Electricity load model forecasting research based on WOABiLSTM-Attention algorithm

Ruxue Wang

doi:10.1117/12.2678903

8 May 2023 Electricity load model forecasting research based on WOABiLSTM-Attention algorithm

Ruxue Wang

Proceedings Volume 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023); 126350C (2023) https://doi.org/10.1117/12.2678903
Event: International Conference on Algorithms, Microchips, and Network Applications 2023, 2023, Zhengzhou, China

Abstract

Short-term load forecasting plays a key role in the overall power dispatch and power sales companies' participation in the spot market. This paper proposes a electricity load forecasting method based on the WOA-BiLSTM-Attention Algorithm. It takes historical load data as input and introduces the Attention mechanism to give different weights to the BiLSTM implied states. At the same time, to address the problem of difficult selection of hyperparameters of this model, the WOA algorithm is proposed to realize the optimal selection of hyperparameters of this model. The WOA-BiLSTM-Attention algorithm not only improves the prediction accuracy of the model but also enhances the prediction efficiency and reliability of the model.

Citation Download Citation

Ruxue Wang "Electricity load model forecasting research based on WOABiLSTM-Attention algorithm", Proc. SPIE 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023), 126350C (8 May 2023); https://doi.org/10.1117/12.2678903

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