Applications and Challenges of Artificial Intelligence in Solar Radiation Forecasting: A Systematic Review

Authors

DOI:

https://doi.org/10.55761/abclima.v36i21.18872

Keywords:

Machine Learning, Deep learning, Prediction model, Artificial Neural Networks

Abstract

Accurate forecasting of solar radiation is essential for many areas, such as solar energy and agriculture. Artificial Intelligence (AI) has proven to be a powerful tool for improving these forecasts. This study analyzed several studies that use AI to predict solar radiation. The most commonly  usedtechniques include neural networks, support vector machines and deep learning. These techniques can identify complex patterns in data and establish relationships betweensolar radiation to factors such as temperature, humidity and cloud cover. AI models are trained on large sets of weather and solar radiation, enabling  them to generate  more accurate predictions. The metrics used to evaluate the performance of these models include the root mean square error (RMSE), the coefficient of determination (R²), and the mean absolute percentage error (MAPE). This review demonstrates the significant potential of AI in enhancing solar radiation forecasting. More accurate predictions can optimize solar energy production, enhance irrigation management, support various other processes dependent on solar radiation.

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Author Biographies

Camila Piacitelli Tieghi, Universidade de São Paulo (USP)

Pós-Doutorado: Utilização de Inteligência Artificial e Programação em Python para Otimização da Geração de Energia Solar. Início: 2023. Universidade de São Paulo - Faculdade de Zootecnia e Engenharia de Alimentos de Pirassununga.Doutora em Agronomia (Energia na Agricultura) pela Faculdade de Ciências Agronômicas - Universidade Estadual Paulista Júlio de Mesquita Filho (FCA UNESP) (2023). Licenciatura em Pedagogia na Faculdade Campos Elísios (2022). Mestre em Agronomia (Energia na Agricultura) pela Faculdade de Ciências Agronômicas - Universidade Estadual Paulista Júlio de Mesquita Filho (FCA UNESP) (2018). Especialização em Gestão de Bancos de dados pela Universidade Luterana do Brasil (ULBRA) (2014). Graduada em Informática para gestão de negócios pela Faculdade de Tecnologia de Botucatu (FATEC) (2013). Entre 2011 à 2015, atuou como instrutora de informática na escola SENAI "Luís Massa" em Botucatu SP, onde ministrou cursos de Operador de microcomputador (pacote MS Office, sistemas operacionais, internet) , MS Word e Excel avançado, CorelDraw, AutoCAD e Web design.Atualmente é:- Professora de programação e desenvolvimento de jogos (JavaScript, HTML, CSS), aplicativos em React Native e Python, Machine learning, Inteligência Artificial e Ciência de dados) na escola Byjus Future School (Home Office).- Professora de Ensino médio e técnico na Etec Dr. Domingos Minicucci Filho de Botucatu como professora de desenvolvimento web e segurança da informação.

Fernando de Lima Caneppele, UNIVERSITY OF SÃO PAULO - USP Pirassununga

Associate Professor at the University of São Paulo (USP) and participating in the Sabbatical Year Program at the Institute for Advanced Studies (IEA/USP) in 2024, working with a focus on Energy and Specialist in Energy Transition and Sustainable Development Goal 7 (SDG7). He has a degree in Electrical Engineering from the Faculty of Industrial Engineering (FEI, 1999), a Master's and a PhD in Agronomy - Energy in Agriculture from the Faculty of Agronomic Sciences at UNESP (FCA, 2007 and 2011). He also holds a Bachelor's Degree in Mathematics from UNINOVE (2008), a Post-Doctorate from UNESP (2018) and a Post-Doctorate from USP (2020). With over 15 years of academic experience, he currently teaches undergraduate courses in Biosystems Engineering and Food Engineering at USP's School of Animal Science and Food Engineering (FZEA). In Postgraduate Studies, he is responsible for subjects and for supervising Master's and Doctorate courses in the Postgraduate Program in Agricultural Engineering at the Faculty of Agronomic Sciences at UNESP (FCA). He has consolidated experience in Electrical and Energy Engineering, working on Energy Efficiency, Alternative and Renewable Energy Sources, Energy Matrix Studies, and Energy Generation and its Environmental Impact. He also has practical experience gained as an intern at BRF during his engineering training, which contributes to the quality of his lectures.

Alexandre Dal Pai, São Paulo State University

He has a degree in Physics from the University of São Paulo (1998), a master's degree in Agronomy (Energy in Agriculture) from the Universidade Estadual Paulista Júlio de Mesquita Filho (2001) and a doctorate in Agronomy (Energy in Agriculture) from the Universidade Estadual Paulista Júlio de Mesquita Filho (2005). He is currently a lecturer at the Faculty of Agronomic Sciences at UNESP/Botucatu, where he teaches physics on the undergraduate course in Bioprocess Engineering and Biotechnology. She also teaches and supervises postgraduate courses in Agronomy, in the Energy in Agriculture and Irrigation and Drainage programs. He has experience in the areas of renewable energies, solar and biomass energy conversion processes, models for estimating solar and photosynthetically active radiation, as well as studies on methods for measuring diffuse solar radiation.

Emmanuel Zullo Godinho, Universidade de São Paulo (USP)

Graduated in Agronomic Engineering from the Paraguaçu Paulista School of Agronomy - ESAPP. Master's degree in Bioenergy from the State University of Western Paraná - UNIOESTE. PhD in Agronomy from Universidade Estadual Paulista - UNESP. Post-doctoral student in Biosystems Engineering at the University of São Paulo - USP. Postgraduate degree from the Getúlio Vargas Foundation (FGV-RJ) with an MBA in International Agribusiness Management. Postgraduate in Teaching in Higher Education and in Special and Inclusive Education from the Venda Nova do Imigrante College - FAVENI. Graduated with a degree in Mathematics from the Federal Technological University of Paraná - UTFPR and a degree in Pedagogy from Intervale College. Postgraduate student in Chemistry at the University of ABC. Undergraduate student in Physical Education at Faculdade Venda Nova do Imigrante - FAVENI. Member of the USP/UNESP Agroenerbio research group. Knowledge of statistical and mathematical modeling programs: Fuzzy Logic, Statistica, Action, Origin. Line of research is RENEWABLE ENERGIES and FUZZY LOGIC. Has experience as a teacher in primary and secondary schools, undergraduate and postgraduate courses.

Carlos Frederico Meschini Almeida, Universidade de São Paulo (USP)

Graduated in Electrical Engineering from the Polytechnic School of the University of São Paulo (2003). He holds a Master's and PhD in Electrical Engineering, with an emphasis on Electrical Power Systems, from the same institution (2007 and 2011, respectively). He is currently a Professor at EPUSP, in the Department of Electrical Energy and Automation. He has experience in the areas of Electricity Transmission and Distribution, working mainly on the following subjects: Electricity Quality, Smart Grids, Electricity Systems Planning and Electrical Installation Projects.

Diego Cunha Malagueta, Universidade Federal do Rio de Janeiro (UFRJ)

Adjunct Professor IV in engineering at iPoli-UFRJ/Macaé since 2013, working in Energy Planning and Mechanical Engineering. He has a PhD in Energy Planning from COPPE/UFRJ, with a focus on renewable energies, especially solar. He works in research, teaching and university extension, developing projects in energy systems simulation, solar technologies and public policies. He is a guest lecturer at various institutions and coordinator of specialization courses. He is also an active science communicator, with a podcast and YouTube channel dedicated to energy. His experience ranges from the Brazilian energy matrix to practical applications of renewable energies, such as desalination and solar cooling.

Murilo Miceno Frigo, Universidade Federal de Mato Grosso do Sul (UFMS)

Graduated in electrical engineering from the Federal University of Mato Grosso do Sul (2010). Master's degree in Electrical Engineering from UFMS (2013), research area: Energy, Planning, Operation and Control of Electrical Systems. He is currently an EBTT professor at the Federal Institute of Mato Grosso do Sul (IFMS) on the Electrotechnical Technician, Industrial Automation Technology and Control and Automation Engineering courses. He was a lecturer on the Electrical Engineering course at the Federal University of Tocantins - UFT (2013-2016). He develops research and extension in the areas of energy management and efficiency, alternative energy sources and education applied to professional and technological education.

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Published

04/02/2025

How to Cite

Tieghi, C. P., Caneppele, F. de L., Dal Pai, A., Godinho, E. Z., Almeida, C. F. M., Malagueta, D. C., & Frigo, M. M. (2025). Applications and Challenges of Artificial Intelligence in Solar Radiation Forecasting: A Systematic Review. Brazilian Journal of Climatology, 36(21), 170–201. https://doi.org/10.55761/abclima.v36i21.18872

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Vol. 36 (2025)

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