Proceedings | |
Forecasting Algorithms and Optimization Strategies for Building Energy Management & Demand Response | |
Weng, Kui1  Shallal, Balsam2  Meng, Fanlin3  | |
[1] Author to whom correspondence should be addressed.;Presented at the Sustainable Places 2018 (SP 2018), Aix-les Bains 73100, France 27â29 June 2018;School of Engineering, Cardiff University, Cardiff CF24 3AA, UK | |
关键词: building energy management; dem; response; dem; -side management; energy forecasting; energy optimization; | |
DOI : 10.3390/proceedings2151133 | |
学科分类:社会科学、人文和艺术(综合) | |
来源: mdpi | |
【 摘 要 】
In this paper, we look at the key forecasting algorithms and optimization strategies for the building energy management and demand response management. By conducting a combined and critical review of forecast learning algorithms and optimization models/algorithms, current research gaps and future research directions and potential technical routes are identified. To be more specific, ensemble/hybrid machine learning algorithms and deep machine learning algorithms are promising in solving challenging energy forecasting problems while large-scale and distributed optimization algorithms are the future research directions for energy optimization in the context of smart buildings and smart grids.
【 授权许可】
CC BY
【 预 览 】
Files | Size | Format | View |
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RO201910254455726ZK.pdf | 203KB | download |