Application of Artificial Intelligence in Optimization of Renewable Energy Systems: A Case Study of Iran

This study investigates the role of AI in enhancing the effectiveness of renewable energy systems, with a focus on the Iranian environment. It builds on a thorough literature search and formal data from global energy catalogs to propose a well-rounded theoretical model connecting AI forecasting and optimization of efficiency and market management. The study uses a quantitative approach and implements AI models, such as artificial neural networks for energy production forecasting, genetic algorithms, particle swarm optimization for enhancing storage and distribution efficiency, and dynamic market prediction tools. These models are simulated using industry-standard tools like MATLAB, Simulink and Python. The results show that the forecasting module achieved a mean squared error of ۰.۰۰۰۲, while the optimization techniques improved overall energy utilization efficiency by ۱۵% and reduced operational costs by ۱۰% compared to conventional methods. Furthermore, AI-driven market models reduced pricing forecast errors by ۲۰%, facilitating more effective peer-to-peer energy transactions. These results point to the game-changing capabilities of AI in the optimization of the technical performance of renewables and help in strategic decision-making in operational and commercial energy markets. The proposed model would provide helpful information for policymakers and industry practitioners striving for a sustainable energy transition. The potential for future work, including field validations and integration of near-real-time data, assures us of the continuous improvement of these AI models, ultimately supporting the development of innovative and economically resilient renewable energy infrastructures.

Keywords:

Artificial Intelligence , Renewable Energy Systems , Iran