Forecasting Algorithms and Optimization Strategies for Building Energy Management & Demand Response

Proceedings

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 27–29 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