为提升典型天气下光伏功率预测的准确性与稳定性,提出一种基于多目标算法（NSGAⅡ）优化改进谱聚类和双向长短期记忆网络（BiLSTM）结合多头注意力机制（MHA）的光伏功率预测模型。首先,对气象及历史光伏数据进行异常值检测与处理并分析主要影响特征。其次,通过动态时间规整（DTW）对谱聚类中度矩阵的构建进行改进,并利用NSGAⅡ对相似性矩阵稀疏度与高斯核参数进行优化得到最优聚类模型,将天气划分为晴天、阴天与雨天。最后,建立典型天气下NSGAⅡ-BiLSTM-MHA最优参数模型,并与4种基准模型进行对比。结果表明,在3种天气条件下,所提模型的均方根误差（RMSE）与稳定性指标（SDEX）相比SVR分别降低50.74%~62.95%和55.85%~60.08%,决定系数（R²）指标提升8.99%~17.07%,表明该模型在多种气象条件下均具备更高的预测准确性与稳定性。
Abstract
To enhance the accuracy and stability of photovoltaic power forecasting under typical weather conditions, this paper proposes a forecasting model that integrates an improved spectral clustering method optimized by the NSGAⅡ multi-objective algorithm with a BiLSTM network enhanced by a multi-head attention mechanism （MHA）. Firstly, outlier detection and preprocessing are performed on meteorological and historical PV data, and key influencing features are identified. Then, the construction of the degree matrix in spectral clustering is improved using dynamic time warping （DTW）, and NSGAⅡ is employed to optimize the sparsity of the similarity matrix and the Gaussian kernel parameter, yielding an optimal clustering model that categorizes weather into sunny, cloudy, and rainy types. Finally, optimal NSGAⅡ-BiLSTM-MHA models are established for each weather type and compared with four baseline models. Results show that, under three weather conditions, the proposed model achieves 50.74%-62.95% lower RMSE and 55.85%-60.09% lower SDEX than that of SVR, while improving the R² by 8.99%-17.07%.
关键词
多目标优化 /
光伏功率 /
预测模型 /
动态时间规整 /
多头注意力机制
Key words
multiobjective optimization /
photovoltaic power /
prediction models /
dynamic time warping /
multi-head attention mechanism
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基金
新疆省重点研发计划（2022B01020-4）; 国家自然科学基金（52266017）; 新疆天山英才科技创新团队项目（2023TSYCTD0009）; 新疆自治区研究生教育创新计划项目（XJ2023G052）
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