Residential Energy Consumption Research Articles

Residential energy consumption by Japan’s super-aging society: visioning a more sustainable future up to 2040

Residential energy consumption by Japan’s super-aging society: visioning a more sustainable future up to 2040

Exploring the correlation between rising temperature and household Electricity Consumption: an empirical analysis in China

This study investigates the nexus between rising temperatures and household energy consumption using data from respondents' electricity bills in the "China Residential Energy Consumption Survey." Our analysis reveals a significant correlation, with an 8.9% increase in yearly energy consumption observed when the average temperature exceeds 32°C. Additionally, we explore potential shifts in power usage due to global warming by integrating baseline estimates with daily temperature forecasts from eight contemporary climate models. Our findings project alarming trends: without interventions to curb greenhouse gas emissions, home electricity consumption could surge by 9.59–30.09% in the medium term and by 9.77–47.70% in the long run. By shedding light on these critical connections, our research underscores the urgent need for policy actions to mitigate the adverse impacts of climate change on energy consumption patterns.

Heat loss from non-circulating domestic hot water pipes increases water consumption and energy demand

Hot water use in showers is a major contributor to residential water and energy consumption, and associated costs and carbon emissions. This study aims to quantify how heat loss from non-circulating pipes contributes to water and energy consumption in residential showers. Heat loss from pipes was modelled for detached dwellings in Melbourne, Australia, using Monte Carlo analysis to quantify variability. Sensitivity analysis was conducted to identify key factors contributing to heat loss. This is the first study to quantify the variability of the impact of heat loss from pipes into increased water (and hence energy) consumption in showers. Under Melbourne conditions, we predicted that heat loss from pipes contributes approximately 2 to 10 % in average shower hot water consumption. Longer pipes, smaller diameter, longer showers and longer intervals between showers were the primary factors driving additional hot water consumption.

The Roles of Housing and Household Characteristics in U.S. Residential Heat Risk

This study uses data from the 2020 nationally-representative U.S. Residential Energy Consumption Survey (RECS) to analyze the under-explored relationship between housing type, household characteristics, and two novel heat health risk measures. We use a combination of descriptive analysis and logistic regression models. The results show that renters and residents of lower-cost housing types – multifamily and manufactured housing – are much more likely to keep their homes at unhealthy temperatures and require medical attention due to hot indoor temperatures. We also find stark disparities in heat risk for households with lower incomes, seniors, and Black, Native American, multiracial, and Hispanic households.

Residential building energy consumption estimation: A novel ensemble and hybrid machine learning approach

In recent decades, there has been a substantial rise in both worldwide energy consumption and the accompanying increase in Carbon Dioxide (CO2) emissions, primarily propelled by population growth and the escalating demand for personal comfort. Operational energy consumption in buildings constitutes about 30% of the world's total final energy usage, underscoring the significance of predicting building energy usage for effective energy planning, management, and conservation. This study has enhanced the prediction of heating load (HL) and cooling loads (CL) for residential buildings using novel and dependable machine learning (ML) techniques. The study utilized two base models: Adaptive Boosting (ADA) and Extreme Gradient Boosting (XGBoost). An ensemble consisting of ADA and XGB was constructed to improve the model's performance, aligning with the principles of the Dempster-Shafer theory. To optimize the efficiency of ADA and XGB, five innovative optimizers, namely Victoria Amazonica Optimization (VAO), Giant Trevally Optimizer (GTO), Covariance Matrix Adaptation Evolution Strategy (CMAES), Coyote Optimization Algorithm (COA), and Mountain Gazelle Optimizer (MGO), were integrated. Statistical analysis has been employed to evaluate the performance of the proposed models. The results highlight the effectiveness of CMAES in optimizing the XGB and ADA models for predicting HL and CL. The most accurate result was achieved by the XGCM hybrid model, as evidenced by the impressive total R2 value of 0.9934 in HL and 0.9911 in CL prediction. The experimental findings illustrate that the suggested approach exhibits superior predictive performance across various scenarios of building energy consumption.

Strategies for Mitigating Urban Residential Carbon Emissions: A System Dynamics Analysis of Kunming, China

To effectively combat environmental challenges, it is necessary to evaluate urban residential building carbon emissions and implement energy-efficient, emission-reducing strategies. The lack of a specialized carbon emission monitoring system complicates merging macro- and micro-level analyses to forecast urban residential emissions accurately. This study employs a system dynamics (SD) model to examine the influence of social, economic, energy, and environmental factors on carbon emissions in urban residences in Kunming, China. The SD model forecasts household carbon emissions from 2022 to 2030 and establishes three scenarios: a low-carbon scenario (LCS), a medium low-carbon scenario (MLCS), and a high low-carbon scenario (HLCS) to assess emission reduction potentials. It predicts emissions will climb to 4.108 million tons by 2030, significantly surpassing the 2014 baseline, with economic growth, urbanization, residential energy consumption, and housing investment as key drivers. To curb emissions, the study suggests enhancing low-carbon awareness, altering energy sources, promoting research and development investment, and expanding green areas. The scenarios indicate a 5.1% to 16.1% emission reduction by 2030 compared to the baseline. The study recommends an 8.3% to 11.4% reduction in MLCS as a practical short-term target for managing urban residential emissions, offering a valuable SD approach for optimizing carbon strategies and aiding low-carbon development.

Time-series data clustering with load-shape preservation for identifying residential energy consumption behaviors

Categorizing residential energy demand patterns is a principal task for demand-side management (DSM) and energy-saving strategies. While deep learning (DL)-based clustering offers a promising alternative to conventional machine learning (ML), DL’s advantages and disadvantages over ML still remain unclear in identifying energy demand patterns. Moreover, prevalent DL-based clustering can suffer from catastrophic feature distortion when capturing load-shape information from energy-load data, leading to erroneous pattern identification. To address these issues, we propose integrating a load-shape preservation mechanism into representative DL-based clustering and investigate its effectiveness in categorizing energy demand patterns, compared to existing ML and DL. We experiment and compare the three clustering approaches using one-year residential energy-load data. Results show that the proposed DL, equipped with load-shape preservation, outperformed ML quantitatively and closely aligns with the baseline DL’s performance. This is particularly significant considering that the baseline DL prioritizes quantitative enhancements, sometimes compromising load-shape precision. Furthermore, the proposed DL discovered more diverse energy demand patterns than the baseline ML and DL, while producing more human-agreeable results. This finding underscores the pivotal role of load-shape preservation in enhancing data clustering and demand pattern recognition in the real-world. These benefits will facilitate personalized DSM interventions and foster residents’ energy-saving behaviors.

Study on the Influence of the Application of Phase Change Material on Residential Energy Consumption in Cold Regions of China

As the impact of climate change intensifies, meeting the energy demand of buildings in China’s cold regions is becoming increasingly challenging, particularly in terms of cooling energy consumption. The effectiveness of integrating phase change material (PCM) into building envelopes for energy saving in China’s cold regions is unclear. The aim of this study is to assess the effectiveness of PCM integration in building enclosures for energy efficiency in these regions. The research monitored and recorded indoor temperature data from typical residential cases from May to September. This measured data was then used to validate the accuracy of EnergyPlus22-1 software simulation models. Subsequently, the calibrated model was utilized to conduct a comparative analysis on the effects of PCM on indoor temperatures and cooling energy consumption across these regions. The results of these comparative analyses indicated that PCM can alleviate indoor overheating to varying degrees in severe cold regions of China. Focusing on north-facing bedrooms, applying PCMs reduced the duration of overheating in non-air-conditioned buildings in severe cold regions of China by 136 h (Yichun), 340 h (Harbin), 356 h (Shenyang), and 153 h (Dalian). In terms of cooling energy consumption, the energy saved by applying PCMs ranged from 1.48 to 13.83 kWh/m2. These results emphasize that the performance of PCM varies with climate change, with the most significant energy-saving effects observed in severe cold regions. In north-facing bedrooms in Harbin, the energy-saving rate was as high as 60.30%. Based on these results, the study offers guidance and recommendations for feasible passive energy-saving strategies for buildings in severe cold and cold regions of China in the face of climate change. Additionally, it provides practical guidance for applying PCMs in different climatic zones in China.

Pioneering historical LCA - A perspective on the development of personal carbon footprint 1860–2020 in Denmark

Mitigating climate change by reducing anthropogenic greenhouse gas emissions is considered an urgent challenge for society. In this context, different philosophies exist. A classic debate, referred to as “boomsters vs. doomsters”, asks whether humanity's drive for technological innovations will mitigate climate change, or whether it is justifiable to compromise our living standards, affluence, and consumption patterns. A Danish research project has elucidated this topic through cross-collaboration between historians and engineers by quantifying the personal consumption-based carbon footprint (CF) of Danish citizens, taking into account the consumption segments of food, transport, housing, and furniture. Quantification is conducted through life-cycle assessments (LCA) of five decadal timesteps during the period 1860–2020. As only one other study conducting historical (retrospective) LCA with an extensive time horizon was identified in the scientific literature, our own study provides novel insights by exploring possible approaches in this field. Historical LCA has many similarities with prospective (future) LCA, providing inspiration to the approach. The main challenge of the study is to adjust both foreground and background systems in the inventory modelling phase. Data is largely based on national statistics, books, museum case studies, expert testimonials, and proxies such as using the steam turbine as an indicator of improvements to technological efficiency over time. The results show a slightly declining carbon footprint in the period 1860–1930, from 7.7 to 6.6 t CO2-eq per capita annually. Unsurprisingly the footprint peak is not found until the 1970s, at 11 t, reducing to 5.4 t in the 2010s. The residential energy consumption segment contributes half of this CF, followed by transportation by road and rail. Discussion of the results shows that whether technology can mitigate the impact from the increase in affluence depends on the specific consumption segment. For housing combined with residential energy, the results show that technology can mitigate increases in CF, despite the comforts of home having only increased over time. For the transportation segment, however, the past century has seen an eightfold increase in CF due to the pronounced evolution of personal modes of transportation. The findings suggests that the “boomsters vs. doomsters” debate is not so black and white after all. We further suggest that historical LCA, in tandem with prospective LCA, can provide valuable insights for decision support to facilitate a green transition and hence a green future.

Smart meter-based energy consumption forecasting for smart cities using adaptive federated learning

Forecasting short-term residential energy consumption is critical in modern decentralized power systems. Deep learning-based prediction methods that can handle the high variability of residential electrical loads have made models more accurate. On the other hand, these methods need a lot of sensitive information about how much people use something gathered centrally to train a forecasting model. This is not good for privacy and scalability. Moreover, models may become less accurate over time due to changing conditions. In this work, we propose a framework for energy consumption forecasting that exploits adaptive learning, federated learning, and edge computing concepts. A central server aggregates numerous long short-term memory (LSTM) models that users at various locations train with their energy consumption data to create a generalized model that uses adaptive learning to detect data drifts and enhance forecasting at the edge layer. Our findings show that adaptive federated learning performs better than centralized learning while preserving privacy, reducing communication overhead, lowering the forecast error rate by 8%, and decreasing the training time by approximately 80%.

Multi-objective optimization of residential building energy consumption, daylighting, and thermal comfort based on BO-XGBoost-NSGA-II

The energy consumption, daylighting, and thermal comfort of buildings directly affect the three key goals of residents. However, there is little research on the optimization of energy consumption, daylighting, and thermal comfort in residential buildings in China. Therefore, this study proposes an optimization framework that combines Bayesian optimization with extreme gradient boosting trees (BO-XGBoost) and non-dominated genetic algorithm-II (NSGA-II) to study the multi-objective optimization of residential building performance. This paper first uses Grasshopper to simulate and obtain a dataset through Latin hypercube sampling (LHS). BO-XGBoost is used to establish the regression relationship between building envelope design parameters and residential building performance. Then, the obtained regression model is used as the fitness function of NSGA-II to get the Pareto optimal solution set. Finally, the ideal point method is used to obtain the optimal combination of building envelope structure parameters for residential buildings. Taking a residential building in a hot summer and cold winter area as an example, the effectiveness of this method is verified. The results show that (1) BO-XGBoost has excellent predictive performance, with R2 values of 0.997, 0.960, and 0.994 for energy consumption, thermal comfort, and daylighting, respectively. (2) The proposed BO-XGBoost-NSGA-II can effectively achieve multi-objective optimization. Compared with the initial scheme of the case building, energy consumption is reduced by 44.1%, thermal comfort index is reduced by 10.9%, and daylighting performance is improved by 1.7%. Therefore, the proposed method can effectively optimize the performance goals of residential buildings and provide practical ideas for similar problems.

The “energy rebound effect” within the framework of environmental sustainability

AbstractThe energy rebound effect, characterized by increasing energy use despite enhanced efficiency, has emerged as a complex interdisciplinary topic in the literature, reflecting its significance in both production and consumption sustainability. Recognizing the pivotal role of academic research in shaping energy management strategies and policies, this study conducted a comprehensive bibliometric review, accessing 530 articles from major citation indexes. This analysis provides insights into the evolution of the energy rebound field and potential future directions. Understanding the existing literature on the rebound effect serves as a valuable foundation for forthcoming research endeavors, addressing the intricate challenge of reconciling energy efficiency with sustainable energy consumption and production. The review explores the extensive literature on the energy rebound effect, highlighting a predominant focus on residential (e.g., cooling, heating, lighting, transportation) and industrial energy consumption in the majority of studies. Empirical applications primarily emphasize the measurement of the energy rebound effect, with a notable trend of increasing research concentration in China in recent years. Additionally, there is a growing body of economy‐wide analyses in this field. However, the literature exhibits complexity in both the analyses conducted and the methods employed for measuring the rebound effect. Research gaps are apparent, numerous studies tend to concentrate solely on the rebound effect at the overall energy consumption level, neglecting variations in energy types and the behavioral patterns of economic actors. Furthermore, despite consistent findings of a higher rebound effect in developing nations, there is a noticeable lack of scholarly literature addressing this issue about these countries.This article is categorized under: Sustainable Energy > Energy Efficiency Human and Social Dimensions > Behavioral Science Policy and Economics > Governance and Regulation

Household fuel and direct carbon emission disparity in rural China

Universal access to clean fuels in household use is one explicit indicator of sustainable development while currently still billions of people rely on solid fuels for daily cooking. Despite of the recognized clean transition trend in general, disparities in household energy mix in different activities (e.g. cooking and heating) and historical trends remain to be elucidated. In this study, we revealed the historical changing trend of the disparity in household cooking and heating activities and associated carbon emissions in rural China. The study found that the poor had higher total direct energy consumption but used less modern energy, especially in cooking activities, in which the poor consumed 60 % more energy than the rich. The disparity in modern household energy use decreased over time, but conversely the disparity in total residential energy consumption increased due to the different energy elasticities as income increases. Though per-capita household CO2 and Black Carbon (BC) emissions were decreasing under switching to modern energies, the disparity in household CO2 and BC deepened over time, and the low-income groups emitted ∼ 10 kg CO2 more compared to the high-income population. Relying solely on spontaneous clean cooking transition had limited impacts in reducing disparities in household energy and carbon emissions, whereas improving access to modern energy had substantial potential to reduce energy consumption and carbon emissions and its disparity. Differentiated energy-related policies to promote high-efficiency modern heating energies affordable for the low-income population should be developed to reduce the disparity, and consequently benefit human health and climate change equally.

Energy efficiency in household sector

Abstract Energy efficiency and behavioural changes are among the key pillars of decarbonization of the global energy system. Residential sector is responsible for a large share of fossil fuels final consumption, therefore the growth of its energy efficiency can bring a valuable impact on decarbonization speed and scale. In countries with growing economies residential energy consumption can increase dramatically due to the desire of the population to improve their standard of living, therefore striking a balance between economic growth and energy efficiency improvement is a complex policy challenge. The purpose of this study is to analyze attitudes and assess barriers of energy efficient behavior in Republic of Armenia as a country with growing economy and standards of living. The paper contributes to the literature by providing new empirical evidence of consumers’ attitude toward different forms of energy efficient behavior in Armenia and reveals how general pro-environmental consciousness and social conditions influence on it. The results of the study prove that the level of general environmental consciousness of the population has a direct impact on the frequency of practical application of various patterns of energy-efficient behavior. As a policy application for emerging economies, our study proposes that the main direction of energy efficiency policy can be education of the population and raising its level of environmental self-awareness.

Multidimensional well-being of US households at a fine spatial scale using fused household surveys

Social science often relies on surveys of households and individuals. Dozens of such surveys are regularly administered by the U.S. government. However, they field independent, unconnected samples with specialized questions, limiting research questions to those that can be answered by a single survey. The presented data comprise the fusion onto the American Community Survey (ACS) microdata of select donor variables from the Residential Energy Consumption Survey (RECS) of 2015, the National Household Travel Survey (NHTS) of 2017, the American Housing Survey (AHS) of 2019, and the Consumer Expenditure Survey - Interview (CEI) for the years 2015–2019. This results in an integrated microdataset of household attributes and well-being dimensions that can be analyzed to address research questions in ways that are not currently possible. The underlying statistical techniques, designed under the fusionACS project, are included in an open-source R package, fusionModel, that provides generic tools for the creation, analysis, and validation of fused microdata.

Addressing energy insecurity: Policy Considerations for enhancing energy assistance programs

Household energy consumption is crucial for a productive and safe life. Despite its importance, 33.7 million U.S. households experienced energy insecurity in 2020. This paper examines the prevalence, correlates, and effects of energy bill assistance programs, which aim to alleviate the hardship. This analysis relies on logistic regressions and the 2020 Residential Energy Consumption Survey, a nationally representative survey administered by the Energy Information Administration. Results reveal 16 percent of the energy insecure population received assistance to pay its energy bill in 2020. Several socioeconomic and housing characteristics are associated with receipt of assistance; however, logistic regression estimates suggest prior participation in energy assistance and receipt of a disconnection notice from a utility provider are indicators substantively and significantly associated with energy assistance participation that warrant attention from scholars and practitioners. Lastly, outcomes generally indicate previous participation in energy assistance does not impact the odds a household will experience energy insecurity. Based on findings, I offer three policy recommendations: 1) increase resources spent on energy assistance to align with demand; 2) enhance communication between utility providers and low-income households regarding available assistance opportunities; and 3) prioritize engagement with populations that never participated in energy assistance to facilitate successful application processes.

Reducing carbon emissions through green renewal: insights from residential energy consumption in Chinese urban inventory districts from an evidence-based decision-making perspective

The green regeneration of urban inventory is now much more viewed as a strategy to create “green finance”, in contrast to the urban renewal movement that was started in the 1950s and focused on the physical renovation of the built environment. This study draws on evidence-based theory to examine the spatial forms of residential communities in 36 urban districts of China. From a morphological perspective, it summarizes the spatial forms of residential environments at the level of buildings and microdistricts. The results indicate that in urban areas, the aspect ratio and shape coefficient of residential buildings have a significant impact on household energy consumption, and an appropriate increase in building density will help reduce carbon emissions. In conclusion, this study emphasizes the role of carbon reduction as a kind of “green finance” and provides a reference for subsequent urban renewal decisions in combination with China’s actual cases.

Simulation of rural residential energy consumption in different climate zones based on Dest-H

Simulation of rural residential energy consumption in different climate zones based on Dest-H

Analysis and Modeling of Residential Energy Consumption Profiles Using Device-Level Data: A Case Study of Homes Located in Santiago de Chile

The advancement of technology has significantly improved energy measurement systems. Recent investment in smart meters has enabled companies and researchers to access data with the highest possible temporal disaggregation, on a minute-by-minute basis. This research aimed to obtain data with the highest possible temporal and spatial disaggregation. This was achieved through a process of energy consumption measurements for six devices within seven houses, located in different communes (counties) of the Metropolitan Region of Chile. From this process, a data panel of energy consumption of six devices was constructed for each household, observed in two temporal windows: one quarterly (750,000+ observations) and another semi-annual (1,500,000+ observations). By applying a panel data econometric model with fixed effects, calendar-temporal patterns that help explain energy consumption in each of these two windows have been studied, obtaining explanations of over 80% in some cases, and very low in others. Sensitivity analyses show that the results are robust in a short-term temporal horizon and provide a practical methodology for analyzing energy consumption determinants and load profiles with panel data. Moreover, to the authors’ knowledge, these are the first results obtained with data from Chile. Therefore, the findings provide key information for the planning of production, design of energy market mechanisms, tariff regulation, and other relevant energy policies, both at local and global levels.

Rural digital credit and residential energy consumption: Evidence from the agricultural production perspective

Lack of capital hindered the development of agricultural production and the residential energy consumption transition in rural areas, but, with the continuous advancement of digital finance technologies, rural digital credit eased the liquidity constraint of agricultural production, which influenced residential energy consumption. However, few studies explored the relationship between rural digital credit and residential energy consumption. Hence, the instrumental variable (IV) method, the mediation effect model and the survey data that covered 295 effective samples were used to analyze the impact of rural digital credit on residential energy consumption from the agricultural production perspective in this study. The results indicated that rural digital credit resulted in a significant decline of the consumption of traditional energy products, but impacts of rural digital credit on electricity consumption and the new-style energy products consumption were not significant. Further, rural digital credit reduced traditional energy products consumption via boosting the level of agricultural mechanization and the adoption of precision fertilizer applying technique. This paper was conducive to not only developing the green credit markets in rural areas, but also enhancing the residential energy consumption transition and sustainable development.