Application of Artificial Intelligence and Simulation Techniques for Enhancing Efficiency in Renewable Energy Systems

Abstract

Driven by rising expectations for clean and sustainable power generation, renewable energy system development is being fast-tracked. At the same time, its large-scale application is constantly challenged by issues of energy efficiency, power fluctuation, and operation optimization. A joint frame of AI and simulation is proposed in this paper to improve energy utilization and reliability of renewable energy systems. The method uses ML-based prediction models and simulation-based optimization to improve generation capacity, power balancing, and system stability in solar- and wind-powered environments. The framework used data preprocessing and AI-based forecasting and simulation, with parameters such as power output, energy utility, forecast accuracy, and operating efficiency in the renewable energy sector. The experimental part uses a simulation approach with multiple performance metrics, including forecast accuracy, energy conversion efficiency, response time, and system stability index, to evaluate the proposed AI-based system. The AI-assisted system's experimental results yield a forecast accuracy of 96.4%, which is 14.8% better than that of traditional analytical methods. The energy utility efficiency increases from 78.5% to 91.2%. Through simulation analysis, load balancing improves by 17.9% and system stability increases by 14.6% under a variety of atmospheric conditions. This research demonstrates the significant impact of combining AI with simulation on renewable energy management, enabling intelligent decision-making and ensuring system stability. It is concluded that AI-powered renewable energy systems have significant potential for future smart-grid and energy-management applications.