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A short-term building cooling load prediction method using deep learning algorithms

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  • Fan, Cheng
  • Xiao, Fu
  • Zhao, Yang

Abstract

Short-term building cooling load prediction is the essential foundation for many building energy management tasks, such as fault detection and diagnosis, demand-side management and control optimization. Conventional methods, which heavily rely on physical principles, have limited power in practice as their performance is subject to many physical assumptions. By contrast, data-driven methods have gained huge interests due to their flexibility in model development and the rich data available in modern buildings. The rapid development in data science has provided advanced data analytics to tackle prediction problems in a more convenient, efficient and effective way.

Suggested Citation

  • Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    • Handle: RePEc:eee:appene:v:195:y:2017:i:c:p:222-233
    DOI: 10.1016/j.apenergy.2017.03.064
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    References listed on IDEAS

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