Residential Energy Usage Research Articles

Analyzing the Determinants of U.S. Residential Energy Usage and Spending: A Machine Learning Approach

This study explores the factors that impact residential energy usage and spending in the United States. Using data from the 2020 Residential Energy Consumption Survey (RECS), we investigate the significance of different energy consumption determinants at various analysis levels. Our analysis covers residential energy usage, electricity, natural gas, propane, and fuel oil consumption. We also examine energy usage for space heating, cooling, and water heating. To leverage the extensive RECS data, which includes over 180 variables, we utilized machine learning (ML) techniques for feature selection and determined their Shapley contribution for different target outcomes. Our results indicate that the CatBoost algorithm outperforms other ML techniques on the 2020 Residential Energy Consumption Survey sample. Our findings demonstrate that it is not appropriate to aggregate consumption and expenditure, as each level has distinct important features. JEL Classification: D12, Q41, R21

From roads to roofs: How urban and rural mobility influence building energy consumption

Understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

Forecasting residential real estate prices and energy usage in Flanders

Purpose Currently, residential forecasting literature is limited with few to no forecasting papers that consider residential prices and energy consumption for the selling and rental market within a single study. The purpose of this paper is to study the prices and energy usage for both markets by combining an explanatory model with the predictions of a predictive model, which will lead to a better understanding of the forecasting results. Design/methodology/approach This study presents an econometric comparison between 508,612 selling properties and 174,007 rental properties transacted in Flanders between January 2019 and December 2023. The authors study their prices and energy usage by combining the causal results of a hedonic price model (HPM) with the predictions of an autoregressive integrated moving average model (ARIMA). Findings The authors find that both markets are extremely sensitive to changes in related factors, whereby selling prices are primarily influenced by macroeconomic factors, but rental prices and especially energy usage are also sensitive to market standards. Policies that target energy efficiency can therefore steer its future trend. In addition, in line with previous literature, the HPM shows an increasingly strong relationship between residential prices and energy efficiency over the years, indicating that these steering energy policies also indirectly monitor residential prices. Originality/value This study underscores the importance of integrating the results of an explanatory model with a predictive model. It demonstrates that combining them provides a more comprehensive understanding which is helpful for policymakers, real estate professionals and investors during their decision-making process.

Exploring attitudes and behavioral patterns in residential energy consumption: Data-driven by a machine learning approach

The present study focuses on two main objectives: firstly, to clarify the mechanisms by which attitudes impact behavioral changes related to household energy consumption, and secondly, to offer valuable insights to enhance the understanding of residential energy usage through a novel technique called Support Vector Regression (SVR). This method employs several feature space transformations to convert nNar relationships into linear ones. The results highlight the crucial role of psychological factors in determining energy consumption behaviors, demonstrating that cognitive factors significantly influence attitudes and behavioral patterns. The findings show that psychological variables have a major role in determining how people consume energy, with cognitive variables having a particularly large impact on attitudes and behavior patterns. Our findings demonstrate the superior performance of Support Vector Regression (SVR) with radial basis function kernels over traditional predictive models, with a prediction accuracy of 93.7 % for changes in behavior patterns (CHP) and 94.4 % for changes in attitudes (CHA). These results highlight the value of applying cutting-edge machine-learning approaches to create precise models for comprehending and directing energy-saving actions. The policy implications suggest that reducing cognitive barriers can significantly encourage energy-saving behaviors and contribute to a comprehensive approach for energy-efficiency initiatives

Bridging the Energy Efficiency Gap for Low-Income Households in Charlottesville

Residential energy usage in Charlottesville, Virginia, accounts for nearly 30% of the city’s greenhouse gas emissions. Without significant changes, Charlottesville is unlikely to meet its goal of reducing emissions by 45% by 2030. Improving residential energy efficiency is key to lowering emissions and reducing energy costs for residents. However, the high upfront cost of these upgrades is unaffordable for over a third of Charlottesville households. As electricity prices rise due to Virginia’s upcoming carbon dioxide cap-and-trade program, low-income households that can’t afford efficiency upgrades will face higher energy bills and greater financial strain. This study explores the feasibility of a residential efficiency financing program designed to improve the affordability and accessibility of energy upgrades for low-income households in Charlottesville. Through a detailed benefit-cost analysis, various policy alternatives, including On-Bill Financing, Revolving Loan Fund, and Loan Loss Reserve, were evaluated for their effectiveness, equity, and implementation feasibility. The recommended option, On-Bill Financing, would allow Charlottesville Gas to cover the upfront costs of energy efficiency upgrades. Customers would repay the investment over time through their gas bills, thereby enjoying immediate reductions in energy consumption and costs. This policy would fairly enhance residential energy efficiency and provide critical financial assistance to households that otherwise couldn’t afford these upgrades.

A Data Mining model using ANN for predicting seasonal variance in residential Electricity consumption

The role of electricity consumption is crucial in daily life, and by analyzing readily available data on electricity usage, we can uncover hidden patterns that aid in forecasting seasonal energy usage. This research paper introduces a Data mining algorithm that utilizes an Artificial Neural Network (ANN) to predict the residential Electric Energy usage. Additionally, it forecasts the fluctuations in energy consumption during various monsoons, as electricity usage varies greatly across seasons. These predictions can guide energy conservation efforts to address the demand for energy more effectively.

Privacy-Preserving and Hierarchically Federated Framework for Short-Term Residential Load Forecasting

Short-term Load Forecasting (STLF) plays a fundamental role in modern energy systems. This paper proposes a privacy-preserving STLF framework for residential energy users. The framework enables users to collaboratively train an STLF model without exchanging their load data. The system works in two stages: (i) a federated user clustering method is developed to divide the users into multiple clusters based on load pattern similarity. Instead of aggregating the users’ data to do clustering, the developed method determines the user clusters in a distributed manner. (ii) after the user clustering stage, a hierarchically federated STLF model training is developed, which applies federated learning principles to facilitate intra-and inter-user cluster model training. In the process, the individual users also do not expose their load data. Further, an asynchronous communication mechanism is designed and integrated into the framework, making it high fault-tolerant and adaptable to the residential environment with communication uncertainties. Comprehensive experiments based on a real Australian load dataset are conducted to validate the system. The simulation results show that the proposed framework can effectively train the load forecasting model with a fast coverage rate and achieve up to 37.25% prediction accuracy improvement (in terms of the Mean Absolute Percentage Error metric) compared with the other benchmark methods.

Social license to automate batteries? Australian householder conditions for participation in Virtual Power Plants

Energy companies and new aggregation businesses have recently promoted Virtual Power Plants (VPPs) to solve many problems in energy systems by aggregating and coordinating rooftop solar and household batteries to act in unison. If successful, they could support deployment of renewable generation into electricity distribution networks and enable households to access multiple value streams for their assets, through participation in markets for wholesale energy and system services and payments for network support. However, while residential battery purchase may be more financially attractive if combined with VPP participation, households remain cautious about participation. This paper examines the social factors behind this gap between industry promise and the very low household uptake of VPPs in Australia. We present the key findings from a qualitative study (n = 47) of Australian residential energy users' motivations towards, and conditions for, participation in a hypothetical battery-based virtual power plant. We find that users' willingness to participate in a VPP is affected by their motivations for solar and battery purchase. In particular, VPP participation can conflict with goals of independence from the grid and security of supply. Furthermore, the financial benefits may be more attractive to households who have not already embarked on a ‘sustainability journey’ by purchasing solar and/or a battery. We also find that environmental and social benefits can help motivate VPP participation, provided transparency and fairness in the distribution of benefits are assured through regulation or operation by a trusted broker.

Sustainability at Home: The Development of an Efficient Framework for Home Energy-Saving Applications

Energy consumption is increasing due to the rise in the world population, industrialization, and urbanization, particularly in the residential sector, attributed to a lack of user-friendly tools. This study seeks to create a research framework and wireframe for home energy-saving applications. A systematic literature review (SLR) was conducted using the VOSviewer software version 1.6.18 tool to pinpoint the research problems. Three key research problems were identified: Inadequate information presentation for both experts and non-experts, insufficient consideration for middle-aged and elderly users, and difficulties in interpreting graphics or images on the application’s display screens. This qualitative research involved three rounds of co-creation activities with nine experts and nine non-experts to identify major problems and preliminary solutions. As a result, two key issues were addressed from the qualitative data: The problem of area calculation, resolved by simplifying data entry processes, and the issue of material selection within homes, improved by incorporating illustrative images with concise, easily understandable descriptions. The outcome of this research is a framework and wireframe that lays the groundwork for developing user-friendly applications that promote sustainable behaviors in residential energy usage. This research contributes valuable guidelines for developers and stakeholders to create more efficient and user-friendly applications, thus promoting environmental action and sustainable practices in residential settings.

Application of a Community Battery-Integrated Microgrid in a Blockchain-Based Local Energy Market Accommodating P2P Trading

This paper presents the application of a community battery energy storage system (CBESS)-integrated microgrid (MG) in a blockchain-enabled local energy market (LEM). The proposed LEM balances the community energy requirement while facilitating frequent peer-to-peer (P2P) energy transactions between several energy users in the presence of both energy supplier and energy operator. The architecture is formulated by taking a number of local market and network constraints, that include residential battery energy storage system (RBESS) constraints; CBESS constraints; P2P traded price constraints; P2P traded power constraints; margin constraints of the stakeholders; power grid export and import constraints; and network energy balance constraints, so as to not only incentivise energy users but also reduce import/export from/to power grid while keeping the margins of energy supplier and energy operator unaffected. Different types of transactions data including energy users’ P2P pricing bids and P2P traded energy volume are also stored in the blockchain database. Further, the developed LEM framework is also validated through a case study executed on an actual Australian power grid network, comprising 260 residential energy users; two energy suppliers; an energy operator; and a CBESS, and the performance of the proposed P2P trading-based LEM strategy is compared with the existing business-as-usual (BAU) that directs energy users to buy/sell energy at the time-of-use (ToU)/ feed-in-tariff (FiT) rate. The extensive and comparative simulation results confirm the superior performance of the proposed LEM mechanism in terms of minimising energy users’ electricity bill; lowering power grid import and export; and retaining margins of energy suppliers and the energy operator; and thus, emphasise its application suitability in the current electricity market.

Energy usage and human behavior modeling for residential bottom-up energy simulation

We introduce a probabilistic modeling for a disaggregated Bottom-up simulation of residential energy usage. Parametric probability distributions are modeled with parameters that have a natural explanation in terms of usage and appliance power. Human behavior such as sleep and home occupancy variables are considered too, with its corresponding trained probabilistic Models. Model parameters are adjusted by the minimization of the Kullback–Leibler divergence from known appliance and behavior usage data. Self-generated photovoltaic Energy is included in the simulation with a battery for storage and electric vehicle usage. Simulations match individual and aggregated usage load profiles in the European REMODECE and RSE Italian load data sets. Obtained Models are useful for residential disaggregated simulations allowing individual appliances to change from house to house. Probabilistic distributions can be used as prior knowledge for energy management systems, risk management, and grid failure prediction and can be adapted based on non-stationary real-time house behavior and appliance usage.

Optimal Planning of Residential Energy Hubs Considering Customer Desire Function

This paper proposes an optimal residential energy hub (REH) design framework where the desired tradeoff between maximizing the satisfaction level of residential user energy usage and minimizing the overall costs is obtained by establishing a customer desire function (CDF). Considering the major household appliances, the REH demands are categorized into several groups, and CDFs are formulated and applied to each group of loads. The developed mathematical model results in a mixed-integer linear programming optimization problem. Simulations were carried out on a residential building as a case study. Numerical results confirmed the presented framework effectiveness in improving the end-user convenience level and enhancing the investment benefits. They also demonstrated the significance of various energy networks and the optimal configuration of REH components on the financial and technical of the REH.

Residential solar energy consumption and greenhouse gas nexus: Evidence from Morlet wavelet transforms

Global warming is one of the most important environmental problems in the world. One might state that the most potential considerable cause of global warming is the increase in greenhouse gas emissions in the atmosphere. The commonly suggested method used to struggle with this potential problem is to consume renewable energy sources instead of fossil fuels. In today's World, where the rate of urbanization is very high, the research on the effect of the energy demand of the residential sector on global warming has become a crucial research topic in the energy and climatic literature.Among different solar sectors, the residential sector is a major energy consumer. Considering the residential, commercial, and industrial solar sectors, the residential solar sector has become a major component of total solar energy. It accounts for approximately 43% of total solar. On the other hand, to the best of my knowledge, there exists no empirical study in the literature considering the influence of residential solar consumption on environmental quality. Thus, determining the effect of the most used energy source, residential solar, energy consumption on the environmental quality has arisen the key research problem of this paper. This research analyzes the effects of residential solar energy usage on CO2 emissions for the period 1989:1–2020:1 in the USA. It has been observed throughout Morlet wavelet analyses that the use of residential solar energy has a strong effect on mitigating CO2 emissions in the 1–2 year frequency. Eventually, this paper provides relevant potential energy policies that can enhance the efficiency of residential solar energy consumption.

Behavioural and environmental sustainability determinants of residential energy management information systems use

Notwithstanding appliance energy efficiency and advances in Smart Home Technologies (SHT), residential energy consumption is still on the rise. Residential Energy Management Information Systems (REMIS) that enable users to monitor, analyse, and benchmark energy use and consumption can contribute to energy savings and mitigate unnecessary consumption. Nevertheless, the withdrawal of Microsoft's Hohm and Google's Powermeter and the low adoption rate of smart meters in the US, UK, Canada, and Australia signposts missed opportunities in reducing energy intensity. Thus, the question of how to influence the successful adoption of REMIS and other related sustainable technologies captivates both researchers and practitioners. In this paper, based on a conceptual model developed by integrating the theory of planned behaviour and pro-environment behaviour research, we analysed survey data from 208 Singapore testbed eco-town apartment precinct households. The study found that environmental altruism and sustainability information exposure induce normative pressures towards REMIS use and a sense of volitional control over REMIS use. Furthermore, presence of children and the apartment size moderate the causal relationship between attitude, subjective norm and perceived behavioural control concerning REMIS adoption. These findings offer theoretical and empirical insights to cleaner production, residential energy consumption research and other sustainable technologies use that can contribute to UN's Sustainable Development Goals (SDGs). It also discusses the strategic, policy and design implications for energy and technology providers, and residential energy users.

Realizing multifractality of smart meter data for household characteristic prediction

Driven by the efficient and personalized services and better management of the distribution networks, power utilities are always willing to recognize the household characteristics which reflect the dwelling and social-demographic characteristics of customers. As a key component of the distribution grid monitoring systems, smart meters installed at each customer’s household offer a non-intrusive approach to predict the household characteristics. The resolution of energy consumption data has a great impact on prediction accuracy as fine granularity data can better characterize the household energy usage profile features. However, collecting high-resolution energy consumption data may not be feasible for power utilities due to the substantial cost of upgrading the existing Advanced Metering Infrastructure (AMI). This paper aims to propose a cost-effective framework for the accurate prediction of household characteristics by realizing the multifractal characteristics of residential electric energy consumption data recorded from smart meters. First, Multiscale Multivariate Multifractal Detrended Fluctuation Analysis (MMV-MFDFA) is performed on the energy consumption data to analyze the multiscale relationships of the residential energy usage profile and reveal the multifractal structures embedded in the energy data. Then, Self-Adaptive Multifractal Interpolation (SAMI) is developed to reconstruct quasi high-resolution energy consumption data from which distinctive features relevant to household characteristics are extracted by Mathematical Morphological Decomposition (MMD). Such features are further integrated with an advanced deep learning algorithm - Deep Forest (DF) for household characteristic prediction. The proposed method is validated using real-life smart meter data from Ireland.

Evolution of Solar Energy in Chile: Residential Opportunities in Arica and Parinacota

Chile has set itself to achieve Greenhouse Gas emission neutrality, with at least 70% of electricity coming from renewable energy sources by 2050. To this end, institutional and regulatory frameworks have been improved, resulting in significant progress in medium and large-scale projects. However, solar energy production at residential level and its surplus injection to all distribution networks has been very limited. This paper analyzes the evolution of the regulatory energy policies in Chile in order to contrast it with an economic evaluation of residential projects. The analysis focuses on the city of Arica, one of the highest potential regions in terms of solar energy within the country. There, a particularly low penetration of residential solar energy usage has been observed. Based on the current situation, projections are made for Arica in 2050, through the identification of barriers and opportunities on a residential scale development. According to some recommendations, there is the need to design policies that take into account the particular characteristics of each region within the country.

Design of Grid-connected and Stand-alone Photovoltaic Systems for Residential Energy Usage: A Technical Analysis

Towards realizing the United Nations sustainable development goals, access to clean, cheap and reliable energy, especially electricity, has been considered as one of the vital indices in any community. Hence, this paper presents the design analysis of both a grid-connected and an offgrid photovoltaic (PV) systems for household users in the highly residential city of Ogbomoso in Nigeria using PVGIS software. For the off-grid design, it is estimated that, given a total daily load of 9.57 kWh, a 3.5 kWp PV array size and a battery capacity of 86 kWh are enough to power the load with 5 days of autonomy and 70% depth of battery discharge. For the grid-connected PV system, the annual energy output for a building-integrated PV system is found to be around 4006 kWh; and a total of eight PV modules (each rated 250 Wp, 30.93 V) are stringed to arrive at the desired capacity of 2 kWp. In terms of performance, the performance ratio (PR) of a building integrated grid-tied PV system at the study location was found to be 71.2% while for a free-standing PV system, the PR was 75%.

An examination of factors affecting residential energy consumption using a multiple discrete continuous approach

Residential energy use has become an important source of global energy demand growth and carbon emissions growth. Residential building energy usage accounts for about 22% of the total energy use in the United States. In the current study, we address residential energy usage by addressing two decisions: (1) source of energy (such as electric and natural gas) and (2) consumption by energy source for various purposes. A Multiple Discrete Continuous Extreme Value model that allows us to analyze the source and consumption decisions in an integrated framework is developed. The model is estimated using data drawn from the 2015 Residential Energy Consumption Survey that provides energy use details for electricity, natural gas, fuel oil and Liquefied Petroleum Gas for residential units across United States. An exhaustive set of independent variables including location characteristics, household characteristics, housing characteristics, appliance use and climatic characteristics were employed in the model estimation. The model estimation results are augmented with a comprehensive policy analysis to illustrate how various independent variables affect energy use by source. A comparison of energy use between urban and rural regions, by varying household size and housing unit size are examined.

THE EFFECTS OF GREEN ENERGY ON ECONOMIC DEVELOPMENT, AND GREEN HUMAN RESOURCE MANAGEMENT ON NONGREEN WORK OUTCOMES IN SRI LANKA

ABSTRACT This paper combines a macroeconomic assessment of the effects of green energy in upper- income, middle-income, and lower-income developing countries, with the examination of the impact of green human resource management policies on job satisfaction, empowerment, job performance, and perceived financial performance among a sample of Sri Lankan managers. Only in upper income developing countries with industrial use of renewable energy does renewable energy output lead to economic development. Sri Lanka is a middle- income developing country. Sri Lankan managers are satisfied with green human resource management policies, which leads to superior job performance and perceptions of financial performance. The challenge is for Sri Lankan managers to view green energy as a means of advancement to economic development by supporting large-scale green energy investments in industrial production to supplement current residential green energy usage. Keywords Green Energy, Economic Development; Green Human Resource Management, Work Outcomes, Job Satisfaction, Job Performance

EXAMINING THE ECONOMIC IMPACT OF RENEWABLE ENERGY IN GREEN BUILDINGS: A CASE STUDY OF JORDAN

This paper aims at testing whether an increase in energy spending has placed a negative or positive impact on the Jordanian economy. Following the economic scale theory and the results of model simulation, we reached to a conclusion that such level of growth in public spending threatens the next generations living in Jordan, as the gap between produced energy and the total energy used. The question is how to shrink this energy gap, or even eliminate it. The paper contributes to bridging such gap by suggesting an increase in the production of domestic energy which depends on what resources Jordan has and whether these resources are renewable or fossil as well as the economic feasibility of using such resources. The influential use is related to social awareness as well as to the supports that are given by government regulations. The paper concludes that it is about time to reduce the amount of support needed by residential energy users in order to invest in other energy projects such as hydroelectric, wind, or large solar farms.Keywords: Renewable energy, green building, energy spending, JordanJEL Сlassifications: O13, Q43, Q48, Q51, Q56DOI: https://doi.org/10.32479/ijeep.9947