Abstract
This study develops an approach consisting of a stacking model integrated with a multi-objective optimisation algorithm aimed at predicting and optimising the ecological performance of buildings. The integrated model consists of five base models and a meta-model, which significantly improves the prediction performance. Specifically, the R2 value was improved by 9.19% and the error metrics MAE, MSE, MAPE, and CVRMSE were reduced by 69.47%, 79.88%, 67.32%, and 57.02%, respectively, compared to the single prediction model. According to the research on interpretable machine learning, adding the SHAP value gives us a deeper understanding of the impact of each architectural design parameter on the performance. In the multi-objective optimisation part, we used the NSGA-III algorithm to successfully improve the energy efficiency, daylight utilisation and thermal comfort of the building. Specifically, the optimal design solution reduces the energy use intensity by 31.6 kWh/m2, improves the useful daylight index by 39%, and modulated the thermal comfort index, resulting in a decrement of 0.69 °C for the summer season and an enhancement of 0.64 °C for the winter season, respectively. Overall, this study provides building designers and decision makers with a tool to make better design decisions at an early stage to achieve a better combination of energy efficiency, daylight utilisation and thermal comfort optimisation in an integrated manner, providing an important support for achieving sustainable building design.
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Acknowledgements
This work was funded by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX23-2117).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yeqin Shen, Yubing Hu, Hainan Yan, Jianye Hua, Xuemin Fei, Qinyu Wang and Kaixiang Cai. The first draft of the manuscript was written by Yeqin Shen and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Shen, Y., Hu, Y., Cheng, K. et al. Utilizing interpretable stacking ensemble learning and NSGA-III for the prediction and optimisation of building photo-thermal environment and energy consumption. Build. Simul. 17, 819–838 (2024). https://doi.org/10.1007/s12273-024-1108-7
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DOI: https://doi.org/10.1007/s12273-024-1108-7