Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework
Sameer Al-Dahidi,
Manoharan Madhiarasan,
Loiy Al-Ghussain,
Ahmad M. Abubaker,
Adnan Darwish Ahmad,
Mohammad Alrbai,
Mohammadreza Aghaei,
Hussein Alahmer,
Ali Alahmer,
Piero Baraldi,
Enrico Zio
Affiliations
Sameer Al-Dahidi
Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan
Manoharan Madhiarasan
Department of Electronics and Computers, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, B-dul Eroilor 29, 500036 Brasov, Romania
Loiy Al-Ghussain
Argonne National Laboratory, Energy Systems and Infrastructure Analysis Division, Lemont, IL 60439, USA
Ahmad M. Abubaker
Institute of Research for Technology Development (IR4TD), University of Kentucky, Lexington, KY 40506, USA
Adnan Darwish Ahmad
Institute of Research for Technology Development (IR4TD), University of Kentucky, Lexington, KY 40506, USA
Mohammad Alrbai
Department of Mechanical Engineering, School of Engineering, University of Jordan, Amman 11942, Jordan
Mohammadreza Aghaei
Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway
Hussein Alahmer
Department of Automated Systems, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan
Ali Alahmer
Department of Mechanical Engineering, Tuskegee University, Tuskegee, AL 36088, USA
Piero Baraldi
Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Enrico Zio
Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.
