Advanced AI for Electricity Forecasting and Renewable Energy
*Corresponding Author: Dr. Hassan Ali, Dept. of Energy Systems UET Lahore, Pakistan, Email: h.ali@uet-demo.pkReceived Date: Jul 01, 2025 / Accepted Date: Jul 29, 2025 / Published Date: Jul 29, 2025
Citation: Ali DH (2025) Advanced AI for Electricity Forecasting and Renewable Energy. Innov Ener Res 14: 467.DOI: 10.4172/2576-1463.1000467
Copyright: © 2025 Dr. Hassan Ali This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricteduse, distribution and reproduction in any medium, provided the original author and source are credited.
Abstract
This compilation reviews state-of-the-art forecasting techniques for electricity load and renewable energy. It covers deep learning
models, ensemble methods, hybrid AI approaches, probabilistic forecasting, and the impact of weather data. Advanced models like
LSTMs, CNNs,GBMs,ST-GNNs,andtransformers are discussed for their efficacy in capturing complex patterns and dependencies.
The research underscores the importance of accuracy, uncertainty quantification, and seamless integration of renewables for grid
stability and energy management.
