Predictive Modelling with Machine Learning for Sustainable Hybrid Energy System

Abstract

In order to achieve sustainability and energy efficiency, hybrid energy systems must incorporate renewable energy sources. In order to maximize the performance of hybrid sustainable energy systems, this study investigates the use of machine learning (ML) approaches in predictive modelling. The study combines advanced machine learning (ML) methods like random forests (RF), support vector machines (SVM), and artificial neural networks (ANN) to forecast energy generation, demand, and system efficiency. These models improve grid stability, lessen dependency on fossil fuels, and improve energy management tactics by utilizing historical data. Through case studies, the suggested method is assessed and shown to be successful in forecasting energy outputs, allocating resources optimally, and reducing operating expenses. The results demonstrate how ML-driven predictive models can improve the sustainability and dependability of hybrid energy systems, assisting in the global shift to cleaner energy sources