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Energies 2018, 11(6), 1570; https://doi.org/10.3390/en11061570

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

1
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
2
College of Engineering and Science, Victoria University, Melbourne 8001, Australia
3
School of Engineering, RMIT University, Melbourne 3000, Australia
*
Author to whom correspondence should be addressed.
Received: 26 May 2018 / Revised: 12 June 2018 / Accepted: 14 June 2018 / Published: 14 June 2018
(This article belongs to the Special Issue Energy Efficiency in Plants and Buildings)
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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. View Full-Text
Keywords: prediction model; thermal load; thermal comfort; building design; data mining prediction model; thermal load; thermal comfort; building design; data mining
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Lin, Y.; Zhou, S.; Yang, W.; Shi, L.; Li, C.-Q. Development of Building Thermal Load and Discomfort Degree Hour Prediction Models Using Data Mining Approaches. Energies 2018, 11, 1570.

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