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

Abstract 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.