Research on short-term load forecasting based on Adam-SGD-LSTM

Renshuo Liu; Yan Zhao; Tianshuo Zhang; Huazhi Chi

doi:10.1117/12.3034422

16 October 2024 Research on short-term load forecasting based on Adam-SGD-LSTM

Renshuo Liu, Yan Zhao, Tianshuo Zhang, Huazhi Chi

Proceedings Volume 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024); 1329134 (2024) https://doi.org/10.1117/12.3034422
Event: Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 2024, Changchun, China

Abstract

Aiming at the problem of low prediction certainty of existing load forecasting methods, this paper proposes a short-term load forecasting system that is based on the LSTM neural network. The original dataset is first processed by the median elimination method, Markov-Monte Carlo algorithm and improved hierarchical clustering algorithm. Then, the data are input into the LSTM prediction model optimized by the Adam method and SGD method for prediction. The example analysis proves that the method proposed in this paper has better prediction accuracy, which is helpful for the planning and movement of the power system.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Renshuo Liu, Yan Zhao, Tianshuo Zhang, and Huazhi Chi "Research on short-term load forecasting based on Adam-SGD-LSTM", Proc. SPIE 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 1329134 (16 October 2024); https://doi.org/10.1117/12.3034422

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