Short-term power load forecasting based on GRU neural network optimized by an improved sparrow search algorithm

Xuanyu Song; Qiutong Wu; Yinong Cai

doi:10.1117/12.2680053

31 May 2023 Short-term power load forecasting based on GRU neural network optimized by an improved sparrow search algorithm

Xuanyu Song, Qiutong Wu, Yinong Cai

Author Affiliations +

Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 1270431 (2023) https://doi.org/10.1117/12.2680053
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China

Abstract

Short-term power load forecasting is a very significant content in the operation and dispatch of power system, and it is a significant side to make certain the secure and economic operation of the power system and realize the scientific administration and dispatch of the power grid. By way of eliminating the matters of difficult parameter selection and insufficient forecasting accuracy in traditional forecasting methods, this paper use an improved sparrow search algorithm to optimize gated recurrent unit neural network. Firstly, Preprocess the raw load data.Secondly, use the processed data to train the model, and optimize model parameters with firefly sparrow search algorithm. Finally, carry out the power load forecast on the day to be forecasted, and comparative analysis with other two models, SSA-GRU and GRU , the results of the example indicate that the model established in this paper can advance the prognosis preciseness degree effectively and is effective in the application of short-term power load forecasting.

Citation Download Citation

Xuanyu Song, Qiutong Wu, and Yinong Cai "Short-term power load forecasting based on GRU neural network optimized by an improved sparrow search algorithm", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 1270431 (31 May 2023); https://doi.org/10.1117/12.2680053

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