ScholarWorks@숙명여자대학교

초록

Accurate power demand forecasting is essential for maximizing the efficiency of power grid operations. In this paper, daily peak electricity demands are predicted by using time-series models and artificial intelligence (machine learning and deep learning) models and their performances are compared. The analysis reveals that the XGBoost method outperforms others for one-step-ahead forecasts, while deep learning models, LSTM and GRU, exhibit superior performance for forecasts from two to seven steps ahead. For time-series models, predictions based on bias-corrected log-transformation and back-transformation methods yield better results than those using raw data.

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