Journal article

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

Yaolin Lin, Shiquan Zhou, Wei Yang, Long Shi, Chun-Qing Li

ENERGIES | MDPI | Published : 2018


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 interac..

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Awarded by Natural Science Foundation of Hubei Province

Awarded by Australian Research Council

Funding Acknowledgements

This research was funded by Natural Science Foundation of Hubei Province grant number [2017CFB602] and Australian Research Council under grants (DP140101547, LP150100413 and DP170102211)