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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 to the decomposed data, and its parameters are optimized using the particle swarm technique, reducing errors 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.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(PSO) attention mechanism BiLSTM |
| DOI：10.11916/j.issn.1005-9113.24080 |
| Clc Number:TM614 |
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| Descriptions in Chinese: |
| 基于CEEMDAN-PSO-BiLSTM-Attention的短期风速预测 田中大，邵薪如 (沈阳工业大学 人工智能学院, 沈阳 110870) 摘要: 风速预测是保证风力发电和电力系统稳定运行的基石之一。考虑到风速的高度随机性和规律性，本文提供一种基于粒子群优化（PSO）的方法来优化双向长短期记忆网络（BiLSTM），以提高风速预测精度。首先将风速时间序列进行自适应噪声完备集合经验模态分解（CEEMDAN），将其分解为具有不同局部特征信息的分量，降低风速的复杂性。然后将分解后的数据带入到引入注意力机制的BiLSTM模型中进行预测，并利用粒子群算法对BiLSTM模型的参数进行优化，降低预测模型中的误差。最后对各分量预测值相加，得出最终的预测值。实验结果表明，CEEMDAN-PSO-BiLSTM-Attention模型与BiLSTM及其他模型相比，RMSE 降低了 15%~44%，MAE 降低了 18%~45%，MAPE 降低了 24%~52%，R2降低了 1.4%~2.7%，验证了 CEEMDAN-PSO- BiLSTM-Attention 模型在短期风速预测中的准确性。 关键词: 短期风速; 预测; 粒子群优化算法; 注意力机制；双向长短期记忆网络 |
