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<p>Accurate prediction of building thermal demands is essential for evaluating energy efficiency and optimizing performance within energy management systems. This need has become increasingly critical due to growing environmental concerns surrounding energy consumption and its ecological impact. Reliable estimation of energy demands supports informed decision-making in resource management and carbon footprint reduction, promoting sustainable and technologically advanced construction practices. This study investigates the predictive accuracy of several ensemble boosting algorithms in estimating thermal energy demands (heating and cooling loads) using different architectural parameters. The performance evaluation using RMSE metrics demonstrated XGBoost’s dominance across both thermal prediction tasks. For heating load estimation, this algorithm yielded the lowest error rate of 0.309&#xa0;kW, surpassing CatBoost’s 0.318&#xa0;kW and LightGBM’s 0.351&#xa0;kW results. In cooling load forecasting, XGBoost maintained its leading position with 0.731&#xa0;kW RMSE, outperforming LightGBM (0.907&#xa0;kW) and CatBoost (0.917&#xa0;kW), which occupied the subsequent ranking positions. Testing phase findings across heating and cooling load predictions confirmed that the XGBoost ensemble methodology successfully improved estimation accuracy when applied to thermal demand modeling with associated parameters. Moreover, SHAP analysis revealed that glazing area distribution and orientation had minimal impact on heating load predictions, while overall height and relative compactness were the most influential. Similarly, orientation and glazing area distribution parameters contribute the least to the prediction of cooling load, whereas overall height and relative compactness parameters have the most significant influence. These findings confirm the effectiveness of XGBoost in enhancing prediction accuracy for residential thermal demand modeling.</p> Graphical abstract <p></p>

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Enhancing the prediction of heating and cooling loads in residential buildings using explainable XGBoost: a comparative analysis of SHAP and LIME techniques

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Meysam Alizamir,
  • Sungwon Kim,
  • Salim Heddam,
  • Aliakbar Gholampour,
  • Hyounseok Moon

摘要

Accurate prediction of building thermal demands is essential for evaluating energy efficiency and optimizing performance within energy management systems. This need has become increasingly critical due to growing environmental concerns surrounding energy consumption and its ecological impact. Reliable estimation of energy demands supports informed decision-making in resource management and carbon footprint reduction, promoting sustainable and technologically advanced construction practices. This study investigates the predictive accuracy of several ensemble boosting algorithms in estimating thermal energy demands (heating and cooling loads) using different architectural parameters. The performance evaluation using RMSE metrics demonstrated XGBoost’s dominance across both thermal prediction tasks. For heating load estimation, this algorithm yielded the lowest error rate of 0.309 kW, surpassing CatBoost’s 0.318 kW and LightGBM’s 0.351 kW results. In cooling load forecasting, XGBoost maintained its leading position with 0.731 kW RMSE, outperforming LightGBM (0.907 kW) and CatBoost (0.917 kW), which occupied the subsequent ranking positions. Testing phase findings across heating and cooling load predictions confirmed that the XGBoost ensemble methodology successfully improved estimation accuracy when applied to thermal demand modeling with associated parameters. Moreover, SHAP analysis revealed that glazing area distribution and orientation had minimal impact on heating load predictions, while overall height and relative compactness were the most influential. Similarly, orientation and glazing area distribution parameters contribute the least to the prediction of cooling load, whereas overall height and relative compactness parameters have the most significant influence. These findings confirm the effectiveness of XGBoost in enhancing prediction accuracy for residential thermal demand modeling.

Graphical abstract