基于混合麻雀算法改进反向传播神经网络的短期光伏功率预测
摘要：精准地预测短期光伏发电功率，是提高光伏电站运行效率、保障光伏并网后安全稳定运行的关键。因此，提出了一种基于精英反向学习策略并结合Metropolis准则的混合麻雀搜索算法(hybrid sparrow search algorithm, HSSA)改进反向传播神经网络(back propagation neural network, BPNN)的预测模型。首先采用皮尔逊相关系数公式选择与光伏输出相关性最好的气象特征集作为模型的输入，避免冗余的气象因子影响光伏输出。再利用欧式距离公式计算时序相似度来选取训练集，以提高训练集的可靠性。最后，使用HSSA-BPNN的权阈值建立预测模型，并利用新疆某光伏电站的实际数据进行实验分析。分析结果表明，与BPNN、粒子群算法(particle swarm algorithm, PSA)-BPNN、麻雀搜索算法(sparrow search algorithm, SSA)-BPNN相比，混合麻雀搜索算法(hybrid sparrow search algorithm, HSSA)-BPNN模型具有良好的适应性、较好的预测性能。
Abstract:Accurately predicting short-term photovoltaic (abbr. PV) power generation is the key to increase operational efficiency of PV station and to ensure secure and stable operation of post-grid-connected PV station. Therefore, a back propagation neural network prediction model improved by Elite reverse learning strategy-based hybrid sparrow search algorithm combined with Metropolis criterion was proposed. Firstly, Pearson correlation coefficient formula was utilized to select the meteorological feature set with the best correlation with photovoltaic output as the input of the proposed model to avoid the affection of redundant meteorological factors on photovoltaic output. Secondly, the Euclidean distance formula was utilized to calculate the time series similarity to select the training set to improve the reliability of the training set. Finally, weight threshold value of HSSA-BPNN was used to establish the prediction model, and actual data of a certain PV station located in Xinjiang was used to conduct the experimental analysis. Results of experimental analysis show that the hybrid sparrow search algorithm-BPNN model possesses good adaptability than those from BPNN, particle swarm algorithm-BPNN, and sparrow search algorithm-BPNN, and the prediction performance of the proposed model is better.
标题：基于混合麻雀算法改进反向传播神经网络的短期光伏功率预测
英文标题：Short-Term Photovoltaic Power Prediction Based on Back Propagation Neural Network Improved by Hybrid Sparrow Algorithm
作者：常东峰, 南新元,
关键词：光伏发电预测, 麻雀算法, 精英反向学习策略, Metropolis准则, 神经网络,
发表日期：2022-06-10
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