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Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters

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  1. Lima, Marcello Anderson F.B. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M. & Braga, Arthur P.S., 2020. "Improving solar forecasting using Deep Learning and Portfolio Theory integration," Energy, Elsevier, vol. 195(C).
  2. 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.
  3. Seyed Morteza Seyedian & Ozgur Kisi & Abbas Parsaie & Mojtaba Kashani, 2024. "Improving the Reliability of Compound Channel Discharge Prediction Using Machine Learning Techniques and Resampling Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4685-4709, September.
  4. Xing Zhang & Zhuoqun Wei, 2019. "A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting," Sustainability, MDPI, vol. 11(15), pages 1-20, July.
  5. Han, Tian & Li, Ruimeng & Wang, Xiao & Wang, Ying & Chen, Kang & Peng, Huaiwu & Gao, Zhenxin & Wang, Nannan & Peng, Qinke, 2024. "Intra-hour solar irradiance forecasting using topology data analysis and physics-driven deep learning," Renewable Energy, Elsevier, vol. 224(C).
  6. David Puga-Gil & Gonzalo Astray & Enrique Barreiro & Juan F. Gálvez & Juan Carlos Mejuto, 2022. "Global Solar Irradiation Modelling and Prediction Using Machine Learning Models for Their Potential Use in Renewable Energy Applications," Mathematics, MDPI, vol. 10(24), pages 1-21, December.
  7. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
  8. Ali Jallal, Mohammed & Chabaa, Samira & Zeroual, Abdelouhab, 2020. "A novel deep neural network based on randomly occurring distributed delayed PSO algorithm for monitoring the energy produced by four dual-axis solar trackers," Renewable Energy, Elsevier, vol. 149(C), pages 1182-1196.
  9. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2019. "Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model," Renewable Energy, Elsevier, vol. 140(C), pages 70-87.
  10. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
  11. Chen, Ji-Long & He, Lei & Yang, Hong & Ma, Maohua & Chen, Qiao & Wu, Sheng-Jun & Xiao, Zuo-lin, 2019. "Empirical models for estimating monthly global solar radiation: A most comprehensive review and comparative case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 91-111.
  12. 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.
  13. Zhao, Shuting & Wu, Lifeng & Xiang, Youzhen & Dong, Jianhua & Li, Zhen & Liu, Xiaoqiang & Tang, Zijun & Wang, Han & Wang, Xin & An, Jiaqi & Zhang, Fucang & Li, Zhijun, 2022. "Coupling meteorological stations data and satellite data for prediction of global solar radiation with machine learning models," Renewable Energy, Elsevier, vol. 198(C), pages 1049-1064.
  14. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
  15. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
  16. Yuansheng Huang & Lei Yang & Chong Gao & Yuqing Jiang & Yulin Dong, 2019. "A Novel Prediction Approach for Short-Term Renewable Energy Consumption in China Based on Improved Gaussian Process Regression," Energies, MDPI, vol. 12(21), pages 1-17, November.
  17. Qin, Shujing & Liu, Zhihe & Qiu, Rangjian & Luo, Yufeng & Wu, Jingwei & Zhang, Baozhong & Wu, Lifeng & Agathokleous, Evgenios, 2023. "Short–term global solar radiation forecasting based on an improved method for sunshine duration prediction and public weather forecasts," Applied Energy, Elsevier, vol. 343(C).
  18. Wang, Yuhan & Zhang, Chu & Fu, Yongyan & Suo, Leiming & Song, Shihao & Peng, Tian & Shahzad Nazir, Muhammad, 2023. "Hybrid solar radiation forecasting model with temporal convolutional network using data decomposition and improved artificial ecosystem-based optimization algorithm," Energy, Elsevier, vol. 280(C).
  19. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
  20. Jiaojiao Feng & Weizhen Wang & Jing Li, 2018. "An LM-BP Neural Network Approach to Estimate Monthly-Mean Daily Global Solar Radiation Using MODIS Atmospheric Products," Energies, MDPI, vol. 11(12), pages 1-14, December.
  21. Youssef Kassem & Hüseyin Çamur & Salman Mohammed Awadh Alhuoti, 2020. "Solar Energy Technology for Northern Cyprus: Assessment, Statistical Analysis, and Feasibility Study," Energies, MDPI, vol. 13(4), pages 1-29, February.
  22. Wongchai Anupong & Muhsin Jaber Jweeg & Sameer Alani & Ibrahim H. Al-Kharsan & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq," Energies, MDPI, vol. 16(2), pages 1-14, January.
  23. Jun Su & Zhiyuan Zeng & Chaolong Tang & Zhiquan Liu & Tianyou Li, 2024. "A Photovoltaic Fault Diagnosis Method Integrating Photovoltaic Power Prediction and EWMA Control Chart," Energies, MDPI, vol. 17(17), pages 1-22, August.
  24. Ahmed Aljanad & Nadia M. L. Tan & Vassilios G. Agelidis & Hussain Shareef, 2021. "Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-20, February.
  25. 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.
  26. Nourani, Vahid & Sharghi, Elnaz & Behfar, Nazanin & Zhang, Yongqiang, 2022. "Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data," Applied Energy, Elsevier, vol. 315(C).
  27. 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.
  28. Zheng, Xidong & Chen, Huangbin & Jin, Tao, 2024. "A new optimization approach considering demand response management and multistage energy storage: A novel perspective for Fujian Province," Renewable Energy, Elsevier, vol. 220(C).
  29. Zhang, Chu & Hua, Lei & Ji, Chunlei & Shahzad Nazir, Muhammad & Peng, Tian, 2022. "An evolutionary robust solar radiation prediction model based on WT-CEEMDAN and IASO-optimized outlier robust extreme learning machine," Applied Energy, Elsevier, vol. 322(C).
  30. 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.
  31. Kumari, Pratima & Toshniwal, Durga, 2021. "Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting," Applied Energy, Elsevier, vol. 295(C).
  32. Fatemeh Barzegari Banadkooki & Vijay P. Singh & Mohammad Ehteram, 2021. "Multi-timescale drought prediction using new hybrid artificial neural network models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 2461-2478, April.
  33. Mohammad Ehteram & Ali Najah Ahmed & Chow Ming Fai & Haitham Abdulmohsin Afan & Ahmed El-Shafie, 2019. "Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy," Energies, MDPI, vol. 12(14), pages 1-26, July.
  34. Ağbulut, Ümit & Gürel, Ali Etem & Biçen, Yunus, 2021. "Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
  35. Hossein Moayedi & Amir Mosavi, 2021. "An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework," Energies, MDPI, vol. 14(4), pages 1-18, February.
  36. Luo, Xilin & Duan, Huiming & He, Leiyuhang, 2020. "A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy," Energy, Elsevier, vol. 205(C).
  37. Azizi, Narjes & Yaghoubirad, Maryam & Farajollahi, Meisam & Ahmadi, Abolfzl, 2023. "Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output," Renewable Energy, Elsevier, vol. 206(C), pages 135-147.
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