Very‐short‐term load forecasting based on empirical mode decomposition and deep neural network

• Published in: IEEJ transactions on electrical and electronic engineering Vol. 15; no. 2; pp. 252 - 258
• Main Authors: Cheng, Li‐Min, Bao, Yu‐Qing, Tang, Lai, Di, Hui‐Fang
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01-02-2020; Wiley Subscription Services, Inc
• Subjects: Artificial neural networks; Electric power systems; empirical mode decomposition; Forecasting; Model accuracy; neural network; Neural networks; System effectiveness; very‐short‐term load forecasting
• ISSN: 1931-4973; 1931-4981
• DOI: 10.1002/tee.23052

Very‐short‐term load forecasting (VSTLF) predicts the load from minutes to 1‐hour timescale. Effective forecasting is important for in‐day scheduling of the power systems. In this paper, a VSTLF method based on empirical mode decomposition and deep neural network is proposed. The extreme point span is used to determine a proper empirical modal number, so as to successfully decompose the load data into different timescales, based on which the deep‐neural‐network‐based forecasting model is established. The accuracy of the proposed method is verified by the testing results in this paper. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.