Residential Electricity Usage Research Articles

Planning for Heat Resilience and the Future of Residential Electricity Usage

Community resilience refers to the ability for a geographic area to respond and adapt to acute shocks and long-term stresses. Geography research is well positioned to examine how communities can adapt to the compounding effects of climate change. Our work aims to analyze the factors that influence residential electricity in the context of increasing urban temperatures in Maricopa County, Arizona, coupled with the COVID-19 pandemic. Using census tracts as our basis for analysis, we quantified factors that influence electricity use related to neighborhood characteristics, home structures, social situations, pricing policies, and work-from-home estimates. Our findings suggest that the structure of the home (e.g., number of rooms and house size) influences the variation in residential electricity use, whereas trees and small-area temperatures were not factors. We then compared above-median-income census tracts to those that were below the median income. We found fewer factors influenced electricity use in the lower income census tracts and that they were also related mostly to the home structure. Our analysis did not reveal that working from home was significant, but we did find that the average household size was significant and that the amount of influence increased from 2019 to 2020, suggesting that the stay-at-home policies from the pandemic did affect electricity use. As we consider and implement work-from-home strategies, families need to recognize that the home structure plays a crucial role in determining electricity usage.

Enhancing Residential Electricity Safety and Management: A Novel Non-Intrusive Load Monitoring-Based Methodology for Accurate Appliance Operational State Identification

Non-intrusive load monitoring (NILM) technology, crucial for intelligent electricity management, has gained considerable attention in residential electricity usage studies. NILM enables monitoring of total electrical current and voltage in homes, offering insights vital for enhancing safety and preventing domestic electrical accidents. Despite its importance, accurately discerning the operational status of appliances using non-intrusive methods remains a challenging area within this field. This paper presents a novel methodology that integrates an advanced clustering algorithm with a Bayesian network for the identification of appliance operational states. The approach involves capturing the electrical current signals during appliance operation via NILM, followed by their decomposition into odd harmonics. An enhanced clustering algorithm is then employed to ascertain the central coordinates of the signal clusters. Building upon this, a three-layer Bayesian network inference model, incorporating leak nodes, is developed. Within this model, harmonic signals are used as conditions for node activation. The operational states of the appliances are subsequently determined through probabilistic reasoning. The proposed method’s effectiveness is validated through a series of simulation experiments conducted in a laboratory environment. The results of these experiments (low mode 89.1%, medium mode 94.4%, high mode 92.0%, and 98.4% for combination) provide strong evidence of the method’s accuracy in inferring the operational status of household electrical appliances based on NILM technology.

Do conservation contests work? An analysis of a large-scale energy competitive rebate program

Contests are widely used as mechanisms to incentivize efforts in various contexts, from research innovation to athletic and employee performance. This paper builds an analytical model and provides empirical evidence to assess the effectiveness of a large-scale, real-world residential energy conservation contest in Vietnam. The model suggests that contests offering multiple prizes and requiring a minimum amount of effort can effectively promote conservation. It also identifies factors that influence the conservation effort, such as the cost of effort, size of prizes, and types of contestants. To empirically estimate the effect of the energy conservation contest, this paper uses a unique confidential dataset of monthly residential electricity usage with over 45 million observations. The results indicate that the contest significantly reduced electricity consumption among contestants, and the effect of the contest could persist for months after its conclusion, leading to an average abatement cost of $27 per ton of CO2 emissions.

A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment

The increasing population migration to urban and peri-urban areas increases basic service needs for cities worldwide. Residential electricity demand increases with more customers and varies with novel uses, such as charging electric vehicles, which may add additional dynamics to the residential electricity demand profile. Widespread installation of smart electricity meters provides fine temporal resolution data reflecting current demands and supports predicting future demands. As part of a demand-side management program, understanding the main drivers of current electricity usage based on demand-driven and exogenous predictors represents a valuable tool for utilities facing new demand scenarios. This work presents the application of multiple models to forecast electricity demand based on the input data and the forecasting horizon. Models with exogenous variables as predictors are part of the input–output category, including a Feed Forward Neural Network, Random Forest, and a Linear Gaussian State Space model. The second category is demand-driven models, where predictors include only previous demand values. The demand-driven models in our analysis include a univariate Nonlinear Autoregressive Neural Network and a Linear Gaussian State Space. Using smart electricity meter data from the greater Chicago area, we compare the performance of the models on two different types of accounts: single- and multi-family residential users when forecasting one and multiple steps. Results show that the Linear Gaussian model reports an R2 of 0.99 compared to an average R2 of 0.92 from the Feed Forward Neural Networks and Random Forest when forecasting single- and multi-family electricity demand one step ahead. However, Nonlinear Autoregressive Neural Networks report an average R2 of 0.85 compared to the Linear Gaussian R2 of 0.58 when forecasting 48 steps. We also found that the most important predictors for single-family demand are temporal variables like weekdays, working and non-working days, and day hours. For multi-family demand, electricity demand at the same hour as the previous week replaces weekdays as a significant predictor. Different forecasting models can assist utilities and city planners to manage demand under different and novel residential electricity usage conditions.

Analysis of Residential Electricity Usage Characteristics and the Effects of Shifting Home Appliance Usage Time under a Time-of-Use Rate Plan

Carbon reduction programs are being introduced for carbon neutrality and energy transition to clean energy sources in various sectors, such as energy, buildings, transportation, and agriculture. In the residential electricity energy of the energy sector, the time-of-use (TOU) rate plan, which employs dynamic rates depending on energy usage times based on the advanced metering infrastructure (AMI), is being implemented for efficient electricity energy consumption. For broad expansion of the TOU rate plan, customers need information about its benefits, such as potential savings on electricity bills. In this paper, we first analyze the statistical characteristics of electricity energy usage using the metering data collected from 10 apartment complexes through AMI and develop a model to calculate the electricity bill savings. We next introduce examples of major home appliances, of which usage times can be shifted, and offer projected bill savings from the developed model. We analyze the examples from both the perspectives of households and apartment complexes. The information from this analysis is helpful in practically investigating customers’ willingness to shift the usage time for a successful implementation of the demand response program.

Predicting residential water and electricity usage profiles with a temporal histogram model

The temporal histogram model and synthesiser that is presented in this paper statistically models the unique usage behaviour of electricity or hot water usage for individual residential households. These can be used to model behaviour and do simulations for demand management and planning. The model takes into consideration both seasonal variations and weekday differences and the synthesiser autonomously generates hourly synthetic profiles. We use 1,200 household electricity profiles, each spanning over one year, and 77 hot water usages, each spanning at least one month for each season, to obtain the results. The results showed that the model can accurately characterise the usage behaviour for a household and accurately predict future usage. The model also has a novel capability to model the relationship between usages at different times of the day, which further increases its usefulness in city-wide utility planning. This reduced hourly prediction errors by 19.6% for electricity usage and 25.0% for hot water usage when the first twelve hours of a day are known. This model can be used as an effective tool for demand-side management strategies and providing insight into utility planning in cities.

A hybrid machine learning approach for forecasting residential electricity consumption: A case study in Singapore

Ensuring effective forecasting of buildings' energy consumption is crucial in establishing a greater understanding and improvement of buildings' energy efficiency. In Singapore, domestic electricity usage in public residential buildings takes up a significant portion of the country's annual energy consumption. Having effective forecasting approaches is thus important in supporting relevant strategies and policy making. In this research, we proposed a hybrid approach that was based on a combination of building characteristics and urban landscape variables to predict residential housing electricity usage in Singapore. XGboost was also incorporated inside the hybrid approach as the preferred machine learning approach for energy consumption predictions. To demonstrate our proposed approach's predictive strength, the performance of our proposed hybrid machine learning approach was compared with two other models, Geographically Weighted Regression (GWR) model and the Random Forest (RF) model. Results showed that our proposed hybrid model had outperformed these abovementioned approaches with higher accuracy (r2 value of 0.9). The proposed approach had thus been effective in forecasting electricity consumption for public housing in Singapore, and it could also be utilised in other similar urban areas for future electricity consumption forecasting.

Assessment of building energy performance integrated with solar PV: Towards a net zero energy residential campus in India

Building Applied Photovoltaics (BAPV) such as Roof-top Solar PV has gained significant attention in recent years for harnessing the untapped potential of renewable energy sources. However, rooftop PV poses hurdles of space restriction and shadowing in densely packed urban residential neighborhoods. This study aims to design and assess the feasibility of an integrated grid-connected Rooftop and Façade Building Integrated Photovoltaic (BIPV) for meeting the energy demand of residential buildings on an academic campus. Three distinctive groups of residential typologies have been investigated in this study, categorized based on built area and occupants’ past energy usage. Additionally, the variation in the measured Energy Performance index of the three different residential groups is illustrated to pave the path for the development of a typology-based residential energy benchmarking and labelling system. The Solar PV system has been designed for the maximum household energy demand recorded in CoVID-affected years due to high residential electricity usage in this period. The study showcases that integration of façade BIPV for low-rise residential buildings increases the system energy production to up to 62.5 % based on the utilized surface area for active PV. Furthermore, the Net Zero Energy Building (ZEB) potential for each typology has been achieved by integration of the proposed Solar PV, evaluated as a function of the Energy Performance Index (EPI) and Energy Generation Index (EGI). The designed nominal PV power of the proposed grid-connected plant is 5.6 MW, producing 7182 MWh annually, meeting the maximum residential energy demand in the studied academic campus in CoVID affected year.

What is the value of peer-to-peer energy trading? A discrete choice experiment with residential electricity users in Colombia

Peer-to-peer (P2P) energy trading is the electricity exchange between end-users with distributed energy resources, mainly photovoltaic panels. In P2P exchanges, electricity supply options can be differentiated by new energy attributes traced with smart meters and digital applications. Given the new range of supply options, energy users' preferences are crucial for designing P2P energy trading business models that engage consumers and advance the energy transition. This work identifies the preferences of a Colombian energy users' segment towards energy attributes in P2P trading. Contributing to a substantial evidence gap in the application of P2P energy trading in Latin America, we conducted a choice experiment with 1101 residential energy end-users in the Aburrá Valley (Medellín, Colombia). We evaluated the following energy attributes: generation source, ownership, location, and destination of profits. The results confirm a differentiated valuation of energy attributes among respondents across population segments. The perceived value of each attribute is represented as marginal willingness-to-pay over the usual energy bill—calculated by analysing choice data with a Mixed Logit Model. The average respondent is willing to pay the most for getting 100 % solar power, followed by a power supply where generation profits go to a social cause. Next in order of importance are energy from cooperative ownership, regional location, and neighbourhood location. Respondents would be willing to pay less for energy owned by an anonymous individual. This article contributes to understanding end-users' values around P2P energy trading and unveiling a potential market segment and preference structure. The results intend to pave the way for further research and the development of P2P energy models, especially in the Global South.

JUE Insight: Powering work from home

This paper documents a shift in energy consumption toward residential usage during the COVID-19 pandemic in the United States. Focusing on electricity, I find a 7.9% increase in residential consumption, and a 6.9% and 8.0% reduction in commercial and industrial usage, respectively, from a monthly panel of electric utilities. Natural gas consumption also shifted toward residential use, so that aggregate electricity and gas expenditure only fell by 1% on net during a period in which GDP fell by 5%. Hourly smart meter data from Texas reveal how daily routines changed during the pandemic, with residential electricity usage during weekdays closely resembling those of weekends. In total, residential energy expenditures were an estimated $13B higher during Q2-Q4 2020, with the largest increases occurring in areas with a greater propensity to work from home. I find that transportation fuel consumption declined about 16%, so that total energy consumption in the U.S. economy fell by 8%.

Resilience from Below: Technicians, Repair and Maintenance Works in Post-socialist Dar es Salaam, 1985-2020

Abstract Since its establishment by the German East Africa Railway Company in 1906, Dar es Salaam’s electricity infrastructure has been functioning precariously. Tanzania’s national power company (Tanesco) collaborated with donors to ameliorate these problems, but to no avail. Tanzania’s cities continued to experience both cascading and rolling blackouts, and such breakdowns of infrastructure became critical, especially from the 1980s onward, making electricity consumers vulnerable. This article illustrates that the failure of Tanesco’s systems does not mean that inhabitants in the city have remained passively exposed to the consequences of power breakdowns. Based on interviews, a literature review, and archival material, it demonstrates that electricians have become part and parcel of a socio-technical landscape that has enhanced household resilience—the ability to survive in a country partly plagued by failing critical infrastructure. The article reveals the active role played by formally and informally trained electricians (mafundi) in forging alternative solutions, increasing electricity users’ ability to cope with power outages. Employing innovative skills to repair electrical appliances and tinker with Tanesco’s infrastructure, street electricians increased the resilience of residential electricity users in the face of recurrent power failures. The author argues that urban resilience studies in African cities like Dar es Salaam need to consider street technicians to gain a full understanding of households’ responses to vulnerable electric infrastructure.

Evaluation of wind energy potential for different regions in Lebanon based on NASA wind speed database

The grid-connected system can be an attractive solution to reduce electricity consumption, dependence on utility power, and increase electricity generation from renewable energy resources like wind energy for residential electricity users. Based on 33-year wind data (1983-2020), this study investigates the potential of wind energy at different locations ((Akkar, Baalbek, Beirut, Zahlé, Baabda, Nabatieh, Tripoli, and Sidon) in Lebanon using the Weibull distribution function. Monthly NASA wind speed data during the period (1983-2020) were used to estimate the wind energy potential. The result showed that the averaged wind speeds at the selected regions are varied from 3.695m/s to 4.457m/s at the height of 10m. Furthermore, the annual wind power density was estimated at various heights (10m, 30m, and 50m). The results demonstrated that small-scale wind turbines are recommended to be used for generating electricity from wind in the selected regions. Finally, the performance of WRE.060 / 6 kW (vertical axis wind turbine) and Proven WT 6000 (horizontal axis wind turbine) was done based on the monthly NASA wind speed database.

Impacts of COVID-19 related stay-at-home restrictions on residential electricity use and implications for future grid stability

“Stay-at-home” orders and other health precautions enacted during the COVID-19 pandemic have led to substantial changes in residential electricity usage. We conduct a case study to analyze data from 390 apartments in New York City (NYC) to examine the impacts of two key drivers of residential electricity usage: COVID-19 case-loads and the outdoor temperature. We develop a series of regression models to predict two characteristics of residential electricity usage on weekdays: The average occupied apartment’s consumption (kWh) over a 9am-5pm window and the hourly peak demand (Watt) over a 12pm-5pm window. Via a Monte Carlo simulation, we forecast the two usage characteristics under a possible scenario in which stay-at-home orders in NYC, or a similar metropolitan region, coincide with warm summer weather. Under the scenario, the 9am-5pm residential electricity usage on weekdays is predicted to be 15% – 24% higher than under prior, pre-pandemic conditions. This could lead to substantially higher utility costs for residents. Additionally, we predict that the residential hourly peak demand between 12pm and 5pm on weekdays could be 35% – 53% higher than that under pre-pandemic conditions. We conclude that the projected increase in peak demand - which might arise if stay-at-home guidelines coincided with hot weather conditions - could pose grid management challenges, especially for residential feeders. We also note that, if there is a longer lasting shift towards work and study-from-home, utilities will have to rethink load profile considerations. The applications of our predictive models to managing future smart-grid technology are also highlighted.

Peak and off-peak demand for electricity: Is there a potential for load shifting?

In this paper, we explore whether there is a potential for shifting load between different times of the days, i.e., between peak and off-peak hours. In particular, we explore whether the fact that electricity is a necessity to modern life puts restrictions on the possibilities for load shifting. To do that we provide a structural framework for peak and off-peak electricity demand, where households are assumed to have Stone-Geary utility functions with subsistence levels for electricity demand that varies within the day, and that depends on household characteristics and temperature. As an empirical illustration, we fit our model to Swedish data on residential electricity usage at the sub-daily level. Our results indicate that the potential to shift load from peak to off peak is limited. One reason for this is that the subsistence levels are larger during peak than off-peak, implying that households assign a high value on electricity during peak time, relative to off-peak time. Overall, the results have important policy implications, not the least with respect to effects of real time pricing, as it suggests that there are limits to households' price responsiveness.

Effect and mechanism of monetary incentives and moral suasion on residential peak-hour electricity usage

One of the most important economic principles is that people respond to incentives. Economics is essentially the study of incentives. However, whether these incentives can replace attitudes and norms that deserve our protection is still unclear especially in the field of environment. And to answer this question, residents’ demand response (DR) for energy conservation and emission reduction trials have been taken. Large-scale, hybrid frequency historical data for electricity usage and microcosmic behavior data have been integrated and analyzed. Empirical results show that monetary incentives have a significant effect on residents’ electricity saving behavior, which saved 0.116 kgwatt-hours (kWh), accounting for 12% of the total demand in peak-hour consumption. Moral suasion has a significant effect only for residents from high-income communities with a reduction in usage by 8.9%. Through matching survey data analysis, we found that families participating in DR save electricity through adjusting other major and small appliances rather than reducing the usage of air conditioners. Families with large number of major and small appliances response 0.51 kWh and 0.38 kWh more, respectively. Monetary incentive is indispensable in China's current stage of encouraging families’ reducing peak-hour electricity usage.

Impact of the Lockdown during the COVID-19 Pandemic on Electricity Use by Residential Users

The COVID-19 pandemic and the associated lockdown can be regarded as a forced social experiment, the results of which show how to use energy under specific conditions. During this period, there was a reduction in electricity consumption at the level of the power system, but a different specificity distinguishes the group of household users. The article aims at presenting and analysing the identified issues concerning residential electricity users based on the experience from the COVID-19 pandemic lockdown. Data from energy meters from almost 7000 flats in Warsaw’s housing estates during the lockdown in 2020 and the analogous period before the pandemic were used. The analysis showed that, on average, residential users staying practically the whole day in their flats increased their energy consumption, but without increasing their average daily peak power, smoothing the profile in the morning hours to the level reaching the peak power that had occurred in the analogous period before the lockdown. The peak power of the sections feeding the different numbers of dwellings also remained practically unchanged during the lockdown compared to the pre-pandemic period. The pressure to work and educate remotely should contribute to an increase in the digital competence of society, which may result in an increased interest in new forms of activity and cooperation based on demand-side response and prosumption mechanisms, with digital settlements for energy exchange and services.

Power Resilience Enhancement of a Residential Electricity User Using Photovoltaics and a Battery Energy Storage System under Uncertainty Conditions

Even in today’s modern electric grid infrastructure, the uncertainty in the power supply is more often seen and is mainly due to power outages. The reasons for power outages might be any of the following: extreme weather events, asset failure, natural disasters, power surges, acute accidents, and even operational errors by the workforce. Such uncertain situations are permitting us to think of it as a resilience problem. In most cases, the power outages may last from a few minutes to a few weeks, depending on the nature of the resilience issue and the power supply system (PSS) configuration. Therefore, it is imperative to understand and improve the resilience of a PSS. In this paper, a four-component resilience framework is proposed to study and compare the resilience of three different PSS configurations of residential electricity users (REUs) considering the realistic power outage conditions in the humid subtropical ecosystem. The proposed PSS configurations contain electric grid (EG), natural gas power generator (NGPG), battery energy storage (BES), and photovoltaics (PV) as the assets. The three PSS configurations of a REUs are EG + BES, EG + NGPG + BES, and EG + PV + BES, respectively, and in these, one REU is only the consumer and the other two REUs are prosumers. By using the proposed framework, simulations are performed on the three PSS configuration to understand the increasing load resiliency in the event of a power outage. Also, a comparative techno-economic and life cycle based environmental assessment is performed to select the most resilient PSS configuration among the EG + BES, EG + NGPG + BES, and EG + PV + BES for an REU. From the results, it was established that EG + PV + BES configuration would enhance the power resilience of an REU better than the other two PSS configurations. Besides, it is also observed that the identified resilient PSS configuration is cost-effective and environmentally efficient. Overall, the proposed framework will enable the REUs to opt for the PSS configuration that is resilient and affordable.

Wastage amidst shortage: Strategies for the mitigation of standby electricity in residential sector in Nigeria

Due to rising population and the increasing rate of urbanization, residential electricity usage accounts for a large chunk of Nigeria’s electricity consumption. However, little attention is paid to electricity conservation in the country. In response to this, several studies are been tailored to ensure a rapid reduction in energy consumption through various alternatives including energy efficient technologies given the current state of inadequate electricity supply in the country. On this note, this article discusses the significance of standby electricity in Nigeria. The electricity generation and consumption patterns were briefly discussed while the current electricity saving behaviour and practices among the urban dwellers were detailed with a case study analysed. Based on the case study, it was discovered that the mean standby load across the 30 households were estimated at 60 W ranging from 34-144 W. Also, standby consumption accounts for 13-44% of the annual electricity consumption across the households. Finally, the strategies for electricity saving and sustainable consumption, most especially the mitigation of standby electricity were highlighted.

Real-time prediction and anomaly detection of electrical load in a residential community

Regression model-based electrical load anomaly detection shows great potential to improve the quality of demand side management (DSM) because the load prediction and detection requirements can be satisfied by a single framework simultaneously. However, compared with other detection methods, both prediction and detection accuracy need improvement. To overcome this limitation, this work proposes a residential electrical load anomaly detection framework (RELAD) that includes a hybrid one-step-ahead load predictor (OSA-LP) and a rule-engine-based load anomaly detector (RE-AD). Considering that the diversity and randomness of residential electricity usage may render prediction difficult, the OSA-LP cascades an autoregressive integrated moving average (ARIMA) model and artificial neural networks (ANN) to achieve high precision in linear and nonlinear regression. Meanwhile, through employing the Bayesian information criterion (BIC), the OSA-LP efficiently reduces the influence of the over- or underfitting problem in real-time prediction and improves the prediction accuracy. To remedy the deficiency of overreliance on prediction outcomes in regression-model-based anomaly detection methods, the RE-AD integrates a support vector machine (SVM), the k-nearest neighbors (kNN) method and the cross-entropy loss function to develop an independent detection process to analyze the correctness of data. This method was applied to detect the load of the off-grid solar power plant in Ngurudoto, a rural area in Tanzania with 44 households and nearly 150 residents. The results of the practical application demonstrate that the proposed predictor and anomaly detector exhibit better predictive and detective accuracy than that achieved in previous work, which demonstrates the practicality of the proposed method.

Estimation of maximum loads of residential electricity users

The subject matter of coefficient factors is not a frequent object of analysis in the scientific literature. However, because of the use of this term in engineering tasks and because of the constantly changing manner of energy use (resulting from the dissemination of new appliances), there is a need for current analysis and possible revision of the approach based on coefficient factors. The aim of the paper is to compare currently recommended values of this coefficient in the task of estimating the peak output of installations on the example of standards applied in Poland. On the basis of smart metering data and statistical methods, coefficient factors for residential consumers supplied from the municipal distribution network were determined. The results were compared with the currently applied values in accordance with the industry standard issued by the Association of Polish Electrical Engineers (standard N SEP-E-002).