Residential Electricity Consumption Research Articles

Temperature change and daily urban-rural residential electricity consumption in northwestern China: Responsiveness and inequality

Daily temperature plays an important role in the comparison of the electricity consumption of urban and rural residents. Previous studies highlighted the impact of income on the relationship between temperature change and residential electricity consumption; however, urban-rural electricity consumption disparity and temperature-electricity response inequality have been neglected. This paper examines the response of urban and rural residents in a China’s northwestern province to temperature changes in terms of electricity consumption, and uncovers its impact on daily electricity consumption inequality between these two groups by using daily households’ electricity consumption dataset from 2017 to 2019. The study results show: (1) The urban response function exhibits a U-shaped distribution, whereas that of rural residents demonstrates a fluctuating upward trend. (2) When the daily mean temperature falls below 24 °C, the gap between urban and rural electricity consumption increases. (3) During the Spring Festival, the negative effects of extremely low temperatures will be reduced, while at the end of October, they will be increased. These findings can help policymakers reduce the electricity consumption inequality between urban and rural areas in the context of climate change.

Causality between residential electricity consumption and explanatory factors

This research adds to the assessment of the causal relationships between residential electricity consumption and its explanatory factors. Johansen cointegration, autoregressive distributed lag model, the vector error correction model, and Wald causality tests are used. In both the short and long run, a unidirectional causality towards electricity consumption is identified. The sustainable development of the electricity sector in Cameroon, as defined by the national development strategy for 2030 and the long-term electricity sector development plan for 2030, requires an increase in GDP per capita, the adoption of a compact less energy-intensive urban model, the strengthening of control over the evolution of the number of subscribers on the distribution networks, and the reduction of CO2 emissions from thermal electricity generation.

Household structure and electricity consumption in Ghana

Ghana has a rising residential electricity consumption of 47% of total generation while at the same time experiencing a worsening household age-dependency ratio considered to be above the global average. Using the most recent Ghana Living Standards Survey (2016/17 i.e., the seventh round), and employing logistic regression analyses, this paper examines how and the extent to which household age-dependency (0–14 and 64+) and other sociodemographic characteristics of Ghanaian households influences residential electricity consumption. In the face of worsening climate change partly attributable to high energy consumption, understanding the role household structure in residential electricity consumption across gender and location is critical in designing appropriate demand-side management policies. The results show that dependency ratio increases electricity consumption by approximately 12.4%. Furthermore, female-headed households with dependents tend to use less; or have reduced electricity usage compared to a male-headed household with dependents. The study recommends among others, the use of local government to spearhead education on energy efficiency especially at the household level and the establishment of green financing scheme for importers, manufacturers, and households.

Climate and health benefits of a transition from gas to electric cooking

Household electrification is thought to be an important part of a carbon-neutral future and could also have additional benefits to adopting households such as improved air quality. However, the effectiveness of specific electrification policies in reducing total emissions and boosting household livelihoods remains a crucial open question in both developed and developing countries. We investigated a transition of more than 750,000 households from gas to electric cookstoves-one of the most popular residential electrification strategies-in Ecuador following a program that promoted induction stoves and assessed its impacts on electricity consumption, greenhouse gas emissions, and health. We estimate that the program resulted in a 5% increase in total residential electricity consumption between 2015 and 2021. By offsetting a commensurate amount of cooking gas combustion, we find that the program likely reduced national greenhouse gas emissions, thanks in part to the country's electricity grid being 80% hydropower in later parts of the time period. Increased induction stove uptake was also associated with declines in all-cause and respiratory-related hospitalizations nationwide. These findings suggest that, when the electricity grid is largely powered by renewables, gas-to-induction cooking transitions represent a promising way of amplifying the health and climate cobenefits of net-carbon-zero policies.

Optimization of Residential Electricity Consumption based on Multienergy Coordinated Control of Generation, Load, and Storage

Introduction: The power system is translating to the “generation, network, load, and storage multiple coordination control” model, which helps to increase the proportion of renewable energy consumption and reduce the operation cost. Materiala and Methods: The residential electrical system is an important component in the power system, which includes distributed photovoltaic systems, flexible loads, electric vehicles, and battery storage systems, which have the potential to realize multiple coordination control and minimize the net cost of the electricity consumption in a residential electrical system by the coordinated cooperation of generation, flexible loads, and the battery storage system. The comfort level of the residents is also maintained during control. Therefore, a nonlinear economic model is introduced to represent coordinated cooperation of generation, flexible loads, and the battery storage system, and a nonlinear economic predictive controller is proposed to solve the problems of this model. Results: Based on the forecasted generation/load for a tumbling prediction window, the impact of different control trajectories is estimated, then the minimum cost trajectory for flexible loads and battery is selected, and only the first control action is implemented. Then, the prediction window is moved to the next time interval, and the same process is repeated. The case study shows that the proposed method reduces about 24.36% of the net cost of the residential electrical system. Conclusion: Meanwhile, the hot-water temperature and indoor temperature of the household are maintained without affecting the end-user comfort, and the state-of-charge (SOC) for the electric vehicle and the battery system is always kept under constraints without affecting usage. The proposed method has a good performance for the coordinated cooperation of generation, flexible loads, and battery storage system in the residential electrical system. The proposed method also increases the consumption of PV, with the implementation in more residential buildings that will benefit in achieving carbon neutrality by 2060 in China.

Heterogeneous responses to climate: evidence from residential electricity consumption

Heterogeneous responses to climate: evidence from residential electricity consumption

Implications of the COVID-19 Pandemic on China's Power Consumption: A comprehensive Analysis Derived from Monthly Electricity Consumption Forecasts

The COVID-19 pandemic profoundly altered the total and pattern of electricity consumption across Chinese industries and households. However, current research has not fully and accurately measured the impact of the pandemic. This paper builds a novel discrete gray seasonal model (referred to as the FDDGSM model) to predict the scenario of different domestic electricity-consuming sectors and residential electricity consumption without a pandemic backdrop, by comparing it with the actual electricity consumption in 2020, the impact of the COVID-19 pandemic on China's electricity consumption is better revealed. Through empirical analysis, the following conclusions are reached in this study: (1) The novel discrete grey season model shows more stable and accurate characteristics in forecasting time series with seasonal trends. (2) The pandemic did not significantly impact electricity consumption in primary industries and households, while actual usage in secondary and tertiary industries was significantly lower than expected in a non-pandemic scenario. (3) The impact on secondary and tertiary industries varied in magnitude and duration, and they differed in the rate of recovery in electricity consumption.

Computational intelligence for residential electricity consumption assessment: Detecting air conditioner use in households

This article presents an approach applying computational intelligence for detecting the use of air conditioners in households. The main objective is determining the intensive use of air conditioning with a high level of confidence. Unsupervised (k-means) and supervised (Artificial Neural Networks) approaches are developed for classifying consumers for a case study in Uruguay, using data collected by a smart meters network and open weather data. Data from 29 Uruguayan cities were considered in the period from January 1, 2022, to December 31, 2022. Two thermal models are developed for estimating the temperature inside households. The main results indicate that the proposed approach is able to reach a high classification accuracy, up to 94.5% and a high classification recall, up to 95%, for the considered real case study. The final scope of the work is developing smart tools for classifying consumers, to design and suggest specific commercial products that promote energy efficiency.

Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model

Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of load demand at the distribution level, which is smoother and benefits from statistical compensation of errors. This paper presents a day-ahead forecasting technique for individual residential load demand that is based on the Long Short-Term Memory encoder–decoder architecture, which is extended to consider possibly differing sets of past and future exogenous variables. A novel focus is posed on the validation of the proposed approach considering that it is tailored for use by energy management systems. A publicly available dataset was used for validation, and the approach was compared with three other methods, resulting in a reduction of the Mean Absolute Scaled Error by up to 8%.

Residential Electricity Consumption and Adaptation to Climate Change by Colombian Households

Residential Electricity Consumption and Adaptation to Climate Change by Colombian Households

COVID-19 triggered residential behavioral changes and electricity consumption of detached houses in Japan

Many studies conducted previously have reported that due to lockdowns or stay-at-home orders associated with the COVID-19 pandemic in April 2020 residential power consumption has increased in countries, particularly in cities worldwide. This study compared the power consumption of 1,339 detached houses in Japan over the past three years as well as a year after the pandemic and analyzed living behavioral changes in the 12months after the pandemic using a questionnaire survey of occupants. As of March 2021, which is after 12months of the beginning of the pandemic, it was confirmed that the way of life had returned to almost normal, and as a factor in increasing consumption, working from home would remain the only behavioral change that may take root in Japanese society.

Forecasting residential electricity consumption using the novel hybrid model

Forecasting residential electricity consumption using the novel hybrid model

Analysis of residential electricity consumption patterns utilizing smart-meter data: Dubai as a case study

Analysis of residential electricity consumption patterns utilizing smart-meter data: Dubai as a case study

Urban residential water and electricity consumption behavior: A systematic literature review

Consumer behavior is an understudied determinant of residential water and electricity demand and, when studied, capturing, measuring, and quantifying attitudes and behaviors is often problematic. This research explores behavioral determinants of the demand for water and electricity, contributing a set of categories and items that may be useful for understanding knowledge, attitudes, and practices related to consumption and constructing valid scales. These results may help researchers capture the complexity of consumer behavior regarding water and electricity and decision-makers in designing tailored demand management strategies.

A synthetic dataset of Danish residential electricity prosumers

Conventional residential electricity consumers are becoming prosumers who not only consume electricity but also produce it. This shift is expected to occur over the next few decades at a large scale, and it presents numerous uncertainties and risks for the operation, planning, investment, and viable business models of the electricity grid. To prepare for this shift, researchers, utilities, policymakers, and emerging businesses require a comprehensive understanding of future prosumers’ electricity consumption. Unfortunately, there is a limited amount of data available due to privacy concerns and the slow adoption of new technologies such as battery electric vehicles and home automation. To address this issue, this paper introduces a synthetic dataset containing five types of residential prosumers’ imported and exported electricity data. The dataset was developed using real traditional consumers’ data from Denmark, PV generation data from the global solar energy estimator (GSEE) model, electric vehicle (EV) charging data generated using emobpy package, a residential energy storage system (ESS) operator and a generative adversarial network (GAN) based model to produce synthetic data. The quality of the dataset was assessed and validated through qualitative inspection and three methods: empirical statistics, metrics based on information theory, and evaluation metrics based on machine learning techniques.

Construction of a smart grid load forecasting platform based on clustering algorithm

Abstract With the rapid development of economic levels, the demand for electricity usage in various industrial sectors has continued to rise. In order to adequately meet the growth in demand for electricity usage at each user end within the power system, accurate forecasting of electricity load is required. This paper uses the K-Means algorithm as the basis, combined with the Canopy algorithm, whose simple and efficient advantages can be used as the initial clustering step for demanding clustering algorithms. The PCA algorithm is used to reduce the dimensionality of the residential multidimensional electricity consumption feature set, and after optimization, the feature vector weights are calculated to obtain the appropriate selection to determine the training and testing samples. The improved K-Means algorithm carries out five clustering tests, and its average accuracy is above 97%. The average relative error is then used as an indicator to compare the algorithm with the time series method, curve extrapolation method, and WKNR method for analysis. The average relative error of the smart grid load forecasting method proposed in this paper is 0.87%, while the average relative errors of the other three algorithms are 1.64%, 1.57%, and 0.93%, respectively. The distribution of relative errors falls less than 1% more than the other methods. It can be seen that the improved K-Means algorithm has higher prediction accuracy and also makes the smart grid load testing platform more practical due to its simple implementation principle.

Reliable Long-Term Energy Load Trend Prediction Model for Smart Grid Using Hierarchical Decomposition Self-Attention Network

Extending the length of time series forecasting has a long-term impact on smart grid energy consumption planning, residential electricity monitoring, extreme weather warning, and other real applications. This article studies the reliable long-term load trend forecasting approaches in smart grid environment. In recent years, the improved neural networks models based on self-attention mechanism show good performances in many sequence tasks, but most studies focus on reducing the complexity of networks layer, and can not restrain the increase of calculation error in longer distance forecasting scenarios. Also, most models lack the ability to mine potential high-dimensional features of time series. Based on these problems, we design a reliable hierarchical self-attention model named as long-term stability network (LTSNet), which adopts a tree-shaped decomposition neural network architecture based on hierarchical residual self-attention blocks, to top-down incrementally mine high-dimensional features of temporal components. At the same time, the attention matrix is used for feature interaction at each layer, to reduce the distribution gap between time series fragments in different domains. Compared with existing study models, LTSNet maintains stable forecasting performance and speed in long-term forecasting services, achieved the most reliable multivariate and univariate forecasting results in multiple domain scenarios. Compared with the latest models, the forecasting accuracy of the proposed model is improved by 22.8% and 13.8%, respectively, covering three applications: energy consumption, residential electricity consumption, and weather forecasting. At the same time, our experiments verify that the hierarchical decomposition networks can be used as a backbone architecture to effectively extended to longer dimensional load trend forecasting scenarios.

A novel short-term household load forecasting method combined BiLSTM with trend feature extraction

Aiming to reduce the short-term household load prediction error caused by small load scale and differently residential electricity consumption behavior, a novel hybrid forecasting model based on wavelet threshold denoising (WTD), variational mode decomposition (VMD) and bidirectional long short-term memory (BiLSTM) network is proposed in this paper. In this hybrid model, WTD is used to denoise the original data firstly. Then trend feature is extracted by VMD. Finally the trend feature and historical load data are put into BiLSTM model for training and testing. The prediction effect of the proposed model is demonstrated with experiment using total household electricity consumption in the United States in a region, and comparison with common short-term household load forecasting models are presented. The experiment results show that the hybrid model improves the accuracy of short-term household load forecasting by providing more stable and more precise forecast under trend feature extraction.

Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption

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%.

Influencing residential electricity consumption with tailored messages: long-term usage patterns and effects on user experience

BackgroundTo transition our energy system toward sustainable production and consumption, it is important to successfully engage consumers to become active participants in this process. One form this can take is manual demand response, where end users respond to fluctuations in energy production and help balance the grid through adjustment of their consumption. This paper presents a trial of such a system that took place with tenants in subsidized housing in Catalonia, Spain. The aim of the trial was to motivate the load shifting behavior of the participants by forecasting expected consumption curves and tailoring suggestions for optimized behavior. The forecasts and suggestions were based on the users’ past consumption patterns and the hourly day-ahead electricity prices. This information was made available to the users on a web-based platform, and participants were actively informed with text messages sent to their mobile phones in case of attractive saving potentials for the following day. The trial was carried out in 2 phases from November 2019 to May 2020 (Phase 1) and from August to October 2020 (Phase 2). Data were collected on interaction with the platform, the perceived user experience of the platform and text messages, and the perceived energy saving success.ResultsOur results showed that there is a general interest of the participants in the concept, but that there are also important barriers to integrating load shifting behavior into everyday life. The biggest barriers here are limitations in the flexibility potential of households and limited perceived benefits. Feedback from our participants also suggests high acceptance and relevance of more automated demand-side management (DSM) concepts.ConclusionsBased on this, we recommend paying special attention to the accommodation of varying flexibility potential in manual demand response (DR) programs, ensuring that communicated benefits are sufficiently attractive to motivate behavior change, and consideration of a phase of manual DR as an entry point to automated DSM.