摘要
光伏出力受气象因素影响,气象数据的有效程度影响着预测结果的准确性。本文提出了气象数据与光伏出力弱相关时短期光伏出力的预测方法。首先采用Pearson关联系数分析法得到影响光伏发电的主要因素,而后采用模糊聚类理论构建相似日,建立了具有优秀小样本学习能力的支持向量回归机预测模型。针对该模型,提出了两阶段确定模型参数的方法,首先采用全局网格搜索确定核参数p和正则化参数C的取值范围,再通过自适应差分进化算法寻找最优核参数p和正则化参数C,以提高参数ε选取范围设置较大时的预测精度。实例测试表明,使用本文提出的SVR方法预测的平均RMSE为5.551%,满足预测要求,比常规BP预测方法提高精度1.238%,在气象数据弱相关时对光伏短期出力有更好的预测能力。
Photovoltaic（PV）output is influenced by meteorological factors,and the significant degree of meteorological data influences the accuracy of forecasting result.In this paper,a short-term PV output forecasting method is presented in such weather situation that the weather data and PV output data are weakly correlated.The main factors affecting PV generation are found out by Pearson correlation coefficient method.Based on relevant factors,fuzzy clustering analysis method is used to build similar days,and support vector regression（SVR）forecasting model that has excellent learning ability for small sample is built.In order to determine model parameters,a two-step parameter-determining method is proposed,in which the global grid searching method is applied to determine the value region of kernel parameter p and regularization parameter C which are optimized through self-adaptive differential evolution algorithm,then the prediction accuracy is increased when the appropriate ranges of parameterε are wider.Examples show that the proposed SVR method has good forecasting ability when the weather data and PV output data are weakly correlated,and the average prediction value,RMSE,is 5.551% which meets the requirement of prediction,and the accuracy is increased to 1.238% by comparison with general BP forecasting method.
出处
《现代电力》
北大核心
2015年第6期1-6,共6页
Modern Electric Power
关键词
分布式光伏
光伏出力预测
支持向量机(SVM)
参数选择
distributed photovoltaic
PV output forecasting
support vector machine（SVM）
parameter selected
