Year: 2025
Type: article
Abstract: In recent years, artificial intelligence (AI) has emerged as a transformative force in optimizing solar energy systems. This review presents a comprehensive, decade-long analysis of AI methodologies applied to various facets of solar energy, including irradiance forecasting, power output prediction, system optimization, and fault detection. The study synthesizes findings from over 30 key publications, categorizing AI techniques such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Long Short-Term Memory (LSTM) networks, and hybrid models. Experimental results reveal that deep learning, particularly CNN-LSTM architecture, offers superior forecasting accuracy, while ensemble methods like Random Forest and XGBoost are highly effective for classification tasks. The work also delves into emerging themes like Explainable AI (XAI), Federated Learning, and Edge AI, stressing the requirements of more interpretable, privacy-protecting, and generalizable models. By summarizing existing issues and directions for the future, this review is intended to act as an opening reference for researchers, engineers, and policymakers wanting to apply AI to sustainable solar energy development.
Author: Shilesh Karunakaran
Institution: University of Cincinnati
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414534301
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