Utilizing interpretable stacking ensemble learning and NSGA-III for the prediction and optimisation of building photo-thermal environment and energy consumption-Reference-Cited by-同舟云学术

Utilizing interpretable stacking ensemble learning and NSGA-III for the prediction and optimisation of building photo-thermal environment and energy consumption

Published:2024-02-23 Issue:5 Volume:17 Page:819-838
ISSN:1996-3599
Container-title:Building Simulation
language:en
Short-container-title:Build. Simul.

Author:

Shen Yeqin,Hu Yubing,Cheng Kai,Yan Hainan,Cai Kaixiang,Hua Jianye,Fei Xuemin,Wang Qinyu

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12273-024-1108-7.pdf

Reference56 articles.

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3. Bröde P, Fiala D, Błażejczyk K, et al. (2012). Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). International Journal of Biometeorology, 56: 481–494.

4. Chang X, Li W, Ma J, et al. (2020). Interpretable machine learning in sustainable edge computing: a case study of short-term photovoltaic power output prediction. In: Proceedings of ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.

5. Chegari B, Tabaa M, Simeu E, et al. (2021). Multi-objective optimization of building energy performance and indoor thermal comfort by combining artificial neural networks and metaheuristic algorithms. Energy and Buildings, 239: 110839.

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