\) and the predicted value ( \(\hat{y}\) ). Several of these metrics were identified in the literature. However, we examined the most used in electrical energy demand forecasting to enable new beginners in the field of electricity demand forecasting to have a firm grip on which to apply in their study. Figure  3 shows the top four (4) most used error metrics in two or more studies. The results revealed that root-mean-square error RMSE (38%) was the most used error metric among EED forecasters, followed by mean absolute percentage error MAPE (35%). Due to the effectiveness in measure predictive model performance and their usefulness for short-term prediction, it was also observed that the MAPE is a standard primary metric as it is easy to both calculate and understand. The results affirm the findings in [ 12 , 19 , 22 ] that MAE, MAPE, and RMSE are the most commonly used evaluation metrics in EED forecasting model.

Most used error metrics in electricity load forecasting

Forecast type

Based on short-term load forecasting (STLF), medium-term load forecasting (MTLF), and long-term load forecasting (LTLF), it was observed that 80%, 15%, and 5% of the electrical energy demand forecasting were STLF, MTLF, and LTLF, respectively. The massive number of studies is based on STLF as compared with LTLF and MTLF call for further studies into the challenges associated with LTLF and MTLF electricity load forecasting. Again, 80% STLF forecasting affirms the 38% use of RMSE error metric, since it is for STLF forecasting. The results affirm the report in [ 22 ] that 43.6% of electricity forecasting are short-term prediction.

Model input parameters

The efficiency of every predictive model is believed to be partially dependent on the independent (input) variable [ 7 ]. At this level, the focus was to examine the different input variables used for electricity load forecasting. The type of independent variables (input features) used by electricity load forecasters was also examined. The current study observed that sixty (60) out of the sixty-seven (67) papers made known the input parameter to the proposed model. Table  2 presents the type of input variables and the percentage of studies that utilised it. It was observed that a high percentage (50%) of electricity demand forecasting was based on weather parameters; next was the historical electricity consumption pattern. The outcome exposed that little attention is given to household lifestyle in electricity demand forecasting. However, Nishida et al. [ 58 ] argue that residential (domestic) energy consumption differs depending on the lifestyle of the family. Family lifestyle, according to [ 56 ], cannot be undermined in electrical load forecasting. According to these studies, these factors include the life stage family composition, house type, age, home appliances possessed and their usage, family income, cultural background, social life, and lifestyle habits, which include how long to stay at home and how to spend holidays. The observations open the opportunity for further studies on the association between EED and household lifestyle.

Conclusions

The current study sought to reviewed state-of-the-art literature on electricity load forecasting to identify the challenges and opportunities for future studies. The outcome of the study revealed that electricity load forecasting is seen to be complicated for both engineers and academician and is still an ongoing area of research. The key findings are summarised as follows.

Several studies (90%) have applied AI in electrical energy demand forecasting as compared with traditional engineering and statistical method (10%) to address energy prediction problems; however, there are not enough studies benchmarking the performance of these methods.

There are few studies on EED forecasting in Africa countries (12 out of 67). Though the continent has progressive achievement in the creation of Regional Power Pools (PPP) over the last two decades, the continent still suffers from a lousy power network in most of its countries, leaving millions of people in Africa without electricity.

Temperature and rainfall as an input parameter to the EED forecasting model are seen to have a divergent view. At the same time, some sections of research recorded an improvement in accuracy and reported no improvement in accuracy when introduced and input. However, the current study attributes this to the difference in automorphic temperature globally and the different economic status among countries. An additional investigation will bring more clarity to the literature.

This study revealed that EED forecasting in the residential sector had seen little attention. On the other hand, Guo et al. [ 78 ] argue that the basic unit of electricity consumption is home.

It was observed that there had been a global increase in residential electricity demand, this according to the report in [ 49 ] can be attributed to the growing rate of buying electrical equipment and appliances of low quality due to higher living standards. However, a further probe into Soares et al. [ 49 ] assertion will bring clarity to literature because of the discrepancy in opinions in literature.

Lastly, the study revealed that there is a limited number of studies on load forecasting studies in Ghana. We, therefore, recommend rigorous researchers in this field in the country to enhance the economic growth of the country.

Our future study will focus on identifying the relationship between household lifestyle factors and electricity consumption in Ghana and predict load consumption based on identified factors since it is an area that has seen little or no attention in Ghana.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

Abbreviations

• Electrical energy demand
• Artificial intelligence

Artificial neural network

Root-mean-square error

Mean absolute percentage error

Very short-term load forecasting

Short-term load forecasting

Medium-term load forecasting

Long-term load forecasting

Preferred-reporting items for systematic-review and meta-analysis

Electricity prediction

Energy forecasting

• Machine learning

Support vector machine

Random forest

k-nearest neighbours

Neural networks

Decision trees

Autoregressive integrated moving average

Particle swarm optimisation

Differential evolution

Curve fitting algorithm

Generalised linear model

Long short-term memory

Genetic algorithm

Mean absolute error

Convolutional neural network

Fuzzy logic

Adaptive neuro-fuzzy inference system

Generalised regression neural network

Taiwan stock exchange capitalisation-weighted stock index

Gross domestic product

Coefficient of variance

Self-organising maps

Autoregressive distributed lag

Long-range energy alternative planning

Quantile regression

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Nti, I.K., Teimeh, M., Nyarko-Boateng, O. et al. Electricity load forecasting: a systematic review. Journal of Electrical Systems and Inf Technol 7 , 13 (2020). https://doi.org/10.1186/s43067-020-00021-8

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Determinants of household electricity consumption in Greece: a statistical analysis

• Dimitra Kotsila 1 &
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Journal of Innovation and Entrepreneurship volume  10 , Article number:  19 ( 2021 ) Cite this article

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Over the last decades, the contemporary way of living, as well as the technology development, has increased the household electricity consumption. The excessive use of electricity has a negative impact on the environment, increasing the carbon footprint and contributing to the climate change. Governments are more and more concerned about the way our societies consume energy and are committed to reduce the greenhouse emissions. As the residential sector contributes significantly to energy use, it is crucial to investigate the socio-economic parameters, dwellings’ characteristics, and climate conditions that determine the electricity consumption in households. The data of this study are collected from 1801 dwellings from all regions of Greece and two statistical models are built. Both of them conclude that the most significant determinants influencing the electricity consumption are the number of occupants, the size of the dwelling, the heating type, the heating and cooling hours, and the weather conditions.

Introduction

In the last decades due to the increased demand and the improved lifestyle, energy demand in the residential sector has increased rapidly causing the policymakers’ concern. The international climate negotiation is an opportunity for decision-makers to promote enhancement of energy efficiency and reduction of greenhouse emissions and set commitments for renewable energy consumption. The recent “2030 Energy Strategy” and the Paris climate conference (COP21) focus on the previous objectives to keep a global temperature rise this century below 2 °C. According to IEA ( 2020a ), based on the most recent data of 2018 in Greece, the CO2 emissions from fuel combustion only come from electricity and heat producers (46.8%), transport (27.4%), industry (8.1%), other energy industries (8.1%), residential (6.5%), commercial and public services (1.6%), and other (1.6%).

In the last two decades, the Greek governments have given a high priority to the environmental protection; thus, they promoted the Renewable Energy Sources (RES). The main aim concerning the RES is to be able to participate in the electricity consumption for at least 40% by the end of 2020. Specifically, under the European Renewable Energy Directive 2009/28/EC (European Parliament and Council, 2009 ) and the Law 3851/2010 (Official Government Gazette, 2018 ), Greece needs to meet the following targets for RES contribution:

20% of the final energy consumption for heating and cooling

40% of the final electricity consumption

10% of the final energy consumption in transportation

According to the most recent provided data by IEA ( 2017 ), Greece has achieved its goal in the sector of heating and cooling (58.90%), but is far from its aim in the other two sectors (22.09% and 1.43%, respectively). However, Greece has made an impressive progress in the electricity sector, thanks to the rapid growth in installed wind and solar parks and the decrease in the total electricity demand.

In 2019, RES and gas are the primary fuels of electricity production in Greece (IEA, 2020b ). In particular, the total electricity production is based on RES (35.2%), gas (35.2%), coal (22.2%), and oil (9.2%), while in 2017 the corresponding percentages were the following: RES (25.1%), gas (31%), coal (34%), and oil (10%).

Policymakers must focus on the distribution of the electricity consumption, in order to detect and improve the most inefficient sectors. In 2018, the distribution of electricity consumption per sector has been the following: commercial and public services (36%), residential (33.9%), industry (25.1%), agriculture/foster (4.6%), and transport (0.6%); it must be noted that this is similar in the last ten years (IEA, 2020c ).

In the residential sector, the final energy consumption is used to heat the space (57%), for lighting and appliances (20%), for heater heating (12%), for cooking (7%), and for space cooling (4%) (Eurostat, 2018 ).

Consumption per capita is an important indicator to observe the tendency of electricity consumption through the years, as it offers a clear view of the electricity that every individual consumes. In Greece, in 2014 the per capita electricity consumption was 5062.61 kWh/capita while in 2010 was 5333.44 kWh/capita and in 2008 was 5805.19 kWh/capita (DataBank, 2020 ). The economic crisis in Greece since 2008–2009 influenced the electricity consumption as in 2013 the average consumption fell to 5029 kWh/capita. Consumption per household is another indicator to monitor the electricity consumption in the residential sector. In 2000, the electricity per household (hh) was 3717 kWh/hh, while in 2010 it was 4023 kWh/hh (World Energy Council, 2016 ).

The increasing trend of electricity consumption is a reality. Since the residential sector is one of the most demanding sectors regarding electricity consumption, it should be studied. Unfortunately, in Greece, the socio-economic parameters that influence the electricity consumption have never been extensively examined. From the previous studies, that are outlined in the “Literature review” section, the electricity consumption is relevant with demographic data, dwelling characteristics and geographical and climate conditions. This study analyzes the aforementioned parameters in Greece and it is based on real data from 1801 customers of a Greece’s electricity provider. The variables and the methodology that are used in the current analysis are in line with the deep literature review that was conducted by Jones et al. ( 2015 ).

This paper is organized as follows: in the “Literature review” section, an overview of the literature review relevant for this study is included. A description of the regression models and the data sources is given in the “Method” section. In the “Results and discussion” section, the main results are summarized. Finally, the conclusions are presented in the “Conclusions” section.

Literature review

On the international stage, residential electricity consumption is a considerably studied subject. Relevant studies are sequenced with the oldest first and the most recent last.

Studies regarding European countries

Halicioglu ( 2007 ) examines how the energy demand in residential sector in Turkey was influenced from the price and income. The income positively influences the electricity demand and accelerates purchases of electrical goods and services. The price of electricity negatively influences the electricity demand, while the urbanization positively influences the electricity demand, as it provides greater access to electricity.

Gram-Hanssen ( 2011 ) points out that the user’s practices influence the energy consumption. He examines 8500 detached houses in Denmark and he finds that the income, the size of the house, and the presence of teenagers (13–19 years old) all have a positive effect on electricity consumption.

Wiesmann et al. ( 2011 ) examine the relationship between the per capita electricity consumption and dwelling characteristics in Portuguese consumers. They conclude that the income, the appliance ownership, and the floor area have a positive influence on per capita electricity consumption. People who live in single-family houses and/or in urban households consume more electricity than those living in a block of flats and/or rural households. People per household, dwellings per building and more heating degree-days negatively influence the consumption per capita, since in colder regions households consume less electricity per capita than those in more moderate climates.

McLoughlin et al. ( 2012 ) examine the influence of dwelling and occupant characteristics on electricity consumption of 3941 Irish dwellings. Dwelling type, number of bedrooms, age of the head of household, and electrical appliances used for water heating and cooking have a positive effect on electricity consumption.

Bedir et al. ( 2013 ) point out that in Netherlands the household size, the dwelling type, and the duration of electrical appliances’ use (such as dryers and washing machines) have significant effect on the electricity consumption. Also, in the Netherlands, Brounen et al. ( 2012 ) analyze data of 300,000 dwellings. The dwelling type affects electricity consumption as the detached and semi-detached houses consume more electricity per capita than row houses or apartments. Houses with children and especially those that have teenagers are found to have a positive effect on per capita electricity consumption. The income has positive influence whereas the number of persons in household has a negative effect on per capita electricity consumption.

Studies regarding non-European countries

Ndiaye and Gabriel ( 2011 ) analyze electricity consumption of 62 dwellings of Oshawa (Ontario, Canada). Number of residents, house status, type of fuel used to heat the pool, type of fuel used in the heating system, type of fuel used in the domestic hot water heater, type of air-conditioning, and number of air changes per hour at 50 Pa are found to have a positive effect on the electricity consumption. On the other hand, the average number of weeks that the family leaves for vacation and the existence or not of an air conditioning system have a negative effect on the electricity.

Sanquist et al. ( 2012 ) base their research on data from the Residential Energy Consumption Survey (RECS) conducted on 2005 in the USA. The air-conditioning, laundry usage, personal computers, climate zone of dwelling, and TV use significantly influence the electricity consumption. Kavousian et al. ( 2013 ) examine the residential electricity consumption of 952 US dwellings in a view of daily maximum and minimum. Daily minimum consumption is influenced by weather, location, dwelling size, and the number of refrigerators when daily maximum consumption is influenced by the use of appliances that consume a lot and the number of residents. In the summer model, the primary factor that influences the electricity consumption is the cooling degrees days.

Tewathia ( 2014 ) conducts a survey in Delhi to find the determinants of electricity consumption. The household income, the number and the usage of electrical appliances, the size of the house, the family size, time spent out of the house, and the higher educational level influence the monthly electricity consumption through all the seasons. The educational level has a negative relationship as the higher educated families tend to consume less electricity. Filippini and Pachauri ( 2004 ) analyze the electricity demand in urban Indian households. The price is inelastic in electricity demand, so the price is not an inhibiting factor in residential electricity consumption. The income, the size, the regions, and the degrees of urbanity have a significant influence into the electricity consumption. Dwellings with more residents and younger households head had the tendency to consume less electricity from those that had less elder people.

Jones et al. ( 2015 ) conduct a broad literature review to investigate the factors that influence or not the domestic electricity consumption. They study 62 factors as potential factors that determine the electricity consumption. In relation to socio-economic factors, the higher household and disposable income, the more occupants and presence of teenagers have a positive effect on electricity consumption. In relation to dwelling factors, the dwelling age, the number of rooms, the number of bedrooms, and the floor area influence the electricity consumption. Regarding appliance factors, the following ones have a positive effect: more appliances, the existence of desktop computer, television, electric oven, refrigerator, dishwasher, tumble dryer, and higher use of washing machines and tumble dryer.

Esmaeilimoakher et al. ( 2016 ) accomplish an introduction to the factors that influence the electricity consumption. Their analysis is based on a survey conducted in nine households of Perth of Western Australia. The main results are that the average annual electricity consumption per person per m 2 floor area (AAEC/P m 2 ) has a negative correlation with the number of occupants and the dwellings size.

Studies regarding Greece

The research in the field of socio-economic determinants that influence electricity consumption in Greece is poor. There are a lot of works focused on macroeconomic factors that determine energy consumption. Only the study of Sardianou ( 2007 ) provides research that includes demographic data and examines electricity conservation behavior.

Donatos and Mergos ( 1991 ) examine the residential electricity demand in Greece during the period 1961–1986. Data are collected from a public database. It is found that the electricity demand is price inelastic and income elastic. The sales of appliances, as well as the heating degrees-days, are found to have an insignificant effect on electricity demand in contrast with the number of consumers that has a significant effect.

Hondroyiannis ( 2004 ) examines the elasticity of price and income in long-run and short-run demand for residential electricity. The examined period is 1986–1999 employing monthly data. In the short-run, the electricity demand is income inelastic and independent of the price, while in the long-run period, all variables, income, price, and weighted average temperature are found to affect electricity demand.

Polemis and Dagoumas ( 2013 ) conduct a similar with Hondroyiannis ( 2004 ) research. They use cointegration techniques and the vector error correction model to observe the long-run and short-run electricity demand. The data that are taken into account are for a longer period, from 1970 to 2011. In the long-run, the electricity demand is price inelastic and income elastic, while in the short-run the relevant elasticities are inelastic.

Sardianou ( 2007 ) investigates the determinants of household energy conservation. The analysis is based on a survey that has been conducted in 586 households of five main Athens’ regions. One of the findings is that people with higher income that own their houses, and have a large family are more willing to conserve energy. Also, the number of rooms, the dwelling’s size, sex, educational level, and marital status are found that do not have a significant influence in energy conservation. However, it is found that the larger electricity expenditures negatively influence the energy conservation behaviors and the older people are more energy-intensive users than the younger ones.

All variables that have been studied in the above papers are indicated in Tables 6 and 7 in the Appendix section. Tables 6 and 7 also present which of these variables have been studied in this paper.

Model specification

The ordinary least squares (OLS) regression is used to estimate the determinants that affect the electricity consumption in Greek households. A variety of studies that examined the determinants of electricity consumption have been conducted using OLS regression (Bedir et al., 2013 ; Brounen et al., 2012 ; Filippini & Pachauri, 2004 ; Gram-Hanssen, 2011 ; Halicioglu, 2007 ; Kavousian et al., 2013 ; McLoughlin et al., 2012 ; Ndiaye & Gabriel, 2011 ; Sanquist et al., 2012 ; Sardianou, 2007 ; Wiesmann et al., 2011 ). Two different models are employed to determine the electricity consumption. More specifically, a simple OLS regression model and a log-linear regression model are used to build the aforementioned models.

Data sources and description

Data are collected from a Greek electricity provider and refer only to residential dwellings. Data for the consumption and square meters area have been provided through the actual bills. The period of actual bills is not the same between bills and differs among dwellings. Thus, in order to calculate the consumption with accuracy, the consumption per day is calculated at first. Afterwards, consumption per month is calculated and only dwellings that have consumption 2017 are selected.

Demographics and behavior data are retrieved from a questionnaire that was provided through the electricity provider’s online platform. The questionnaire was answered from every individual that covers the bill. Only dwellings that already had a monthly consumption for 2017 and have also answered all the questions are selected. The dwellings with invalid values have been excluded from the data, so the final number of dwellings that are included in the analysis is 1801. Table 1 illustrates all available variables, as well as the type and the units of the variables of both models. Table 2 illustrates the summary of statistics of the variables of both models and it shows the levels of ordinal variables and their correspondence to the converted numbers. In addition, the factor type of each variable is indicated.

In the literature review, we have found out that many studies (Donatos & Mergos, 1991 ; Kavousian et al., 2013 ; Ndiaye & Gabriel, 2011 ; Sanquist et al., 2012 ; Wiesmann et al., 2011 ) include in their models the weather factors of heating degrees-days (HDD) and cooling degrees-days (CDD). HDD and CDD is the difference, in degrees, of outside temperature and base (18.3 °C ) temperature. HDD and CDD data for each prefecture and for 2017 have been downloaded from the weather stations of National Observatory of Athens (NOA). Data have been available in the weather website ( www.meteo.gr ) and have been recorded at a daily basis (Petrou, 2018 ). The yearly HDD and CDD have been calculated per prefecture and the weather data are then connected to the consumption data based on prefecture. Units of HDD and CDD are in 1000 °C days (the values of these variables are big numbers and they should be fitted in the model as in Wiesmann et al., 2011 ).

The data preparation, manipulation, visualization, and the regression analysis is conducted using programming language R through RStudio program.

Before conducting a regression analysis, an advanced statistical analysis is conducted to observe any associations or correlation between variables. Thus, pairwise comparisons and a correlation analysis (correlation matrix can be found in Table 8 in the Appendix section) are used. The insights of the analysis show a very high negative correlation between HDD and CDD, approximately −0.809 (see Table 8 ), so HDD is removed from the model. Furthermore, a high correlation between family type and number of occupants is observed, so family type is removed from the model.

In the sample, the final independent variables along with the dummy variables count to thirty three (33). It is essential to determine whether the subset of all independent variables yields to an adequate and appropriate model. Stepwise regression is a method that attempts to find the best regression model, without examining all the possible models (Berenson et al., 2014 ). There are two approaches of stepwise regression, the “forward selection” and the “backward elimination”. Both approaches use the Akaike Information Criterion (AIC) to find the best combination of variables based on balancing the model’s complexity and accuracy. The best model is the one with minimum AIC value. The regression model is built using the forward selection, where an initial model is defined that contains only the constant and each independent variable is retained in the model only if it improves the ability of the model to predict the dependent variable.

Results and discussion

The ols regression method.

The results of the OLS regressions are presented in Table 3 . In general, the results from both models are in agreement with the literature, as it will be indicated in this section. Most of the significant variables are common in both models, but there are variables that influence one model and not the other. Τhe log-linear model (model 2) has better goodness of fit from the linear model, in terms of the R 2 . Both models explain approximately 30% of the variance in electricity consumption. Even if this value does not seem satisfactory for a regression analysis, when comparing to the literature the R 2 is within the range published. Indeed, in Sardianou ( 2007 ), R 2 is approximately 10%; in Wiesmann et al. ( 2011 ), R 2 is approximately 33%; in Bedir et al. ( 2013 ), R 2 is approximately 50%; and in most other papers presented in the literature review, the R 2 value has not been given. One basic reason for this low value is that even though we have studied thirty-three variables (most given by the electricity provider and others found by the authors), more parameters need to be studied.

Square meters area has a significant effect on the average monthly electricity consumption in both models. To be more specific, if the square meters area increases by 1m 2 then an increase of 1.389 kWh/month according to model 1 and an increase on average yearly consumption by 0.3% according to model 2 are expected. These results are in line with previous studies (Bedir et al., 2013 ; Filippini & Pachauri, 2004 ; Gram-Hanssen, 2011 ; Jones et al., 2015 ; Kavousian et al., 2013 ; Tewathia, 2014 ; Wiesmann et al., 2011 ).

Number of occupants is strongly related with the electricity consumption (because of high beta coefficient and p -value < 0.01). In both models, more occupants consume more electricity. Studies of Gram-Hanssen ( 2011 ), Jones et al. ( 2015 ), Kavousian et al. ( 2013 ), and Ndiaye and Gabriel ( 2011 ) reach the same conclusion.

Heating hours, heating type, and the presence of secondary heaters have a significant positive effect on electricity consumption in both models. That is, heating hours influence the electricity consumption regardless the heating type. On the other hand, heating type of local units significantly influences the electricity consumption. Dwellings that use local units for heating the space seem to consume more electricity. The results reveal that houses with heating type of local units use possibly electrical appliances to heat their space which has an impact on their total consumption. The presence of secondary heaters has also a significant positive effect on the electricity consumption. Those results enhance the perspective that the electrical appliances used to heat the space have an impact on electricity consumption. Jones et al. ( 2015 ) mention that there are eight studies that found a positive effect of presence of electric space heating system on electricity consumption.

Cooling hours is also related with the electricity consumption. Both models agree to the positive relationship between cooling hours and electricity consumption, as expected. CDD also is found that has a positive effect on electricity consumption in both models. The presence of air-conditioning to cool the space has a significant effect in model 2 while in model 1 it is not found that influences the electricity consumption. This result is aligned with the literature review presented by Jones et al. ( 2015 ), since they present studies with both results.

According to model 1, the dwelling type has a significant effect on electricity consumption, as the single family houses seem to consume more electricity. This result is aligned with past studies of Bedir et al. ( 2013 ), Brounen et al. ( 2012 ), McLoughlin et al. ( 2012 ), and Wiesmann et al. ( 2011 ).

Both models reveal that the age of inhabitants positively influences the electricity consumption. So, as long as the occupants that cover the bill are elderly, the more electricity is consumed. This result is aligned with and also mentioned that there are eight studies that marked the positive effect of age of head of the household on the electricity consumption.

According to model 2, dwellings with occupants that spent more time in home are found to have a positive significant effect on electricity consumption, as it is expected and it is aligned with Ndiaye and Gabriel ( 2011 ) and Tewathia ( 2014 ), respectively.

Many variables of the collected data are found that have no significant influence on the electricity consumption. Those variables are gender, educational level, house status, marital status, dwelling floor, income, light behavior, and summer holidays.

As mentioned above, most of the variables that are found significant are common in both models, but there are variables that influence one model and not the other. This happens because in model 2 a non-linear relationship exists between the independent and dependent variables.

Two-stage least squares method (2SLS)

It is possible that a two-way causation exists between variables of the above models; thus, there is the need to test for endogeneity. Variables that are tested for endogeneity are those of square meter area, number of occupants, and heating hours. Square meter area is found as significant at the level of 10%; thus, 2SLS is conducted with endogenous variable the square meters area. Number of bedrooms and number of rooms have been inserted in the model as instrumental variables. Tables 4 and 5 illustrate the results of OLS, first stage of 2SLS and second stage of 2SLS for model 1 and model 2, respectively. The coefficients of square meter area and other variables have been fitted and the significant variables are the same with the OLS method.

Conclusions

This study focuses on the investigation of the socio-economic determinants, the dwellings characteristics, and the climatic conditions that influence the household electricity consumption. Two regression models, one linear and one log-linear, are built and the results have been presented in the previous section. The two-stage last squares method (TSLS) is used to explore the two-way causation of squared meter area. Most significant variables are the number of occupants, the size of the dwelling, the heating type, the heating and cooling hours, and the weather conditions. The results of this study are aligned with the ones presented in the literature review.

When acknowledging the conclusions of this analysis, policy makers could use suitable incentives to motivate customers to reduce their electricity consumption. The variables regarding heating and cooling the space are found as significant in both models. A good practice would be for the government to motivate people to renovate their houses in order to maintain the houses’ temperature and as a consequence it could reduce the cooling and heating electricity demand. As it is shown from this study, in Greece, houses use electricity to heat their spaces and in many cases they use secondary heaters. Thus, another measure could be to give a subsidy to urge consumers to change their heating systems to more efficient and cheaper ones, such as natural gas boilers.

Another strategy that policy makers could follow is to incentivize electricity providers for the electricity per hh reduction. The utilities then could provide a discount to those households that achieve an electricity reduction year by year. Thus, both utilities and consumers will win from this strategy. The customers will have the incentive of lower cost and will try to reduce their consumption.

Simultaneously, government and utilities could enhance and promote the usage of new technology electricity appliances, such as smart appliances and appliances with A++ energy label, that consume less electricity. By large campaigns, the consumers could be informed about how they can save if they change their old wasteful appliances.

To conclude, this research shows that the dwelling and household characteristics, as well as the climatic conditions, are essential predictors in models of electricity consumption. Further research on occupants’ behavior and on presence and functions of appliances will enhance the determinants of electricity consumption in housing.

Availability of data and materials

Data of this study were collected from the database of a Greek electricity provider. The research data cannot be shared publicly, because the individual privacy could be compromised. The authors after the approval of the Greek electricity provider could provide the final data set to a third researcher.

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In the following table, the correlation matrix between types of variables is presented. Due to the varying type of variables, a compass correlation matrix is created as follows:

Continuous/discrete/ordinal pair: Pearson correlation

Continuous/discrete/categorical pair: correlation coefficient or squared root of R 2 coefficient of linear regression of integer/numeric variable over factor/categorical variable. The value lies between 0 and 1.

Categorical pair: Cramer’s V value is computed based on Chi-squared test using. The value lies between 0 and 1.

The cutoff point is set 0.6. So, the variables of Family type and HDD are omitted from the analysis.

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Kotsila, D., Polychronidou, P. Determinants of household electricity consumption in Greece: a statistical analysis. J Innov Entrep 10 , 19 (2021). https://doi.org/10.1186/s13731-021-00161-9

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Global electricity consumption plummeted an unprecedented amount during the first six weeks of the COVID-19 pandemic. Just six months later it had recovered fully. Moreover, factors that were strongly associated with initial declines in electricity use were not tightly linked to its rebound.

An empty railway station in Osaka, Japan, in 2020. The unprecedented plunge in electricity use around the world at the beginning of the global pandemic was tied to shut-down policies and other factors. Surprisingly, the recovery to pre-COVID levels was quite fast and not linked to those same factors. (Image credit: Ryutaro Tsukata)

That’s what a team of Stanford University and Oregon State University researchers found in a study published online by the journal iScience in January. In April 2020, at the beginning of the COVID-19 pandemic, electricity consumption had declined by 7.6 percent across the globe, which was larger and much more rapid than the 7 percent drop seen during the 2008 global financial crisis. Apparent causes of early consumption reductions – like government restrictions on in-person work, schooling, travel and social interaction, as well as declines in personal mobility and economic production – were much less correlated with recovery times. In general, the larger a country’s initial decrease in consumption, the slower it recovered, with the extreme examples being India and Italy.

“Our question was: How is the global electricity system responding to the pandemic?” said Ram Rajagopal , senior co-author of the study and an associate professor in the Department of Civil & Environmental Engineering at Stanford. “We focused specifically on electricity consumption due to changes in habits and behavior throughout the pandemic.”

A global scale

The team of researchers drew their conclusions from a pool of data encompassing 58 countries and regions within countries, 60 percent of the world’s population and 75 percent of global electricity use. The initial decline was usually heavily correlated with the stringency of a government’s lockdown. Due to stay-at-home orders and consequential reduced mobility, residential consumption increased, while commercial and industrial consumption decreased, leading to a net reduction in usage.

Other factors, like geography, were not so strongly correlated. Declines varied among countries in the same continent and among regions in the same country. Nearly every continent saw at least one country’s electricity use decline dramatically and at least one country’s use remain nearly unaffected. In South America, Argentina took a big hit while Chile’s consumption was about the same. In the United States, the Carolinas saw a severe drop, while declines were mild in New England and the Northeast.

Researchers did not expect electricity use to rebound so quickly. Nor did they expect that factors associated strongly with decline would delink from patterns of recovery.

“By September or October 2020, global consumption had recovered. This was surprising, especially because a lot of countries still had restrictions in place. It seems as though changes in electricity use were most tightly linked to restriction levels early in the pandemic, but they decoupled as consumption recovered,” said Lily Buechler , a PhD candidate and one of two lead student researchers of the study.

People likely began to deviate from habits initially adopted by returning to work and school, shopping and socializing more regardless of changes in government restrictions, the researchers think.

Searching for an explanation

The study considered several factors that could have contributed to the recovery of consumption rates.

“The magnitude of change was not necessarily related to the magnitude of restrictions or mobility changes. That’s what people had expected before, but it turned out those were not the best predictors,” said Rajagopal.

Lower gross domestic product (GDP) seemed to be linked to initial reductions in power use, but not to the rebounds in countries’ power use.

“I think the pattern reflects that the world has changed,” said Siobhan Powell , co-lead author of the study with Buechler. “The pandemic affected the connection between economic activity and electricity use as many people shifted to new ways of working. The different world we found ourselves in during the second half of 2020 was reflected in the relationships in this study.”

India and Italy stood out in their respective regions as having the most severe declines in electricity use, averaging 26 percent at their lowest point in late March. They were also among the countries with the slowest return to pre-pandemic consumption levels. Their average consumption returned to within 5 percent of forecasted use in July. In contrast, power consumption in quick-recovering countries, which usually had a less severe initial drop, recovered before the end of May.

In terms of geographic scope, the study was the largest to examine pandemic electricity patterns. While several previous studies attempted to estimate the effects of the pandemic, most of their data had been limited to countries where data was the most readily available, primarily in the United States and Europe.

Moving forward

The team sees many future applications for their research.

“It’s actually unusual to have an event like the pandemic that affects so many different countries at the same time. It gave us an interesting opportunity to compare responses among countries that experienced very different COVID-related policies,” said Buechler.

“One of the applications of our study is in preparing for future shocks to electricity systems,” said Powell.

As climate change causes more and more unpredictable phenomena across the globe, it is important that power systems can handle the shock of sudden, sweeping changes in electricity consumption. In order to build resilience to events that may affect grid operation and forecasting, electricity system operators need to understand and anticipate the effects of such events,

“If utilities could make use of what we’ve uncovered here about how electricity consumption changes when things are shut down, they could be better informed about what will happen when things may shut down in the future because of the pandemic or other crises,” said Hilary Boudet , PhD ’10, senior co-author, and associate professor of sociology and public policy at Oregon State.

The researchers hope their work continues to contribute to that understanding. They have released their code and data with the hope that other researchers can use their results to further analyze the pandemic’s impacts on the power system, explained Buechler.

Other co-authors of this study – all at Stanford ­– are June Flora, senior research scholar; Tao Sun, PhD student; and postdoctoral scholars Chad Zanocco, Jose Bolorinos and Nicolas Astier (former).

Funding for this work was provided by the National Science Foundation and a Stanford Graduate Fellowship.

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How to reform federal permitting to accelerate clean energy infrastructure: A nonpartisan way forward

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Rayan sud , rayan sud former research assistant - economic studies , center on regulation and markets sanjay patnaik , and sanjay patnaik director - center on regulation and markets , bernard l. schwartz chair in economic policy development, senior fellow - economic studies @sanjay_patnaik robert l. glicksman robert l. glicksman j. b. and maurice c. shapiro professor of environmental law - the george washington university law school.

February 14, 2023

Executive Summary

As we laid out in a previous article , the process for the permitting of renewable energy generation and electric transmission projects in the United States is multi-layered and often extremely long. If the U.S. is to achieve its climate ambitions and fully implement transformative legislation like the Inflation Reduction Act, Congress will also have to enable a massively accelerated build-out of clean energy infrastructure.

At the same time, valuable environmental safeguards, and the established public participatory and related administrative processes used to adopt and implement them, cannot simply be sidestepped. Congress should approach federal permitting reform in a way that maximizes efficiency in government decisionmaking through shorter timelines for regulatory approvals without sacrificing the value of the current process in protecting the environment and local stakeholders. Further, it is essential that reforms are evidence-based in targeting the major sources of current delays. Our research in this article indicates that striking such a balance is possible—a targeted set of six reforms laid out here could significantly accelerate federal permitting for clean energy infrastructure, without compromising environmental protections.

Permitting reforms will also need to be able to attract bipartisan support to pass through Congress. The most recent high-profile attempt at permitting reform by Senator Joe Manchin (D-WV) was not palatable to both progressive Democrats , who argued it would eliminate environmental protections, and to Republicans , who, among other concerns, argued that it was a federal power grab and that it did not go far enough in reducing regulatory red tape.

In this article, we provide an analysis of the specific points of delay within the federal permitting process for clean energy infrastructure. We then discuss six major areas of potential reform, including evaluations of existing reform provisions on the table, such as Senator Manchin’s proposed legislation. In each area, we provide policy options that would make a significant impact on shortening permitting timelines, avoid affecting the integrity of environmental review, and attract support across the political spectrum. We conclude with options for permitting reform at the local and state levels, both of which have important planning and compliance roles in clean energy infrastructure development, making them critical pieces of the puzzle. Below are the highlights of the policy options we present:

Policy Roadmap

• Under such a program, Congress could direct federal land-management agencies to prepare national-level maps of environmental sensitivity, with corresponding pre-designated “go-to areas” for renewable energy projects in areas of lowest environmental sensitivity.
• Congress could also direct federal land-management agencies to prepare programmatic environmental impact statement reports for low-sensitivity areas with high potential for renewable energy infrastructure, and if it does so, it should appropriate sufficient funding for these mapping and reviewing functions.
• Furthermore, the U.S. Army Corps of Engineers could expand Clean Water Act Section 404 general permitting to include offshore wind transmission line construction.
• Siting authority for all interstate transmission lines could be federalized with the Federal Energy Regulatory Commission (FERC). Interstate transmission lines are critical for decarbonization of the U.S., with national benefits but local costs, that are frequently rejected by state authorities. Natural gas pipelines have similar cost-benefit tradeoffs, but they are permitted much faster due to FERC’s existing siting authority over them. Expanding FERC’s partial, pre-existing backstop authority over transmission lines to complete siting authority is therefore a step with precedent and high expected benefits. FERC could also ensure that interstate transmission lines allocate a fair fraction of their capacity to the states and communities through which they pass, thereby increasing local support for transmission and more equitably distributing its benefits.
• The Biden administration could conduct a staff capacity, funding, and technology needs assessment across agencies involved with critical permitting for clean energy. If the assessment finds substantial gaps, Congress could appropriate funds to increase resources available to these agencies, earmarking them for permitting capacity.
• Congress could transfer initial authority for Clean Air Act permitting for offshore wind from the Environmental Protection Agency (EPA) to the Bureau of Ocean Energy Management (BOEM) within the Department of the Interior. Such a step would help shorten a part of the permitting timeline for offshore wind and bring it on fairer footing with the treatment of Clean Air Act permitting for offshore fossil fuel production, an industry that generates much more pollution. Congress could also create a separate legislative title for offshore wind under the Outer Continental Shelf Lands Act (OCSLA), thereby improving planning, permitting, and leasing processes.
• Congress could support multi-agency coordination by allocating additional funding to the Federal Permitting Improvement Steering Council (FPISC) , and by expanding its scope to cover mid-sized as well as large clean energy projects. Further, all agencies could adopt the process of lead agency coordination of multi-agency reviews created by Title 41 of the Fixing America’s Surface Transportation Act (FAST-41).
• Narrow expansions of categorical exclusions under NEPA, as detailed by the Bipartisan Policy Center, are likely to accelerate some permitting actions. An overly aggressive expansion of categorical exclusions may not have beneficial effects, as categorical exclusions are already widely used, and misclassifying projects that deserve a higher level of review may not necessarily shorten permitting timelines.
• Strict and automatically-enforced NEPA time limits for pre-designated low-environmental-sensitivity areas for clean energy infrastructure, modeled on a recent European Union plan, are likely to significantly accelerate clean energy permitting and deployment. Broader time or page limits on NEPA reviews without further study and targeting are unlikely to be helpful.
• Congress could direct legal challenges to solar, wind, and transmission infrastructure Environmental Impact Statements (EIS) directly to the federal Court of Appeals for the D.C. Circuit to expedite approval of large clean energy infrastructure projects, as suggested by James Coleman. Any broader limitations on NEPA litigation may have unintended consequences, as the evidence of excessive frivolous litigation or excessive litigation-induced delay is currently mixed.

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The Effect of Energy Consumption on Economic Growth: a Scientometric Analysis

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This research employs science mapping techniques to conduct a comprehensive bibliometric analysis of 409 scholarly articles extracted from Scopus, spanning the period from 2003 to 2023. The primary objective of this study is to compile and categorize existing literature, identifying its primary thematic focuses within the domain of energy consumption and economic growth. Additionally, we aim to pinpoint gaps in the existing literature and propose potential research opportunities within this realm. Our findings reveal several significant insights. Firstly, prior studies predominantly examine the linkage between energy consumption and economic growth at an aggregate level, with limited exploration of specific energy sources such as fossil fuels, renewable energy, and hydroelectric power. Secondly, a dearth of research exists on the nexus between CO 2 emissions, energy consumption, and GDP growth in Asian countries. Lastly, urbanization, globalization, government spending, financial development, and population factors remain largely unexplored in relation to energy consumption and economic growth. This study holds substantial value for both researchers and policymakers. By delineating the thematic focuses and identifying gaps in the literature, it offers crucial insights to guide future research endeavors. Furthermore, it provides a foundation for policymakers to formulate informed strategies aimed at addressing the intricate interplay between energy consumption and economic growth.

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Gahlot, R., Garg, M. The Effect of Energy Consumption on Economic Growth: a Scientometric Analysis. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02048-y

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Using AI to improve building energy use and comfort

U niversity of Waterloo researchers have developed a new method that can lead to significant energy savings in buildings. The team identified 28 major heat loss regions in a multi-unit residential building with the most severe ones being at wall intersections and around windows. A potential energy savings of 25% is expected if 70% of the discovered regions are fixed.

Their research paper was published in Energy Conversion and Management .

Building enclosures rely on heat and moisture control to avoid significant energy loss due to airflow leakage, which makes buildings less comfortable and more costly to maintain. This problem will likely be compounded by climate change due to volatile temperature fluctuations. Since manual inspection is time-consuming and infrequently done due to a lack of trained personnel, energy inefficiency becomes a widespread problem for buildings.

Researchers at Waterloo, which is a leader in sustainability research and education and a catalyst for environmental innovation, solutions and talent, created an autonomous, real-time platform to make buildings more energy efficient. The platform combines artificial intelligence, infrared technology, and a mathematical model that quantifies heat flow to better identify areas of heat loss in buildings.

Using the new method, the researchers conducted an advanced study on a multi-unit residential building in the extreme climate of Canadian prairies, where elderly residents reported discomfort and higher electricity bills due to increased demand for heating in their units. Using AI tools, the team trained the program to examine thermal images in real time, achieving 81% accuracy in detecting regions of heat loss in the building envelope.

"The almost 10% increase in accuracy with this AI-based model is impactful, as it enhances occupants' comfort as well as reduces energy bills," said Dr. Mohamad Araji, director of Waterloo's Architectural Engineering Program and head of the Symbiosis Lab, an interdisciplinary group at the university that specializes in developing innovative building systems and building more environmentally friendly buildings.

The new AI tools helped to remove the element of human error in examining the results and increased the speed of getting the data analyzed by a factor of 12 compared to traditional building inspection methods.

Future expansions to this work will include utilizing drones equipped with cameras to inspect high-rise buildings.

"The hope is that our methodology can be used to analyze buildings and lead to millions in energy savings in a much faster way than previously possible," Araji said.

More information: Ali Waqas et al, Machine learning-aided thermography for autonomous heat loss detection in buildings, Energy Conversion and Management (2024). DOI: 10.1016/j.enconman.2024.118243

Provided by University of Waterloo

Carbon-capture batteries developed to store renewable energy, help climate

Researchers at the Department of Energy's Oak Ridge National Laboratory are developing battery technologies to fight climate change in two ways, by expanding the use of renewable energy and capturing airborne carbon dioxide.

This type of battery stores the renewable energy generated by solar panels or wind turbines. Utilizing this energy when wind and sunlight are unavailable requires an electrochemical reaction that, in ORNL's new battery formulation, captures carbon dioxide from industrial emissions and converts it to value-added products.

ORNL researchers recently created and tested two different formulations for batteries that convert carbon dioxide gas, or CO 2 , into a solid form that has the potential to be used in other products.

One of these new battery types maintained its capacity for 600 hours of use and could store up to 10 hours of electricity. Researchers also identified, studied and overcame the primary challenge, a deactivation caused by chemical buildup, that had been an obstacle for the other battery formulation.

"The Transformation Energy Science and Technology, or TEST, initiative at ORNL is precisely the kind of effort needed to address climate change. We are excited that ORNL is investing in innovative ideas and approaches that can transform the way we think about storing energy beyond lithium-ion batteries and other conventional electrochemical energy storage systems," said Ilias Belharouak, an ORNL Corporate Fellow and initiative director. "What a fantastic scenario: Using free electrons to store CO 2 and converting it to revenue-generating products is a concept I never would have imagined 10 years back, but this is just a start."

Batteries operate through electrochemical reactions that move ions between two electrodes through an electrolyte. Unlike cell phone or car batteries, those designed for grid energy storage do not have to function as a portable, closed system. This allowed ORNL researchers to create and test two types of batteries that could convert CO 2 from stationary, industrial sources.

For example, CO 2 generated by a power plant could be pumped through a tube into the liquid electrolyte, creating bubbles similar to those in a carbonated soft drink. During battery operation, the gas bubbles turn into a solid powder.

How it works

Each component of a battery can be made of different elements or compounds. These choices determine the battery's operational lifetime, how much energy it can store, how big or heavy it is, and how fast it charges or consumes energy. Of the new ORNL battery formulations, one combines CO 2 with sodium from saltwater using an inexpensive iron-nickel catalyst. The second combines the gas with aluminum.

Each approach uses abundant materials and a liquid electrolyte in the form of saltwater, sometimes mixed with other chemicals. The batteries are safer than existing technology because their electrodes are stable in water, said lead researcher Ruhul Amin.

Very little CO 2 battery research has been conducted. The previously-tried approach relies on a reversible metal-CO 2 reaction that regenerates carbon dioxide, continuing to contribute greenhouse gases to the atmosphere. In addition, solid discharge products tend to clog the surface of the electrode, degrading the battery performance.

However, the CO 2 batteries developed at ORNL do not release carbon dioxide. Instead, the carbonate byproduct dissolves in the liquid electrolyte. The byproduct either continuously enriches the liquid to enhance battery performance, or it can be filtered from the bottom of the container without interrupting battery operation. Battery design can even be tuned to create more of these byproducts for use by the pharmaceutical or cement industries. The only gases released are oxygen and hydrogen, which do not contribute to climate change and can even be captured to produce energy or fuel.

ORNL researchers used an almost completely new combination of materials for these CO 2 batteries. The few similar previous designs worked for only short periods or incorporated expensive metals.

Pros, cons and challenges overcome

The sodium-carbon dioxide, or Na-CO 2 , battery was developed first and faced some obstacles. For this system to function, the electrodes must be separated in wet and dry chambers with a solid ion conductor between them. The barrier slows the movement of ions, which in turn slows down battery operation, reducing battery efficiency.

One significant challenge for this Na-CO 2 battery is that after prolonged use, a film forms on the electrode surface, which eventually causes the battery to deactivate. Amin's research team used highly specialized microscopes and X-ray techniques to examine the battery cell when it failed and at various stages of operation.

Studying how the film formed helped researchers understand how to break it down again. They were intrigued to realize the battery could be reactivated, or prevented from deactivating at all, simply through operational changes in the charge/discharge cycle. Uneven pulses of charging and discharging prevented film buildup on the electrode.

"We are reporting for the first time that the deactivated cell can be reactivated," Amin said. "And we found the origin of the deactivation and activation. If you symmetrically charge-discharge the battery too long, it's dead at one stage. If you use the protocol we established for our cell, the chance of failure is very slim."

A second design for long-term storage

Next, researchers focused on the design of the aluminum-carbon dioxide, or Al-CO 2, battery. The team experimented with various electrolyte solutions and three different synthesis processes to identify the best combination. The result was a battery which provides enough storage for more than 10 hours of electricity to be used later.

"That's huge for long-duration storage," Amin said. "This is the first Al-CO 2 battery that could run with stability for a long time, which is the goal. Holding just a few hours of stored energy doesn't help."

Testing found that the ORNL battery could operate more than 600 hours without losing capacity, Amin said -- far more than the only previously reported Al-CO 2 battery, which was only tested for eight hours of cycling.

The cherry on top is that this battery captures almost twice as much carbon dioxide as the Na-CO 2 battery. It can be designed for the system to operate in a single chamber, with both electrodes in the same liquid solution, so there is no barrier to ion movement.

The challenge for the Al-CO 2 battery is to bring it closer to scale-up, Amin said. Even so, the team will continue systematically studying its properties to extend the operating lifetime and capture CO 2 more efficiently. For the Na-CO 2 battery to be competitive, the team will focus on developing a very fine, dense, mechanically stable ceramic membrane to separate the battery chambers.

Other ORNL scientists who contributed to the project include Marm Dixit, Mengya Li, Sabine Neumayer, Yaocai Bai, Ilias Belharouak, Anuj Bisht, Yang Guang and former ORNL researcher Rachid Essehli. The research was funded through the ORNL Laboratory Directed Research and Development, or LDRD, program. The sodium-CO 2 battery research utilized the Center for Nanophase Materials Sciences, a DOE user facility at ORNL.

• Energy and Resources
• Energy Technology
• Energy and the Environment
• Renewable Energy
• Environmental Science
• Global Warming
• Climate change mitigation
• Renewable energy
• Energy development
• Climate engineering
• Carbon dioxide
• Hydroelectricity
• Geothermal power

Story Source:

Materials provided by DOE/Oak Ridge National Laboratory . Note: Content may be edited for style and length.

Journal Reference :

• Ruhul Amin, Marm Dixit, Mengya Li, Rachid Essehli, Sabine Neumayer, Yaocai Bai, Anuj Bisht, Yang Guang, Ilias Belharouak. Origin of deactivation of aqueous Na–CO2 battery and mitigation for long-duration energy storage . Journal of Power Sources , 2024; 609: 234643 DOI: 10.1016/j.jpowsour.2024.234643

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