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

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

Applied Energy, 2009, vol. 86, issue 10, 2249-2256

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.

Keywords: Support; vector; machine; Building; Cooling; load; Prediction; Artificial; neural; network (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (95)

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