Development of Building Thermal Load and Discomfort Degree Hour Prediction Models Using Data Mining Approaches

Yaolin Lin, Shiquan Zhou, Wei Yang, Long Shi and Chun-Qing Li
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Yaolin Lin: School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
Shiquan Zhou: School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
Wei Yang: College of Engineering and Science, Victoria University, Melbourne 8001, Australia
Long Shi: School of Engineering, RMIT University, Melbourne 3000, Australia
Chun-Qing Li: School of Engineering, RMIT University, Melbourne 3000, Australia

Energies, 2018, vol. 11, issue 6, 1-14

Abstract: Thermal load and indoor comfort level are two important building performance indicators, rapid predictions of which can help significantly reduce the computation time during design optimization. In this paper, a three-step approach is used to develop and evaluate prediction models. Firstly, the Latin Hypercube Sampling Method (LHSM) is used to generate a representative 19-dimensional design database and DesignBuilder is then used to obtain the thermal load and discomfort degree hours through simulation. Secondly, samples from the database are used to develop and validate seven prediction models, using data mining approaches including multilinear regression (MLR), chi-square automatic interaction detector (CHAID), exhaustive CHAID (ECHAID), back-propagation neural network (BPNN), radial basis function network (RBFN), classification and regression trees (CART), and support vector machines (SVM). It is found that the MLR and BPNN models outperform the others in the prediction of thermal load with average absolute error of less than 1.19%, and the BPNN model is the best at predicting discomfort degree hour with 0.62% average absolute error. Finally, two hybrid models—MLR (MLR + BPNN) and MLR-BPNN—are developed. The MLR-BPNN models are found to be the best prediction models, with average absolute error of 0.82% in thermal load and 0.59% in discomfort degree hour.

Keywords: prediction model; thermal load; thermal comfort; building design; data mining (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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