An AI-Based System for Predicting Renewable Energy Power Output Using Advanced Optimization Algorithms
1Faculty of Artificial Intelligence, Horus University, New Damietta, Egypt
2Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
3School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain
4Jadara University Research Center, Jadara University, Jordan
5Applied Science Research Center. Applied Science Private University, Amman, Jordan
6Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
Emails: mgafaar@horus.edu.eg; eng.mo.gamal@mans.edu.eg; islam_m@mans.edu.eg; skenawy@ieee.org; hossammoustafa@mans.edu.eg
Abstract
Accurate generation forecasting of Renewable Energy Sources (RES) is becoming more and more crucial for effective grid operation and energy management as RES are incorporated into the electrical grid. Because Machine Learning (ML) and Deep Learning (DL) algorithms can learn complicated relationships from data and provide accurate forecasts, they have become more popular than traditional forecasting approaches, which have limits. This article examines the state of the art and future directions in the field of ML and DL-based forecasting of renewable energy generation. This paper reviews the several approaches and models that have been used to project renewable energy. It also highlights the challenges, such as managing the uncertainty and unpredictability of renewable energy output, data accessibility, and model interpret ability. To sum up, this study emphasizes how important it is to develop accurate and dependable renewable energy forecasting models to facilitate the future transition to sustainable energy sources and enable the integration of RES into the electrical grid.
Keywords: Renewable Energy; Machine Learning; Deep Learning