Applying support vector machine to predict hourly cooling load in the building |
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| Authors: | Qiong Li Qinglin Meng Jiejin Cai Hiroshi Yoshino Akashi Mochida |
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| Affiliation: | 1. Building Environment and Energy Laboratory, State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510640, China;2. Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan;3. School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan |
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| 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. |
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| Keywords: | Support vector machine Building Cooling load Prediction Artificial neural network |
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