考虑大气颗粒物对辐照度影响的光伏功率预测

Photovoltaic power prediction considering the influence of atmospheric particles on irradiance

太阳辐射强度受大气颗粒物浓度影响显著,对光伏发电功率预测准确度的影响不容忽视。为提高太阳辐射强度以及光伏发电功率预测准确度,通过K-means算法对大颗粒物浓度以及相对湿度历史数据进行聚类,基于聚类数据利用径向基神经网络分别建立大气气溶胶光学厚度预测模型;在预测的大气气溶胶光学厚度基础上,结合双波段太阳辐射大气传输模型与倾斜面辐射模型,预测光伏电池板面接收到的太阳辐射强度;最后利用气温和预测光伏电池板面接收到的太阳辐射强度,结合光电转换模型实现光伏发电功率预测。通过实验验证,预测模型平均误差为6.07%,考虑大气颗粒物浓度对太阳辐射强度影响的光伏发电功率预测模型具有较高准确度。

The intensity of solar radiation was significantly affected by the concentration of atmospheric particulate matter,and its impact on the accuracy of photovoltaic power generation forecasting cannot be ignored.In order to improve the prediction accuracy of solar radiation intensity and photovoltaic power,Kmeans algorithm was used to cluster the historical data of large particle concentration and relative humidity.Based on the clustering data,radial basis function neural network is used to establish the prediction models of atmospheric aerosol optical thickness,Based on the predicted aerosol optical thickness,the solar radiation intensity received by the photovoltaic panel was predicted by combining the two band solar radiation atmospheric transmission model and the tilted surface radiation model,Finally,the solar radiation intensity received by the photovoltaic panel was predicted by using the air temperature and the photoelectric conversion model to realize the photovoltaic power prediction.Through experimental verification,the average error of the prediction model is 6.07%,and the photovoltaic power generation power prediction model considering the influence of atmospheric particle concentration on solar radiation intensity has high accuracy.