Sky image-based solar irradiance prediction methodologies using artificial neural networks

Abstract

In order to decelerate global warming, it is important to promote renewable energy technologies. Solar energy, which is one of the most promising renewable energy sources, can be converted into electricity by using photovoltaic power generation systems. Whether the photovoltaic power generation systems are connected to an electrical grid or not, predicting near-future global solar radiation is useful to balance electricity supply and demand. In this work, two methodologies utilizing artificial neural networks (ANNs) to predict global horizontal irradiance in 1 to 5 minutes in advance from sky images are proposed. These methodologies do not require cloud detection techniques. Sky photo image data have been used to detect the clouds in the existing techniques, while color information at limited number of sampling points in the images are used in the proposed methodologies. The proposed methodologies are able to capture the trends of fluctuating solar irradiance with minor discrepancies. The minimum root mean square errors of 143 W/m2, which are comparable with the existing prediction techniques, are achieved for both of the methodologies. At the same time, the proposed methodologies require much less image data to be handled compared to the existing techniques.