V1I7P11

AI Powered Digital Twins for Predictive Maintenance and Operational Optimization of Renewable Energy Systems

Habeeb A. Shittu^1*, Mujeeb A. Shittu¹

Abstract

This study presents the design, implementation, and evaluation of an AI powered hybrid digital twin framework for predictive maintenance and operational optimization in renewable energy systems. By integrating physics-based modeling with machine learning techniques, the system was deployed across pilot wind and solar installations to enable real time fault detection and performance optimization. The digital twins were constructed using MATLAB/Simulink for high fidelity simulation and paired with AI models trained on historical and live sensor data. A cloud edge architecture was used to ensure real time data processing, system scalability, and low latency inference. Results demonstrated high fault prediction accuracy (F1 scores up to 0.88), improved energy yield (4.2%–4.8%), and reduced operational latency (1.2 seconds on average). The system also featured explainable AI components to enhance interpretability and user trust. Compared to traditional rule based and schedule driven maintenance strategies, the proposed framework showed substantial gains in reliability, responsiveness, and energy efficiency. The findings underscore the potential of AI enhanced digital twins as intelligent, scalable solutions for managing distributed renewable energy assets and support their broader integration into future smart grid infrastructures.

Keywords: Digital twin, predictive maintenance, renewable energy, artificial intelligence, machine learning, operational optimization, real time monitoring,explainable AI, smart grid