Nowcasting of Spatial Average Irradiance in Several km Radius Area using All-sky Image

Abstract

Because of the unstable power output characteristics of a photovoltaic power generation system (PVS), the on-line monitoring or nowcasting of the aggregated PVS power output is important for the stable operation of electric power system. In this paper, we propose a nowcasting method of the spatial average irradiance in the area of several km radius using the all-sky image and the observed irradiance both at single point. First, the proposed method classifies the sky condition based on the color information of the all-sky image. If the sky is classified as the uniform condition (very fine or very cloudy), the spatial average irradiance is estimated as the same with the single point observed irradiance. If not, the spatial average irradiance is estimated by using a neural network (NN) model utilizing the color information as the inputs. As a result of a demonstrative study for several months, the Root Mean Square Error (RMSE) of the NN model-based estimation for the periods of the non-uniform sky condition is 103.8W/m² (15.2%). Because the performance of the sky condition classification is high, RMSE for the periods of the uniform sky condition is 41.8W/m² (8.7%), resulting in RMSE of 77.4W/m² (13.5%) for the entire periods.