Artificial Intelligence for Optimizing Renewable Energy Systems: Techniques, Applications, and Future Directions
International Journal of Applied Power Engineering (IJAPE), (2026), Vol. 15, No. 1, pp. 275-288
Ian B. Benitez
a,b
,
Edwin C. Cuizon
c
,
Jose Carlo R. Dizon
d
,
Kristina P. Badec
e
,
Daryl Anne B. Varela
f
a Research Office, FEU Institute of Technology, Manila, Philippines
b Electrical Engineering Department, FEU Institute of Technology, Manila, Philippines
c Industrial Systems Engineering Department, Asian Institute of Technology, Pathum Thani, Thailand
d Department of Agricultural and Food Engineering, Cavite State University, Cavite, Philippines
e Electronics Engineering Department, University of Science and Technology of Southern Philippines, Cagayan de Oro City, Philippines
f Faculty of Climate Change and Sustainability, Asian Institute of Technology, Pathum Thani, Thailand
Abstract: The integration of artificial intelligence (AI) is critically transforming the renewable energy sector. This review synthesizes AI's role in optimizing solar and wind energy systems, focusing on power forecasting, system optimization, and predictive maintenance. The research goal was to systematically analyze how diverse AI techniques enhance these critical aspects. Key findings indicate AI's capacity to substantially improve short-term solar irradiance and wind power forecasts (e.g., via SARIMAX, long short-term memory (LSTM), and hybrid deep learning models), dynamically manage energy flow in smart grids and microgrids, optimize maximum power point tracking (MPPT) in photovoltaic (PV) systems, and enable proactive maintenance through anomaly detection in wind turbines using IoT-integrated AI. Key conclusions reveal that AI significantly enhances the efficiency, reliability, and economic viability of solar photovoltaic and wind power generation, offering superior adaptability and predictive capabilities over traditional methods. While AI is important for the global transition to cleaner energy, persistent challenges related to data quality and availability, model interpretability, and cybersecurity must be addressed to fully unlock its potential in practical renewable energy applications.