Improved Bi-LSTM short-term power load forecasting considering multi-meteorological factors

Wenyuan Du; Bing Wang; Xinyi Ying; Gang Luo; Yuan Huang

doi:10.1117/12.2646830

7 September 2022 Improved Bi-LSTM short-term power load forecasting considering multi-meteorological factors

Wenyuan Du, Bing Wang, Xinyi Ying, Gang Luo, Yuan Huang

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123291O (2022) https://doi.org/10.1117/12.2646830
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

Based on the characteristics of power load and considering various meteorological factors, this paper improved BiLSTM model for forecasting. On the improved Bi-LSTM model, the prediction effect of historical power load data at the peak is significantly better than the original model; Then, considering the meteorological factors mined by KNN algorithm, different meteorological factors and historical load data are used as the input end of the prediction model to predict, and the corresponding evaluation criteria are obtained. Simulation results show that the prediction accuracy is improved after considering the influence of multiple meteorological factors. Compared with the previous methods, the proposed method has higher prediction accuracy.

Citation Download Citation

Wenyuan Du, Bing Wang, Xinyi Ying, Gang Luo, and Yuan Huang "Improved Bi-LSTM short-term power load forecasting considering multi-meteorological factors", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123291O (7 September 2022); https://doi.org/10.1117/12.2646830

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