Chodakowska, E.; Nazarko, J.; Nazarko, Ł.; Rabayah, H.S. Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends. Energies2024, 17, 3156.
Chodakowska, E.; Nazarko, J.; Nazarko, Ł.; Rabayah, H.S. Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends. Energies 2024, 17, 3156.
Chodakowska, E.; Nazarko, J.; Nazarko, Ł.; Rabayah, H.S. Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends. Energies2024, 17, 3156.
Chodakowska, E.; Nazarko, J.; Nazarko, Ł.; Rabayah, H.S. Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends. Energies 2024, 17, 3156.
Abstract
Effective solar forecasting has become a critical topic in the scholarly literature in recent years due to the rapid growth of photovoltaic energy production worldwide and the inherent variability of this source of energy. Due to the technological and econonomic limitations of energy storage solutions, using other, mosty conventional, sources to cover energy shortfalls and at the same time utilising solar surpluses production becomes necessary. The need to optimize energy systems, ensuring power continuity, and balancing energy supply and demand has led to the development of various forecasting methods and approaches based on meteorological data or photovoltaic plant characteristics. This article presents the results of a meta-review of the solar forecasting literature, including the current state of knowledge and methodological discussion. It presents a comprehensive set of forecasting methods, evaluates current classifications, and proposes a new synthetic typology. The article emphasizes the increasing role of artificial intelligence (AI) and machine learning (ML) techniques in improving forecast accuracy, alongside traditional statistical and physical models. It explores the challenges of hybrid and ensemble models, which combine multiple forecasting approaches to enhance performance. The paper addresses the emerging trends in solar forecasting research, such as the integration of big data and advanced computational tools. Additionally, from a methodological perspective, the article outlines a rigorous approach to the meta-review research procedure, addresses the scientific challenges associated with conducting bibliometric research, and highlights best practices and principles. This includes defining research questions, selecting eligibility criteria, literature search, data extraction, synthesis, and assessing bias and quality. The article contributes to the solar forecasting field by providing up-to-date knowledge, along with insights on the emerging trends, future research directions, and anticipating implications for the theory and practicce.
Keywords
forecasting; solar; energy; irradiance; photovoltaic; state of the art; systematic literature review (SLR); meta-review; overviews of review; bibliometric; classification; method
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
