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Improved BAS-BiLSTM-Attention model for wind power forecasting
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DOI：10.1093/ijlct/ctaf146.png)
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摘要
En 中文
为优化风电功率预测中的复杂非线性时序关系，提出了一种改进的BAS-BiLSTM-Attention模型以提升风电功率预测的精度和鲁棒性。利用甲虫触角搜索算法获取不同频带下的隐藏特征，双向长短期记忆算法捕捉隐藏特征的双向时序关联，最后加入注意力机制以确保特征组件的权重选择不受影响。结果表明，该方法在风电功率预测任务中表现优异，相比单一模型及其他常用算法，在多个评价指标上展现出更高的预测精度和鲁棒性。
Keyword:
BiLSTM
attention
wind power prediction
BAS
wind power optimization
期刊
IF:
2.3
论文数:
113
被引数:
2.3K
机构
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