考虑多种实时气象因素的分布式光伏出力预测模型构建与优化  

Construction and Optimization of Distributed Photovoltaic Output Forecast Model Considering Multiple Real⁃Time Meteorological Factors

作　　者：卢新星 瞿振宇 黄际元 赵子鋆 李煜亮 吴东琳 LU Xinxing;QU Zhenyu;HUANG Jiyuan;ZHAO Zijun;LI Yuliang;WU Donglin(State Grid Changsha Power Supply Company,Changsha 410015,China)

机构地区：[1]国网湖南省电力有限公司长沙供电分公司,湖南长沙410015

出　　处：《湖南电力》2024年第6期128-133,共6页Hunan Electric Power

摘　　要：针对光伏电源输出功率受多种因素影响,呈现随机性和间歇性,导致准确预测光伏出力困难问题,提出一种考虑多种实时气象因素的分布式光伏出力预测模型构建与优化方法。首先,按照太阳辐照度、温度、风速、每日辐照时长及相对湿度进行筛选,使用牛顿插值法,将异常光伏数据全部归零,对缺失数据进行填补,完成光伏数据的预处理。在此基础上,量化选取不确定性因素下的数值天气预报特征,基于模糊加权卷积神经网络提取光伏预测因子。最后,基于最小二乘支持向量回归构建光伏发电出力预测模型,并完成模型的求解。实验结果表明,研究方法对分布式光伏出力的预测具有非常高的精度。Aiming at the problem that the output power of photovoltaic(PV)power is affected by a variety of factors,showing randomness and intermittency,which leads to the difficulty of accurately predicting PV output.A method of distributed PV output forecastmodel construction and optimization considering multiple real⁃time meteorological factors is proposed.Firstly,according to solar irradiance,temperature,wind speed,daily irradiation duration and relative humidity,the abnormal PV data return to by Newton interpolation,and the missing data are filled in to complete the preprocessing of PV data.On this basis,the characteristics of numerical weather prediction under uncertainties are quantitatively selected and the PV predictors are extracted based on fuzzy weighted convolutional neural network.Finally,a PV power output forecastmodel is constructed based on least square support vector regression,and the model is solved.The experimental results show that the research method has a very high precision for the prediction of distributed PV output.

关 键 词：多种实时气象因素 分布式光伏 出力预测 

分 类 号：TM714[电气工程—电力系统及自动化]