基于天气聚类的光伏电站辐照度预测

太阳辐照度是影响光伏输出功率最直接、最显著的因素,其预测精度关系到电力系统部门的调度以及规划.受到地理纬度、海拔高度、气候复杂性的影响,传统地表辐照度预测模型精度较低.基于辐照度的天文学模型结合天气类型进行辐照度预测,通过建立太阳光照与地球大气层的几何模型,求解地外辐照度计算公式并修正晴天透明度系数,然后依据天气类型与历史辐照度进行聚类分析,计算相应的权重系数,建立基于天气聚类的太阳辐照度预测模型.通过实测数据验证,与传统的晴天模型、递推最小二乘法(recursive least square,RLS)模型、小波变换(wavelet transform,WT)模型、相比,天气聚类模型的适应性更强、准确率更高、误差更小.

The Prediction of Irradiation Degree of Photovoltaic Power Station Based on Weather Clustering

Solar irradiance is the most direct and significant factor affecting photovoltaic output power, and its prediction accuracy is related to the scheduling and planning of power system departments. Affected by geographic latitude, altitude, and climate complexity, the accuracy of traditional surface irradiance prediction models is low. In this paper, based on the irradiance astronomy model combined with the weather type to predict irradiance, By establishing a geometric model of the sun"s illumination and the Earth"s atmosphere, the calculation formula for extraterrestrial irradiance is solved and the transparency coefficient of the sunny day is corrected, Then according to the weather type and historical irradiance, the cluster analysis is performed, the corresponding weight coefficient is calculated, and the solar irradiance prediction model based on weather clustering is established. Through the verification of the measured data, compared with the traditional sunny model, RLS model, and WT model, the weather clustering model has stronger adaptability, higher accuracy, and smaller error.