Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning
Anas Al-lahham Obaidah Theeb Khaled Elalem Tariq A. Alshawi Saleh A. Alshebeili
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.