Analysis of the Factors Affecting Energy Consumption by the Physical Elements and the Household Features in Residential Buildings

Kim, Mansu; Jung, Sung Won

doi:10.6107/jkha.2019.30.1.013

Cited by 7 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various studies have been conducted based on economic and non-economic perspectives. Recent studies have considered regional perspectives and found that energy consumption and consumer behavior are determined by socioeconomic, built environment, and climatic factors (Azevedo et al, 2016;Kim et al, 2017;Kim and Jung, 2019). However, the stochastic modeling of household energy consumption at the regional level has been challenging owing to uncertainties, e.g., an end-user's consumption behavior and environmental characteristics, which can significantly influence the outcome.…”

Section: Determinants Of Household Energy Consumptionmentioning

confidence: 99%

Determinants of Electricity Consumption of Energy-Vulnerable Group Using Ensemble Gradient-Boosting Algorithm

2022

View full text Add to dashboard Cite

The increasing energy burden on vulnerable households is critical in modern cities, it is crucial to understand how cities can characterize energy vulnerability and its relationship with the environment. This study modeled relationships between energy consumption and built environmental factors to compare determinants in average and energy-vulnerable households. While the conventional approach of identifying energy vulnerability often relies on household income, this study suggested a new approach by considering the energy-vulnerable group as a low-income class with high energy expenditure. A traditional regression model (semi-log regression) and advanced machine learning algorithm (ensemble gradient boosting, XGboost) were employed to maximize the performance of the modeling processes. The results indicated that the overall modeling performance was superior with regard to the machine learning algorithm, producing the r-squared value of 0.92 for the energy-vulnerable households, compared to the 0.34 of the semi-log regression model. While the direction of the association of the determinants was similar in the average and energy-vulnerable households, the level of association exhibited a clear difference, especially for the effect of income (comparing 0.30 to 0.03) and housing type (comparing -0.45 to -0.63). The study identified several implications regarding urban energy management and policy based on the findings.

show abstract

Section: Determinants Of Household Energy Consumptionmentioning

confidence: 99%

Determinants of Electricity Consumption of Energy-Vulnerable Group Using Ensemble Gradient-Boosting Algorithm

2022

View full text Add to dashboard Cite

show abstract

“…Luis Lopez et al [27] conducted a survey of energy-saving potential in 13 households using a questionnaire, and evaluated the impact of the resulting changes in people's lifestyles on energy consumption. Mansu et al [28] combined building factors and household characteristics and used multiple regression analysis to derive building factors and household characteristics that can be used to effectively manage residential energy. Xie et al [29] used multiple regression models to quantitatively analyze factors such as building characteristics, household composition, lifestyle, and household equipment.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption and Carbon Emissions of Nearly Zero-Energy Buildings in Hot Summer and Cold Winter Zones of China

Liu

Zhang

et al. 2023

Sustainability

View full text Add to dashboard Cite

Issues of energy efficiency and sustainability in buildings are gaining increasing attention in the context of the “3060” dual-carbon initiative. In recent years, nearly zero-energy buildings (nZEBs) have emerged as a potentially viable solution to the challenges of the energy crisis in the building sector, and it is important to study the factors influencing their energy consumption and carbon emissions. However, existing research lacks analyses of multifactor interactions, and the problem of high energy consumption has not been sufficiently addressed. Taking a typical residential building in the Yangtze River basin as the study subject, this study, jointly funded by the University of Nottingham and Hubei University of Technology, proposes a hybrid approach that combines building energy simulation and orthogonal experiments to investigate factors pertaining to buildings, people, and the environment to identify key influencing factors and explore the energy consumption and carbon emission characteristics of residential buildings in hot summer and cold winter (HSCW) zones. Our findings reveal the following: (1) The use of renewable energy sources, such as solar photovoltaic power generation and solar hot water, and renewable energy systems such as ground-source heat pumps, in the operation phase of a baseline building can result in a 61.76% energy-saving and a 71% renewable energy utilization rate. (2) To more easily meet the requirements of nZEB standards, it is recommended to keep KE within the range of 0.20–0.30 W/(m2·K), KR within the range of 0.15–0.20 W/(m2·K), and VT within the range of 0.6–0.7 h−1. This study will help to identify the critical factors affecting energy consumption and provide a valuable reference for building energy efficiency in HSCW zones.

show abstract

Time-series analysis of the effects of building and household features on residential end-use energy

Lee

Song

2022

Applied Energy

View full text Add to dashboard Cite

Analysis of the Factors Affecting Energy Consumption by the Physical Elements and the Household Features in Residential Buildings

Cited by 7 publications

References 9 publications

Determinants of Electricity Consumption of Energy-Vulnerable Group Using Ensemble Gradient-Boosting Algorithm

Determinants of Electricity Consumption of Energy-Vulnerable Group Using Ensemble Gradient-Boosting Algorithm

Energy Consumption and Carbon Emissions of Nearly Zero-Energy Buildings in Hot Summer and Cold Winter Zones of China

Time-series analysis of the effects of building and household features on residential end-use energy

Contact Info

Product

Resources

About