Harnessing Artificial Intelligence to Enhance Efficiency in Industrial Renewable Energy Systems

Abstract

This study aims to explore the use of AI in industrial renewable energy systems, majoring in efficiency, storage, and prognosis of maintenance techniques. The research analyses various renewable power technologies – solar, wind, hydroelectric, and bioenergy- and evaluates the impact of AI on increasing the effectiveness, stability, and profitability of the technology. Machine learning is used to predict energy production, control and predict energy storage and other real-time control systems, and implement predictive maintenance models to minimize equipment failure. Best studies show that augmenting solar and wind energy with AI increased the predictive capability to 95% and overall energy efficiency by 7%. Furthermore, new charging/discharging plans provided by the optimization process based on the AI technology increased energy density by 12% and cut the energy cost by 15%. Preventive maintenance models produced through artificial intelligence solutions decreased rates of unwanted time loss by 20%, while biomass power systems achieved higher fuel efficiency rates of 9%. The study also shows that through AI improvement of energy management, storage, and system reliability, operational costs have been cut by 18%. Application to the industry stakeholders, policy makers, and researchers are pushing for AI's ability to enhance efficient electricity generation using sustainable resources. Finally, limitations are discussed in the light of the study: first, regarding the accessibility of different operational datasets and second, the need for sophisticated AI models to enhance energy systems in various geographical and industrial conditions.