Exploring spatial and environmental heterogeneity affecting energy consumption in commercial buildings using machine learning

Lu, Yijun; Chen, Qi‐Yue; Yu, Mengqing; Wu, Zihao; Huang, Chenyu; Fu, Jiayan; Yu, Zhuoping; Yao, Jiawei

doi:10.1016/j.scs.2023.104586

Cited by 9 publications

(1 citation statement)

References 43 publications

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“…The proposed approach not only enhances decision-making accuracy for the layout of various business service spaces in communities but also integrates considerations for energy consumption. Factors such as regional location, overall planning, and investment priorities are systematically analyzed to optimize both the functional arrangement of commercial services and energy-efficient practices [9].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%