How solar radiation forecasting impacts the utilization of solar energy: A critical review

Abstract

The demand for energy generation from solar energy resource has been exponentially increasing in recent years. It is integral for a grid operator to maintain the balance between the demand and supply of the grid. Solar radiation forecasting paves the way for proper planning, reserve management, and elude penalty since solar energy is sporadic in nature. Several methods can forecast solar radiation; the prior classifications are machine learning models, numerical weather prediction models, satellite imaging, sky imager and hybrid model. This article presents a comprehensive review of all those models with the working principle, challenges and future research direction. Sky imagers provide the Normalized Root Mean Square Error (nRMSE) value of 6%–9% for a time horizon of 30 min, and the satellite imagery technique provides the Root Mean Square Error (RMSE) value of 61.28 W/m2 – 346.05 W/m2 for a time horizon of 4 h ahead. Similarly, NWP mesoscale models provide the RMSE value of 411.6 W/m2 - for three days ahead of forecasting with a spatial resolution of 50 km. Machine learning models are good at delivering accurate results with the time horizon up to 1 day ahead by yielding the results of RMSE in the range of 0.1170 W/m2 – 93.04 W/m2. Deep learning and hybrid models are being developed to overcome the issues faced by the standalone techniques. In many research works, artificial intelligence techniques are integrated with NWP models, sky imagers and satellite imagers to improve the data handling algorithm, which implicitly results in forecasting accuracy.