These days’ smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory（i.e., artificial systems,computational experiments, and parallel computing） will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.
类型: 期刊论文
作者: Abdulaziz Almalaq,Jun Hao,Jun Jason Zhang,Fei-Yue Wang
来源: IEEE/CAA Journal of Automatica Sinica 2019年06期
年度: 2019
分类: 信息科技,工程科技Ⅱ辑
专业: 建筑科学与工程,自动化技术
单位: IEEE,the Department of Electrical Engineering, Engineering College, University of Hail,the Department of Electrical and Computer Engineering, Ritchie School of Engineering and Computer Science, University of Denver,the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences,the Research Center for Military Computational Experiments and Parallel Systems Technology, National University of Defense Technology
分类号: TU855;TU111.195
页码: 1452-1461
总页数: 10
文件大小: 831K
下载量: 24
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