Machine Learning-Based Renewable Energy Systems Fault Mitigation and Economic Assessment

In an era increasingly focused on sustainability, the adoption of renewable energy stands as a promising avenue for fostering local economic growth. This study presents a novel approach, merging advanced fault mitigation techniques and machine learning, to assess the economic impact of renewable energy systems (RES) at the local level. Leveraging random forest, support vector machines (SVM), and gradient boosting, customized algorithms are deployed for regression analysis and defect identification. Hyperparameter optimization ensures optimal performance, with a linear regression meta-learner facilitating the fusion of predictions. An advanced anomaly detection component effectively identifies and rectifies errors within RES. Performance evaluation metrics, including an root mean square error (RMSE) of 2.18 and an overall system efficiency of 98%, underscore the success of the fault mitigation strategy. Precision, recall, and F1-score metrics further highlight its robustness. This comprehensive framework not only provides precise estimates of the financial impact of renewable energy adoption but also enhances the reliability of RES through sophisticated fault mitigation. Empowering decision-makers with actionable insights, it facilitates sustainable energy planning, effective policy implementation, and the establishment of resilient energy systems.