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Title	Toward Explainable Electrical Load Forecasting Of Buildings: A Comparative Study Of Tree-Based Ensemble Methods With Shapley Values
ID_Doc	57684
Authors	Moon J.; Rho S.; Baik S.W.
Year	2022
Published	Sustainable Energy Technologies and Assessments, 54
DOI	http://dx.doi.org/10.1016/j.seta.2022.102888
Abstract	Electrical load forecasting of buildings is crucial in designing an energy operation strategy for smart city realization. Although artificial intelligence techniques have demonstrated excellent energy forecasting performance, explaining how outcomes are obtained when they are inaccurate is challenging. Explainable artificial intelligence (XAI) has recently received considerable attention in addressing this issue. This study proposes an explainable electrical load forecasting (XELF) methodology. We first preprocess data for input variable configuration and build the following tree-based ensemble models that produce outstanding results in tabular data: random forest, gradient boosting machine (GBM), extreme gradient boosting, light GBM (LightGBM), and categorical boosting. We evaluate performance in terms of the mean absolute percentage error, coefficient of variation of the root mean square error, and normalized mean absolute error. Finally, we provide the rationale for interpreting the influence of the input variables and decision-making process via Shapley additive explanations (SHAP), an XAI technique, on the best model. The experiments were conducted with an electrical load dataset from educational buildings to validate the practicality and validity of this methodology. We applied the SHAP to the LightGBM model and performed its respective analyses and visualizations for XELF. © 2022 The Authors
Author Keywords	Building energy data analytics; Ensemble learning; Explainable artificial intelligence; Explainable electrical load forecasting; Shapley additive explanations; Short-term load forecasting

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