A novel numerical model for investigating macro factors influencing building energy consumption intensity

Lin, Changqing; Gao, Yafeng; Huang, Jinxin; Shi, Dongdong; Feng, Wei; Liu, Qiaoyue; Du, Xiaoze

doi:10.1016/j.spc.2020.07.014

Cited by 22 publications

(2 citation statements)

References 55 publications

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“…The flexibility in the layout and architectural design of commercial spaces enables the implementation of energy-efficient building practices, including energy-efficient lighting, sustainable building materials, and intelligent technologies. Linhave shown that smart thermostats and HVAC control systems can be programmed to enhance heating and ventilation efficiency, lowering energy consumption while ensuring comfort [37]. Integrating commercial spaces with green landscapes promotes biodiversity, reducing the urban heat island effect and improving air quality in communities, as Yang observed [38].…”

Section: Future Directionsmentioning

confidence: 96%

Exploring the Layout Optimization of Commercial Service Space Affecting Energy Consumption in Communities Using Machine Learning

Liu,

Wang

et al. 2023

Buildings

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The current design of commercial service spaces in many communities faces issues like incoherence, irrational resource allocation, and low utilization rates. These challenges contribute to increased energy consumption in communities, hindering the overall sustainable development of cities. As a representative community space in the urban environment, the commercial space within the campus requires continuous energy input. Its energy-efficient layout aligns with the principles of sustainable development. This paper uses the university campus as a case study to examine energy-efficient commercial space layout and community practices for environmental protection. Various factors influence the layout of inter-community commercial spaces, and the parameters for measuring the layout structure are diverse, considering the large sample size. Employing machine learning and big data processing to quantify development indicators across various industries and optimize their structure, resource allocation, and energy use has emerged as a viable tool for sustainable urban planning practices. This research seeks to utilize machine learning and data-driven optimization techniques to formulate a comprehensive framework for the sustainable allocation and design of business service spaces within communities. Firstly, we conduct a comprehensive investigation, which includes data collected by applying questionnaire surveys and field research, to assess and model the factors influencing the spatial layout of commercial services on university campuses. Secondly, the AEL machine learning model is constructed by combining the analytic hierarchy process to determine subjective weights, the entropy weight method to calculate objective weights, and the Lagrange algorithm to determine comprehensive weights. Thirdly, we assess and improve the layout of commercial service spaces. Then, by training and testing the Neural Network Model, we apply cases to ensure the accuracy of the machine learning calculation results. Qualitative analysis elucidates the varying factors influencing the sustainable layout of different commercial spaces. Quantitative analysis indicates that, within university campuses, the distance between commercial service spaces and residence halls is a crucial factor in fostering a more sustainable layout. Other significant factors include their location along major student routes and proximity to teaching areas. This study makes contributions not only to the specific field of optimizing commercial service space in communities but also to the broader discourse on sustainable urban development. It advances our understanding of the complex dynamics involved in crafting urban environments that are both efficient and environmentally friendly. Beyond theoretical considerations, the study provides practical solutions and recommendations applicable to implementing tangible improvements in resource allocation. These contributions aim to foster urban environments that are not only environmentally conscious but also economically viable.

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Section: Future Directionsmentioning

confidence: 96%

Exploring the Layout Optimization of Commercial Service Space Affecting Energy Consumption in Communities Using Machine Learning

Liu,

Wang

et al. 2023

Buildings

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“…These factors are interconnected, and together, can significantly impact the energy performance of buildings. In recent years, the interactions between influencing factors have been the focus of a growing number of research scholars worldwide [18]. Yang et al [19] found that reducing the roof heat transfer coefficient, exterior wall heat transfer coefficient, ground heat transfer coefficient, exterior window heat transfer coefficient, and north window-towall ratio could effectively enable buildings to meet the standards of nZEBs in the cold and arid regions of northwest China.…”

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

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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.

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China’s commercial building carbon emissions toward 2060: An integrated dynamic emission assessment model

Huo

Liu

et al. 2022

Applied Energy

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A novel numerical model for investigating macro factors influencing building energy consumption intensity

Cited by 22 publications

References 55 publications

Exploring the Layout Optimization of Commercial Service Space Affecting Energy Consumption in Communities Using Machine Learning

Exploring the Layout Optimization of Commercial Service Space Affecting Energy Consumption in Communities Using Machine Learning

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

China’s commercial building carbon emissions toward 2060: An integrated dynamic emission assessment model

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