Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning
This addresses the problem of grid stability for renewable energy integration by providing efficient solar irradiance forecasts, though it appears incremental as it builds on existing sky image-based methods.
The paper tackles short-term solar irradiance forecasting from sky images using a machine learning algorithm, achieving competitive results with reduced computational complexity compared to state-of-the-art methods for nowcasting and forecasting up to 4 hours ahead.
Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. The~proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The~performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The~datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time.