Improving Predictions of Renewable Energy Systems with the Application of Machine Learning Integrated with Explainable AI

Abstract

Renewable energy installations are rising at a fast pace because societies r./uire both emission reduction and alternative clean energy sources. Policymakers, together with industry stakeholders, find it troublesome to use traditional energy prediction models because these systems operate without clarity and fail to handle intricate market systems properly. This research solves these issues through a machine learning (ML) model prediction of renewable energy use. Then, it enhances predictions through explainable artificial intelligence (XAI) methods to achieve better accuracy and trustworthiness. Our analysis includes multiple ML algorithms from the ensemble category consisting of Random Forests (RF) and Gradient Boosting in addition to advanced boosting algorithms XGBoost and Light Gradient Boosting Machines (GBM). Local Interpretable Model-Agnostic Explanations (LIME) reveal the decision-making procedures during predictions while delivering understandable explanations about the model’s conduct to users. The methodology adopts a thorough model testing methodology using extensive datasets, which include multiple variables related to renewable energy consumption, including economic metrics and environmental aspects. Researchers obtained predictive performance excellence with interpretability benefits from their models in predicting renewable energy usage. The Light GBM model delivered 97.40% accuracy when analyzing data, while the LIME process showed GDP growth and electricity access as key determining variables. XAI integration in renewable energy forecasting presents important progress that livers enhanced, transparent yet actionable energy predictions that build trusted reliability for use in the industry. The study demonstrates the power of uniting ML with XAI techniques for better comprehension of renewable energy patterns, which enables better decisions for sustainable energy development.