Household Electricity Demand Research Articles

Off seasons, holidays and extreme weather events: Using data-mining techniques on smart meter and energy consumption data from China

The fine-grained electricity consumption data created by advanced metering technologies offers an opportunity to understand residential demand from new angles. Although there exists a large body of research on demand response in short- and long-term forecasting, household consumption behaviour studies based on smart metering data has not been discussed as much as the demand load studies, especially limited research focusing the residential consumption in China. The study's novelty lies in its use of unsupervised machine learning tools to explore residential customers' demand patterns and response in a finer picture, which provides a better understanding of household electricity demand profiles in China. We investigate behavioural response in three different contexts: 1) seasonal (using weekly consumption profiles); 2) holidays/festivals; and 3) extreme weather situations. The analysis is based on the smart metering data of 2000 households in Chengdu, China over three years from 2014 to 2016. Workday/weekend profiles indicate that there are two distinct groups of households that appear to be white-collar or relatively affluent families. Demand patterns at the major festivals in China, especially the Spring Festival, reveal various types of lifestyle and households. In terms of extreme weather response, the most striking finding was that in summer, at night-time, over 72% of households doubled (or more) their electricity usage, while consumption changes in winter do not seem to be significant. Our research offers more detailed insight into Chinese residential consumption and provides a practical framework to understand households' behaviour patterns in different settings.

A comparative study of commonly used batteries in household rooftop solar battery systems based on test data of commercial batteries

In recent years, uptake of grid integrated household rooftop solar battery systems (RSBSs) has increased significantly. This paper presents a thorough analysis on technical and economic performances of the common types of batteries featuring in RSBSs using test data of different commercial batteries. In this study, number of complete cycles, battery voltage deviation during a discharge period, discharge capacity, energy output and round trip efficiency are considered as the technical performance indicators of a RSBS battery. While evaluating the economic performance, the expected household net incomes from the RSBSs of different battery types have been calculated considering the uncertainties in household electricity demand and solar power generation using Monte-Carlo Simulation. Moreover, the payback period of RSBSs of different battery types have been predicted in this study. The outcomes of this study provide a clear insight to the performance of commonly used batteries in the RSBS, which will aid the end users in making educated decisions while choosing RSBSs.

ICT, carbon emissions, climate change, and energy demand nexus: The potential benefit of digitalization in Taiwan

The global rise in energy consumption makes managing energy demands a priority. Here, the potential of Information and Communication Technology (ICT) in controlling energy consumption is still debated. Within this context, the main objective of the current study is to measure the impact of ICT, its potential benefit, and environmental factors on household electricity demand in Taiwan. A panel of data from 20 cities in Taiwan was collected during the period 2004–2018. We adopted PMG estimation and applied the DH-causality test for analysis. The estimation results show that ICT, carbon emissions, and climate change will drive household electricity demand in Taiwan in the long term. However, ICT has a higher potential to reduce electricity demand in the short-term period. In addition, the results of the causality test reveal a two-way interrelationship between ICT and electricity demand. Our study also found that climate change indirectly affects the use of electricity through household appliances. We also presented several policy implications at the end of this paper.

Household Effective Demand for Electricity in Ghana: Analysis and Implication for Tariffs

Outcomes of most developing country projects to secure inclusive growth through electricity provision appear to hinge on available information regarding households’ response to electricity. To provide the needed information for policy, this study assessed the determinants of household electricity demand and estimated the mean willingness to pay for electricity by households in Ghana. The study used a Contingent Valuation modelling procedure involving over 3000 households to derive an effective demand function for electricity in Ghana. This was done through a national household survey. A mathematical programming analytical procedure was employed to fully account for the block pricing tariff system used in Ghana. The study found that Ghanaian households are willing to pay a monthly mean electricity tariff of 50.40 Ghana cedis (US$11.56), which is lower than the average monthly tariff of 73.67 Ghana cedis (US$16.90) paid by households. Thus, the average tariff paid by households monthly is 46% higher than the mean willingness to pay. The study also found that the highest impact determinants of demand for electricity in Ghana were affordability of tariffs, usage of electrical appliances, and availability of electricity, respectively. This study employs a mathematical programming procedure to determine the mean willingness to pay for electricity in Ghana. This procedure is theoretically more robust than the often-used differential calculus approach since it incorporates the block pricing of electricity in Ghana, which the calculus approach ignores. Also, it uses the largest and most inclusive known sample, specifically designed to elicit households’ willingness to pay for electricity in Ghana. The study is also unique in its findings.

Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method

Household electricity demand has substantial impacts on local grid operation, energy storage and the energy performance of buildings. Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management. However, such type of data is often expensive and time-consuming to collect, process and integrate. Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process. Incomplete data due to confidentiality concerns or system failure can further increase the difficulty of modeling and optimization. In addition, methods using historical data to make predictions can largely vary depending on data quality, local building environment, and dynamic factors. Considering these challenges, this paper proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recombining them into synthetics. The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics. A reference building was used to provide empirical parameter settings and validations for the studied buildings. An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method. The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data. The average monthly error for the best month reached 15.9% and the best one was below 10% among 11 tested months. Less than 0.6% improper synthetic values were found in the studied region.

Predicting Residential Energy Consumption by Explainable Deep Learning with Long-Term and Short-Term Latent Variables

Recently, deep learning models proliferate in the prediction of power demand for efficient planning of power consumption. However, the “black-box” characteristics of deep learning hinders from establishing a specific plan because it cannot explain the cause of the prediction. Recently, there are several attempts to explain the result of deep learning through the analysis of the input attributes that influence the prediction, but they lack of appropriate explanation because of ignoring the time-series property of the input data. In this paper, we propose a deep learning model to explain the impact of the input attributes on the prediction by taking account of the long-term and short-term properties of the time-series forecasting. The model consists of (i) two encoders to represent the power information for prediction and explanation, (ii) a decoder to predict the power demand from the concatenated outputs of encoders, and (iii) an explainer to identify the most significant attributes for predicting the energy consumption. Kullback–Leibler divergence in the loss function induces the long-term and short-term dependencies in latent space constructed by the second encoder. Several experiments on the benchmark dataset of household electric energy demand show that the proposed method explains the prediction appropriately with the most influential input attributes in the long-term and short-term dependencies. We can trade off the gain of the time-series explanation of the result against a slight degradation of the prediction performance.

Household electricity demand in Latin America and the Caribbean: A meta-analysis of price elasticity

Studying the price elasticity of demand is essential when considering rate policies in the electricity sector. This research aims to perform a meta-analysis and a meta-regression of the price elasticity of electricity demand for the residential sector in Latin American and Caribbean countries. Results show that the true value of the short-term price elasticity is between −0.197 and −0.468 and, for the long-term, is between −0.252 and −0.331. Findings differ substantially when studies are classified by their quality in econometric procedures. The long-term price elasticity seems to be lower than estimates found for other regions.

The Household Appliance Stock, Income, and Electricity Demand Elasticity

This paper estimates household electricity demand using data from the Energy Information Administration’s (EIA) 2015 Residential Energy Consumption Survey (RECS). Previous research has focused on estimating price elasticities, and we contribute to this literature by examining how price elasticity is impacted by household income and the appliance stock. Results show that as income increases, households rely less on electricity for space and water heating, but the number of electronic appliances increases with income. The changing stock suggests that the mechanism through which energy reduction occurs differs across income levels. The results can aid policymakers concerned about electricity demand, rising electricity rates, and the impact on low-income households. The results can also inform the design of demand response and demand side management programs.

Price Elasticity of Household Electricity Demand Under Block Pricing

Price Elasticity of Household Electricity Demand Under Block Pricing

Characteristics and Determinants of Household Electricity Demand from Decentralized Renewable-Based Mini-Grids: An Econometric Analysis Using Metered Data

Characteristics and Determinants of Household Electricity Demand from Decentralized Renewable-Based Mini-Grids: An Econometric Analysis Using Metered Data

Household electricity demand estimation: a regression discontinuity design approach

Household electricity demand estimation: a regression discontinuity design approach

Faktor-Faktor Yang Mempengaruhi Permintaan Listrik Rumah Tangga Di Kabupaten Pemekaran Dalam Provinsi Jambi

Electricity resources are one of the important factors in supporting the economic and social development of society (human resources). This is proven still not optimal utilization of electricity in The Expansion Regency (Tebo). This study aims to analyze the factors that affect household electricity demand in Expansion District in Jambi Province, for this first step of study in Tebo Regency. Using a quantitative approach supported by qualitative approach, which selected 100 respondents in a Proforsional Stratifide Random Sampling. Based on regression methods using SPSS found the result that the average income of the family (X1), the Number of Family Members (X2) and the Number of Power Tools (X3) both together (over all test) and partially significantly influence to household electricity demand (Y). Therefore, it is necessary to synergize the government, private and the community to meet the electricity needs in Tebo Regency.

The problem of energy poverty in the activities of agricultural advisory centres in Poland.

It is necessary in agricultural consulting to take into account the current problems as well as economic and social challenges facing rural areas. Undoubtedly, sustainable economy and energy policy is such a problem in Poland, including the issue of access to energy from renewable sources and financial possibilities of meeting the electricity demand of households and agriculture. Therefore, advisory and information activities in the field of reducing energy poverty and improving air quality are important. The main purpose of the paper was to identify and assess the role of advisory entities in the process of counteracting energy poverty in rural areas in Poland. The basic research method was an expert (survey) study. Research shows that the subject of energy poverty is relatively rarely taken up by consulting institutions as part of training courses, especially issues related to saving electricity in the household/agricultural sector are marginalized; monitoring and analysis of energy consumption; selection of technical devices in terms of energy efficiency. Based on expert research, there were identified consultancy activities that are and may be important in the topic under study. Experts indicated co-financing of activities in the field of energy efficiency as the preferred way to fight energy poverty in agriculture and rural areas. The results may constitute an important direction in the development of consultancy, the basis for building priority programs, which in turn may affect the behaviour and actions of farmers and other inhabitants of rural areas in the context of energy transformation. The performed research may constitute the basis for further, in-depth analyses in other countries and on an international scale.

Optimal planning of solar photovoltaic and battery storage for electric vehicle owner households with time‐of‐use tariff

This paper presents a practical optimal planning of solar photovoltaic (SPV) and battery storage system (BSS) for electric vehicle (EV) owner households with time of use (TOU) electricity pricing. The main aim of the optimisation problem is to minimize the Cost of Electricity (COE) while satisfying the design constraints over 20-year project lifespan. Novel rule-based home energy management systems are created to investigate the optimal sizing of two system configurations: (1) SPV-EV, and (2) SPV-BSS-EV. The arrival and departure times of the EV's availability, as well as its initial state-of-charge are incorporated into the energy management system via stochastic functions. Sensitivity analyses of the feed in tariff, grid constraint, electricity demand, and available rooftop area are provided to investigate the variations of cost of electricity and capacities of SPV and BSS. From the study results, practical guidelines for grid-connected households, based on TOU energy pricing, are provided to select the optimal capacity of SPV and BSS to accommodate the existing EV. The optimisation method is general and applicable to any household owning EV; however, in this study realistic data of solar insolation and temperature as well as household electricity demand and electricity prices are implemented for South Australia. Uncertainty analysis is investigated based on 10-year real and stochastic data to prove the optimal results. It is found that the optimal configuration for a typical SA household utilising TOU pricing with an EV requires a 10-kW SPV with a 10-kWh capacity of BSS.

Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method

The long-term electricity demand forecast of the consumer utilization is essential for the energy provider to analyze the future demand and for the accurate management of demand response. Forecasting the consumer electricity demand with efficient and accurate strategies will help the energy provider to optimally plan generation points, such as solar and wind, and produce energy accordingly to reduce the rate of depletion. Various demand forecasting models have been developed and implemented in the literature. However, an efficient and accurate forecasting model is required to study the daily consumption of the consumers from their historical data and forecast the necessary energy demand from the consumer’s side. The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows one to reduce the demand for electricity by studying the daily usage pattern of consumers, which would significantly help to cope with the accurate evaluation. The efficiency of the proposed forecasting model is compared with various conventional models. In addition, by the utilization of power consumption data, power theft detection in the distribution line is monitored to avoid financial losses by the utility provider. This paper also deals with the consumer’s energy analysis, useful in tracking the data consistency to detect any kind of abnormal and sudden change in the meter reading, thereby distinguishing the tampering of meters and power theft. Indeed, power theft is an important issue to be addressed particularly in developing and economically lagging countries, such as India. The results obtained by the proposed methodology have been analyzed and discussed to validate their efficacy.

Explainable prediction of electric energy demand using a deep autoencoder with interpretable latent space

Recently, many studies have exploited the potential of deep learning to forecast energy demand, but they cannot explain the results. They only analyze the simple correlations between the input and output to discover the most important input features, or they depend on the manual investigation of the latent space embedded with power demand patterns. In this paper, to overcome these shortcomings, we propose a deep autoencoder that can explain the prediction results by manipulating the latent space. It consists of 1) a power encoder that embeds power information, 2) an auxiliary encoder that embeds auxiliary information for an interpretable latent space in two dimensions, 3) a predictor that predicts power demand by using concatenated values of the latent variables extracted from the two encoders, and 4) an explainer that provides the most important input features in predicting the future demand by utilizing the interpretable latent variables. Several experiments on a dataset of household electric energy demand show that the proposed model not only performs better than conventional models, with a mean squared error of 0.376 in predicting electricity demand for 60 min, but also provides the capacity to explain the results by analyzing the correlation of inputs, latent variables, and energy demand predicted.

DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany

Real-world domestic electricity demand datasets are the key enabler for developing and evaluating machine learning algorithms that facilitate the analysis of demand attribution and usage behavior. Breaking down the electricity demand of domestic households is seen as the key technology for intelligent smart-grid management systems that seek an equilibrium of electricity supply and demand. For the purpose of comparable research, we publish DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. The dataset contains recordings of 15 homes over a period of up to 3.5 years, wherein total 50 appliances have been recorded at a frequency of 1 Hz. Recorded appliances are of significance for load-shifting purposes such as dishwashers, washing machines and refrigerators. One home also includes three-phase mains readings that can be used for disaggregation tasks. Additionally, DEDDIAG contains manual ground truth event annotations for 14 appliances, that provide precise start and stop timestamps. Such annotations have not been published for any long-term electricity dataset we are aware of.

Quantification of climate-induced interannual variability in residential U.S. electricity demand

We assess the sensitivity of residential electricity demand in 48 U S. states to seasonal climate variations and structural changes pertaining to state-level household electricity demand. The main objective is to quantify the effects of seasonal climate variability on residential electricity demand variability during the winter and summer seasons. We use state-level monthly demographic, energy, and climate data from 2005 to 2017 in a linear regression model and find that interannual climate variability explains a significant share of seasonal household electricity demand variation: in 42 states, more than 70% and 50% of demand variability in summer and winter, respectively, is driven by climate. Our work suggests the need for new datasets to quantify unexplained variance in the winter and summer electricity demand. Findings from this study are critical to developing seasonal electricity demand forecasts, which can aid power system operation and management, particularly in a future with greater electrification of end-use demands.

Evaluation of residential power supply by photovoltaics and electric vehicles

Electric vehicles (EVs) and vehicle-to-home (V2H) technologies are expected to be used as domestic electricity storage systems, thereby promoting the self-consumption of photovoltaic (PV) power. The purpose of this study was to evaluate the profitability of V2H installation, the optimal facility configuration and the energy performance of V2H, while considering a diversity of household types and storage facility options. We optimized the facilities and operation strategy of a residential energy system under many scenarios with different electricity demand profiles, automobile usage patterns and available areas for PV installation. The results showed that a V2H system should generally be installed instead of a stationary battery (SB), but combining a V2H system with a SB is more economical for households with long EV absence times. Additionally, V2H is profitable for households with short EV absence times, large available areas for PV installation and high electricity demand. Furthermore, V2H exhibits superior energy independence and energy-saving effects for households whose annual PV generation exceeds the yearly household electricity demand.

HOUSEHOLD ELECTRICITY LOAD FORECASTING TOWARD DEMAND RESPONSE PROGRAM USING DATA MINING TECHNIQUES IN A TRADITIONAL POWER GRID

At present, the continuous increase of household electricity demand is strategic and crucial in electricity demand management. Household electricity consumers can play an important role in this issue. The rationalization of electricity consumption might be achieved by using an efficient Demand Response (DR) program. In this paper a new methodology is suggested using a combination of data mining techniques namely K-means clustering, K-Nearest Neighbors (K-NN) classification and ARIMA for electricity load forecasting using consumers' electricity prepaid bills data set of an ordinary electricity grid with prepaid electricity meters. As a result of applying this methodology, various DR programs are recommended as an attempt to assist the management of electricity system to manage the electricity demand issues from demand-side in an efficient and effective manner, which can be put into practice. A case study has been carried out in Tulkarm District, Palestine. The performance of applying the suggested methodology is measured, and the results are considered very well.Keywords: Demand Response (DR); K-means Clustering; K-Nearest Neighbor classification (K-NN); ARIMA model; Prepaid electricity metersJEL Classifications: Q4, Q41, Q47, Q49DOI: https://doi.org/10.32479/ijeep.11192