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Incorporating machine learning with building network analysis to predict multi-building energy use

机译：将机器学习与建筑网络分析相结合，以预测多栋建筑的能耗

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摘要

Predicting multi-building energy use at campus or city district scale has recently gained more attention: and more researchers have started to define reference buildings and study inter-impact between building groups. However, how to integrate the relationship to define reference buildings and predict multi-building energy use, using significantly less amount of building data and reducing complexity of prediction models, remains an open research question. To resolve this, this study proposed a novel method to predict multi-building energy use by integrating a social network analysis (SNA) with an Artificial Neural Network (ANN) technique. The SNA method was used to establish a building network (BN) by identifying reference buildings and determine correlations between reference buildings and non-reference buildings. The ANN technique was applied to learn correlations and historical building energy use, and then used to predict multi-building energy use. To validate the SNA-ANN method, 17 buildings in the Southeast University campus, located in Nanjing, China, were studied. These buildings have three years of actual monthly electricity use data and were grouped into four types: office, educational, laboratory, and residential. The results showed the integrated SNA-ANN method achieved average prediction accuracies of 90.67% for the office group, 90.79% for the educational group, 92.34% for the laboratory group, and 83.32% for the residential group. The results demonstrated the proposed SNA-ANN method achieved an accuracy of 90.28% for the predicted energy use for all building groups. Finally, this study provides insights into advancing the interdisciplinary research on multi-building energy use prediction. (C) 2019 Elsevier B.V. All rights reserved.
机译：预测在校园或市区范围内的多建筑物能源使用情况已引起越来越多的关注：越来越多的研究人员开始定义参考建筑物并研究建筑物组之间的相互影响。然而，如何整合关系以定义参考建筑物并预测多座建筑物的能源使用，如何使用更少的建筑物数据并降低预测模型的复杂性仍然是一个悬而未决的研究问题。为了解决这个问题，本研究提出了一种通过将社交网络分析（SNA）与人工神经网络（ANN）技术相集成来预测多层建筑能耗的新方法。 SNA方法用于通过识别参考建筑物并确定参考建筑物与非参考建筑物之间的相关性来建立建筑物网络（BN）。人工神经网络技术被用于学习相关性和历史建筑能耗，然后用于预测多种建筑能耗。为了验证SNA-ANN方法，研究了位于中国南京的东南大学校园内的17座建筑物。这些建筑物具有三年的实际每月用电量数据，并分为四种类型：办公室，教育，实验室和住宅。结果显示，集成SNA-ANN方法的办公室组，教育组为90.79％，实验室组为92.34％，居住组为83.32％，平均预测准确率达到90.67％。结果表明，所提出的SNA-ANN方法对于所有建筑物组的预测能耗均达到90.28％的精度。最后，本研究为推进跨建筑物能源使用预测的跨学科研究提供了见识。 （C）2019 Elsevier B.V.保留所有权利。

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  • 来源
    《Energy and Buildings》 |2019年第3期|80-97|共18页
  • 作者

    Xu Xiaodong; Wang Wei; Hong Tianzhen; Chen Jiayu;

  • 作者单位

    Southeast Univ, Sch Architecture, 2 Sipailou, Nanjing, Jiangsu, Peoples R China;

    Southeast Univ, Sch Architecture, 2 Sipailou, Nanjing, Jiangsu, Peoples R China|Lawrence Berkeley Natl Lab, Bldg Technol & Urban Syst Div, 1 Cyclotron Rd, Berkeley, CA 94720 USA;

    Lawrence Berkeley Natl Lab, Bldg Technol & Urban Syst Div, 1 Cyclotron Rd, Berkeley, CA 94720 USA;

    City Univ Hong Kong, Dept Architecture & Civil Engn, Kowloon, Y6621,AC1,Tat Chee Ave, Hong Kong, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Multi-building; Energy use prediction; Social network analysis; Artificial neural networks; Machine learning; Building network;

    机译：多层建筑;能耗预测;社会网络分析;人工神经网络;机器学习;建筑网络;

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