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The growing global population has significantly increased energy demand, particularly in the residential building sector. This surge underscores the necessity for accurate energy consumption forecasting to facilitate effective planning and future demand projections. However, traditional methods such as regression face challenges in modeling household electricity consumption due to seasonal and monthly variations. Smart grid technology now allows users to manage home energy use more efficiently and effectively. This study explores optimizing Artificial Neural Network (ANN) parameters using meta-heuristic algorithms instead of traditional gradient-based methods to predict residential electricity consumption across different seasons. The experimental data used to train a Radial Basis Function (RBF) neural network were utilized. The meta-heuristic algorithms employed for fine-tuning the ANN's weight and bias parameters include the Genetic Algorithm (GA), Multi-Verse Optimizer (MVO), Moth Flame Optimization (MFO), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Advanced Grey Wolf Optimizer (AGWO), Biogeography-Based Optimization (BBO), and Particle Swarm Optimization with Grey Wolf Optimizer (PSOGWO). These algorithms were evaluated for their effectiveness in adapting to seasonal variations in electricity consumption data. The results revealed that the PSOGWO algorithm consistently outperformed the others across all seasons, particularly in spring and summer. Statistical measures indicated superior accuracy and reliability in predicting energy usage. Specifically, the nine-neuron configuration with the PSOGWO algorithm achieved a high R² value of 0.99077, reflecting lower error metrics. Conclusively, the PSOGWO model's consistent performance across seasonal variations underscores its potential for reliable residential electricity consumption forecasting, making it a valuable tool for energy management in smart grids. [ABSTRACT FROM AUTHOR]

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This study analyses the dwelling, household, socio-economic and appliance-related determinants of urban domestic electricity use in Burkina Faso. A survey of 387 households in Ouagadougou was conducted and combined with their electricity use, collected from energy bills. To the authors' knowledge, this is the first large-scale, city-wide household electricity study undertaken in Burkina Faso. Linear regression models were applied to the data to assess the influence of household, dwelling, socio-economic and appliance-related factors on electricity use. Significant and unambiguous predictors of urban domestic electricity use included household income, dwelling tenure type and ownership, and use of lighting fixtures, television sets, fridges, fans, freezers and air conditioners. Dwelling, household and socio-economic factors explained 70.2% and 70.5% of the variance in electricity use, respectively, when combined with appliance ownership and use factors. This study provides an understanding of the driving factors of domestic electricity use and discusses the implications and applications of this research for a range of stakeholders in the electricity sector. [ABSTRACT FROM AUTHOR]

The share of electricity consumption by urban and rural residents in China's overall electricity consumption is very close to that of the tertiary sector, which has become an important driver of China's electricity consumption growth. At the same time, due to the mismatch between China's regional resource endowments and the level of regional development, the regional supply and demand situation for electricity varies. Therefore, it is urgent to clarify the regional differences in residential electricity consumption and the factors affecting it, and accordingly adopt targeted and feasible measures to regulate residential electricity consumption. This article includes data from 285 Chinese prefecture-level cities from 2006 to 2019, and adopts a "three lines" method of region-partitioning (Qinling–Huaihe line, Huhuanyong line, and Shanhaiguan line) to divide four regions. We used spatial econometric models to examine residential electricity consumption and its influencing factors in China from the standpoint of regional heterogeneity. The results show that there is significant regional heterogeneity in residential electricity consumption in China, and the difference between the north of the Shanhaiguan line and other areas is significant. Moreover, there is a positive spatial correlation in the per capita domestic electricity consumption of urban residents, and each influencing factor has obvious regional heterogeneity, among which household appliances are the significant influencing factor. Based on the regional heterogeneity of residential electricity consumption, management measures should be formulated according to local conditions, and the supply of electricity should be ensured by strengthening multidimensional initiatives. [ABSTRACT FROM AUTHOR]

In pursuing Japan's target of net zero greenhouse gas (GHG) emissions by 2050, decarbonization strategies at the regional level have been taking place nationally. Some successes have been achieved in the residential sector in achieving decarbonization at the regional level due to improvements in the advancement of energy-saving technologies. An important prerequisite to achieving further decarbonization in the residential sector is understanding household electricity consumption of power demand objects. This study constructed a method for predicting residential electricity consumption in a case study region. First, we set up six models of household composition for scenario exercises. Then, we used the residential energy estimation based on daily activities (REEDA) method to calculate the hourly electricity consumption of each household composition in the four seasons based on the duration of daily life activity. Finally, we separately explore the impact of housing performance (insulation, airtightness), air-conditioning patterns (intermittent operation method in a habitable room/continuous operation method in a habitable room/continuous operation method in all rooms), and geographical location on residential air conditional electricity consumption. The output is a regional residential energy estimate method that can consider multiple key variables. We verified the developed model by (1) comparing the estimated output with the Japan Energy Database and (2) testing the method for various residential areas in Japan. The results showed an accuracy level greater than 75% and nationwide applicability. [ABSTRACT FROM AUTHOR]

The residential sector is important for the energy transition to combat global warming. Due to the geographical variability of socio-economic factors, the highly dependent residential electricity consumption (REC) should be studied locally. This study aims to project future French REC considering climate change and air-conditioning (AC) scenarios and to quantify its spatial variability. For this purpose, a linear temperature sensitivity model fitted by annual observed electricity consumption data and historical temperature is applied at an intra-regional scale. Future temperature-sensitive REC is computed by applying the model to temperature projections under the climate change pathway RCP8.5. Three AC scenarios are considered: (1) A 100% AC rate scenario assuming that any region partially equipped with AC systems nowadays will have all its households equipped with AC, but local temperature sensitivity will no longer progress; (2) A gradual spreading scenario mimicking “do like my neighbor” behavior; (3) A combination of the two scenarios. Increasing temperatures lead to an overall REC decrease (−8 TWh by 2040 and down to −20 TWh by 2100) with significant spatial variability, which had never been quantified and mapped due to a lack of suited methodology and limited available data at the finest scale. The evolution of REC is modulated by the evolution of cooling needs and the deployment of AC systems to meet those needs. In the first 2 AC scenarios, the decrease of REC due to climate change could be totally offset in the South of France, which would then display an increase in REC. When the 2 AC scenarios are combined, an increase in REC could be seen over the whole country. The most extreme AC scenario shows a potential REC rise due to AC usage by 2% by 2040 and even 32% by 2100, which could be canceled by increasing the cooling setpoint up to 26–27 °C.

This study utilized different methods, namely classical multiple linear regression (MLR), statistical approach exponential smoothing (EXPS), and deep learning algorithm long short-term memory (LSTM) to forecast long-term electricity consumption in the Kingdom of Saudi Arabia. The originality of this research lies in (1) specifying exogenous variables that significantly affect electrical consumption; (2) utilizing the Bayesian optimization algorithm (BOA) to develop individual super learner BOA-LSTM models for forecasting the residential and total long-term electric energy consumption; (3) measuring forecasting performances of the proposed super learner models with classical and statistical models, viz. MLR and EXPS, by employing the broadly used evaluation measures regarding the computational efficiency, model accuracy, and generalizability; and finally (4) estimating forthcoming yearly electric energy consumption and validation. Population, gross domestic products, imports, and refined oil products significantly impact residential and total annual electricity consumption. The coefficient of determination ( R 2) for all the proposed models is greater than 0.93, representing an outstanding fitting of the models with historical data. Moreover, the developed BOA-LSTM models have the best performance with R 2 > 0.99 , enhancing the predicting accuracy (Mean Absolute Percentage Error (MAPE)) by 59.6% and 54.8% compared to the MLR and EXPS models, respectively, of total annual electricity consumption. This forecasting accuracy in residential electricity consumption for the BOA-LSTM model is improved by 62.7% and 68.9% compared to the MLR and EXPS models. This study achieved a higher accuracy and consistency of the proposed super learner model in long-term electricity forecasting, which can be utilized in energy strategy management to secure the sustainability of electric energy. [ABSTRACT FROM AUTHOR]

This study analyses the dwelling, household, socio-economic and appliance-related determinants of urban domestic electricity use in Burkina Faso. A survey of 387 households in Ouagadougou was conducted and combined with their electricity use, collected from energy bills. To the authors’ knowledge, this is the first large-scale, city-wide household electricity study undertaken in Burkina Faso. Linear regression models were applied to the data to assess the influence of household, dwelling, socio-economic and appliance-related factors on electricity use. Significant and unambiguous predictors of urban domestic electricity use included household income, dwelling tenure type and ownership, and use of lighting fixtures, television sets, fridges, fans, freezers and air conditioners. Dwelling, household and socio-economic factors explained 70.2% and 70.5% of the variance in electricity use, respectively, when combined with appliance ownership and use factors. This study provides an understanding of the driving factors of domestic electricity use and discusses the implications and applications of this research for a range of stakeholders in the electricity sector.

This dataset's data relate to Japan's electricity consumption from 1990 to 2015. It can be used by researchers, industry practitioners, and policymakers to analyze the impact of changes in electricity prices on the demand for electricity by region in the residential and industrial sectors. The dataset is divided into three categories: (i) regional sectoral electricity consumption data, (ii) regional sectoral electricity consumption intensity data, and (iii) regional sectoral electricity price data. Each category was obtained on an annual basis directly from various government databases. All data are aggregated by region.

Existing studies have shown that climate change has important implications for residential electricity consumption, yet how responses to climate vary between rural and urban residents, and more importantly, the roles of electricity pricing regimes in determining such responses remain largely unknown. In this paper, we explore these issues using monthly data in Anhui province in China. Our results suggest that on average rural residents are more sensitive to cooling degree days (CDD) than urban counterparts (0.19% vs 0.08% increase in electricity consumption per unit increase in CDD). Additionally, households who adopt the time of use (TOU) pricing regime tend to be less responsive to temperatures than households choosing tiered pricing regimes (TPHE). Substantial increases in electricity demand induced by climate change are expected in the future. With the pessimistic RCP8.5 scenario, our results suggest an increase of 35.5% and 77.1% in electricity demand respectively for the urban and rural residents in the 2080s relative to 2017. [ABSTRACT FROM AUTHOR]

As the fertility rate declines, it becomes increasingly necessary for governments to guide power companies in introducing preferential tariffs to encourage nuclear families to have children. However, traditional household statistics for residential households are time-consuming and insufficient for enterprises seeking to adopt intelligent marketing schemes for different types of households. To address these issues, this paper proposes a nuclear family type identification method for residential electricity consumption based on a deep forest algorithm. The method first classifies nuclear households according to the number of children in them. Then, features are selected by combining the daily 48-point load and prior knowledge of nuclear families. The Pearson correlation coefficient and random forest importance ranking are used to remove features with low correlation and low importance. Additionally, features are classified based on their importance, and the number of features is balanced by stratified sampling to optimize the multi-granularity scan results and improve the model's generalization. Finally, the improved cascade forest with feature input replacement base learner is trained, and the model is evaluated using accuracy evaluation metrics.The experimental results demonstrate that the proposed model accurately recognizes the number of children in different nuclear families and can be used in power companies to improve lean management. The results show that the improved method is effective in improving recognition com-pared to the original deep forest method, with recognition accuracy 5.1% higher than the random forest method and 0.7% higher than the deep forest method, reaching 94%. [ABSTRACT FROM AUTHOR]

Abstract Energy systems face challenges due to climate change, distributed energy resources, and political agenda, especially distribution system operators (DSOs) responsible for ensuring grid stability. Accurate predictions of the electricity load can help DSOs better plan and maintain their grids. The study aims to test a systematic data identification and selection process to forecast the electricity load of Danish residential areas. The five-ecosystem CSTEP framework maps relevant independent variables on the cultural, societal, technological, economic, and political dimensions. Based on the literature, a recurrent neural network (RNN), long-short-term memory network (LSTM), gated recurrent unit (GRU), and feed-forward network (FFN) are evaluated and compared. The models are trained and tested using different data inputs and forecasting horizons to assess the impact of the systematic approach and the practical flexibility of the models. The findings show that the models achieve equal performances of around 0.96 adjusted R2 score and 4–5% absolute percentage error for the 1-h predictions. Forecasting 24 h gave an adjusted R2 of around 0.91 and increased the error slightly to 6–7% absolute percentage error. The impact of the systematic identification approach depended on the type of neural network, with the FFN showing the highest increase in error when removing the supporting variables. The GRU and LSTM did not rely on the identified variables, showing minimal changes in performance with or without them. The systematic approach to data identification can help researchers better understand the data inputs and their impact on the target variable. The results indicate that a focus on curating data inputs affects the performance more than choosing a specific type of neural network architecture.

The introduction of smart meters and time-use survey data is helping decision makers to understand the residential electricity consumption behaviour behind load profiles. However, it can be difficult to obtain the actual detailed consumption data due to privacy issues. Synthesising residential electricity consumption profiles may be an alternative way to develop synthetic load profiles that initially starts by reviewing the existing synthetic load profile methods. The purpose of this review is to identify the recent methods for synthesising residential electricity load profiles by conducting a rigorous standalone literature review. This review study has been applied and presented transparently and is replicable by other researchers. The review has answered the following research questions: the definition, concept and roles of residential electricity load profile and synthesised data; recent approaches and methods; research purposes; applicable simulations and validation methods of the final selected studies. The results show that the most applied approach in modelling residential electricity load profiles is the bottom-up approach. As it is detailed, it suitable to reflect the local residential behaviour in electricity consumption. Consequently, it is more complex to develop and calibrate the model as identified in the results. Bottom-up models are more powerful in analysing energy consumptions that focus on behavioural patterns, dwelling profiles and control strategies. [ABSTRACT FROM AUTHOR]

Global warming leads to the problem of climate adaptability, which makes residents’ electricity consumption behavior more sensitive to temperature. Understanding the shape of the temperature–electricity consumption response curve helps plan power investment and production and facilitates a green and low-carbon transformation of the power system. Using data regarding electricity consumption in nearly 20,000 households from seven cities in Anhui Province, China, from 2016 to 2017, this study examined the response of residential electricity consumption to temperature. The results show that there is a positive effect of the heating degree day (HDD) and cooling degree day (CDD) on residential electricity consumption. In particular, under the possible influence of the electricity price and weather factor, the electricity-temperature response curve has a “V”-shape when the average temperature is over 30 °C, and an extra day above 34 °C will increase monthly residential electricity consumption by 2.70%. The heterogeneity analysis shows that the temperature and electricity response curve have strong fluctuations under the time-of-use (TOU) pricing policy change. This implies that the price policy helps regulate the power consumption temperature response curve and thus impacts the power load. [ABSTRACT FROM AUTHOR]

Intensifying climate change significantly impacts residential electricity consumption, especially in developing countries, such as China, that are experiencing rapid income growth. By combining meteorological and monthly household consumption survey data, this study explores the response function of residential electricity consumption to temperature in China from a micro perspective. Future residential electricity demands and related CO2 emissions are then forecast under different climate scenarios. Overall, the response function is U-shaped, and one additional day above 34 °C will increase monthly residential electricity consumption by 2.11%. Global warming will more likely increase the electricity burden on low-income groups. There will be notable seasonal changes in electricity demand in the future, and the largest increase will occur in August. The total demand for residential electricity caused by temperature change will show a fluctuating growth trend, from 0.8% and 1% in 2025 to 2% and 2.9% in 2060 under the RCP4.5 scenario and RCP8.5 scenario, respectively; meanwhile, this demand will be accompanied by a cumulative increase in carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

Energy systems face challenges due to climate change, distributed energy resources, and political agenda, especially distribution system operators (DSOs) responsible for ensuring grid stability. Accurate predictions of the electricity load can help DSOs better plan and maintain their grids. The study aims to test a systematic data identification and selection process to forecast the electricity load of Danish residential areas. The five-ecosystem CSTEP framework maps relevant independent variables on the cultural, societal, technological, economic, and political dimensions. Based on the literature, a recurrent neural network (RNN), long-short-term memory network (LSTM), gated recurrent unit (GRU), and feed-forward network (FFN) are evaluated and compared. The models are trained and tested using different data inputs and forecasting horizons to assess the impact of the systematic approach and the practical flexibility of the models. The findings show that the models achieve equal performances of around 0.96 adjusted R2 score and 4–5% absolute percentage error for the 1-h predictions. Forecasting 24 h gave an adjusted R2 of around 0.91 and increased the error slightly to 6–7% absolute percentage error. The impact of the systematic identification approach depended on the type of neural network, with the FFN showing the highest increase in error when removing the supporting variables. The GRU and LSTM did not rely on the identified variables, showing minimal changes in performance with or without them. The systematic approach to data identification can help researchers better understand the data inputs and their impact on the target variable. The results indicate that a focus on curating data inputs affects the performance more than choosing a specific type of neural network architecture. [ABSTRACT FROM AUTHOR]

The increasing energy burden on vulnerable households is critical in modern cities, it is crucial to understand how cities can characterize energy vulnerability and its relationship with the environment. This study modeled relationships between energy consumption and built environmental factors to compare determinants in average and energy-vulnerable households. While the conventional approach of identifying energy vulnerability often relies on household income, this study suggested a new approach by considering the energy-vulnerable group as a low-income class with high energy expenditure. A traditional regression model (semi-log regression) and advanced machine learning algorithm (ensemble gradient boosting, XGboost) were employed to maximize the performance of the modeling processes. The results indicated that the overall modeling performance was superior with regard to the machine learning algorithm, producing the r-squared value of 0.92 for the energy-vulnerable households, compared to the 0.34 of the semi-log regression model. While the direction of the association of the determinants was similar in the average and energy-vulnerable households, the level of association exhibited a clear difference, especially for the effect of income (comparing 0.30 to 0.03) and housing type (comparing -0.45 to -0.63). The study identified several implications regarding urban energy management and policy based on the findings. [ABSTRACT FROM AUTHOR]

In pursuing Japan’s target of net zero greenhouse gas (GHG) emissions by 2050, decarbonization strategies at the regional level have been taking place nationally. Some successes have been achieved in the residential sector in achieving decarbonization at the regional level due to improvements in the advancement of energy-saving technologies. An important prerequisite to achieving further decarbonization in the residential sector is understanding household electricity consumption of power demand objects. This study constructed a method for predicting residential electricity consumption in a case study region. First, we set up six models of household composition for scenario exercises. Then, we used the residential energy estimation based on daily activities (REEDA) method to calculate the hourly electricity consumption of each household composition in the four seasons based on the duration of daily life activity. Finally, we separately explore the impact of housing performance (insulation, airtightness), air-conditioning patterns (intermittent operation method in a habitable room/continuous operation method in a habitable room/continuous operation method in all rooms), and geographical location on residential air conditional electricity consumption. The output is a regional residential energy estimate method that can consider multiple key variables. We verified the developed model by (1) comparing the estimated output with the Japan Energy Database and (2) testing the method for various residential areas in Japan. The results showed an accuracy level greater than 75% and nationwide applicability.

The share of electricity consumption by urban and rural residents in China’s overall electricity consumption is very close to that of the tertiary sector, which has become an important driver of China’s electricity consumption growth. At the same time, due to the mismatch between China’s regional resource endowments and the level of regional development, the regional supply and demand situation for electricity varies. Therefore, it is urgent to clarify the regional differences in residential electricity consumption and the factors affecting it, and accordingly adopt targeted and feasible measures to regulate residential electricity consumption. This article includes data from 285 Chinese prefecture-level cities from 2006 to 2019, and adopts a “three lines” method of region-partitioning (Qinling–Huaihe line, Huhuanyong line, and Shanhaiguan line) to divide four regions. We used spatial econometric models to examine residential electricity consumption and its influencing factors in China from the standpoint of regional heterogeneity. The results show that there is significant regional heterogeneity in residential electricity consumption in China, and the difference between the north of the Shanhaiguan line and other areas is significant. Moreover, there is a positive spatial correlation in the per capita domestic electricity consumption of urban residents, and each influencing factor has obvious regional heterogeneity, among which household appliances are the significant influencing factor. Based on the regional heterogeneity of residential electricity consumption, management measures should be formulated according to local conditions, and the supply of electricity should be ensured by strengthening multidimensional initiatives.

The global energy system is highly vulnerable to climate variability and change. This results in a vast range of impacts on the energy demand sector and production and supply channels. This article aims to estimate the impacts of variables such as heating and cooling temperatures, income, population, and price on residential electricity demand in G7 countries. Methodologically, this study uses the second-generation panel unit root and cointegration approaches (which are robust in the presence of cross-sectional dependence), a panel fixed effects model with Driscoll–Kraay standard errors, and a novel method of moments quantile regression (MM-QR) to determine long-run elasticities. The results suggest that the residential electricity demand of G7 countries is statistically and positively responsive to cold days rather than hot days. This study also presents some policy-relevant issues based on the results. [ABSTRACT FROM AUTHOR]

The impact of average wages on electricity consumption among urban residents in China has generated many fascinating debates for scholarly research, but only a few studies have considered the spatial spillover effect of average wages on residential electricity consumption. With the use of city-level panel data from 278 Chinese cities spanning 2005 to 2016, this preliminary study explores the impacts of the average wage on residential electricity consumption. Specifically, based on the spatial Durbin model with fixed effects, three different spatial weight matrices (the economic distance, the inverse distance, and the four nearest neighbours) are utilised to check the robustness of the results under different standards. The results show that the residential electricity consumption of each city increased during the observation period, presenting obvious spatial correlations. Secondly, the average wage of residents had a positive spatial spillover effect, which promoted the residential electricity consumption of both local and surrounding cities. Thirdly, the population density, electricity intensity, educational level of urban residents, and per capita household liquefied petroleum gas consumption in urban areas are key factors influencing residential electricity consumption. Therefore, improving the educational level of urban residents and reducing the electricity intensity can help reduce electricity consumption by residents in China. This paper also presents policy recommendations. [ABSTRACT FROM AUTHOR]

The importance of accurate energy demand modelling has increased to support the decision making of policymakers for ensuring a safe energy supply. However, forecasting energy demand has several difficulties due to the complexity of the supply line, demand increase, non-linearity of data and volatility of energy usage. In this study, an improved grey Verhulst model with a constant term (GVMCT), which is based on the grey model, is introduced for improving the accuracy of energy demand prediction models. Within this context, the total residential electricity demand of both the USA and Turkey is modelled by way of linear and quadratic trend models, as well as three grey models, including the proposed GVMCT model. The effectiveness of the models is assessed based on the mean absolute error (MAE), mean squared error and root mean square error. The results show that the linear trend is the best-performing model, with an MAE of 34 564.81844, for the US data, whereas the proposed GVMCT, with an MAE of 4130.086917, outperforms all models for the data of Turkey. [ABSTRACT FROM AUTHOR]

Extreme weather induced by climate change has triggered large-scale power outages worldwide, particularly during the cold season. More insight into the climatic impacts (especially those of precipitation) on cold season residential electricity consumption (REC) is needed. This study quantified the climatic impacts on REC, with a focus on precipitation, and projected the associated changes under representative concentration pathways (RCPs) 2.6, 4.5, and 8.5 climate change scenarios in Lanzhou and Lhasa, two cities in China with distinctive cold season climates. The climatic impacts on REC in both cities are driven by heating-related demand. A stronger precipitation impact during the cold season was observed in both cities, since precipitation (particularly snowfall) boosts electricity consumption during the cold season. As the two cities become warmer and wetter, increased precipitation will offset the impact of warming on REC, with Lanzhou being more strongly affected. Based on the projections for Lanzhou, the offsetting effect will be more pronounced during the cold season across all scenarios, and will be particularly strong under RCP 2.6. For the remainder of the year, the effects of increased precipitation and warming will have competing importances under the RCP 4.5 scenario, whereas temperature effects will generally dominate the climatic impacts under the RCP 8.5 scenario. These results provide new insights for future cold season climate–energy studies and can be used to improve regional climate adaptation policies. We also propose a method to project climate-based REC changes which is compatible with potential early-warning projects to protect against extreme weather-induced power outages.

As the fertility rate declines, it becomes increasingly necessary for governments to guide power companies in introducing preferential tariffs to encourage nuclear families to have children. However, traditional household statistics for residential households are time-consuming and insufficient for enterprises seeking to adopt intelligent marketing schemes for different types of households. To address these issues, this paper proposes a nuclear family type identification method for residential electricity consumption based on a deep forest algorithm. The method first classifies nuclear households according to the number of children in them. Then, features are selected by combining the daily 48-point load and prior knowledge of nuclear families. The Pearson correlation coefficient and random forest importance ranking are used to remove features with low correlation and low importance. Additionally, features are classified based on their importance, and the number of features is balanced by stratified sampling to optimize the multi-granularity scan results and improve the model’s generalization. Finally, the improved cascade forest with feature input replacement base learner is trained, and the model is evaluated using accuracy evaluation metrics.The experimental results demonstrate that the proposed model accurately recognizes the number of children in different nuclear families and can be used in power companies to improve lean management. The results show that the improved method is effective in improving recognition com-pared to the original deep forest method, with recognition accuracy 5.1% higher than the random forest method and 0.7% higher than the deep forest method, reaching 94%.

The introduction of smart meters and time-use survey data is helping decision makers to understand the residential electricity consumption behaviour behind load profiles. However, it can be difficult to obtain the actual detailed consumption data due to privacy issues. Synthesising residential electricity consumption profiles may be an alternative way to develop synthetic load profiles that initially starts by reviewing the existing synthetic load profile methods. The purpose of this review is to identify the recent methods for synthesising residential electricity load profiles by conducting a rigorous standalone literature review. This review study has been applied and presented transparently and is replicable by other researchers. The review has answered the following research questions: the definition, concept and roles of residential electricity load profile and synthesised data; recent approaches and methods; research purposes; applicable simulations and validation methods of the final selected studies. The results show that the most applied approach in modelling residential electricity load profiles is the bottom-up approach. As it is detailed, it suitable to reflect the local residential behaviour in electricity consumption. Consequently, it is more complex to develop and calibrate the model as identified in the results. Bottom-up models are more powerful in analysing energy consumptions that focus on behavioural patterns, dwelling profiles and control strategies.

The average electricity consumption per capita in China still has significant room for growth. Under the current rigid residential electricity pricing, it is very important to study how factors other than price can promote electricity conservation among residents. This paper uses micro-survey data of 2018 and 2020 to analyze energy efficiency and the rebound effect through a stochastic energy demand frontier model, as well as to examine the role of news propaganda in this context. The research conclusions are as follows: (1) News propaganda promoting electricity saving can weaken the rebound effect of electricity use, which can lead to better electricity savings. (2) The energy efficiency of residential electricity has improved significantly compared to previous years, and the rebound effect has decreased. (3) Electricity prices have a negative impact on electricity consumption, while income, household size, home area, and extreme temperatures have a positive impact. Based on these conclusions, we propose policy recommendations to address the current inflexibility in residential electricity pricing and to improve the efficiency of electricity use among residents. • News propaganda promoting electricity saving can weaken the rebound effect. • The energy efficiency of residential electricity has improved. • The rebound effect of residential electricity has decreased. • News propaganda can be a complement to the inflexibility of pricing policy. [ABSTRACT FROM AUTHOR]

Predicting electricity demand at city level is vital for stakeholders. There are various data-driven methods in electricity consumption prediction, but their applicability to forecast monthly electricity demand has yet to be systematically explored. This study examines several widely used data-driven methods, namely multiple linear regression (MLR), machine learning (ML) method including support vector machine (SVM) and random forest (RF), and deep learning (DL) method including long short-term memory network (LSTM) and LSTM-gated recurrent unit (GRU), for long-term energy prediction using climatic and historical electricity datasets in Singapore (2005–2019) and Hong Kong (1975–2019). In Singapore, ML outperforms other methods in terms of statistical criteria and time series plots, and SVM provides the best accuracy with mean absolute percentile error (MAPE) of 2.55 %, while MLR exhibits the worst accuracy with MAPE of 3.1 %. In Hong Kong, DL surpasses ML in terms of statistical criteria, time series stability, and generalization ability. MLR achieves the best overall prediction accuracy with R 2 up to 0.95 but shows poor ability for predicting peak and low electricity consumption, while LSTM exhibits the smallest bias in these months. RF shows strong overfitting issues in both cities. Overall, SVM and LSTM are recommended for small and large datasets, respectively. • Regression, machine and deep learning methods were used to predict electricity consumption. • Temperature is the most explanatory climatic factor in Singapore, while it is cooling degree days in Hong Kong. • Machine learning performs best on small datasets, and support vector machine is the most suitable model. • Deep learning shows overall superiority over machine learning on larger datasets, and long-short term memory is recommended. [ABSTRACT FROM AUTHOR]

Integrating renewable energy technologies into a decentralised smart grid presents the 'Duck Curve' challenge — the disparity between peak demand and solar photovoltaic (PV) yield. Smart grid operators still lack an effective solution to this problem, resulting in the need to maintain standby fossil fuel-fired plants. The COVID-19 pandemic-induced lockdowns necessitated a shift to remote work (work-from-home) and home-based education. The primary objective of this study was to explore mitigating strategies for the duck curve challenge by investigating this notable shift in behaviour by examining the effect of remote work and education on grid and decentralised solar PV electricity use in 100 households with battery energy storage in the southwest of the UK. This study examined 1-min granular grid electricity and decentralised solar energy consumption data for April–August 2019 and 2020. The findings revealed statistically significant disparities in energy demand. Notably, there was a 1.4—10% decrease in average electricity consumption from April to August 2020 (during and following the lockdown) compared to the corresponding months of 2019. Furthermore, household grid electricity consumption was reduced by 24—25%, while self-consumption from solar PV systems increased by 7—8% during the lockdown in April and May 2020 compared to 2019. This increase in self-consumption was particularly prominent in the morning and afternoon, possibly attributed to the growing prevalence of work-from-home and home-based education. The dynamic shifts in energy consumption patterns emphasised the role of decentralised solar PV energy in meeting the evolving needs of households during unprecedented societal changes. Additionally, remote work might unlock decentralised solar PV's potential in resolving the 'Duck Curve', urging further investigation into the implications for energy infrastructure and policy development. • Analysed 1-minutely grid and decentralised solar PV energy demand data from 100 houses in a southwestern UK city. • Average electricity consumption decreased by 1.4—10% in April—August 2020 compared to 2019. • Grid electricity consumption was reduced by 24—25%, and from solar PV self-consumption increased by 7—8%. • Increased solar PV self-consumption was prominent in the morning and afternoon. • Might unlock solar PV's potential of resolving the 'Duck Curve.' [ABSTRACT FROM AUTHOR]

Residential electricity consumption (REC) is poised to grow significantly in the emerging markets of the Indian subcontinent and South East Asian region. The study aims to provide a systematic review of studies that have explored the determinant factors of REC in South, East and South East Asia. After screening for eligibility and data quality, 52 studies were selected for systematic review using the PRISMA framework. A systematic review of these studies revealed 113 factors that at least one study reported as having significant impacts on REC. These factors comprised socioeconomic indicators, variables pertaining to building envelopes and occupancies, ownership and usage patterns of appliances, and influence of climate and location. The effect of each of these determinants on REC was recorded, and the relationship was analysed considering the available literature. Using a criterion of being reported as significant by at least five studies, a subset of 21 factors emerged as the key determinants of REC. To our knowledge, this study is the first to present a comprehensive picture of the key determinants of REC in South, East and South East Asia based on a systematic review. The review identifies factors that unambiguously contribute to increasing or decreasing REC and those that require further research. • Systematic review of determinants of residential electricity consumption (REC) in South, East and South East Asia. • Identified 113 factors that have been used to model REC with significant effects. • Recognized 21 widely reported socioeconomic, dwelling, appliance, and location-related factors. • Opportunity to improve studies by including synthetic indicators and regularisation methods. [ABSTRACT FROM AUTHOR]

Quantifying the climatic effect on residential electricity consumption (REC) can provide valuable insights for improving climate–energy damage functions. Our study quantifies the effect of climate on the REC in Tibet using machine learning algorithm models and model-agnostic interpretation tools of feature importance scores and partial dependence plots. Results show that the climate contributes about 16.46% to total Tibet REC while socioeconomic factors contribute about 83.55%. Precipitation (particularly snowfall) boosts electricity consumption during the cold season. The effect of the climate is stronger in urban Tibet (~25.06%) than rural Tibet (~14.79%), particularly in September when electricity-aided heating is considered optional, as higher incomes amplified the REC response to the climate. With urbanization and income growth, the climate is expected to contribute more to Tibet REC. Hence, precipitation should be incorporated in climate–REC functions for the social cost of carbon (SCC) estimation, particularly for regions vulnerable to snowfall and blizzards. Herein, we developed a model-agnostic method that can quantify the total effect of the climate while differentiating between contributions from temperature and precipitation, which can be used to facilitate interdisciplinary and cross-section analysis in earth system science. Moreover, this data-driven model can be adapted to warn against extreme weather induced power outages. [ABSTRACT FROM AUTHOR]

Electricity consumption is crucial for decarbonizing transformation from coal, biomass to clean energy in the residential sector. REC has become the largest contributor to China's electricity consumption growth. However, we have encountered challenges in assessing the effectiveness of relevant energy policies and know little about the variability of macro factors. Consequently, by employing a decomposition analytical framework at different levels (nation, region, and province), we decompose and quantify the driving forces of REC and demonstrate the dominant determinants of REC, the differentiation of these factors across areas, and between urban and rural. The key findings show that urbanization positively affects the urban REC but negatively impacts the rural REC. Increasing per capita consumption expenditure in rural areas has a sustained promoting effect on REC, and the influence degree is higher than that in urban areas. The impact of the coal substitution factor on electricity in inland developing regions is much higher than that in developed coastal areas. However，the effect of the coal substitution factor on electricity in less developed rural areas is much higher than that in urban. These findings provide unique insights into the mechanism and dominant driving forces of REC growth and underscore the equality between urban and rural for a sustainable green energy consumption system. • We build an expanded nation-region-province index decomposition framework. • The main driving forces of residential electricity consumption differ between regions. • We explain the causes of different performance of factors between urban and rural. • Per capita expenditure is the primary driver of residential electricity demand growth. • Rural population scale factor has an inhibiting impact on electricity demand growth. [ABSTRACT FROM AUTHOR]

To understand how specific interventions affect a process observed over time, we need to control for the other factors that influence outcomes. Such a model that captures all factors other than the one of interest is generally known as a baseline. In our study of how different pricing schemes affect residential electricity consumption, the baseline would need to capture the impact of outdoor temperature along with many other factors. In this work, we examine a number of different data mining techniques and demonstrate Gradient Tree Boosting (GTB) to be an effective method to build the baseline. We train GTB on data prior to the introduction of new pricing schemes, and apply the known temperature following the introduction of new pricing schemes to predict electricity usage with the expected temperature correction. Our experiments and analyses show that the baseline models generated by GTB capture the core characteristics over the two years with the new pricing schemes. In contrast to the majority of regression based techniques which fail to capture the lag between the peak of daily temperature and the peak of electricity usage, the GTB generated baselines are able to correctly capture the delay between the temperature peak and the electricity peak. Furthermore, subtracting this temperature-adjusted baseline from the observed electricity usage, we find that the resulting values are more amenable to interpretation, which demonstrates that the temperature-adjusted baseline is indeed effective.

This study investigates income and price elasticities of residential electricity demand and electricity rebound effect using the annual panel data of 18 European Union countries. Quantile regression results show that income elasticities are higher in lower quantiles, whereas price elasticities are uniform in all quantiles. Three fixed-effect estimators provide consistent results, with the scale of long-run income and price elasticities increasing compared to short-run elasticities. In the short run, the derived elasticities of electricity demand are 0.08 for income and −0.03 for price, whereas in the long run, they are at 1.17 for income and −0.43 for price. In addition, asymmetric price response is observed, estimating the rebound effect in the decomposed price model. Our empirical results suggest that European governments implement policies that accelerate improvements to electricity efficiency and invigorate the retail electricity market, making residential electricity prices more transparent to consumers. [ABSTRACT FROM AUTHOR]

This paper estimates the residential electricity demand’s response to price policy and income dynamics in China at both national and provincial levels, specifically in Anhui, Guizhou, Zhejiang, Jiangsu, and Jiangxi provinces, using the unbalanced panel partial adjustment model (PAM) and time-series PAM based on monthly data from January 2006 to October 2016. The empirical results show that the income elasticity of demand is inelastic in the short term on the whole while it is elastic in the long term and ranges from 0.807 to 2.371 in different provinces. The own-price elasticity of electricity demand is insignificant in most cases due to the government’s price regulation, leading to that the regulating effect of the electricity price policy, such as the tiered pricing policy, is weak. Overall, the residents’ income and seasonality are the most important driving forces of residential electricity consumption (REC). The price policy for pipeline natural gas has a significant effect on REC, and it would be an effective alternative option to regulate REC in China. This paper recommends reducing the upper limit of REC in each grade under the tiered tariff policy and further promoting the use of energy-efficient household appliances. [ABSTRACT FROM AUTHOR]

The global energy system is highly vulnerable to climate variability and change. This results in a vast range of impacts on the energy demand sector and production and supply channels. This article aims to estimate the impacts of variables such as heating and cooling temperatures, income, population, and price on residential electricity demand in G7 countries. Methodologically, this study uses the second-generation panel unit root and cointegration approaches (which are robust in the presence of cross-sectional dependence), a panel fixed effects model with Driscoll–Kraay standard errors, and a novel method of moments quantile regression (MM-QR) to determine long-run elasticities. The results suggest that the residential electricity demand of G7 countries is statistically and positively responsive to cold days rather than hot days. This study also presents some policy-relevant issues based on the results.

Understanding how populations' daily behaviors change during the COVID-19 pandemic is critical to evaluating and adapting public health interventions. Here, we use residential electricityconsumption data to unravel behavioral changes within peoples' homes in this period. Based on smart energy-meter data from 10,246 households in Singapore, we find strong positive correlations between the progression of the pandemic in the city-state and the residential electricity consumption. In particular, we find that the daily new COVID-19 cases constitute the most dominant influencing factor on the electricity demand in the early stages of the pandemic, before a lockdown. However, this influence wanes once the lockdown is implemented, signifying that residents have settled into their new lifestyles under lockdown. These observations point to a proactive response from Singaporean residents--who increasingly stayed in or performed more activities at home during the evenings, despite there being no government mandates--a finding that surprisingly extends across all demographics. Overall, our study enables policymakers to close the loop by utilizing residential electricity usage as a measure of community response during unprecedented and disruptive events, such as a pandemic. [ABSTRACT FROM AUTHOR]

At least one-third of the total electricity generation of a country is consumed by the residential sector, which is one of the most unpredictable consumer groups with respect to electricity usage and thus risks network security by producing peaks in demand. Consequently, a growing body of literature has paid particular attention to this sector and explored the household factors responsible for residential electricity consumption. However, comprehensive studies that explicitly considered all these factors in relation to peaks in demand have rarely been undertaken. A comprehensive review of the factors that contribute to residential electricity consumption has thus been conducted. Possible links between these factors and peaks in demand are investigated. As part of this review, in-house energy-use dynamics are presented, and a framework for future studies is introduced. The results of this review would serve as a useful reference to engage in new research dealing with residential factors and peaks in electrical demand. [ABSTRACT FROM AUTHOR]

The impact of average wages on electricity consumption among urban residents in China has generated many fascinating debates for scholarly research, but only a few studies have considered the spatial spillover effect of average wages on residential electricity consumption. With the use of city-level panel data from 278 Chinese cities spanning 2005 to 2016, this preliminary study explores the impacts of the average wage on residential electricity consumption. Specifically, based on the spatial Durbin model with fixed effects, three different spatial weight matrices (the economic distance, the inverse distance, and the four nearest neighbours) are utilised to check the robustness of the results under different standards. The results show that the residential electricity consumption of each city increased during the observation period, presenting obvious spatial correlations. Secondly, the average wage of residents had a positive spatial spillover effect, which promoted the residential electricity consumption of both local and surrounding cities. Thirdly, the population density, electricity intensity, educational level of urban residents, and per capita household liquefied petroleum gas consumption in urban areas are key factors influencing residential electricity consumption. Therefore, improving the educational level of urban residents and reducing the electricity intensity can help reduce electricity consumption by residents in China. This paper also presents policy recommendations.

Residential electrical peak demand has attracted much attention as this demand is unpredictable. Several approaches have been used in the literature to analyze residential electrical demand. However, no previous studies have considered the energy cultures framework (ECF) to assess residential peak demand. This study investigates the applicability of ECF in accounting for residential demand variability. The ECF consists of three elements: material culture, cognitive norms, and energy practices. The ECF has been employed in a sample of 50 households in Bangladesh, of which 32% of households (HHs) were 'peaky' (peak time demand is much higher than average) and 68% HHs 'non-peaky' (average demand during peak hours). The analysis revealed that peaky HHs in Bangladesh is dominated by material culture. Non-peaky HHs' demands are driven by cognitive norms. Spearman test results were found to be in line with ECF analysis. [ABSTRACT FROM AUTHOR]

Quantifying the climatic effect on residential electricity consumption (REC) can provide valuable insights for improving climate–energy damage functions. Our study quantifies the effect of climate on the REC in Tibet using machine learning algorithm models and model-agnostic interpretation tools of feature importance scores and partial dependence plots. Results show that the climate contributes about 16.46% to total Tibet REC while socioeconomic factors contribute about 83.55%. Precipitation (particularly snowfall) boosts electricity consumption during the cold season. The effect of the climate is stronger in urban Tibet (~25.06%) than rural Tibet (~14.79%), particularly in September when electricity-aided heating is considered optional, as higher incomes amplified the REC response to the climate. With urbanization and income growth, the climate is expected to contribute more to Tibet REC. Hence, precipitation should be incorporated in climate–REC functions for the social cost of carbon (SCC) estimation, particularly for regions vulnerable to snowfall and blizzards. Herein, we developed a model-agnostic method that can quantify the total effect of the climate while differentiating between contributions from temperature and precipitation, which can be used to facilitate interdisciplinary and cross-section analysis in earth system science. Moreover, this data-driven model can be adapted to warn against extreme weather induced power outages.

Household characteristics play an important role in helping utilities carry out efficient and personalized services. Current methods to obtain such information, e.g., surveys, are usually costly and time-consuming. The widespread installation of smart meters enables the collection of fine-grained residential electricity consumption data and thus, making the identification of household characteristics from smart meter data possible. This article proposes a time–frequency feature combination based household characteristic identification approach using smart meter data. First, in addition to conventional time-domain statistical features, several frequency-domain features are extracted using discrete wavelet transform. Second, the random forest algorithm is used to select a subset of important features and remove redundant information contained in the original feature set. Third, a support vector machine is used as a classifier with the input of the selected features to infer the household characteristics. Finally, case study using the realistic data from Ireland indicates that the proposed approach shows better performance after incorporating the frequency-domain features. [ABSTRACT FROM AUTHOR]