改进相似度的模糊聚类算法在光伏阵列短期功率预测中的应用

摘要

A method of PV array short-term power prediction is proposed based on improved similarity of fuzzy clustering algorithm. First, the weights of the meteorological factors are obtained through path analysis. An improved similarity is constructed integrating weighted similarity coefficients and weighted distance coefficient according to the weight of each factor. Second, the history day samples are divided into several categories by making fuzzy similarity matrix. The classification of the forecasting day is got by pattern recognition. Then the BP neural network prediction model using differential evolution algorithm to optimize BP neural network weights and threshold value is constructed based on the history data of the classification and the meteorological factors of the forecasting day. Experimental results demonstrate that the model has higher prediction accuracy compared with the traditional prediction model based on similar day selected. It is conducive to the operation of PV system operation and its security economic dispatch.%提出一种基于改进相似度的模糊聚类算法的光伏阵列短期功率预测方法，通过通径分析得到气象因子对光伏阵列日发电功率的影响权重。根据各个因子的权重自定义综合了加权相似系数和加权距离系数的统计量-相似度，建立模糊相似矩阵将历史日样本划分为若干类。然后通过分类识别获得与预测日最相似的一类历史日样本集，将其与预测日的气象因素作为预测模型的输入样本建立BP神经网络发电预测模型，并利用差分进化算法对构建的BP神经网络的参数进行了全局寻优。以实际数据对所提模型进行了验证，并与传统的基于相似日选取的光伏功率预测模型进行了对比，结果表明该模型具有更高的预测精度，有利于光伏发电系统并网运行和电网安全经济调度。