Two-Stage Short-Term Power Load Forecasting Based on RFECV Feature Selection Algorithm and a TCN–ECA–LSTM Neural Network

Hui Liang, Jiahui Wu (), Hua Zhang and Jian Yang
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Hui Liang: School of Electrical Engineering, Xinjiang University, Urumqi 830017, China
Jiahui Wu: School of Electrical Engineering, Xinjiang University, Urumqi 830017, China
Hua Zhang: CGN New Energy Investment (Shenzhen) Co., Ltd., Xinjiang Branch, Urumqi 830011, China
Jian Yang: CGN New Energy Investment (Shenzhen) Co., Ltd., Xinjiang Branch, Urumqi 830011, China

Energies, 2023, vol. 16, issue 4, 1-22

Abstract: To solve the problem of feature selection and error correction after mode decomposition and improve the ability of power load forecasting models to capture complex time series information, a two-stage short-term power load forecasting method based on recursive feature elimination with a cross validation (RFECV) algorithm and time convolution network–efficient channel attention mechanism–long short-term memory network (TCN–ECA–LSTM) is presented. First, the load sequence is decomposed into a relatively stable set of modal components using variational mode decomposition. Then, the RFECV-based method filters the feature set of each modal component to construct the best feature set. Finally, a two-stage prediction model based on TCN–ECA–LSTM is established. The first stage predicts each modal component and the second stage reconstructs the load forecast based on the predicted value of the previous stage. This paper takes actual data from New South Wales, Australia, as an example, and the results show that the method proposed in this paper can build the feature set reliably and efficiently and has a higher accuracy than the conventional prediction model.

Keywords: load forecasting; temporal convolutional network; efficient channel attention; recursive feature elimination with cross validation; long short-term memory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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