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Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components

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  1. Yamashiro, Hirochika & Nonaka, Hirofumi, 2021. "Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem," Operations Research Perspectives, Elsevier, vol. 8(C).
  2. Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
  3. Hasna Hissou & Said Benkirane & Azidine Guezzaz & Mourade Azrour & Abderrahim Beni-Hssane, 2023. "A Novel Machine Learning Approach for Solar Radiation Estimation," Sustainability, MDPI, vol. 15(13), pages 1-21, July.
  4. Ye, Wenlian & Wang, Xiaojun & Liu, Yingwen, 2020. "Application of artificial neural network for predicting the dynamic performance of a free piston Stirling engine," Energy, Elsevier, vol. 194(C).
  5. Zhang, Hongjie & Yao, Runming & Luo, Qing & Wang, Wenbo, 2022. "A mathematical model for a rapid calculation of the urban canyon albedo and its applications," Renewable Energy, Elsevier, vol. 197(C), pages 836-851.
  6. 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.
  7. Niu, Tong & Li, Jinkai & Wei, Wei & Yue, Hui, 2022. "A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting," Applied Energy, Elsevier, vol. 326(C).
  8. Gabriel Mendonça de Paiva & Sergio Pires Pimentel & Bernardo Pinheiro Alvarenga & Enes Gonçalves Marra & Marco Mussetta & Sonia Leva, 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks," Energies, MDPI, vol. 13(11), pages 1-28, June.
  9. Yang, Jianfeng & Suo, Guanyu & Chen, Liangchao & Dou, Zhan & Hu, Yuanhao, 2023. "Prediction method of key corrosion state parameters in refining process based on multi-source data," Energy, Elsevier, vol. 263(PA).
  10. Segarra-Tamarit, Jorge & Pérez, Emilio & Moya, Eric & Ayuso, Pablo & Beltran, Hector, 2021. "Deep learning-based forecasting of aggregated CSP production," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 306-318.
  11. Hoyos-Gómez, Laura S. & Ruiz-Muñoz, Jose F. & Ruiz-Mendoza, Belizza J., 2022. "Short-term forecasting of global solar irradiance in tropical environments with incomplete data," Applied Energy, Elsevier, vol. 307(C).
  12. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms," Applied Energy, Elsevier, vol. 316(C).
  13. Puah, Boon Keat & Chong, Lee Wai & Wong, Yee Wan & Begam, K.M. & Khan, Nafizah & Juman, Mohammed Ayoub & Rajkumar, Rajprasad Kumar, 2021. "A regression unsupervised incremental learning algorithm for solar irradiance prediction," Renewable Energy, Elsevier, vol. 164(C), pages 908-925.
  14. Su-Chang Lim & Jun-Ho Huh & Seok-Hoon Hong & Chul-Young Park & Jong-Chan Kim, 2022. "Solar Power Forecasting Using CNN-LSTM Hybrid Model," Energies, MDPI, vol. 15(21), pages 1-17, November.
  15. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
  16. Kisi, Ozgur & Heddam, Salim & Yaseen, Zaher Mundher, 2019. "The implementation of univariable scheme-based air temperature for solar radiation prediction: New development of dynamic evolving neural-fuzzy inference system model," Applied Energy, Elsevier, vol. 241(C), pages 184-195.
  17. Gonzalo Astray & Benedicto Soto & Enrique Barreiro & Juan F. Gálvez & Juan C. Mejuto, 2021. "Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea," Mathematics, MDPI, vol. 9(19), pages 1-15, October.
  18. Gao, Mingming & Li, Jianjing & Hong, Feng & Long, Dongteng, 2019. "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM," Energy, Elsevier, vol. 187(C).
  19. Yaxin Zhang & Tao Hu, 2022. "Ensemble Interval Prediction for Solar Photovoltaic Power Generation," Energies, MDPI, vol. 15(19), pages 1-30, September.
  20. Yang, Liu & Cao, Qimeng & Yu, Ying & Liu, Yan, 2020. "Comparison of daily diffuse radiation models in regions of China without solar radiation measurement," Energy, Elsevier, vol. 191(C).
  21. Dong, Yan & Han, Han & Wang, Fuqiang & Zhang, Yingjie & Cheng, Ziming & Shi, Xuhang & Yan, Yuying, 2022. "A low-cost sustainable coating: Improving passive daytime radiative cooling performance using the spectral band complementarity method," Renewable Energy, Elsevier, vol. 192(C), pages 606-616.
  22. Sujan Ghimire & Ravinesh C Deo & Nawin Raj & Jianchun Mi, 2019. "Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction," Energies, MDPI, vol. 12(12), pages 1-39, June.
  23. Ben Ammar, Rim & Ben Ammar, Mohsen & Oualha, Abdelmajid, 2020. "Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems," Renewable Energy, Elsevier, vol. 153(C), pages 1016-1028.
  24. Zang, Haixiang & Liu, Ling & Sun, Li & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2020. "Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations," Renewable Energy, Elsevier, vol. 160(C), pages 26-41.
  25. Das, Pronob & Das, Barun K. & Rahman, Mushfiqur & Hassan, Rakibul, 2022. "Evaluating the prospect of utilizing excess energy and creating employments from a hybrid energy system meeting electricity and freshwater demands using multi-objective evolutionary algorithms," Energy, Elsevier, vol. 238(PB).
  26. Liu, Yanfeng & Zhou, Yong & Chen, Yaowen & Wang, Dengjia & Wang, Yingying & Zhu, Ying, 2020. "Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: A case study in China," Renewable Energy, Elsevier, vol. 146(C), pages 1101-1112.
  27. Tingting Zhu & Yiren Guo & Cong Wang & Chao Ni, 2020. "Inter-Hour Forecast of Solar Radiation Based on the Structural Equation Model and Ensemble Model," Energies, MDPI, vol. 13(17), pages 1-15, September.
  28. 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.
  29. 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.
  30. Spyros Theocharides & Marios Theristis & George Makrides & Marios Kynigos & Chrysovalantis Spanias & George E. Georghiou, 2021. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting," Energies, MDPI, vol. 14(4), pages 1-22, February.
  31. Ruan, Zhaohui & Sun, Weiwei & Yuan, Yuan & Tan, Heping, 2023. "Accurately forecasting solar radiation distribution at both spatial and temporal dimensions simultaneously with fully-convolutional deep neural network model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
  32. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  33. Abdallah Abdellatif & Hamza Mubarak & Shameem Ahmad & Tofael Ahmed & G. M. Shafiullah & Ahmad Hammoudeh & Hamdan Abdellatef & M. M. Rahman & Hassan Muwafaq Gheni, 2022. "Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model," Sustainability, MDPI, vol. 14(17), pages 1-21, September.
  34. Na Sun & Nan Zhang & Shuai Zhang & Tian Peng & Wei Jiang & Jie Ji & Xiangmiao Hao, 2022. "An Integrated Framework Based on an Improved Gaussian Process Regression and Decomposition Technique for Hourly Solar Radiation Forecasting," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
  35. Jia, Dongyu & Yang, Liwei & Lv, Tao & Liu, Weiping & Gao, Xiaoqing & Zhou, Jiaxin, 2022. "Evaluation of machine learning models for predicting daily global and diffuse solar radiation under different weather/pollution conditions," Renewable Energy, Elsevier, vol. 187(C), pages 896-906.
  36. Hai Tao & Isa Ebtehaj & Hossein Bonakdari & Salim Heddam & Cyril Voyant & Nadhir Al-Ansari & Ravinesh Deo & Zaher Mundher Yaseen, 2019. "Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme," Energies, MDPI, vol. 12(7), pages 1-24, April.
  37. Dengchang Ma & Guobing Pan & Fang Xu & Hongfei Sun, 2021. "Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System Feasibility," Energies, MDPI, vol. 14(10), pages 1-16, May.
  38. Marzouq, Manal & El Fadili, Hakim & Zenkouar, Khalid & Lakhliai, Zakia & Amouzg, Mohammed, 2020. "Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data," Renewable Energy, Elsevier, vol. 157(C), pages 214-231.
  39. Cecilia Martinez-Castillo & Gonzalo Astray & Juan Carlos Mejuto, 2021. "Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models," Energies, MDPI, vol. 14(8), pages 1-16, April.
  40. Bisoi, Ranjeeta & Dash, Deepak Ranjan & Dash, P.K. & Tripathy, Lokanath, 2022. "An efficient robust optimized functional link broad learning system for solar irradiance prediction," Applied Energy, Elsevier, vol. 319(C).
  41. Simon Liebermann & Jung-Sup Um & YoungSeok Hwang & Stephan Schlüter, 2021. "Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts," Energies, MDPI, vol. 14(11), pages 1-21, May.
  42. Narvaez, Gabriel & Giraldo, Luis Felipe & Bressan, Michael & Pantoja, Andres, 2021. "Machine learning for site-adaptation and solar radiation forecasting," Renewable Energy, Elsevier, vol. 167(C), pages 333-342.
  43. Musaed Alhussein & Syed Irtaza Haider & Khursheed Aurangzeb, 2019. "Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance," Energies, MDPI, vol. 12(8), pages 1-27, April.
  44. Wu, Wei & Tang, Xiaoping & Lv, Jiake & Yang, Chao & Liu, Hongbin, 2021. "Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas," Renewable Energy, Elsevier, vol. 177(C), pages 148-163.
  45. Ochuko Kelvin Overen & Edson Leroy Meyer, 2022. "Solar Energy Resources and Photovoltaic Power Potential of an Underutilised Region: A Case of Alice, South Africa," Energies, MDPI, vol. 15(13), pages 1-29, June.
  46. Hassan, Muhammed A. & Akoush, Bassem M. & Abubakr, Mohamed & Campana, Pietro Elia & Khalil, Adel, 2021. "High-resolution estimates of diffuse fraction based on dynamic definitions of sky conditions," Renewable Energy, Elsevier, vol. 169(C), pages 641-659.
  47. Polasek, Tomas & Čadík, Martin, 2023. "Predicting photovoltaic power production using high-uncertainty weather forecasts," Applied Energy, Elsevier, vol. 339(C).
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