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Machine learning methods for solar radiation forecasting: A review

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Cited by:

  1. Edna S. Solano & Payman Dehghanian & Carolina M. Affonso, 2022. "Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection," Energies, MDPI, vol. 15(19), pages 1-18, September.
  2. Duchaud, Jean-Laurent & Notton, Gilles & Fouilloy, Alexis & Voyant, Cyril, 2019. "Hybrid renewable power plant sizing – Graphical decision tool, sensitivity analysis and applications in Ajaccio and Tilos," Applied Energy, Elsevier, vol. 254(C).
  3. Llinet Benavides Cesar & Rodrigo Amaro e Silva & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira, 2022. "Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates," Energies, MDPI, vol. 15(12), pages 1-23, June.
  4. Li, Guanglei & Wang, Guohao & Luo, Tengqi & Hu, Yuxiao & Wu, Shouyuan & Gong, Guanghui & Song, Chenchen & Guo, Zhiling & Liu, Zhengguang, 2024. "SolarSAM: Building-scale photovoltaic potential assessment based on Segment Anything Model (SAM) and remote sensing for emerging city," Renewable Energy, Elsevier, vol. 237(PA).
  5. Despotovic, Milan & Voyant, Cyril & Garcia-Gutierrez, Luis & Almorox, Javier & Notton, Gilles, 2024. "Solar irradiance time series forecasting using auto-regressive and extreme learning methods: Influence of transfer learning and clustering," Applied Energy, Elsevier, vol. 365(C).
  6. Jawed Mustafa & Shahid Husain & Saeed Alqaed & Uzair Ali Khan & Basharat Jamil, 2022. "Performance of Two Variable Machine Learning Models to Forecast Monthly Mean Diffuse Solar Radiation across India under Various Climate Zones," Energies, MDPI, vol. 15(21), pages 1-32, October.
  7. Andrea Scrocca & Roberto Pisani & Diego Andreotti & Giuliano Rancilio & Maurizio Delfanti & Filippo Bovera, 2025. "Optimal Spot Market Participation of PV + BESS: Impact of BESS Sizing in Utility-Scale and Distributed Configurations," Energies, MDPI, vol. 18(14), pages 1-32, July.
  8. Munir Husein & Il-Yop Chung, 2019. "Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach," Energies, MDPI, vol. 12(10), pages 1-21, May.
  9. Adel Alblawi & M. H. Elkholy & M. Talaat, 2019. "ANN for Assessment of Energy Consumption of 4 kW PV Modules over a Year Considering the Impacts of Temperature and Irradiance," Sustainability, MDPI, vol. 11(23), pages 1-24, November.
  10. Rodríguez, Eduardo & Droguett, Enrique López & Cardemil, José M. & Starke, Allan R. & Cornejo-Ponce, Lorena, 2024. "Enhancing the estimation of direct normal irradiance for six climate zones through machine learning models," Renewable Energy, Elsevier, vol. 231(C).
  11. Francisco A. Ramírez-Rivera & Néstor F. Guerrero-Rodríguez, 2024. "Ensemble Learning Algorithms for Solar Radiation Prediction in Santo Domingo: Measurements and Evaluation," Sustainability, MDPI, vol. 16(18), pages 1-27, September.
  12. Demir, Hasan, 2024. "Simulation and forecasting of power by energy harvesting method in photovoltaic panels using artificial neural network," Renewable Energy, Elsevier, vol. 222(C).
  13. Bouzgou, Hassen & Gueymard, Christian A., 2019. "Fast short-term global solar irradiance forecasting with wrapper mutual information," Renewable Energy, Elsevier, vol. 133(C), pages 1055-1065.
  14. Rosato, Antonello & Panella, Massimo & Andreotti, Amedeo & Mohammed, Osama A. & Araneo, Rodolfo, 2021. "Two-stage dynamic management in energy communities using a decision system based on elastic net regularization," Applied Energy, Elsevier, vol. 291(C).
  15. Marchesoni-Acland, Franco & Alonso-Suárez, Rodrigo, 2020. "Intra-day solar irradiation forecast using RLS filters and satellite images," Renewable Energy, Elsevier, vol. 161(C), pages 1140-1154.
  16. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
  17. Guosheng Duan & Lifeng Wu & Fa Liu & Yicheng Wang & Shaofei Wu, 2022. "Improvement in Solar-Radiation Forecasting Based on Evolutionary KNEA Method and Numerical Weather Prediction," Sustainability, MDPI, vol. 14(11), pages 1-20, June.
  18. Liang Gong & Fei Huang & Wei Zhang & Yanming Li & Chengliang Liu, 2023. "Precise Short-Term Small-Area Sunshine Forecasting for Optimal Seedbed Scheduling in Plant Factories," Agriculture, MDPI, vol. 13(9), pages 1-19, September.
  19. AlSkaif, Tarek & Dev, Soumyabrata & Visser, Lennard & Hossari, Murhaf & van Sark, Wilfried, 2020. "A systematic analysis of meteorological variables for PV output power estimation," Renewable Energy, Elsevier, vol. 153(C), pages 12-22.
  20. Qin, Jun & Jiang, Hou & Lu, Ning & Yao, Ling & Zhou, Chenghu, 2022. "Enhancing solar PV output forecast by integrating ground and satellite observations with deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  21. Khawaja Haider Ali & Marvin Sigalo & Saptarshi Das & Enrico Anderlini & Asif Ali Tahir & Mohammad Abusara, 2021. "Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation," Energies, MDPI, vol. 14(18), pages 1-18, September.
  22. Amin, Amin & Petri, Ioan & Minett, Simon, 2026. "Local energy community with a power-to-gas system: A feasibility case study and performance analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 225(C).
  23. Siripat Somchit & Palamy Thongbouasy & Chitchai Srithapon & Rongrit Chatthaworn, 2023. "Optimal Transmission Expansion Planning with Long-Term Solar Photovoltaic Generation Forecast," Energies, MDPI, vol. 16(4), pages 1-17, February.
  24. Shang, Chuanfu & Wei, Pengcheng, 2018. "Enhanced support vector regression based forecast engine to predict solar power output," Renewable Energy, Elsevier, vol. 127(C), pages 269-283.
  25. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
  26. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
  27. Victor Oliveira Santos & Felipe Pinto Marinho & Paulo Alexandre Costa Rocha & Jesse Van Griensven Thé & Bahram Gharabaghi, 2024. "Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California," Energies, MDPI, vol. 17(14), pages 1-26, July.
  28. Zarazua de Rubens, Gerardo, 2019. "Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market," Energy, Elsevier, vol. 172(C), pages 243-254.
  29. Heo, Jae & Jung, Jaehoon & Kim, Byungil & Han, SangUk, 2020. "Digital elevation model-based convolutional neural network modeling for searching of high solar energy regions," Applied Energy, Elsevier, vol. 262(C).
  30. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
  31. Voyant, Cyril & Motte, Fabrice & Notton, Gilles & Fouilloy, Alexis & Nivet, Marie-Laure & Duchaud, Jean-Laurent, 2018. "Prediction intervals for global solar irradiation forecasting using regression trees methods," Renewable Energy, Elsevier, vol. 126(C), pages 332-340.
  32. Zhang, Linyue & Wang, Jianzhou & Qian, Yuansheng & Li, Zhiwu, 2025. "Photovoltaic power uncertainty quantification system based on comprehensive model screening and multi-stage optimization tasks," Applied Energy, Elsevier, vol. 381(C).
  33. Ceylin Şirin & Fatih Selimefendigil & Hakan Fehmi Öztop, 2023. "Performance Analysis and Identification of an Indirect Photovoltaic Thermal Dryer with Aluminum Oxide Nano-Embedded Thermal Energy Storage Modification," Sustainability, MDPI, vol. 15(3), pages 1-27, January.
  34. Mehmood, Faiza & Ghani, Muhammad Usman & Asim, Muhammad Nabeel & Shahzadi, Rehab & Mehmood, Aamir & Mahmood, Waqar, 2021. "MPF-Net: A computational multi-regional solar power forecasting framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
  35. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
  36. Chetanpal Singh, 2023. "Machine Learning in Pattern Recognition," European Journal of Engineering and Technology Research, European Open Science, vol. 8(2), pages 63-68, March.
  37. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
  38. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha & Wenying Wen, 2019. "Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods," Energies, MDPI, vol. 12(9), pages 1-17, May.
  39. Wang, Lunche & Lu, Yunbo & Wang, Zhitong & Li, Huaping & Zhang, Ming, 2024. "Hourly solar radiation estimation and uncertainty quantification using hybrid models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
  40. Ghadah Alkhayat & Syed Hamid Hasan & Rashid Mehmood, 2022. "SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting," Energies, MDPI, vol. 15(18), pages 1-55, September.
  41. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
  42. Preeti Verma & Sunil Patil, 2023. "A Machine Learning Approach and Methodology for Solar Radiation Assessment Using Multispectral Satellite Images," Annals of Data Science, Springer, vol. 10(4), pages 907-932, August.
  43. Cheng, Hsu-Yung & Yu, Chih-Chang & Lin, Chih-Lung, 2021. "Day-ahead to week-ahead solar irradiance prediction using convolutional long short-term memory networks," Renewable Energy, Elsevier, vol. 179(C), pages 2300-2308.
  44. Tovar Rosas, Mario A. & Pérez, Miguel Robles & Martínez Pérez, E. Rafael, 2022. "Itineraries for charging and discharging a BESS using energy predictions based on a CNN-LSTM neural network model in BCS, Mexico," Renewable Energy, Elsevier, vol. 188(C), pages 1141-1165.
  45. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
  46. Miriam Steurer & Robert Hill, 2019. "Metrics for Evaluating the Performance of Automated Valuation Models," Graz Economics Papers 2019-02, University of Graz, Department of Economics.
  47. Pedro, Hugo T.C. & Lim, Edwin & Coimbra, Carlos F.M., 2018. "A database infrastructure to implement real-time solar and wind power generation intra-hour forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 513-525.
  48. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction," Renewable Energy, Elsevier, vol. 190(C), pages 408-424.
  49. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
  50. Agüera-Pérez, Agustín & Palomares-Salas, José Carlos & González de la Rosa, Juan José & Florencias-Oliveros, Olivia, 2018. "Weather forecasts for microgrid energy management: Review, discussion and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 265-278.
  51. Gürtürk, Mert & Ucar, Ferhat & Erdem, Murat, 2022. "A novel approach to investigate the effects of global warming and exchange rate on the solar power plants," Energy, Elsevier, vol. 239(PD).
  52. Monica Borunda & Adrián Ramírez & Raul Garduno & Gerardo Ruíz & Sergio Hernandez & O. A. Jaramillo, 2022. "Photovoltaic Power Generation Forecasting for Regional Assessment Using Machine Learning," Energies, MDPI, vol. 15(23), pages 1-25, November.
  53. Mandal, Dipak Kumar & Vaishali, & Gupta, Kritesh Kumar & Biswas, Nirmalendu & Manna, Nirmal K. & Santra, Somnath & Cuce, Erdem, 2025. "A novel comparative study of machine learning surrogate models for solar chimney (SC) plant performance evaluation: Thermo-physical insights," Energy, Elsevier, vol. 330(C).
  54. Said Benkaciali & Gilles Notton & Cyril Voyant, 2025. "Comparative Study of Feature Selection Techniques for Machine Learning-Based Solar Irradiation Forecasting to Facilitate the Sustainable Development of Photovoltaics: Application to Algerian Climatic Conditions," Sustainability, MDPI, vol. 17(14), pages 1-28, July.
  55. Liu, Fa & Wang, Xunming & Sun, Fubao & Wang, Hong, 2022. "Correct and remap solar radiation and photovoltaic power in China based on machine learning models," Applied Energy, Elsevier, vol. 312(C).
  56. Terrén-Serrano, Guillermo & Martínez-Ramón, Manel, 2021. "Multi-layer wind velocity field visualization in infrared images of clouds for solar irradiance forecasting," Applied Energy, Elsevier, vol. 288(C).
  57. Ngoc-Lan Huynh, Anh & Deo, Ravinesh C. & Ali, Mumtaz & Abdulla, Shahab & Raj, Nawin, 2021. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition," Applied Energy, Elsevier, vol. 298(C).
  58. Choi, Jung & Son, Seok-Woo & Lee, Seungjik & Park, Sangdae, 2024. "Advancing global solar photovoltaic power forecasting with sub-seasonal climate outlooks," Renewable Energy, Elsevier, vol. 237(PC).
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  105. Shab Gbémou & Julien Eynard & Stéphane Thil & Emmanuel Guillot & Stéphane Grieu, 2021. "A Comparative Study of Machine Learning-Based Methods for Global Horizontal Irradiance Forecasting," Energies, MDPI, vol. 14(11), pages 1-23, May.
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