A Similar Day Based Short Term Load Forecasting Method Using Wavelet Transform and LSTM. Issue 4 (29th December 2021)

• Record Type: Journal Article
• Authors: Zhang, Ruixuan; Zhang, Chuyan; Yu, Miao
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 17:Issue 4(2022)
• Page Start: 506
• Page End: 513
• Publication Date: 2021-12-29
• Subjects: short‐term load forecasting (STLF) -- similar day approach -- wavelet transform (WT) -- long short‐term memory (LSTM); Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.23536
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract : With the deregulation of electricity markets, short‐term load forecasting (STLF) has gained importance for the operation of power systems. However, an effective STLF model is hard to achieve as the load is affected by various factors. Here we present a STLF method based on similar day approach to predict the electricity usage 24 h ahead and by employing long short‐term memory (LSTM) and wavelet transform to further improve the forecasting accuracy. Compared with other methods, the proposed method achieves higher accuracy, and brings out the significance of using similar day's load, wavelet transform, and LSTM network. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.