基于改进鱼鹰优化算法的风电场功率预测

朱兵磊; 王安然; 赵正阳; 罗婷; 汪蒋杰; 张凯

doi:10.19912/j.0254-0096.tynxb.2025-0383

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (7) : 163-173. DOI: 10.19912/j.0254-0096.tynxb.2025-0383

基于改进鱼鹰优化算法的风电场功率预测

朱兵磊¹, 王安然², 赵正阳¹, 罗婷¹, 汪蒋杰¹, 张凯¹

作者信息 +

WIND FARM POWER PREDICTION BASED ON IMPROVED OSPREY OPTIMIZATION ALGORITHM

Zhu Binglei¹, Wang Anran², Zhao Zhengyang¹, Luo Ting¹, Wang Jiangjie¹, Zhang Kai¹

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文章历史 +

摘要

为了提高风电场功率预测的精度和稳定性,提出一种基于改进鱼鹰优化算法（IOOA）的长短期记忆网络（LSTM）的风电场预测模型。将双向长短期记忆网络（BiLSTM）和卷积神经网络（CNN）联合起来,借助二者优势捕捉风电场功率数据呈现出的复杂非线性时空关系,挖掘其中隐藏的规律和特征并引入注意力机制来调节捕获信息的权重。针对鱼鹰优化算法（OOA）算法收敛速度慢、易陷入局部最优解的问题,提出应用莱维（Levy）飞行策略和Logistic混沌映射的方法来提升OOA算法的搜索能力,再使用改进的鱼鹰优化算法（IOOA）对模型参数进行寻优。结果表明,相比较其中较好的CNN-BiLSTM-Attention预测模型,所提出的IOOA-CNN-BiLSTM-Attention模型的均方根误差（RMSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）分别降低55.287%、54.352%和32.283%,同时R²提升到96.635%,具有更好的稳定性和预测精度。

Abstract

To improve the accuracy and stability of wind farm power forecasting, a wind farm prediction model based on a long short-term memory neural network （LSTM） optimized by an improved osprey optimization algorithm （IOOA） is proposed. A combination of bidirectional long short-term memory network （BiLSTM） and convolutional neural network （CNN） is used to leverage the advantages of both models to capture the complex nonlinear spatiotemporal relationships presented by wind farm power data, mine the hidden patterns and features, and introduce an attention mechanism to adjust the weight of the captured information. To address the issues of slow convergence and susceptibility to local optima in the osprey optimization algorithm （OOA）, a method using the Levy flight strategy and Logistic chaotic mapping is proposed to enhance the OOA's search capability, after which the improved osprey optimization algorithm （IOOA） is used to optimize the model parameters. The results show that, compared to the better-performing CNN-BiLSTM-Attention prediction model, the proposed IOOA-CNN-BiLSTM-Attention model reduces the root mean square error （RMSE）, mean absolute error （MAE）, and mean absolute percentage error （MAPE） by 55.287%, 54.352%, and 32.283%, respectively, while R² improves to 96.635%, demonstrating better stability and forecasting accuracy.

导出引用

朱兵磊, 王安然, 赵正阳, 罗婷, 汪蒋杰, 张凯. 基于改进鱼鹰优化算法的风电场功率预测[J]. 太阳能学报. 2026, 47(7): 163-173 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0383

Zhu Binglei, Wang Anran, Zhao Zhengyang, Luo Ting, Wang Jiangjie, Zhang Kai. WIND FARM POWER PREDICTION BASED ON IMPROVED OSPREY OPTIMIZATION ALGORITHM[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 163-173 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0383

中图分类号： TM615

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