影响光伏发电功率的气象因子分析及其预测检验

利用贵州省普安磨舍光伏电站2020年逐15 min的光伏发电功率、辐射资料与气象站资料，对光伏发电功率变化特征及影响光伏发电功率的气象因子进行分析，建立了光伏发电功率的预测模型，并利用CFSv2模式资料开展月内预测检验。结果表明：光伏电站发电功率呈现早晚低、中午高的单峰型日变化特征，其中春季发电功率值最大，夏季次之，冬季最小。影响光伏发电功率最关键的气象因子为总辐射和日照时数，其相关系数均在0.9以上。5种组合的线性回归预测模型检验结果显示，利用平均气温、最高气温、日较差建立的预测模型预测效果最好，而利用单一气象因子的预测效果最差。为增加光伏发电功率的预测准确率，可根据预测服务需求，并用延伸期模式资料开展光伏发电功率滚动订正预测。

Diagnostic Analysis of the Impact of Meteorological Factors on Photovoltaic Power Output and Its Prediction Test

Based on the photovoltaic power output and radiation data in every 15 minutes from Pu''an Moshe photovoltaic power station in Guizhou Province and the meteorological observation data in 2020, the variation of photovoltaic power output and the impact of meteorological factors on photovoltaic power were analyzed. The prediction model of photovoltaic power output was established and carried out intra-month forecast test by using CFSv2 model data. The results show that the photovoltaic power output reaches low in the morning and evening and peaks in the noon. The value of photovoltaic power output is the largest in spring, followed by summer, and the smallest in winter. The key meteorological factors impact on photovoltaic power are total solar radiation and sunshine duration, and their correlation coefficients are both above 0.9. The results of linear regression prediction model test of five combinations show that, the prediction model has a better prediction performance based on the average temperature, maximum temperature and daily range, while the model with single meteorological factor does not perform well. In order to increase the prediction accuracy of photovoltaic power output, the data from forecast model in extended period can be used to make the rolling forecast of photovoltaic power output according to the service demand.