Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
Title Interpretation of Load Forecasting Using Explainable Artificial Intelligence Techniques
Authors 이용건(Yong-Geon Lee) ; 오재영(Jae-Young Oh) ; 김기백(Gibak Kim)
Page pp.480-485
ISSN 1975-8359
Keywords Load forecasting; Machine Learning; XGBoost; Explainable Artificial Intelligence
Abstract Artificial intelligence (AI) is getting popular and has been successfully applied to many applications. However, in many cases, AI is considered as a ‘black box’ which is hard to interpret. Recently, researchers have been attempting to explain AI systems and various explainable AI techniques have been developed. In this paper, we apply explainable AI techniques to interpret the load forecasting based on machine learning method. For load forecasting, we employ XGBoost which is decision tree based gradient boosting algorithm. The XGBoost based load forecasting approach was analyzed in terms of feature importance and partial dependence plot.
The experimental results show that the performance can be improved by selecting features which were found to have high importance in the SHAP analysis.