Household Electricity Usage Research Articles

Simulating appliance-level household electricity data: Accounting for residential behavior and usage patterns in China

Data scarcity presents a significant challenge for improving deep learning algorithms in Non-intrusive Load Disaggregation. These algorithms heavily rely on extensive appliance electricity consumption data, which is expensive to gather through on-site measurements. Therefore, generating simulated data has emerged as a viable alternative. However, existing approaches often randomly combine appliance power, failing to capture realistic household electricity usage patterns. To address this, this study investigates the dynamics of household electricity consumption throughout a 24-h day, considering factors such as household size, age, occupation, and education. Markov chains and Variational Auto-encoder are employed as a data generation model to simulate electrical power consumption data. Appliance power consumption data are aggregated to represent the total household load by incorporating resident electricity consumption behavior. Comparing the generated data with actual data from a self-measurement dataset, the Jensen-Shannon divergence, RMSE, and MAE are utilized to assess their similarity, and the Entropy and the Coefficient of Variation are utilized to evaluate randomness and volatility. The results indicate a high level of distribution similarity between the generated and actual data. Moreover, the observed variations show the model's capability to produce diverse and rich datasets. Overall, this research demonstrates the value of simulated data created as a low-cost method for Non-intrusive Load Disaggregation.

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.

Predicting tropospheric nitrogen dioxide column density in South African municipalities using socio-environmental variables and Multiscale Geographically Weighted Regression.

Atmospheric nitrogen dioxide (NO2) pollution is a major health and social challenge in South African induced mainly by fossil fuel combustions for power generation, transportation and domestic biomass burning for indoor activities. The pollution level is moderated by various environmental and social factors, yet previous studies made use of limited factors or focussed on only industrialised regions ignoring the contributions in large parts of the country. There is a need to assess how socio-environmenral factors, which inherently exhibit variations across space, influence the pollution levels in South Africa. This study therefore aimed to predict annual tropospheric NO2 column density using socio-environmental variables that are widely proven in the literature as sources and sinks of pollution. The environmental variables used to predict NO2 included remotely sensed Enhanced Vegetation Index (EVI), Land Surface Temperature and Aerosol Optical Depth (AOD) while the social data, which were obtained from national household surveys, included energy sources data, settlement patterns, gender and age statistics aggregated at municipality scale. The prediction was accomplished by applying the Multiscale Geographically Weighted Regression that fine-tunes the spatial scale of each variable when building geographically localised relationships. The model returned an overall R2 of 0.92, indicating good predicting performance and the significance of the socio-environmental variables in estimating NO2 in South Africa. From the environmental variables, AOD had the most influence in increasing NO2 pollution while vegetation represented by EVI had the opposite effect of reducing the pollution level. Among the social variables, household electricity and wood usage had the most significant contributions to pollution. Communal residential arrangements significantly reduced NO2, while informal settlements showed the opposite effect. The female proportion was the most important demographic variable in reducing NO2. Age groups had mixed effects on NO2 pollution, with the mid-age group (20-29) being the most important contributor to NO2 emission. The findings of the current study provide evidence that NO2 pollution is explained by socio-economic variables that vary widely across space. This can be achieved reliably using the MGWR approach that produces strong models suited to each locality.

Adept Domestic Energy Load Profile Development Using Computational Intelligence‐Based Modelling

Most studies undertaken on energy usage in buildings have shown that energy utilization is widely influenced by occupancy presence and occupants’ activities relative to the indoor environment, which may be widely dependent on weather conditions and user behaviors. However, the core drawback that has negated the proficient estimation of energy is the modelling of occupant behavior relative to energy use. Occupants’ behavior is a complex phenomenon and has a dynamic nature influenced by numerous internal, individual, and circumstantial factors. This research proposes a computational intelligence‐based model for household electricity usage profile development as impacted by core input variables—household activities, household financial status, and occupancy presence. The incorporation of these variables and their adaptiveness is expected to address and resolve unpredictability or nonlinearity concerns, thus allowing for adept energy usage estimation. The model addresses issues unresolved in many other studies, such as occupancy determination (deduction) and the impact on energy consumption. The performance precision of this approach has been demonstrated by trend series analysis, demand analysis, and correlation analysis. Based on the performance indicators including mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE), the model has shown proficient predictive output with respect to the metered (actual) energy usage data. The proposed model, compared to actual data, showed that average MAPE values for the respective day standard, morning peak, and night peak demand period (TOUs) are 2.8%, 1.88%, and 0.31% for all income groups, respectively. The aptitude to improve on energy prediction and evaluation accuracy, especially in these periods, makes it a highly suited tool for demand‐side management, power generation, and distribution planning activity. This will translate into power system reliability, reduce operation cost (lowest cost), and reduce greenhouse emissions (environmental pollution), thereby cumulating into sustainable cities.

Design a Realtime Datalogger on a Simple House KWH Meter with a Capacity of 1300 VA

At this time, monitoring tools that directly monitor electricity usage in the home are very necessary, because in this era of global warming, it requires us to be a little careful in order to use electricity effectively. Where if we do not control electricity usage, the costs charged per month could increase and become burdensome in the payment process. This research designs a KWH meter monitoring tool with output estimates of usage costs so that the tool created can minimize costs in the field of electricity usage. Design a circuit where the tool can display the values ​​of voltage, current, frequency and cos phi flowing through the KWH, then also display the usage costs for a week which is indicated by a buzzer sound if usage has exceeded normal limits. The next step is the validation or calibration process for the designed tool which is planned to be able to test the tool that has been made. The results obtained from designing a realtime datalogger tool on this KWH meter can monitor household electrical energy usage with a capacity of 1300 VA where the monitoring aspects that can be displayed include voltage, current, power factor, usage power and usage costs while this tool is installed. The main system of this tool is driven by an Arduino UNO microcontroller which is programmed so that it can display voltage, current, power factor, usage power and usage costs. In the tool system that has been created, the error rate of voltage measurements is <1%. The error rate in reading current and power factor (PF) is <3%. Meanwhile, the error rate in reading power and usage costs is <9%. Keywords: Monitoring, KWH, Data Logger

Investigating whether the inclusion of humid heat metrics improves estimates of AC penetration rates: a case study of Southern California

Global cooling capacity is expected to triple by 2050, as rising temperatures and humidity levels intensify the heat stress that populations experience. Although air conditioning (AC) is a key adaptation tool for reducing exposure to extreme heat, we currently have a limited understanding of patterns of AC ownership. Developing high resolution estimates of AC ownership is critical for identifying communities vulnerable to extreme heat and for informing future electricity system investments as increases in cooling demand will exacerbate strain placed on aging power systems. In this study, we utilize a segmented linear regression model to identify AC ownership across Southern California by investigating the relationship between daily household electricity usage and a variety of humid heat metrics (HHMs) for ~160000 homes. We hypothesize that AC penetration rate estimates, i.e. the percentage of homes in a defined area that have AC, can be improved by considering indices that incorporate humidity as well as temperature. We run the model for each household with each unique heat metric for the years 2015 and 2016 and compare differences in AC ownership estimates at the census tract level. In total, 81% of the households were identified as having AC by at least one heat metric while 69% of the homes were determined to have AC with a consensus across all five of the heat metrics. Regression results also showed that the r 2 values for the dry bulb temperature (DBT) (0.39) regression were either comparable to or higher than the r 2 values for HHMs (0.15–0.40). Our results suggest that using a combination of heat metrics can increase confidence in AC penetration rate estimates, but using DBT alone produces similar estimates to other HHMs, which are often more difficult to access, individually. Future work should investigate these results in regions with high humidity.

Untangling electricity demand elasticities: Insights from heterogeneous household groups in Pakistan

This study presents a comprehensive analysis of evaluating household energy demand's income and price elasticities by employing micro data from eight independent household surveys spanning 2001–2019. To achieve this objective, a pseudo-panel is employed; therefore, 1200 cohorts are constructed based on various variables such as region, climatic zone, months, and expenditure deciles. The findings reveal a relatively higher own price elasticity, illustrating a decline in household electricity usage with increased electricity prices. Therefore, any policy that tends to increase electricity prices would cut back household electricity usage. The findings of cross-price elasticities demonstrate that electricity is a substitute for gas and firewood in Pakistan. Moreover, quantile regression was utilized to explore the relationship between energy consumption and predictors. The results indicate that elasticities vary significantly at different levels of electricity usage. In addition, electricity elasticities across different heterogeneous groups are also estimated, their results indicating that low-income groups are more price elastic than the high-income group. Furthermore, electricity demand in rural areas is more price elastic than in urban areas. Therefore, policies induced in the energy sector might cause significant redistribution effects. Therefore, the findings of this study provide valuable insights of the determinant of household electricity usage in Pakistan and emphasize the importance of incorporating income and price elasticities in formulating energy policy. By considering households' heterogeneous characteristics, policymakers can formulate equitable and efficient policies, ensuring that the benefits of such policies are distributed fairly for all segments of society.

Study on the residential load profiles in Brunei Darussalam based on income and location

Brunei Darussalam has the highest electricity consumption per capita among Southeast Asian countries. This high electricity consumption dramatically contributes to environmental issues because gas is the primary source of energy generation in Brunei. Hence, the government must come up with measures to counter the issues. However, before providing solutions, it is also vital to understand electricity consumption behaviour, especially in the residential sector. The residential sector in Brunei has the second highest electricity consumption, and the government heavily subsidizes it. This paper aims to study the residential load profiles in Brunei based on the locations of the households and the total monthly incomes per household. The data for this study was obtained through an online questionnaire to calculate household electrical appliance usage patterns. The result of the study shows that the residential load profiles are almost the same despite the different locations and salaries of the households. However, upon looking at the total daily consumption, Temburong has minor electricity consumption of 50 kWh, while the rest uses about 80 kWh daily. Also, the study showed that the higher the income, the higher the electricity consumption of the households. Lastly, air-conditioning shows the highest daily electricity consumption, more than 50% of the total usage.

Interference Analysis of Postpaid kWh Meters According to Standard Operational Procedures at PT. PLN Ulp Helvetia Medan Indonesia

In this work, we report that various electronic device are continuously being developed to support human life. The scale also varies, ranging from simple household appliances to heavy industrial equipment. In the process of collecting household electrical power, a kWh Meter is needed to simplify and minimize employee errors in inputting data on household and industrial electricity usage in the Medan City area. In its operation, operational and non-operational disturbances often occur such as congestion at the kWh meter, fire at the kWh meter, and other unavoidable disturbances. In overcoming disturbances that occur in kWh Meters, Inspection and Maintenance of kWh meters can be carried out, especially on digital / Postpaid kWh Meters. This report contains the type of kWh that is often used, the components in the kWh meter, the types of disturbances that often occur in the kWh meter, the impact on the damage to the kWh meter, and how to overcome these types of disturbances.

Electricity Usage as A Proxy Indicator For Poverty Targeting

This perspective demonstrates that household electricity usage is a good proxy for poverty and a quick, efficient, and effective targeting mechanism for welfare benefits. As Sri Lanka’s economic crisis continues, up to 50% or more of the population will likely need state support, yet the existing welfare benefit scheme falls far short of its goals. Current targeting through Samurdhi reaches just about a quarter of all households and only 40% of the poorest decile of individuals. The analysis presented in this perspective shows that the alternative of using a threshold of 60kwh of electricity usage per month as a preliminary eligibility criterion will reach approximately 50% of the population and over 80% of the poorest among them and performs best among a set of alternatives, including Samurdhi.

Analysing the Residential Electricity Consumption using Smart Meter

A massive amount of electricity usage may be accessed on an everyday and hourly basis due to the advancement of smart power measuring technology. Electricity demand management and utility load management are made easier by energy usage forecasts. The majority of earlier studies have concentrated on the power consumption of business clients or residential buildings, or they have experimented with individual household electricity usage using behavioral and occupant sensor information. This study used smart meters to examine energy usage at a single household level to enhance residential energy services and gather knowledge for developing demand response strategies.The power usage of various appliances in a single household is estimated, by utilizing Autoregressive Integrated Moving Average (ARIMA) modeling technique, which is applied to daily, weekly, and monthly information granularity. To select the household’s energy consumption dataset for this study, a multivariate time-series dataset describing the four-year electricity usage of a household is provided. The use of Exploratory Data Analysis (EDA) is utilizedfor the selection of features and data visualization. The correlation coefficients with the daily usage of the household have been computed for the characteristics prepared for the forecast. The top three major determinants with the top three positive significance are "temperature," "hour of the day," and "peak index." A single household's usage is inversely related to the variables having negative coefficients. It should be noticed that the correlations among a household's attributes with usage vary from one another. Finally, the power prediction is analyzed in a single household.

Did the energy efficiency upgrades for air conditioners reduce residential electricity consumption? Evidence from China

Energy efficiency standards for household appliances have been adopted in many countries to mitigate negative externalities, reduce emissions, and save energy. Using Chinese provincial-level panel data from 2008 to 2018, this paper builds a difference-in-difference model to examine the mechanism by which energy-efficiency upgrades in China’s air conditioners affect urban household electricity usage. The results show that the upgrades have brought about an obvious reverse effect, with urban household electricity usage increasing rather than decreasing. Reducing the unit costs of energy service has enhanced residents’ sensitivity to cooling demand. Driven by the substitution and income effects, urban residents may increase the intensity and frequency of their AC use. Additionally, this reverse effect is heterogeneously dependent on the family size and urban residents’ age structure rather than their educational level. When families are larger and have more children, the reverse effect is more pronounced. This paper’s findings provide useful recommendations for policy-makers to formulate policies that are synergistic with energy efficiency standards for household appliances.

A data-driven household electricity synthesiser for South Africa using enveloped sum of Gaussians

Accurate modelling of household electricity on a large scale is a cornerstone for demand management and decarbonisation efforts. This paper introduces a data-driven model for South African households. According to the Paris Agreement, a 50 % reduction in global CO2 emissions is required to ensure the global average temperature does not surpass 2 °C above pre-industrial levels. In this context, 26 % of the overall European energy consumption is used by the residential sector and the global energy consumption is estimated to increase by 1.3 % on average per year until 2050. In the last decade, the development and implementation of smart grid technologies have grown to efficiently and cost-effectively meet the electricity demands of the grid and mitigating greenhouse gas emissions. We present a data-driven, enveloped sum of Gaussians-based model and residential household synthesiser that statistically models the household's electricity usage demand based on smart meter data and generates synthetic data that accurately represents the actual demand profile, load peaks, and daily variances for an individual or aggregate group of households. The measured data was gathered over a one year period for 1200 households in South Africa. Our model accounts for temporal variations such as seasonality and the day of week, household uniqueness, and is fully autonomous. Our results show that the root mean square error between the aggregated measured and synthetic electricity profiles is 0.181 A (5.68 %) and the total energy of each profile is 75.9 and 76.3 A·h, respectively.

Anomaly Detection and Classification of Household Electricity Data: A Time Window and Multilayer Hierarchical Network Approach

With the increasing popularity of the smart grid, huge volumes of data are gathered from numerous sensors. How to classify, store, and analyze massive data sets to facilitate the development of the smart grid has recently attracted much attention. In particular, with the popularity of household smart meters and electricity monitoring sensors, a large amount of data can be obtained to analyze household electricity usage so as to better diagnose the leakage and theft behaviors, identify man-made tampering and data fraud, and detect powerline loss. In this article, the time window method is first proposed to obtain the features and potential periodicity of household electricity data. Combining the denoising ability of the autoencoder and the induction ability of the feedforward neural network, a multilayer hierarchical network (MLHN) is then established to detect anomalies in single sensor data and classify multiple groups of sensor data, respectively. The experimental results show that the accuracy of detecting abnormal data and data classification is significantly improved compared with the presented scheme.

Rule Formation Application based on C4.5 Algorithm for Household Electricity Usage Prediction

Electrical energy is one of the essential energies for the world due to the fast growth of the world population and houses. In Indonesia, 95 % of the energy used in the household in 2017 is electrical energy. Therefore, reducing the use of electricity in the household is crucial. In the past decades, customers have carried out several approaches to reduce the use of their electricity. One of the widely used methodologies is the EMS. However, the PDCA model has not been implemented in electricity consumption. Subsequently, this study applies such an approach focusing more on the planning stage and is implemented in Ternate City, North Maluku, Indonesia. The C4.5 algorithm is applied at the planning stage to form a rule in predicting household electricity consumption. Moreover, the system performance is tested using the confusion matrix. The data of electricity consumption is collected and the data is treated with a varying amount depending on the number of the training data applied. The results of the system performance test by applying the confusion matrix are 76.22, 90.3, and 74.4 % for the highest accuracy value, precision, recall, respectively with the number of rules formed by 14. HIGHLIGHTS The need for electrical energy in Indonesia continues to increase, especially the use of household electricity To reduce the consumption of electrical energy, a plan-do-check-act model is used. The research focuses on the plan stage by applying the C4.5 algorithm The highest system accuracy is 73.33 % with 14 rules formed GRAPHICAL ABSTRACT

Persisting effects of social norm feedback letters in reducing household electricity usage in Post-Soviet Eastern Europe: A randomized controlled trial

In a large-scale randomized field experiment in Chisinau, Moldova, with over 120,000 households, we tested the influence of a simple, one-time letter presenting electricity-usage social comparison feedback. The letter was framed in either kilowatt-hour units or monetary values. We find that both letter versions significantly reduce recipient households’ electricity consumption by 1.7–2.1 percent in the following month, and the effect persists for at least three months. The effect's magnitude and duration resemble those seen in similar trials in the U.S., even though Moldova's average household electricity consumption level is much lower. As the first large-scale field experiment testing social comparison energy reports in a post-Soviet environment, this study provides empirical evidence that non-price nudges can be an inexpensive yet useful energy conservation policy tool even in non-Western and less industrialized settings. We explore the relationship between Moldova's socioecological contexts and environmental attitudes as a former Soviet Union country and the relatively strong social norm effect that we observed.

Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

Nudging down household electricity usage during peak hours with small monetary rewards

Two recent studies highlight the potential of a demand response (DR) program as a tool to manage peak-hour electricity consumption by households. Households in these studies respond to the offer of financial rewards or an appeal to the sense of civic duty and significantly reduce electricity consumption during declared peak hours. Due to the small number of peak events included in these studies, however, a question remains whether households will continue to respond to repeated calls to action over a long span of time. We analyze a demand response program of Gangwon Province, South Korea, called “Electricity Piggy Bank” where the incentive offered was in the form of small monetary reward together with an appeal to social consciousness. The program tracked the electricity usage of about 2,000 households over one-year period during which peak events were declared once a week. We find significant reduction in electricity usage during declared peak hours and, more importantly, no decay of effects over time. The magnitude of electricity savings during peak hours is no less than the one reported for the previous financial-incentive-based DR, suggesting that offering small monetary rewards together with pro-social stimuli can be a viable alternative to a financial-reward-oriented scheme.

Financial and economic feasibility of bio-digesters for rural residential demand-side management and sustainable development

This paper addresses the financial and economic feasibility of biogas production to meet energy demands at a household or village scale. To this effect, the potential cost and benefit of biogas digesters were explored. The biogas digesters were characterised under controlled conditions before being deployed to the KrwaKrwa and Upper Ncera villages in the Eastern Cape, South Africa. At the control site, meteorological weather parameters that influence biogas production were monitored continuously. Based on typical rural household electrical usage profile from 2-years of data observed at the sub-station level, assumptions about the cooking times and electrical demands were made. It was found that the bio-digesters are capable of supplying the energy required for cooking, provided the digesters are continuously fed at least every 5th day. Also, 1.0 m3 of CH4 is equivalent to 4.70 kWh of electricity. With reloading after every 5th day, the total biogas produced per day over 31 days is 2.93 m3. This is equivalent to demand-side avoidance of 13.77 kWh/day and monetary savings of R26,88/day or R833,43/month. A financial cost of approximately R 17 000,00 per digester, Net Present Value of R22 000,00 and a payback period of 22 months was obtained for the feasibility of the project. Based on the findings of the study, it can be said that biogas digesters are financially sound investments and economically feasible at both household and societal levels. These digesters can also ensure that the household remains above the poverty threshold.

Demand response beyond the numbers: A critical reappraisal of flexibility in two United Kingdom field trials

Domestic demand response – specifically, changing household electricity usage patterns in response to signals – is an increasingly important part of electricity system decarbonisation. Many forms of demand response exist and there is a live question of the most appropriate and fair way to design it, especially regarding which types of household can participate and how.Using secondary qualitative analysis of published trial documentation, this paper compares two UK based trials in low income households whose headline results – kW peak reduction - differed by two orders of magnitude. Using a framework based on flexibility capital, the contextual factors underlying these different peak reductions are examined, and these headline results are balanced against other outcomes of the trials. The analysis examines the technical and social sources of flexibility capital in each trial, who controlled this capital, and for whom it delivered value. This highlights questions to consider when designing demand response, such as to what extent participants should be expected to understand and actively participate, who should control their energy use and the spread of responsibility and liability across facilitating parties.We argue that critical reappraisals of existing evidence are necessary as the terms for consumer participation in the future energy system are being established. There exist important aspects and consequences of demand response that are overlooked if schemes focus solely on how many Watts can be shifted. This is crucial as governments, the private sector and a growing number of other parties test and implement different demand response strategies.