CNN-LSTM Power System Load Prediction Based on Embedded Wavelet

doi:10.3969/j.issn.1008-0198.2025.01.013

Abstract

Abstract: To address the issue of the accuracy of power consumption prediction at the load end in power systems, a prediction method combining convolutional neural networks and long short-term memory networks is proposed. By utilizing the principle of wavelet transform, the downsampling method in the convolutional neural network is improved, allowing time-frequency characteristicsinformation to be theoretically preserved and enhanced during the downsampling process. Experiments are conducted using actual power consumption data, and the results show that compared with traditional methods, the proposed method significantly improves various evaluation index and the accuracy of short-term load prediction.

Key words: power system, time-frequency characteristics, downsampling, embedded wavelets, power prediction

CLC Number:

TM715.1

WANG Caiqian, HU Qian, MIAO Wenjie, LU Shenxiong. CNN-LSTM Power System Load Prediction Based on Embedded Wavelet[J]. Hunan Electric Power, 2025, 45(1): 86-92.

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