SHAP Value-Based Feature Importance Analysis for Short-Term Load Forecasting

• Published in: Journal of electrical engineering & technology Vol. 18; no. 1; pp. 579 - 588
• Main Authors: Lee, Yong-Geon, Oh, Jae-Young, Kim, Dongsung, Kim, Gibak
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 2023; 대한전기학회
• Subjects: Electrical Engineering; Electrical Machines and Networks; Electronics and Microelectronics; Engineering; Instrumentation; Original Article; Power Electronics; 전기공학; Explainable artificial intelligence; Feature importance; SHAP; Load forecasting
• ISSN: 1975-0102; 2093-7423
• DOI: 10.1007/s42835-022-01161-9

Integrated with the state-of-the-art technologies, Artificial Intelligence (AI) has been successfully applied to diverse industries thanks to the increased availability of data and computing power. As AI applications become more challenging to solve real world problems, various explainable AI techniques have also been developed to interpret AI models. In this paper, we conduct SHAP value analysis, which is one of the explainable AI, to assess the feature importance of the load forecasting model. We point out the limitation of the conventional SHAP value-based feature importance metric and propose a new metric which incorporates the coefficient of determination to consider the distribution of the SHAP values. To evaluate the proposed metric, we conduct feature importance experiments on the XGBoost-based 24-h load forecasting model trained with Korea Power Exchange data. The experimental results show that the proposed SHAP value-based feature importance metric is more relevant in terms of the performance of load forecasting.