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

Author

Listed:
  • Li, Qiong
  • Meng, Qinglin
  • Cai, Jiejin
  • Yoshino, Hiroshi
  • Mochida, Akashi

Abstract

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.

Suggested Citation

  • Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    • Handle: RePEc:eee:appene:v:86:y:2009:i:10:p:2249-2256
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    References listed on IDEAS

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    1. Probst, Oliver, 2004. "Cooling load of buildings and code compliance," Applied Energy, Elsevier, vol. 77(2), pages 171-186, February.
    2. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
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