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作 者:朱明亚[1] 潘毅群[1] 吕岩 王秋涧 李玉明[2] 黄治钟[2] 陶清宝 ZHU Mingya;PAN Yiqun;LYU Yan;WANG Qiujian;LI Yuming;HUANG Zhizhong;TAO Qingbao(School of Mechanical Engineering,Tongji University,Shanghai 200092,China;Sino-German College of Applied Sciences,Tongji University,Shanghai 200092,China;Mormount(Shanghai)Engineering Co.,LTD,Shanghai 201203,China)
机构地区:[1]同济大学机械与能源工程学院,上海200092 [2]同济大学,中德工程学院,上海200092 [3]茂盟(上海)工程技术股份有限公司,上海201203
出 处:《建筑科学》2020年第10期35-46,124,共13页Building Science
基 金:国家自然科学基金项目“基于高维空间理论的建筑能耗预测最小变量集构建方法研究”(51978481);上海市经济和信息化委员会项目(2018-RGZN-02055)。
摘 要:建筑用能分析和以此为基础的能源需求预测、节能效果评估是建筑能效管理的重要基础。人工智能领域的机器学习方法在建筑能耗预测中的广泛应用,不仅拓展了建筑能耗预测的研究路线,更为建筑能效优化提供了新的视角。本文旨在总结建筑能耗预测研究领域中,人工智能机器学习方法的重要应用-数据驱动模型、传统正演模型、以及两类模型的对比和应用,并归纳出预测模型在建筑能效优化研究领域的常见应用场景和技术路线,从而为建筑能耗预测研究人员提供全面的模型方法、应用场景、预测条件等多方位考量依据。在此基础上,本文分别在应用和基础层面提出了建筑能耗预测领域的研究问题和发展需求。Building energy consumption analysis,energy demand prediction and energy-saving effect evaluation based on this constitute an important foundation for building energy efficiency management.The widespread application of machine learning methods in the field of artificial intelligence in building energy consumption prediction has not only contributed to the technology innovation in building energy consumption prediction field,but provided a new perspective on building energy efficiency optimization.In this paper,a review of the important application of artificial intelligence machine learning methods in the research field of building energy consumption prediction:data-driven model and traditional forward model was presented,and the comparison and application of the two models were summarized.Moreover,the common application scenarios and technical routes of the prediction model in the field of building energy efficiency optimization were concluded,thus providing comprehensive model methods,application scenarios,prediction conditions and other multi-dimensional considerations for building energy consumption prediction researchers.Based on the review,existing research gaps and future research directions in the field of building energy consumption prediction were highlighted from two perspectives:application and foundation.
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