Energy Forecasting and Optimization for a Greener Grid

Abstract

The precise estimation of energy supply and demand over the years has become mandatory to integrate renewable energy sources (RES) into contemporary power grids. Our project is going to use machine learning (ML) and data analytics to predict energy consumption and to make sure perfect balance between usage of renewable energy to minimize impacts on the environment and less dependency on fossil fuels for sustainability. The complex dynamics of renewable energy cannot be managed by traditional methods, but the sophisticated approaches in machine learning, such as deep learning and time series models, significantly increase accuracy. The ability to predict can be enhanced through various pieces of information, such as energy storage, weather forecasts, grid measures, and data from smart homes. This study evaluates the effectiveness of the models, addresses the data problems, and highlights the need for interpretable models to promote a sustainable energy future.