Energy Forecasting and Optimization for a Greener Grid

The "Energy     Forecasting and Optimization for a Greener Grid" project aims to leverage advanced machine learning algorithms for forecasting energy     consumption and optimizing grid performance. Using techniques such as Linear Regression (LR), XGBoost, Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Hybrid Algorithms (RNN+LSTM, GRU), this project seeks to predict energy     usage with high accuracy. Additionally, ARIMA or SARIMA models will be used for time-series forecasting, while Particle Swarm Optimization (PSO) will aid in optimizing the energy     grid. The output will be presented as graphical representations of forecasting models, which will provide valuable insights into the future energy     requirements and grid optimization. This project is expected to enhance grid efficiency, promote sustainability, and contribute to a greener energy     future.

Keywords: Energy     forecasting, machine learning, grid optimization, time-series, RNN, LSTM, XGBoost, ARIMA, PSO.