Title: Short-term power load prediction based on CNN-LSTM model

Authors: Jiawen Chen; Chao Cai; Fangbin Yan; Jinfeng Liu

Addresses: State Grid Hubei Electric Power Co., Ltd., Wuhan, Hubei Province, 430072, China ' State Grid Hubei Electric Power Co., Ltd., Wuhan, Hubei Province, 430072, China ' State Grid Hubei Electric Power Co., Ltd., Wuhan, Hubei Province, 430072, China ' Department of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang Province, 310018, China

Abstract: The load forecasting of power system is to forecast the load of the system in a future period of time, considering the influence of historical load, economic condition, meteorological condition and social events. Therefore, a CNN-LSTM model is proposed to predict short-term power load fluctuations in the next few days on the basis of the original, in which the convolutional layer and pooling layer in convolutional neural network (CNN) are used to extract features and reduce dimensions, and then the reconstructed data output by CNN is forecasted. The experimental results show that the prediction accuracy and error of CNN-LSTM model are obviously better than that of long short-term memory (LSTM) model, which also shows that CNN-LSTM model is suitable for short-term power load data prediction.

Keywords: short-term power load; CNN; convolutional neural network; LSTM; long short-term memory network; deep learning.

DOI: 10.1504/IJCSM.2025.146093

International Journal of Computing Science and Mathematics, 2025 Vol.21 No.1, pp.77 - 89

Received: 11 Mar 2024
Accepted: 04 Jun 2024
Published online: 06 May 2025 *

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