CS算法优化VMD-BiLSTM-AM的光伏功率预测

针对光伏发电功率的波动性与随机性对调度部门的负荷预测以及电网安全运行带来的严峻挑战, 提出了一种基于变分模态分解(VMD)和布谷鸟搜索(CS)算法优化的双向长短期记忆网络(BiLSTM)光伏发电功率预测方法. 首先使用VMD将光伏功率序列分解成不同频率的子模态, 通过皮尔逊相关性分析确定影响各模态的关键气象因子. 其次分别构建注意力机制(AM)和BiLSTM混合的光伏发电功率预测模型, 利用CS算法获取网络最优的权重和阈值. 最后, 将不同模态的预测结果相叠加, 得到最终的预测结果. 通过对亚利桑那州地区光伏电站输出功率进行预测, 验证了所提模型的有效性.

Photovoltaic Power Prediction Based on VMD-BiLSTM-AM Optimized by CS Algorithm

For the severe challenges brought by the fluctuation and randomness of photovoltaic power generation to the load prediction of the dispatching department and the safe operation of the power grid, this study proposes a photovoltaic power prediction method of bidirectional long short-term memory (BiLSTM) optimized by variational modal decomposition (VMD) and cuckoo search (CS) algorithm. Firstly, VMD is employed to decompose the photovoltaic power sequence into sub-modes with different frequencies, and Pearson correlation analysis is adopted to determine the key meteorological factors affecting each mode. Secondly, the hybrid photovoltaic power prediction models of attention mechanism (AM) and BiLSTM are constructed, and the CS algorithm is utilized to obtain the optimal weight and threshold of the network. Finally, the prediction results of different modes are superimposed to obtain the final prediction results. The effectiveness of the proposed model is verified by predicting the output power of photovoltaic power stations in Arizona.