提出一种基于混合遗传蚁群算法（GA－ACO）改进BP神经网络的预测模型.通过皮尔逊相关系数公式求出与光伏发电输出功率相关性强的气象特征作为训练模型的输入，减少无关气象特征量对光伏输出功率的预测影响.运用遗传算法（GA）产生寻找最优参数问题的信息素分布，蚁群算法（ACO）在有初始信息素分布的条件下输出最优权阈值，让BP神经网络二次训练，输出预测值.分析结果表明，以晴天为例，GA－ACO－BP神经网络模型比传统BP神经网络模型、ACO－BP神经网络模型、GA－BP神经网络模型的预测结果相对误差分别减少了9.47、4.83和3.27个百分点，因此GA－ACO－BP神经网络模型用于光伏发电功率预测时具有更好的预测精度.

This paper proposes an improved BP neural network prediction model based on hybrid genetic ant colony algorithm （GA－ACO）. Through Pearson correlation coefficient formula， meteorological features with strong correlation with photovoltaic power output are calculated as the input of the training model to reduce the influence of irrelevant meteorological features on the forecast of photovoltaic power output. The genetic algorithm（GA） is used to generate the pheromone distribution in search of the optimal parameter. The ant colony algorithm（ACO） outputs the optimal weight threshold under the condition of the initial pheromone distribution， and the BP neural network is trained twice to output the predicted value. The analysis results show that： taking sunny days as an example， the relative error of GA－ACO－BP neural network model is reduced by 9.47， 4.83 and 3.27 percentage point respectively， compared with the traditional BP neural network model， ACO－BP neural network model and GA-BP neural network model. Therefore， GA－ACO－BP neural network model has better prediction accuracy when applied to photovoltaic power generation prediction.