Household Electricity Consumption Research Articles

Socio-Economic and Demographic Factors Affecting Adoption of Electric Vehicles in India

The purpose of this study is to analyze the diverse socio-economic and demographic determinants that impact the adoption and growth of electric vehicles (EVs) in India. Quantitative research was conducted utilizing data from the Prowess IQ database. Growth of Electric Vehicles (GEV) is represented through the proxy of total income of ten prominent Indian EV manufacturing companies for eight years from 2016 to 2023. This is analyzed against nine independent variables including GDP, Per Capita Income, Age of Company, Population Density, Average Petrol/Diesel Price, Price of Electricity Per Unit, Average Household Electricity Consumption, and Vehicle Electricity Consumption, data for which was extracted from governmental and company websites. The study employed statistical methods, such as correlation and regression analysis, to examine the associations between the dependent and independent variables. The results depict notable positive correlations with GDP, per capita income, age of company, and population density, and negative correlations with the price of electricity and diesel. Regression analysis further substantiated six variables (population density, petrol prices, per capita income, GDP, diesel prices, and average household electricity consumption) as statistically significant contributors to GEV in India These findings point towards the need to leverage the positive drivers and mitigate the effects of negative correlates. Policies offering financial support tailored to demographic and economic contexts, might help achieve accelerated and sustainable growth for EVs in India. This study highlights the unique impact of demographic elements like population density on the EV market in India.

Evaluation of carbon footprint due to household electricity consumption in Iranian provinces

Evaluation of carbon footprint due to household electricity consumption in Iranian provinces

Understanding visual feedback mechanism from three-dimensional of information, time and display: A meta-analysis of feedback research for household electricity conservation

Visual feedback, which refers to the provision of energy information through visual interfaces to households, has emerged as a crucial approach for promoting energy conservation and addressing climate change. However, existing experimental studies lack a unified standard framework design for visual feedback, and have significant divergences in its effectiveness. Hence, we first propose a design framework for visual feedback on household electricity consumption, including information, time and display. On this basis, this study provides comprehensivemeta-analytic evidence on the effectiveness of visual feedback from 156 articles between 1976 and 2024, and finds that visual feedback has a moderate energy-saving effect. Further considering the interaction of 3D influencing factors, we explore the optimal combination of visual feedback using multilevel meta-regression. The results show that information, time and display are all important factors for visual feedback. When designing informational interventions, it is important to choose effective feedback frequency and feedback media. The optimal strategy of 3D visual feedback combination is “self-comparison + monetary incentives + low frequency + digital media”. These findings can help policy makers and utilities to develop effective feedback measures to promote household energy conservation.

Exploring the correlation between rising temperature and household electricity consumption: An empirical analysis in China

This study investigates the nexus between rising temperatures and household energy consumption using data from respondents' electricity bills in the "China Residential Energy Consumption Survey." Our analysis reveals a significant correlation, with an 8.9 % increase in yearly energy consumption observed when the average temperature exceeds 32 °C. Additionally, we explore potential shifts in power usage due to global warming by integrating baseline estimates with daily temperature forecasts from eight contemporary climate models. Our findings project alarming trends: without interventions to curb greenhouse gas emissions, home electricity consumption could surge by 9.59–30.09 % in the medium term and by 9.77–47.70 % in the long run. By shedding light on these critical connections, our research underscores the urgent need for policy actions to mitigate the adverse impacts of climate change on energy consumption patterns.

Sharing Is Caring: Exploring Distributed Solar Photovoltaics and Local Electricity Consumption through a Renewable Energy Community

Renewable Energy Communities (REC) can play a crucial role in enhancing citizen participation in the energy transition. Current European Union legislation enshrines energy communities and mandates Member States to encourage these organizations, promoting adequate conditions for their establishment. Nevertheless, uptake has been slow, and more research is needed to optimize the associated energy sharing. Using a Portuguese case study (REC Telheiras, Lisbon), this research aims to match local generation through four photovoltaic systems (totalizing 156.5 kWp of installed capacity) with household electricity consumption while cross evaluating the Portuguese legislation for energy sharing. The latter aim compares two scenarios: (a) current legislation (generated energy must be locally self-consumed before shared) and (b) equal share for members with a fixed coefficient. The evaluation is performed according to two indexes of self-consumption (SCI) and self-sufficiency (SSI), related to the simulation of four photovoltaic systems in public buildings, their associated consumption profiles, and an average household consumption profile of community members. The results show that, while maximizing self-consumption for the same values of generation and consumption, the number of participants is considerably lower for Scenario A (SCI = 100% is achieved with at least 491 residential members in Scenario A and 583 in Scenario B), implying that legislative changes enabling energy communities to better tailor sharing schemes may be necessary for them to become more attractive. The methods and results of this research can also be applied to other types of facilities, e.g., industrial and commercial consumers, if they are members of a REC and have smart meters in their installations.

A comprehensive study to estimate income and price elasticities of household electricity consumption using Auto-metrics

This study focuses on understanding family electricity consumption behaviors in response to income and price changes from 1994 to 2022 across 12 prominent European countries. We employ a unique econometric approach, Auto-selection Models, to analyze the nuances of energy demand elasticity. Our methodology includes the use of saturation techniques, which are highly effective in identifying anomalies and discontinuities in the data, ensuring the reliability of our results. The Auto-metrics method streamlines the model selection process and enhances the accuracy of elasticity predictions. We use Error Correction Models (ECMs) for each country to examine the long-term equilibrium relationships among key variables such as energy consumption, household income, electricity prices, and weather patterns, taking into account any observed anomalies and significant structural changes. The findings reveal varying levels of income and price elasticity across the countries, reflecting their unique economic and climatic conditions. The study's results hold significant implications for policymaking. By recognizing and adapting to the varied characteristics of electricity demand elasticity, energy policies can be more accurately tailored at both the national and European Union levels.

Income and electricity consumption issue: evidence from European households

Research on electricity consumption plays an important role in the development of sustainable national strategies and mitigation of global climate change. With global economic development in general, and Europe in particular, the demand for electricity in households is increasing. Therefore, based on demand theory, this study evaluates the impact of income on electricity consumption in European households. Data were collected from 35 European countries and analyzed. The instrumental variable regression results indicate that income positively impacts electricity consumption in European households. The electricity consumption of European households provides evidence that supports the prediction of demand theory, as an increase in household income as well as a decrease in price leads to a rise in overall consumption. However, the effect of price is not statistically significant. The findings also show that population growth and inflation positively influence household electricity consumption. The outcomes of this study have theoretical and practical implications for improving energy efficiency and effective electricity use in European households. Therefore, it is necessary to continue implementing energy-saving strategies at the household level in the context of increasing electricity consumption in Europe.

Factors Influencing Electricity Consumption in Rural Households

Over time, several relationships have been defined between electricity consumption and a region’s social and economic variables, with income as the main factor. This paper uses multiple correspondence analysis to identify the categories of dwellings and, from a graphical point of view (positioning maps), the effects of the different characteristics that influence the electricity consumption of households in rural areas of Cundinamarca, Colombia. In this analysis, the consumption of residential users responded mainly to what they can afford or acquire based on their income, consumption habits, and the characteristics of the technology. Furthermore, this study highlights the implications of these findings for policymakers and energy providers, providing valuable insights for developing targeted strategies to promote energy efficiency and sustainability in rural areas. This research contributes to a deeper understanding of the dynamics of electricity consumption and highlights the importance of tailoring energy-related interventions to the specific socio-economic context of rural communities, in this case in Cundinamarca.

CLIMATE CHANGE AND HOUSEHOLD ELECTRICITY CONSUMPTION INEQUALITY IN CHINA

Based on the micro-data of China’s Urban Household Survey, this paper investigates the impact of climate change on household electricity consumption inequality by using a two-way fixed effect model. The result reveals that households are more likely to purchase air conditioning (AC) and consume more electricity in response to extreme weather shocks. Furthermore, it is observed that high-income households are better equipped to mitigate the negative effects of extreme weather, as they possess a larger proportion of AC units and are more likely to acquire new ones to cope with extremely high temperatures. Finally, the study finds that extreme weather shocks exacerbate inequality in household electricity consumption.

Optimasi Sistem Panel Surya dan Baterai Untuk Meminimasi Biaya Pemakaian Energi Listrik Rumah Tangga

The use of photovoltaic-battery energy storage systems to meet the electrical energy needs of house while reducing the cost of using electricity from the grid is increasing. Optimization of this system can be performed by optimizing the energy consumption of solar panels and batteries. This paper presents the optimization of solar panels and battery systems to minimize the cost of using electricity from the grid. Circuit models and mathematical formulations of grid-connected solar panel and battery systems were created for optimization purposes. The optimization problem is solved by using mixed-integer linear programming and Pyomo software based on the Python programming language. Data on household electricity consumption and output power from two solar panels with a capacity of 100 Wp and 200 Wp respectively were obtained from the results of simultaneous measurements for one month. The test results show that the optimization of the solar panel and battery system can reduce the cost of electricity consumption at home with optimal energy consumption from solar panels and batteries.

AN AGENT-BASED COMPUTATIONAL MODEL ON HOUSEHOLD ELECTRICITY CONSUMPTION IN INDIAN CITIES

ABSTRACT Traditional methods of planning and managing cities often adopt a simplified linear approach to understanding their composition and functioning. These approaches have led to the creation of many undesirable and non-functional cities. However, over the last few decades, there has been a radical shift in the approach to investigating cities through the adoption of complexity science and its various concepts. Unfortunately, this paradigm shift has been limited to only a handful of developed Western nations. This study proposes an extendable agent-based computational model for assessing household electricity consumption patterns in cities of developing nations. The model is demonstrated using an urban precinct in India as a representative case. It simulates the monthly electricity consumption of an urban precinct in Nagpur by combining the significant factors impacting household electricity consumption in developing nations, with the household's electricity consumption process in relation to outdoor weather conditions, and the heterogeneity in occupants’ behavior. The predictive validation of the model using the “BehaviorSpace” tool yielded promising results, with the model consistently and accurately predicting the growth of variables and household electricity consumption patterns. The model also successfully generated multiple future scenarios, showcasing its capacity to forecast the future implications of policy interventions. The simulation results indicated that household electricity consumption in Indian cities is driven by population growth and income levels. Additionally, it suggests that widely practiced rooftop solar panel installation policies may not be a viable approach to achieving sustainability in this sector. This study finds that the “emergent urbanism” approach has the potential to revitalize the urban energy planning process through an effective framework for comprehensively understanding the phenomena and addressing its various complexities.

Electrification and residential well-being in China

In the past 20 years, China has undergone a rapid process of electrification, which has contributed to the modernization of people's lives. This paper associated household electricity consumption, appliance service function, total cost of electricity consumption to life well-being for the first time, and explained the structural characteristics of these changes. The welfare index from appliance services is defined as the result of the interaction between electricity consumption and appliance operation, and is used as an indicator to assess the improvement of household well-being. We sorted out 9 types of service functions of appliances, estimated the structure of household electricity consumption, total electricity cost and welfares received, and analyzed the factors that enhance welfare and their contributions. The results show that the electricity consumed by 12 main household appliances per household per year increased from 358 kWh in 2000 to 1419.5 kWh in 2020, and the total household electricity cost increases from 1315.74 yuan (RMB) to 6772.68 yuan. The quantity and structure of both have changed greatly. The service functions provided by appliances are increasingly diversified, have expanded from the basic services such as lighting, watching TV, refrigerated food to nine aspects including communication and transportation. The quality of life of residents continues to be improved, and the welfare index has increased from 2.48 to 7.82. There is a good linear relationship between the index and the three independent variables. The unit contributions of household disposable income, total electricity cost and household population size to the welfare index were 0.0836, 0.1195 and −1.6161, respectively. To accelerate electrification and enhance people's well-being, China needs to further improve the power and communication infrastructure, promote the application of intelligent technology, and implement precise encouragement policies.

A review of electricity consumption and CO2 emissions in Gulf Cooperation Council households and proposed scenarios for its reduction

PurposeThe Gulf Cooperation Council (GCC) of countries has some of the highest electricity consumptions and carbon dioxide emissions per capita in the world. This poses a direct challenge to the GCC government’s ability to meet their CO2 reduction targets. In this review paper the current household electricity consumption situation in the GCC is reviewed.Design/methodology/approachThree scenarios for reducing energy consumption and CO2 emissions are proposed and evaluated using strengths, weaknesses, opportunities and threats (SWOT) as well as the political, economic, social, technical, legal and environmental (PESTLE) frameworks.FindingsThe first scenario found that using solar Photovoltaic (PV) or hybrid solar PV and wind system to power household lighting could save significant amounts of energy, based on lighting making up between 8% to 30% of electricity consumption in GCC households. The second scenario considers replacement of conventional appliances with energy-efficient ones that use around 20% less energy. The third scenario looks at influencing consumer behavior towards sustainable energy consumption.Practical implicationsPilot trials of these scenarios are recommended for a number of households. Then the results and feedback could be used to launch the schemes GCC-wide.Social implicationsThe proposed scenarios are designed to encourage responsible electricity consumption and production within households (SDG12).Originality/valueAll three proposals are found viable for policymakers to implement. However, to ensure successful implementation GCC Governments are recommended to review all the opportunities and challenges associated with these schemes as laid out in this paper.

Energy consumption forecasting with deep learning

This research endeavors to create an advanced machine learning model designed for the prediction of household electricity consumption. It leverages a multidimensional time-series dataset encompassing energy consumption profiles, customer characteristics, and meteorological information. A comprehensive exploration of diverse deep learning architectures is conducted, encompassing variations of recurrent neural networks (RNNs), temporal convolutional networks (TCNs), and traditional autoregressive moving average models (ARIMA) for reference purposes. The empirical findings underscore the substantial enhancement in forecasting accuracy attributed to the inclusion of meteorological data, with the most favorable outcomes being attained through the application of time-series convolutional networks. Additionally, an in-depth investigation is conducted into the impact of input duration and prediction steps on model performance, emphasizing the pivotal role of selecting an optimal duration and number of steps to augment predictive precision. In summation, this investigation underscores the latent potential of deep learning in the domain of electricity consumption forecasting, presenting pragmatic methodologies and recommendations for household electricity consumption prediction.

Detecting changes in price-sensitivity of household electricity consumption: The impact of the global energy crisis on implicit demand response behavior of Finnish detached households

The energy transition combined with the ongoing global energy crisis and soaring inflation has pushed household electricity prices to all-time high levels. The year 2022 was especially rough for customers due to significant electricity price increases, leading to, for instance, a fall in the European consumer confidence index to a new all-time low. When dealing with very high electricity prices, the only things household can do to reduce their utility bills is to reduce consumption, invest in self-generation, or change to a time-based electricity contract and shift consumption to cheaper times. In this study, we present a methodology for analysis of changes in customer electricity use behavior and price-sensitivity based on smart electricity meter data, correlation analysis and the k-means clustering algorithm. Electricity meter data is further used to cluster customers into different primary heating type groups to analyze whether there exist behavioral change differences between these groups. As a case study we utilize these methods to investigate the impact of the global energy crisis and rapidly risen electricity prices of 2022 on the price-sensitivity of Finnish detached house electricity use. Based on our results, almost a third of the analyzed households had a statistically significant increase in implicit demand response behavior from January to December of 2022. The largest shares of households with a statistically significant increase in price-sensitivity were in night-time water heating, oil, and ground-source heating groups.

The impact of adopting an energy information system on household energy consumption: a dynamic difference-in-differences approach

ABSTRACT This study analyzes effects of adopting an energy information system on energy use based on the advanced metering infrastructure (AMI) in South Korea. Adopting the AMI was randomly assigned by the Korean Government. However, the adoption timing varies across household, making it difficult to apply an ordinary difference-in-difference (DID) approach to measure the impact, given the variation in adoption timing across households. We use a dynamic DID that is suitable for estimating a causal effect of a policy whose treatment timing differs depending upon the individual. Results indicate that AMI adoption led to a reduction in household electricity consumption, peaking within 18 months after adoption with a slight rebound effect. Our findings suggest that providing information on energy consumption and cost in the household sector can significantly reduce electricity use by as much as one quarter of average monthly consumption. Reducing electricity use in households can be achieved in several ways, including monetary incentives as well as personal and social feedback provided by adopting the AMI, implying the importance of a dissemination policy.

Energy Community Management Based on Artificial Intelligence for the Implementation of Renewable Energy Systems in Smart Homes

The trade, control, and management of transactive energy have gained significant relevance and are receiving a lot of interest from academia and industry. New strategies for their progress and implementation are emerging. The prosumer concept involves the integration of household loads with solar PV and battery storage systems. It is suggested as a practical technique to include renewable energy sources and reduce strain on the distribution grid. This study indicates establishing a home-smart community connected to a power grid, explicitly emphasizing prosumers. The district employs cooperative game theory to enable the sharing, controlling, and coordinating of excess energy generated by distributed energy resources (DERs) with neighboring prosumers experiencing energy shortages. The energy community examined in this study comprises six identical prosumers, encompassing residential energy use, solar photovoltaic (PV) installations, and battery storage systems. Furthermore, the community consists of an energy community manager and an energy retailer, both tied to a distribution system. The intended market model assesses the economic advantages of incorporating renewable sources into a centralized transactive energy trading system This approach reduces transaction costs and power expenditures, offering cost savings to producers and consumers. The results show that the suggested intelligent method has successfully decreased consumer electricity costs in various circumstances. Indeed, the proposed approach seeks to oversee the exchange, organization, and control of the community’s household electricity consumption effectively. Furthermore, its objective is to diminish energy usage, enhance system effectiveness, and minimize operational strain and carbon emissions.

Household electricity consumption determinants in major Nigeria cities

The increasing epileptic electricity supply, mainly in the residential areas of Nigerian cities, has been linked to the incorrect knowledge of the numerous socio-economic and physical indices that influence household electricity usage. Most of the seemingly identified explanatory factors were done at macro level which does not give a clear estimate of this electricity demand. The thrust of the study is to analyse empirically the household electricity determinants in Nigerian cities with a view to evolving a more informed and sustainable energy policy decision. Multistage area cluster sampling method was adopted in the study where 769 copies of structured questionnaire were distributed to electricity users of prepaid meters in five major Nigerian cities. The research hypothesis was tested using the multiple linear regression statistical tool. The result revealed that nine variables which include age (r = 0.05, p-value: 0.05), household income (r = 0.00, p-value: 0.05), number of hours that people stay outside the house (r = 0.043, p-value: 0.05), number of teenagers at home, (r = 0.006, p-value: 0.01) number of electrical appliances (r = 0.016, p-value: 0.01), type of house (r = 0.012, p-value: 0.01), hours that the electrical appliances are used (r = 0.043, p-value: 0.05), weather condition, (r = 0.011, p-value: 0.05) and the location of the building (r = 0.045, p-value: 0.05) were significant in determining the household electricity consumption. Policies based on the findings will give energy and urban planners an empirical basis for accurate and robust forecasting of the determinants that influence household electricity consumption in Nigeria that is devoid of any speculation or unfounded predictions.

Beyond short-term savings: A ten-year analysis of energy efficiency program outcomes in swiss households

This paper investigates the enduring impacts of Swiss energy efficiency programs on household electricity use and on the continuation of energy-conserving practices. Employing a mixed-methods research design, we analyzed electricity consumption data from 2009 to 2019 and incorporated insights from 2840 survey responses collected in 2018. Findings reveal sustained reductions in electricity use and continued adoption of energy-efficient appliances among program participants, underscoring the role of non-financial incentives—particularly environmental awareness—in promoting long-lasting energy conservation behaviors. Socio-cultural factors emerged as more indicative of household energy use than social status. Scaling the results to the national level, we estimate a potential annual energy saving of 2400 GWh, equivalent to 13 % of Swiss household electricity consumption in 2009. The paper advocates energy policies that prioritize engagement and periodic reinforcement to enhance long-term program efficacy.

Metrics matter: An integrated approach for analyzing household electricity consumption and the potential for conservation

The residential building sector accounts for a significant portion of global energy consumption, necessitating energy-efficient building practices to mitigate climate change. The metric most used in legislation and building codes to promote energy conservation is Energy Use Intensity (EUI). The efficacy of such regulation is often taken for granted, but as this study of a representative sample of over 800 households in Israel demonstrates, energy 'efficiency' is not necessarily translated into lower total consumption. Multiple linear regression and path analysis were employed to identify significant direct and indirect factors shaping electricity consumption measured for the first time by all three metrics: total consumption, per capita consumption, and EUI. Factors considered included building properties as well as occupant demographic characteristics, environmental awareness, and lifestyles. The factors found to be of greatest importance were, in descending order, house floor area, number of occupants, and building type (detached single-family or multi-family apartment). Other factors, including income, were shown to have an indirect effect. EUI was found to be an ineffective metric, because it allows greater consumption in larger apartments with the same efficiency rating for climatization as smaller but otherwise similar ones. In our sample, EUI for climatization decreased by about 0.14 kWh/m2/year for every 1 m2 increase in floor area. To address this, the research proposes implementing stricter efficiency requirements for large and detached houses based on a novel 'size-normalized EUI'. In addition, to promote behavior change in a socially equitable way the paper proposes the introduction of tiered electricity bills based on consumption levels adjusted for the number of household occupants.