A short-term load forecasting of BiLSTM based on grey relational analysis and attention model

Bing Zeng; Pengfei Jiang; Xiaopin Yang; Yunmin Xie; Yifan Wang; Zhilong Li

doi:10.1117/12.2661194

9 December 2022 A short-term load forecasting of BiLSTM based on grey relational analysis and attention model

Bing Zeng, Pengfei Jiang, Xiaopin Yang, Yunmin Xie, Yifan Wang, Zhilong Li

Proceedings Volume 12492, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022); 124920D (2022) https://doi.org/10.1117/12.2661194
Event: International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022), 2022, Wuhan, China

Abstract

In this paper, a short-term load forecasting of bi-directional long short-term memory (BiLSTM) neural network based on grey relational analysis (GRA) and attention model (AM) is proposed. Firstly, the GRA is used to analyze the correlation between the load and weather factors, and the optimal feature set affecting load is extracted and selected as the input of the prediction model. Then, the AM is used to tune the BiLSTM neural network model parameters. Finally, the BiLSTM neural network model is used for load prediction and being verified with sample data. Compared with other prediction models, the model proposed in this paper shows higher prediction accuracy.

Citation Download Citation

Bing Zeng, Pengfei Jiang, Xiaopin Yang, Yunmin Xie, Yifan Wang, and Zhilong Li "A short-term load forecasting of BiLSTM based on grey relational analysis and attention model", Proc. SPIE 12492, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022), 124920D (9 December 2022); https://doi.org/10.1117/12.2661194

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