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.
Original language | English |
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Pages (from-to) | 2249-2256 |
Number of pages | 8 |
Journal | Applied Energy |
Volume | 86 |
Issue number | 10 |
DOIs | |
Publication status | Published - 2009 Oct |
Keywords
- 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