Enhancing Solar Energy Systems Using Artificial Intelligence: Optimization, Forecasting, and Smart Grid Integration

Abstract

The integration of artificial intelligence (AI) in solar energy systems has revolutionized the efficiency, reliability, and scalability of renewable energy solutions. This research explores the multifaceted applications of AI in the solar energy domain, focusing on three key areas: photovoltaic (PV) performance optimization, solar irradiance and energy output forecasting, and intelligent grid integration. Machine learning algorithms such as artificial neural networks (ANN), support vector machines (SVM), and deep learning models are employed for real-time prediction and adaptive control. Furthermore, AI-based fault detection and predictive maintenance strategies enhance system reliability and reduce downtime. The study also investigates the role of AI in demand-side management and the optimization of energy storage systems, promoting effective smart grid operations. The findings demonstrate that AI-driven approaches significantly contribute to maximizing solar energy utilization and advancing the global shift towards sustainable energy systems.

Cite as:

Raju P, & Jagruti P. (2025). Enhancing Solar Energy Systems Using Artificial Intelligence: Optimization, Forecasting, and Smart Grid Integration. Recent Innovations in Material Engineering, 1(1), 25–28.

https://doi.org/10.5281/zenodo.15246236

Cite as:

Raju P, & Jagruti P. (2025). Enhancing Solar Energy Systems Using Artificial Intelligence: Optimization, Forecasting, and Smart Grid Integration. Recent Innovations in Material Engineering, 1(1), 25–28.

https://doi.org/10.5281/zenodo.15246236