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

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

Research output: Contribution to journalArticlepeer-review

348 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2249-2256
Number of pages8
JournalApplied Energy
Issue number10
Publication statusPublished - 2009 Oct


  • Artificial neural network
  • Building
  • Cooling load
  • Prediction
  • Support vector machine

ASJC Scopus subject areas

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


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