Effect of the mood of indoor activities on thermal comfort after winter commutes with different metabolic levels

Wu, Yuxin; Jiang, Angchen; Liu, Hong; Li, Baizhan; Liu, Mengjia; Wang, Weining; Zheng, Xu

doi:10.1016/j.enbuild.2023.113746

Cited by 6 publications

(1 citation statement)

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“…The challenge is compounded by the diversity in regional climates, building designs, and occupant preferences [22]. Different regions, influenced by their geographical positioning and topographical attributes, experience different temperature ranges and climatic conditions.…”

Section: Introductionmentioning

confidence: 99%

An Explainable Evaluation Model for Building Thermal Comfort in China

Liu,

2023

Buildings

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The concentration of atmospheric greenhouse gases is being amplified by human activity. Building energy consumption, particularly for heating and cooling purposes, constitutes a significant proportion of overall energy demand. This research aims to establish a smart evaluation model to understand the thermal requirements of building occupants based on an open-access dataset. This model is beneficial for making reasonable adjustments to building thermal management, based on factors such as different regions and building user characteristics. Employing Bayesian-optimized LightGBM and SHAP (SHapley Additive exPlanations) methods, an explainable machine learning model was developed to evaluate the thermal comfort design of buildings in different areas and with different purpose. Our developed LightGBM model exhibited superior evaluation performance on the test set, outperforming other machine learning models, such as XGBoost and SVR (Support Vector Regression). The SHAP method further helps us to understand the interior evaluation mechanism of the model and the interactive effect among input features. An accurate thermal comfort design for buildings based on the evaluation model can benefit the carbon-neutral strategy.

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Section: Introductionmentioning

confidence: 99%