A comparative study of the data-driven day-ahead hourly provincial load forecasting methods: From classical data mining to deep learning

• Published in: Renewable & sustainable energy reviews Vol. 119; p. 109632
• Main Authors: Liu, Xin, Zhang, Zijun, Song, Zhe
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2020
• Subjects: Data-driven model; Deep learning; Feature encoding; Feature selection; Short-term load prediction; Deep learning; Feature selection; Data-driven model; Short-term load prediction; Feature encoding

This paper aims at studying the data-driven short-term provincial load forecasting (STLF) problem via an in-depth exploration of benefits brought by the feature engineering and model selection. Three core issues regarding model selections, feature selections, and feature encoding mechanism selection...