Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks

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

Research output: Contribution to journalArticlepeer-review

199 Citations (Scopus)

Abstract

This study presents four modeling techniques for the prediction of hourly cooling load in the building. In addition to the traditional back propagation neural network (BPNN), the radial basis function neural network (RBFNN), general regression neural network (GRNN) and support vector machine (SVM) are considered. All the prediction models have been applied to an office building in Guangzhou, China. Evaluation of the prediction accuracy of the four models is based on the root mean square error (RMSE) and mean relative error (MRE). The simulation results demonstrate that the four discussed models can be effective for building cooling load prediction. The SVM and GRNN methods can achieve better accuracy and generalization than the BPNN and RBFNN methods.

Original languageEnglish
Pages (from-to)90-96
Number of pages7
JournalEnergy Conversion and Management
Volume50
Issue number1
DOIs
Publication statusPublished - 2009 Jan 1

Keywords

  • Cooling load
  • Energy conservation
  • Neural networks
  • Prediction
  • Support vector machine

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology

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