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Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models

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  1. Mohammed, Safwan & Arshad, Sana & Bashir, Bashar & Vad, Attila & Alsalman, Abdullah & Harsányi, Endre, 2024. "Machine learning driven forecasts of agricultural water quality from rainfall ionic characteristics in Central Europe," Agricultural Water Management, Elsevier, vol. 293(C).
  2. Eduardo Rangel-Heras & César Angeles-Camacho & Erasmo Cadenas-Calderón & Rafael Campos-Amezcua, 2022. "Short-Term Forecasting of Energy Production for a Photovoltaic System Using a NARX-CVM Hybrid Model," Energies, MDPI, vol. 15(8), pages 1-23, April.
  3. Salcedo-Sanz, S. & Cornejo-Bueno, L. & Prieto, L. & Paredes, D. & García-Herrera, R., 2018. "Feature selection in machine learning prediction systems for renewable energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 728-741.
  4. Jiale Tang & Kuixing Liu & Weijie You & Xinyu Zhang & Tuomi Zhang, 2023. "Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining," Energies, MDPI, vol. 16(14), pages 1-19, July.
  5. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2019. "Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China," Renewable Energy, Elsevier, vol. 135(C), pages 984-1003.
  6. Afroz, Zakia & Urmee, Tania & Shafiullah, G.M. & Higgins, Gary, 2018. "Real-time prediction model for indoor temperature in a commercial building," Applied Energy, Elsevier, vol. 231(C), pages 29-53.
  7. Hussain, Sajid & AlAlili, Ali, 2017. "A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks," Applied Energy, Elsevier, vol. 208(C), pages 540-550.
  8. Mohamed A. Ali & Ashraf Elsayed & Islam Elkabani & Mohammad Akrami & M. Elsayed Youssef & Gasser E. Hassan, 2023. "Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods," Energies, MDPI, vol. 16(17), pages 1-30, August.
  9. Rao K, D.V. Siva Krishna & Premalatha, M. & Naveen, C., 2018. "Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 248-258.
  10. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
  11. Dach, J. & Koszela, K. & Boniecki, P. & Zaborowicz, M. & Lewicki, A. & Czekała, W. & Skwarcz, J. & Qiao, Wei & Piekarska-Boniecka, H. & Białobrzewski, I., 2016. "The use of neural modelling to estimate the methane production from slurry fermentation processes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 603-610.
  12. Kılıç, Fatih & Yılmaz, İbrahim Halil & Kaya, Özge, 2021. "Adaptive co-optimization of artificial neural networks using evolutionary algorithm for global radiation forecasting," Renewable Energy, Elsevier, vol. 171(C), pages 176-190.
  13. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
  14. Mohamed A. Ali & Ashraf Elsayed & Islam Elkabani & Mohammad Akrami & M. Elsayed Youssef & Gasser E. Hassan, 2024. "Artificial Intelligence-Based Improvement of Empirical Methods for Accurate Global Solar Radiation Forecast: Development and Comparative Analysis," Energies, MDPI, vol. 17(17), pages 1-42, August.
  15. Hussain, Sajid & Al-Alili, Ali, 2016. "A new approach for model validation in solar radiation using wavelet, phase and frequency coherence analysis," Applied Energy, Elsevier, vol. 164(C), pages 639-649.
  16. Messner, Wolfgang, 2024. "Distance is the spice, but not the whole enchilada: Country-pair psychic distance stimuli and country fixed effects in a deep learning implementation of the trade flow model," International Business Review, Elsevier, vol. 33(1).
  17. Shamshirband, Shahaboddin & Mohammadi, Kasra & Yee, Por Lip & Petković, Dalibor & Mostafaeipour, Ali, 2015. "A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1031-1042.
  18. Mohammadi, Kasra & Shamshirband, Shahaboddin & Kamsin, Amirrudin & Lai, P.C. & Mansor, Zulkefli, 2016. "Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 423-434.
  19. 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).
  20. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
  21. Mohammadi, Kasra & Shamshirband, Shahaboddin & Petković, Dalibor & Khorasanizadeh, Hossein, 2016. "Determining the most important variables for diffuse solar radiation prediction using adaptive neuro-fuzzy methodology; case study: City of Kerman, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1570-1579.
  22. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
  23. Fucheng Song & Anling Zhang & Hui Liang & Lianhua Cui & Wenlian Li & Hongzong Si & Yunbo Duan & Honglin Zhai, 2016. "QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs," IJERPH, MDPI, vol. 13(11), pages 1-14, November.
  24. 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.
  25. Yadav, Amit Kumar & Chandel, S.S., 2015. "Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model," Renewable Energy, Elsevier, vol. 75(C), pages 675-693.
  26. 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.
  27. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2015. "Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1093-1106.
  28. Bocca, Alberto & Chiavazzo, Eliodoro & Macii, Alberto & Asinari, Pietro, 2015. "Solar energy potential assessment: An overview and a fast modeling approach with application to Italy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 291-296.
  29. 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).
  30. Şenol, Halil & Ali Dereli̇, Mehmet & Özbilgin, Ferdi, 2021. "Investigation of the distribution of bovine manure-based biomethane potential using an artificial neural network in Turkey to 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
  31. Aggarwal, S.K. & Saini, L.M., 2014. "Solar energy prediction using linear and non-linear regularization models: A study on AMS (American Meteorological Society) 2013–14 Solar Energy Prediction Contest," Energy, Elsevier, vol. 78(C), pages 247-256.
  32. Yadav, Amit Kumar & Chandel, S.S., 2017. "Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using Artificial Neural Network and Multiple Linear Regression Models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 955-969.
  33. Yadav, Amit Kumar & Sharma, Vikrant & Malik, Hasmat & Chandel, S.S., 2018. "Daily array yield prediction of grid-interactive photovoltaic plant using relief attribute evaluator based Radial Basis Function Neural Network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2115-2127.
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