Journal
ENERGY
Volume 212, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.118676
Keywords
Residential building; Space heating and cooling; Load intensity; Machine learning; Occupant behaviour
Categories
Funding
- National Key R&D Programme 'Solutions to Heating and Cooling of Buildings in the Yangtze River Region' [2016YFC0700301]
- 'LoHCool project'- Low carbon climate responsive heating and cooling of cities - Natural Science Foundation of China [NSFC 51561135002]
- UK Engineering and Physical Sciences Research Council [EPSRC EP/N009797/1]
- China Fundamental Research Fund for Central Universities [2018CDJDCH0015]
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Energy consumption for space heating and cooling typically accounts for more than 40% of residential household energy consumption. An accurate and fast prediction of space heating and cooling loads aids energy conservation and carbon emission reduction by relieving the simulation burden for optimisation design, which consider various building characteristics combinations. This study aims to develop machine learning based load prediction model for residential building, five machine-learning models have been utilised for the prediction of residential building space heating and cooling load intensities, with occupant behaviour innovatively accounted as predictor variable. Their prediction performances are compared with each other. The five machine-learning models used in this study are linear kernel support vector regression, polynomial kernel support vector regression, Gaussian radial basis function kernel support vector regression, linear regression, and artificial neural networks. The results indicate that the Gaussian radial basis function kernel support vector regression is the best-performing model, with training time of less than 35s as well as less than 4% normalised mean absolute error and normalised root-mean-square error for both cooling and heating load prediction. The sample size of training and validation set for Gaussian radial basis function kernel support vector regression model is suggested as 200 samples. A data-driven machine-learning-based prediction model is an alternative to complex simulation tools in aiding the decision making of both building design and retrofit processes. (C) 2020 Elsevier Ltd. All rights reserved.
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