Power load forecasting based on deep neural network

Yuhang Zhang; Guoyang Li

doi:10.1117/12.2685798

15 August 2023 Power load forecasting based on deep neural network

Yuhang Zhang, Guoyang Li

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 127190V (2023) https://doi.org/10.1117/12.2685798
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

The accuracy of power load prediction has great influence on the actual power generation, distribution, system maintenance and the operation plan of energy supplier related to electricity price. This paper studies the application of feedforward deep neural network and recursive deep neural network in medium term power load forecasting and analyzes its accuracy and computing power. Firstly, a feature extraction method of time-frequency domain analysis is proposed to preprocess the collected original data set, which can fully excavate the deep information hidden in the original data set. Then the feedforward deep neural network and recursive deep neural network model are used to forecast the power load in the medium term. Finally, the actual load data of a city in 5 years are used to predict the load in different seasons in the next 1 year. The simulation results show that the collaborative use of time-frequency domain analysis method and deep neural network has higher accuracy in medium-term load prediction.

Citation Download Citation

Yuhang Zhang and Guoyang Li "Power load forecasting based on deep neural network", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 127190V (15 August 2023); https://doi.org/10.1117/12.2685798

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