Optimizing renewable energy system maintenance: an integrated approach of GA with ML techniques

T. N. Nisha and S. Vijayakumar Bharathi

Chapter 20 in Handbook on Artificial Intelligence and the Circular Economy, 2026, pp 320-341 from Edward Elgar Publishing

Abstract: We are experiencing a transition from traditional energy sources to sustainable energy. The transition is fueled by growth witnessed by renewable energy systems, including wind turbines and solar panels. It requires frequent evaluation of these complex devices to guarantee optimal performance by providing security from natural conditions. This research proposes a phased approach combining machine learning-based classification with genetic algorithm-based feature optimization for the predictive maintenance of renewable energy systems. Genetic algorithm-based feature selection selects key parameters for optimizing the maintenance prediction. The sensor data from different sensors are analyzed to predict and detect possible failures, thereby enabling the maintenance schedules to be planned. This approach addresses critical factors, such as equipment deterioration, environmental fluctuations, and grid integration, in order to predict system failures, thereby reducing the downtime of the system and maintenance costs. By applying different machine learning (ML) algorithms, this work proposes the best ML algorithms and the sensitivity of the classification to the genetic algorithm-based feature selection. This research suggests methods for establishing the reliability of renewable energy systems contributing to cost-effective and resilient infrastructure.

Keywords: Renewable energy; Solar panels; Predictive maintenance; Machine learning; Feature optimization; Genetic algorithm; Sustainability (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035343379
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