基于PFA-MBAS-BP神经网络模型的光伏发电短期预测

针对光伏发电短期预测模型的输入变量多且关系复杂、BP神经网络稳定性差且易陷入局部最优解等问题,建立了一种基于主因子分析法(PFA)和优化天牛须搜索算法(MBAS)的改进BP神经网络光伏发电短期预测模型。该模型首先对光伏历史发电数据和气象数据进行降维简化分析,利用主因子分析法对影响光伏发电的主要因素进行相关性分析,选取主因子作为预测模型输入量。然后利用MBAS算法的空间寻优搜索,选取BP神经网络训练的最优权值阈值。最后,利用实测历史数据对不同预测模型进行仿真对比。仿真结果表明,所建立的改进模型的预测精度可达92.5%,图像数据拟合程度高且适用多种天气类型的光伏发电预测。

Short-term prediction of photovoltaic power generation based on a PFA-MBAS-BP neural network model

A BP neural network has problems associated with having many input variables and complex relationships, and poor stability. It is also easy for it to fall into local optimal solution. Thus an improved BP neural network short-term prediction model for photovoltaic power generation is established, one which combines Principal Factor Analysis (PFA) and Beetle Antennae Search Algorithm Majorization (MBAS). First, the model simplifies the dimension reduction analysis of photovoltaic historical power generation data and meteorological data, and uses the principal factor analysis method to analyze the correlation of the main factors affecting photovoltaic power generation. It chooses the principal factor as the input of the prediction model. Then, the optimal weight threshold of the BP neural network training is selected by using the spatial search of the MBAS algorithm. Finally, the simulation results show that the prediction accuracy of the optimized model can reach 92.5%, the image data has a high degree of fitting and it can be used to forecast photovoltaic power generation in various weather types. This work is supported by National Natural Science Foundation of China (No. 61563034).