Residential Electricity Consumption Research Articles

Residents' willingness to be compensated for power rationing during peak hours based on choice experiment

There is growing interest in demand-side interventions aimed at reducing residential peak-hour electricity consumption in response to energy transition. However, limited knowledge exists regarding residents' willingness to accept compensation (WTC) for power rationing during peak hours. This study empirically evaluates the compensation standards based on residents' willingness to accept power rationing in peak hours based on the Choice Experiment (CE). The results indicate that residents are concerned most with the attributes of power rationing seasons and power rationing periods, requiring higher compensation for power rationing in summer and at night, whether on weekdays or non-weekdays. Further analysis reveals significant regional and group heterogeneity. Specifically, residents in northern regions are not significantly concerned about the power rationing durations compared to those in southern regions, while they are more on power rationing degrees. Analysis based on the Latent Class Analysis (LCA) model shows that two classes of residents are willing to participate in the power rationing agreement for free on both weekdays and non-weekdays, as well as two categories of residents unwilling to participate in the compensated power rationing on non-weekdays. Based on this, we estimate that the adjusted participation rate of residents in compensated power rationing is 96.59% on weekdays, with compensation ranges from 0.67 ￥/kWh to 13.38 ￥/kWh, and a participation rate of 69.69% on non-weekdays, with compensation levels ranging from 3.99 ￥/kWh to 15.34 ￥/kWh. This paper also assesses the costs of power rationing compensation to provide evidence for policymakers when determining the compensation level of the power rationing program.

Consumption as the catalyst: Analyzing rural power infrastructure and agricultural growth through panel threshold regression and data-driven prediction

With ongoing improvements in infrastructure, extensive data consistently demonstrate that the development of rural power infrastructure plays a significant role in driving agricultural growth. However, the existing research has only scratched the surface in comprehensively assessing the multifaceted impact of different aspects of power infrastructure, primarily owing to the narrow scope of performance indicators. To address this gap, this study employs principal component analysis (PCA) to develop panel threshold models and data-driven predictive models based on an indicator framework. Using Fujian Province as a case study, this research systematically analyzes the impact of rural power infrastructure development on agriculture across four stages, i.e., generation, transmission, distribution, and consumption. The findings highlight the crucial role of the consumption stage in nurturing agricultural growth through rural power infrastructure construction. Notably, the investigation reveals that residential electricity consumption and the number of service users have a significantly positive impact on rural agricultural development, while the influence of agricultural electricity consumption remains comparatively limited when compared with the secondary and tertiary sectors. Furthermore, the data-driven predictive model introduced in this study provides a more precise forecast of future trends in rural agricultural development. This research underscores the critical importance of conducting in-depth explorations to uncover the actual role of power infrastructure development in the context of rural agriculture. With regards to rural power infrastructure construction, it is advisable to invest prudently, formulate relevant policies, with a specific focus on the consumption stage, and implement targeted technology upgrades to enhance productivity and ultimately promote agricultural development.

Estimating the direct rebound effect for residential electricity use in seventeen European countries: Short and long-run perspectives

The energy saved by improving the building efficiency of European households may not be as much as expected due to a rebound effect. Based on Eurostat and World Bank annual panel data covering the period 1996–2020, this article develops dynamic panel approaches, a generalized method of moments, and first-difference specifications, to investigate the direct rebound effect of residential electricity consumption in selected European countries. Instrumental variable techniques are used to tackle the reverse causality of the electricity price in a demand model. Estimates of the direct rebound effect in residential electricity use are 18% in the short-run and 43% in the long-run. Our findings reject the hypothesis of a backfire effect in residential electricity demand. They have important implications for policy makers, suggesting the need for smart policies that consider energy consumption behaviors and decision-making processes among various households.

Matchmaking in Off-Grid Energy System Planning: A Novel Approach for Integrating Residential Electricity Demands and Productive Use of Electricity

Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must be carefully evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis and energy system modeling to optimize decision making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends significantly on three factors: (1) The residential electricity consumption patterns, which are influenced by the socio-economic composition of the community; (2) The degree of flexibility of (i) PUE assets and (ii) operational preferences of the PUE user; and (3) The capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy systems can reduce the financial risk for operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project’s financial sustainability and is achieving the overall objectives of decentralized energy system planning.

Sustainable EnergySense: a predictive machine learning framework for optimizing residential electricity consumption

In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application utilizes Linear Regression as a machine learning (ML) algorithm to develop the electricity consumption forecasting model for the next months, based on past utility bills. Linear regression is often considered one of the most computationally lightweight ML algorithms, making it suitable for smartphones. The application also offers users practical tips for optimizing their electricity consumption habits.

A discrete time-varying grey Fourier model with fractional order terms for electricity consumption forecast

Accurate electricity consumption forecast plays an important role in managing and operating a country’s power system and is a challenging task due to the data exhibiting the mixture characteristics of trend, periodicity and randomness. To accurately describe the dynamic changes of seasonal fluctuations of electricity consumption, a novel discrete time-varying grey Fourier model with fractional order terms is proposed by coupling Fourier functions with fractional order time-varying terms, as the grey action, to fit the amplitude variations hidden in seasonal pattern and improve the fitting and forecasting accuracy. Moreover, the mechanism and properties of the proposed model are discussed. Then the optimal fractional order is determined by the Dingo Optimization Algorithm (DOA) and the optimal Fourier order is selected by the hold-out method to enhance the robustness of the model. Subsequently, the results of designed experiments based on Monte-Carlo method testify the validity of the Fourier order selection method and the proposed model. Finally, this model is applied to predict the monthly residential electricity consumption of China from 2015 to 2022. The results indicate that the proposed model captures the dynamic amplitude variations of the time series and has better prediction performances than other benchmark grey prediction models and non-grey prediction models.

Classification model of electricity consumption behavior based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering

The development of electrical measurement technology has brought high latitude residential electricity consumption data to power companies, which contains the characteristics of users' electricity consumption behavior and provides data support for the behavior classification. In order to improve the efficiency of data feature extraction and the accuracy of electricity consumption behavior identification, a classification model based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering is proposed in this paper. Firstly, the sparse denoising autoencoder (SDAE) and the manually defining electricity consumption characteristic indicators are deployed to extract features from the residential daily electricity consumption data, and then the spectral clustering is employed to classify the extracted electricity consumption characteristics. Secondly, the t-distributed stochastic neighbor embedding (t-SNE) is applied to visualize and analyze the classification results, and on this basis, the secondary classification is implemented to fix the issue of the confused electricity consumption behaviors. Finally, the typical consumption behavior curves are calculated by Gaussian distance weighting method, and the characteristics of power consumption behavior are analyzed and summarized. The proposed approach is evaluated and verified by using the electricity dataset in Fujian, China.

Elicitation of electricity consumption habit formation among new subscribers

In this study, our aim is to explore the formation of electricity consumption habits in Cote d'Ivoire, specifically focusing on new subscribers. The growth of residential electricity consumption globally has been influenced by these habits, which can lead to inertia and inefficient energy usage. Using the unique database of the National Electricity Company, we uncover that electricity consumption habits exhibit a high level of persistence, and this persistence tends to strengthen over time. These findings highlight the need to develop policies that encourage electricity efficiency among new subscribers, to combat energy inefficiency and foster positive consumption habits at the household level.

Optimal Operation of Residential Battery Energy Storage Systems under COVID-19 Load Changes

Over the past few years as COVID-19 was declared a worldwide pandemic that resulted in load changes and an increase in residential loads, utilities have faced increasing challenges in maintaining load balance. Because out-of-home activities were limited, daily residential electricity consumption increased by about 12–30% with variable peak hours. In addition, battery energy storage systems (BESSs) became more affordable, and thus higher storage system adoption rates were witnessed. This variation created uncertainties for electric grid operators. The objective of this research is to study the optimal operation of residential battery storage systems to maximize utility benefits. This is accomplished by formulating an objective function to minimize distribution and generation losses, generation fuel prices, market fuel prices, generation at peak time, and battery operation cost and to maximize battery capacity. A mixed-integer linear programming (MILP) method has been developed and implemented for these purposes. A residential utility circuit has been selected for a case study. The circuit includes 315 buses and 100 battery energy storage systems without the connection of other distributed energy resources (DERs), e.g., photovoltaic and wind. Assuming that the batteries are charging overnight, the results show that energy costs can be reduced by 10% and losses can decrease by 17% by optimally operating batteries to support increased load demand.

Examining the magnitude and causes of variation in electricity use in a sample of Indian dwellings with and without air conditioning

ABSTRACT Understanding the factors that drive the use of residential electricity in India is essential for designing policies that can reduce its expected growth. This study empirically examines the magnitude and causes of variation in electricity use in a sample of Indian dwellings with and without air conditioning (AC), using an interview-based survey approach applied in 41 dwellings located across two cities representing the composite climate of India. Statistical analysis was used to unpack the relationship between electricity use and socio-technical factors. Despite the small sample size and having the same climatological realm, there was a wide variation observed in electricity use by income groups and the presence of ACs. The mean annual residential electricity consumption (REC) was observed to be highest in high-income group dwellings (5,618 kWh/year), followed by middle-income group dwellings (3,870 kWh/year) and lowest in low-income group dwellings (2,169 kWh/year). The mean REC of AC dwellings (4,208 kWh/year) was found to be nearly double that of non-AC dwellings (2,260 kWh/year) with significant seasonal variation. Multivariate regression analysis revealed that the presence of ACs, household size (number of occupants), annual non-AC appliance hours, dwelling size (number of rooms) and income group accounted for 80% of the variability in REC across the study sample. Residential energy policy in India should consider these key factors that drive REC. To be effective, such policy programmes need to be customised for different income groups, adopting incentives as well as energy awareness campaigns to influence both purchasing and habitual behaviours.

The potential of residential load flexibility: An approach for assessing operational flexibility

This study presents a novel methodology for evaluating the flexibility of residential electricity consumers under diverse operational conditions introducing innovative flexibility indicators. The increasing demand for energy flexibility from the demand side is crucial for ensuring the power grid’s economic stability and resilience. Buildings offer a range of potential flexibility sources that can be effectively leveraged for this purpose. Accordingly, this paper formulates energy flow management in a Smart House using a Mixed-Integer Linear Programming (MILP) framework, incorporating the scheduling of shiftable appliances and an Energy Storage System (ESS). By defining different consumption strategies, the feasible region for Energy-Time Profiles (ETPs) is explored through the establishment of the corresponding optimization problems. A series of case studies are presented to evaluate the impact of operational constraints on the feasible region. Finally, by meticulously analyzing the characteristics of ETPs extracted from these case studies, the proposed flexibility indicators are introduced, quantifying the Smart House’s energy flexibility potential. Furthermore, a sensitivity analysis is conducted to scrutinize the influence of key parameters on the new flexibility indicator, encompassing ESS characteristics. The findings substantiate the efficacy of the proposed indicators in providing comprehensive insights into the potential energy flexibility under a range of operational conditions.

Determinants of Urban Residential Electricity Consumption in Burkina Faso: A Study of Dwelling, Household, Socio-Economic and Appliance-Related Factors

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.

Analysis of multi-objective cooperative optimization strategy for residential load under extreme weather

In recent years, the problem of seasonal power supply shortages in extreme weather has frequently occurred across China, which has brought great inconvenience to people’s production and life. At the same time, with the advancement of the energy revolution, the proportion of electric energy in the terminal energy is increasing, which also poses a severe challenge to China’s energy security. Our research, by constructing a residential electricity consumption model and conducting multiple electricity price response experiments, has collected data that assesses various factors involved in user responses. It has also explored and inferred the impact of these factors on user-side responses. This study introduces new research directions aimed at alleviating electricity supply-demand imbalances during different periods, contributing to the optimization and enhancement of the national power system.

A Mixed-Methods Approach for Evaluating the Influence of Residential Practices for Thermal Comfort on Electricity Consumption in Auroville, India

With rapid global economic growth and a rise in disposable household income, particularly within a progressively warming planet, the escalating demand for energy to achieve thermal comfort has become a salient concern in the Global South, notably in emerging economies like India. This burgeoning need for cooling solutions has not only underscored the vital role of energy consumption but has also accentuated the imperative of comprehending the ensuing implications for electricity policy and strategic planning, particularly within the ambit of the Global South. This study explored the nuanced landscape of active cooling within an intentional community, Auroville, in southern India, aiming to discern the factors underpinning household preferences and practices in the pursuit of thermal comfort. Employing a mixed-methods approach, this study contributed empirically and methodologically to the interdisciplinary discourse by analysing residential electricity consumption patterns and cooling practices within selected households in the specified community. The study unfolded in three methodological stages: firstly, an analysis of climatic data coupled with an environmental stress index (ESI) assessment; secondly, the monitoring of end-user electricity consumption followed by rigorous data analysis; and lastly, the utilisation of qualitative in-depth interviews and observational techniques. This study’s outcome yielded empirical insights into the unprecedented shifts in the ESI for Auroville since 2014. Furthermore, the study unravelled the intricate complexities inherent in occupant behaviour within residential structures, thereby offering valuable insights into the practices that shape householders’ cooling preferences. This research enriched the understanding of the dynamics of energy consumption in the pursuit of thermal comfort and contributes to the broader discourse on sustainable development and energy policy in the context of climate change.

Mitigating the Energy Consumption and Carbon Emissions of a Residential Area in a Tropical City Using Digital Twin Technology: A Case Study of Bertam, Penang

As of 2022, roughly 79.8% of Malaysia’s population resides in urban areas, increasing the population density of its cities. The hot and humid climate in Malaysia necessitates the constant use of air conditioning, especially in cities, resulting in high residential electric consumption and carbon emissions. The residential sector significantly contributes to global climate change, accounting for 27% of global energy consumption and 17% of carbon emissions. To address this concern, the local framework of the National Low Carbon Cities Masterplan (NLCCM) is advocating for a minimum 33% reduction in carbon emissions by 2030 in urban settings, aligning with the commitments made at the 15th Conference of Parties (COP 15). The aim of this study was to determine the energy consumption and carbon emission of residential areas in a tropical city, as well as explore potential energy and carbon savings. Utilizing the Intelligent Communities Lifecycle–Intelligent Community Design (iCL-iCD) energy simulation software, a digital twin of Bertam City, Penang, was developed. This digital model included 65.4% residential housing, which, in the specific scenario under study, accounted for 36% of the city’s electrical energy consumption for cooling purposes only. An early simulation of the residential areas of the city estimated the baseline energy consumption and carbon emissions to be 607 GWh and 314,736 tCO2e, respectively. Several energy-efficient measures were applied to the residential area of Bertam City, revealing a potential saving of 37.3% in both energy and carbon emissions.

Relationship between Socio-economic Factors and Electrical Energy Consumption of Customer Groups in Indonesia

The research aim was to study Socio-Economic Analysis and its Relationship with Electrical Energy Consumption of Customer Groups in Indonesia using secondary data from 2018-2022 covering 34 provinces in Indonesia. Data sources generally come from Central Bureau of Statistics (BPS) and State Electricity Company (PLN) Indonesia. The results of the analysis show that GDP per capita, education and MSMEs have a significant negative influence on poverty in Indonesia. Furthermore, GDP per capita is the only variable that has a significant positive effect on electricity consumption, while education, MSMEs and poverty have no effect on residential electricity consumption. In the industry group, all variables have a significant effect on household consumption, except for education which has an insignificant effect, GDP per capita and MSMEs have a significant positive effect on electricity consumption, apart from that, education and poverty also have an effect but are not significant in the Business group. The research results also show that poverty does not have a significant impact, but GDP per capita, education and MSMEs have a significant positive influence on social electricity consumption. Furthermore, in the Government Office Buildings (GOB) group, GDP per capita and MSMEs have a significant positive effect, however, education and poverty do not have an influence on electricity consumption in this group. Finally, in the Public Street Lighting (PSL) group, GDP per capita, MSMEs and poverty have no effect, but MSMEs have an influence on electricity consumption.

A Study of Households’ Perception towards Solar Panel Installation Case Study: Villagers of Techno-park nearby Faculty of Technology, University of Sri Jayewardenepura

In Sri Lanka, energy demand has been exponentially increasing with the outage of non-renewable energy sources. There is significant solar intensity in most of the areas in Sri Lanka. This study focused on the domestic solar power system since domestic solar panel usage (700 MW) is the country's highest contributor to solar energy usage. A photovoltaic (PV) system can supply electric energy to loads by directly converting solar energy through the photovoltaic effect with a flexible structure. This study aimed to analyze households' perceptions of solar panel installation. The study was conducted within a 5 km radius around the Faculty of Technology, the University of Sri Jayewardenepura, to analyze the awareness of solar-related technologies. A systematically developed questionnaire was used for the survey to collect the peoples' awareness based on environmental, social & economic beliefs. Thirty residential places (30) were interviewed. According to the results, a residential house's average monthly electricity consumption is 137.67 kWh. The mean monthly income in the area is sixty-eight thousand one hundred and sixty-six rupees and sixty-seven cents. Moreover, most people had solar energy knowledge (93.33%). Among them, 53.57% of the majority were willing to install Domestic Solar PV systems for residential places. From the consumers, 76.66% of the majority had an average level of awareness of the Environmental benefits of using renewable energy and 70.00% of the majority had an excellent awareness of government intervention in rooftop solar energy projects such as "Soorya Bala Sangramaya‖. From the defined awareness levels, further analysis was done on the lowest level of Awareness of Solar Energy to identify their characteristics. The majority of them were those who had lower energy consumption, were over the age of 60 and had a monthly income below twenty thousand rupees. According to the consumers, the best strategy for promoting Solar PV Systems was to lower the prices of existing Solar PV systems. Regulating the efficiency of the Solar PV system was identified by the existing Solar PV system owners. 
 Keywords: Solar PV system, Techno-park, Photovoltaic panels

Income elasticity of residential electricity consumption in rural South Africa

Analyses of residential electricity demand primarily rely on expenditure or aggregate data. However, newer sources of data, such as that from meter readings, are becoming available. In most circumstances, these newer sources cannot be matched to detailed information about the household. In this research, we make use of South Africa’s Domestic Electrical Load Study, one of the only sources available in a developing country that includes both meter and household level data. Due to some gaps in the meter readings, we focus our attention on average peak electricity consumption, estimating the income elasticity with respect to morning, evening, and the average across both morning and evening peaks. Although we find differences in income responsiveness in the morning, relative to the afternoon, and across quantiles of electricity consumption, these differences tend not to be statistically significant. We do, however, find heterogeneities in those elasticities that can be correlated with, in particular, appliance ownership, suggesting that the ownership of appliances makes electricity more of a necessity, or at least makes the services derived from electricity more necessary for the household.

Residential Electricity Consumption Prediction Method Based on Deep Learning and Federated Learning Under Cloud Edge Collaboration Architecture

Traditional residential electricity prediction methods have problems, such as difficulty in ensuring user privacy and poor convergence speed due to the influence of Different Residential Electricity Consumption (REC) habits. A REC prediction method based on Deep Learning (D-L) and Fed-L under the Cloud Edge Collaboration (CEC) architecture is proposed to address the above issues. First, based on the CEC architecture, combining edge computing and cloud computing center server, the overall model of REC prediction is built. Then, Federated Learning (Fed-L) and D-L model Empirical Mode Decomposition - Long Short-Term Memory (EMD-LSTM) were introduced on the edge side, and the edge side Fed-L depth model was personalized by using EMD-LSTM. Finally, aggregation of edge side models was achieved in the cloud by receiving encrypted model parameters from the edge side and updating and optimizing all edge side models. The results show that the proposed method has the highest REC prediction accuracy, reaching 96.56%, and its performance is superior to the other three comparative algorithms.

Application of dynamic models in forecasting the total population of the United States

Dynamic models have been widely cited in predicting criminal population, residential electricity consumption, food prices and other objects. However, for total population predictions, dynamic models are rarely used. In this study, we will analyse the relationship between 13 variables such as CPI, grain prices, and medical expenditures and the total population of the United States, then combine it with the ARIMA model to generate a time series dynamic regression model. The conclusion is that, according to the parameters of the final model, two predictors (CPI and the number of crimes) and one interaction term (the product of the poverty rate and unemployment rate) are significantly related to changes in the population. Ultimately, the model performed well on the test set and was remarkably accurate for population prediction five years later. This report screens various factors influencing the total population and provides a broader background for applying dynamic models. In addition, this study also provides directions for subsequent research on more efficient dynamic models.