As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned.

This research article explores the potential of Pumped Storage Hydroelectric Power Plants across diverse locations, aiming to establish a sustainable electric grid system and reduce per-unit energy costs. A distinctive feature of the study involves forecasting solar irradiance on large-scale hydroelectric dam locations to identify optimal sites for a PV-integrated hydropower system. The research focuses on advancing the integration of floating solar power modules on water storage systems in eight selected regions across India, emphasizing precise solar irradiance estimation. The paper introduces a state-of-the-art hybrid intelligent deep learning model, combining time series analysis and deep learning through residual ensembling to address these challenges. The primary objective is to pinpoint the optimal location for a Power Storage System (PSS) with the highest solar irradiation for PV-integrated hydro system integration. A secondary goal involves minimizing errors within computational time constraints by the proposed model. The study also employs various optimization techniques to enhance its effectiveness and fine-tune the model’s performance, contributing to the advancement of sustainable energy solutions. The proposed model performs best with a Whale optimization algorithm with mean absolute error varying from 0.34 to 3.63 W/m² and root mean square error from 0.75 to 9.51 W/m² on PSS locations. The analysis also confirms average solar irradiance is high on PSS 7 with 221.0 W/ m² followed by PSS 1 with 221.1 W/ m² among the eight designated sites.