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| Abstract: |
| One of the cornerstones for guaranteeing the stability of wind generation and electric power system operation is wind speed prediction. This research offers a method based on Particle Swarm Optimization (PSO) to optimize the Bidirectional Long Short-term Memory Network (BiLSTM) in order to improve the wind speed prediction accuracy, taking into account the highly stochastic and regular aspects of wind speed. Firstly, the wind speed time sequence is subjected to the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The complexity of the wind speed pattern is reduced by decomposing it into components with different local feature information. The BiLSTM model, which incorporates the attention mechanism for prediction, is then fitted with the decomposed data, and optimization of BiLSTM parameters using the particle swarm technique, cutting down on mistakes in predictive modeling. To get the final prediction, the components are finally superimposed. The empirical evidence shows that the CEEMDAN-PSO-BiLSTM-Attention model decreases the RMSE(root-mean-square-error) by 15%-44%, the MAE by 18%-45%, the MAPE by 24%-52%, and the R2 by 1.4%-2.7% in comparison to the BiLSTM and other models, which the validation of CEEMDAN-PSO- BiLSTM-Attention model in short-term wind speed prediction is verified. |
| Key words: short-term wind speed prediction Particle Swarm Optimization attention mechanism Bidirectional Short-term Memory Network |
| DOI：10.11916/j.issn.1005-9113.24080 |
| Clc Number:TM614 |
| Fund: |
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| Descriptions in Chinese: |
| One of the cornerstones for guaranteeing the stability of wind generation and electric power system operation is wind speed prediction. This research offers a method based on Particle Swarm Optimization (PSO) to optimize the Bidirectional Long Short-term Memory Network (BiLSTM) in order to improve the wind speed prediction accuracy, taking into account the highly stochastic and regular aspects of wind speed. Firstly, the wind speed time sequence is subjected to the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The complexity of the wind speed pattern is reduced by decomposing it into components with different local feature information. The BiLSTM model, which incorporates the attention mechanism for prediction, is then fitted with the decomposed data, and optimization of BiLSTM parameters using the particle swarm technique, cutting down on mistakes in predictive modeling. To get the final prediction, the components are finally superimposed. The empirical evidence shows that the CEEMDAN-PSO-BiLSTM-Attention model decreases the RMSE(root-mean-square-error) by 15%-44%, the MAE by 18%-45%, the MAPE by 24%-52%, and the R2 by 1.4%-2.7% in comparison to the BiLSTM and other models, which the validation of CEEMDAN-PSO- BiLSTM-Attention model in short-term wind speed prediction is verified. |
