Modeling and forecasting building energy consumption: A review of data-driven techniques

建筑能耗建模与预测:数据驱动技术的综述

Mathieu Bourdeau, Xiao Qiang Zhai, Elyes Nefzaoui, Xiaofeng Guo, Patrice Chatellier

DOI: 10.1016/j.scs.2019.101533

期刊: Sustainable Cities and Society

摘要

Building energy consumption modeling and forecasting is essential to address buildings energy efficiency problems and take up current challenges of human comfort, urbanization growth and the consequent energy consumption increase. In a context of integrated smart infrastructures, data-driven techniques rely on data analysis and machine learning to provide flexible methods for building energy prediction. The present paper offers a review of studies developing data-driven models for building scale applications. The prevalent methods are introduced with a focus on the input data characteristics and data pre-processing methods, the building typologies considered, the targeted energy end-uses and forecasting horizons, and accuracy assessment. A special attention is also given to different machine learning approaches. Based on the results of this review, the latest technical improvements and research efforts are synthesized. The key role of occupants’ behavior integration in data-driven modeling is discussed. Limitations and research gaps are highlighted. Future research opportunities are also identified.

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期刊信息

期刊:

ISSN: 2210-6707

国际分区

类目分区
GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY1

国内分区

类目分区
工程技术1
工程技术, 结构与建筑技术1
工程技术, 能源与燃料1
工程技术, 绿色可持续发展技术1
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