Applying support vector machine to predict hourly cooling load in the building

Qiong Li, Qinglin Meng, Jiejin Cai, Hiroshi Yoshino, Akashi Mochida

研究成果: Article査読

263 被引用数 (Scopus)

抄録

In this paper, support vector machine (SVM) is used to predict hourly building cooling load. The hourly building cooling load prediction model based on SVM has been established, and applied to an office building in Guangzhou, China. The simulation results demonstrate that the SVM method can achieve better accuracy and generalization than the traditional back-propagation (BP) neural network model, and it is effective for building cooling load prediction.

本文言語English
ページ(範囲)2249-2256
ページ数8
ジャーナルApplied Energy
86
10
DOI
出版ステータスPublished - 2009 10

ASJC Scopus subject areas

  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

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