This project aims to develop a predictive model capable of forecasting future energy production based on weather trends and other relevant characteristics. By leveraging regression and classification machine learning models, our objective is to predict energy output and assist in power generation decision-making.
This initiative is crucial for evaluating the efficiency of renewable energy production and supporting grid management, contributing to the sustainability and reliability of power distribution. The insights generated from this project can optimize energy usage, particularly in home and community-level grids, by addressing the challenges posed by irregular power generation and its impact on grid stability.
Through the analysis of historical weather and energy data, the ultimate goal is to enhance energy planning and distribution, ensuring more effective management of power resources.