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A neural networks approach for wind speed prediction

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Cited by:

  1. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
  2. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
  3. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
  4. Keshab Raj Dahal & Nawa Raj Pokhrel & Pramesh Subedi & Santosh Gaire & Ramesh Bhandari & Mimansa Dahal & Modesola Giwa, 2025. "Predicting Coronary Artery Disease Using Machine Learning," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 13(2), pages 1-1, January.
  5. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
  6. Yeji Lim & Minjae Son & Kyungnam Park & Minsoo Kim & Keunju Song & Haejoong Lee & Hongseok Kim, 2025. "Power System Decision Making in the Age of Deep Learning: A Comprehensive Review," Energies, MDPI, vol. 18(18), pages 1-49, September.
  7. A. Alexandridis & A. Zapranis, 2013. "Wind Derivatives: Modeling and Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 41(3), pages 299-326, March.
  8. Kiplangat, Dennis C. & Asokan, K. & Kumar, K. Satheesh, 2016. "Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition," Renewable Energy, Elsevier, vol. 93(C), pages 38-44.
  9. Shrivastava, Nitin Anand & Lohia, Kunal & Panigrahi, Bijaya Ketan, 2016. "A multiobjective framework for wind speed prediction interval forecasts," Renewable Energy, Elsevier, vol. 87(P2), pages 903-910.
  10. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
  11. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
  12. Rehman, Shafiqur & Al-Abbadi, Naif M., 2007. "Wind shear coefficients and energy yield for Dhahran, Saudi Arabia," Renewable Energy, Elsevier, vol. 32(5), pages 738-749.
  13. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
  14. Rehman, S. & El-Amin, I.M. & Ahmad, F. & Shaahid, S.M. & Al-Shehri, A.M. & Bakhashwain, J.M. & Shash, A., 2007. "Feasibility study of hybrid retrofits to an isolated off-grid diesel power plant," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(4), pages 635-653, May.
  15. Chen, Juntao & Fu, Xueying & Zhang, Lingli & Shen, Haoye & Wu, Jibo, 2024. "A novel offshore wind power prediction model based on TCN-DANet-sparse transformer and considering spatio-temporal coupling in multiple wind farms," Energy, Elsevier, vol. 308(C).
  16. Rehman, S & Halawani, T.O & Mohandes, M, 2003. "Wind power cost assessment at twenty locations in the kingdom of Saudi Arabia," Renewable Energy, Elsevier, vol. 28(4), pages 573-583.
  17. Rehman, Shafiqur & Ahmad, Aftab, 2004. "Assessment of wind energy potential for coastal locations of the Kingdom of Saudi Arabia," Energy, Elsevier, vol. 29(8), pages 1105-1115.
  18. 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.
  19. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
  20. Philippopoulos, Kostas & Deligiorgi, Despina, 2012. "Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography," Renewable Energy, Elsevier, vol. 38(1), pages 75-82.
  21. Drisya, G.V. & Asokan, K. & Kumar, K. Satheesh, 2018. "Diverse dynamical characteristics across the frequency spectrum of wind speed fluctuations," Renewable Energy, Elsevier, vol. 119(C), pages 540-550.
  22. Ivan Marović & Ivana Sušanj & Nevenka Ožanić, 2017. "Development of ANN Model for Wind Speed Prediction as a Support for Early Warning System," Complexity, Hindawi, vol. 2017, pages 1-10, December.
  23. He, Jinhua & Hu, Zechun & Wang, Songpo & Mujeeb, Asad & Yang, Pengwei, 2024. "Windformer: A novel 4D high-resolution system for multi-step wind speed vector forecasting based on temporal shifted window multi-head self-attention," Energy, Elsevier, vol. 310(C).
  24. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
  25. Laslett, Dean & Creagh, Chris & Jennings, Philip, 2016. "A simple hourly wind power simulation for the South-West region of Western Australia using MERRA data," Renewable Energy, Elsevier, vol. 96(PA), pages 1003-1014.
  26. Manoj Verma & Harish Kumar Ghritlahre, 2023. "Forecasting of Wind Speed by Using Three Different Techniques of Prediction Models," Annals of Data Science, Springer, vol. 10(3), pages 679-711, June.
  27. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
  28. Cheng-Yu Ho & Ke-Sheng Cheng & Chi-Hang Ang, 2023. "Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan," Energies, MDPI, vol. 16(3), pages 1-18, January.
  29. Keshab Raj Dahal & Nirajan Budhathoki & Anuja Dahal & Shreya Dhital, 2026. "Comparative Performance of Machine Learning Models for Diabetes Prediction Among US Adults Using NHANES Data," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 15(1), pages 1-12, April.
  30. Narayana, Mahinsasa & Sunderland, Keith M. & Putrus, Ghanim & Conlon, Michael F., 2017. "Adaptive linear prediction for optimal control of wind turbines," Renewable Energy, Elsevier, vol. 113(C), pages 895-906.
  31. Bouzgou, Hassen & Benoudjit, Nabil, 2011. "Multiple architecture system for wind speed prediction," Applied Energy, Elsevier, vol. 88(7), pages 2463-2471, July.
  32. Ahmed, Adil & Khalid, Muhammad, 2018. "An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks," Applied Energy, Elsevier, vol. 225(C), pages 902-911.
  33. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
  34. Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems & M. Mujahid Rafique, 2018. "Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review," Energies, MDPI, vol. 11(3), pages 1-34, February.
  35. Rehman, S. & El-Amin, I.M. & Ahmad, F. & Shaahid, S.M. & Al-Shehri, A.M. & Bakhashwain, J.M., 2007. "Wind power resource assessment for Rafha, Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(5), pages 937-950, June.
  36. Shukai Li & Jifeng Chen & Wei Dai & Fangyuan Li & Yuting Gong & Hongmei Gong & Ziyi Zhu, 2025. "Multi-Province Collaborative Carbon Emission Forecasting and Scenario Analysis Based on the Spatio-Temporal Attention Mechanism—Empowering the Green and Low-Carbon Transition of the Transportation Sector Through Technological Innovation," Sustainability, MDPI, vol. 17(19), pages 1-30, September.
  37. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
  38. M. Mujahid Rafique & Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems, 2018. "Feasibility of a 100 MW Installed Capacity Wind Farm for Different Climatic Conditions," Energies, MDPI, vol. 11(8), pages 1-18, August.
  39. Sun, Fei & Jin, Tongdan, 2022. "A hybrid approach to multi-step, short-term wind speed forecasting using correlated features," Renewable Energy, Elsevier, vol. 186(C), pages 742-754.
  40. Tasadduq, Imran & Rehman, Shafiqur & Bubshait, Khaled, 2002. "Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia," Renewable Energy, Elsevier, vol. 25(4), pages 545-554.
  41. Yang, Qiuling & Deng, Changhong & Chang, Xiqiang, 2022. "Ultra-short-term / short-term wind speed prediction based on improved singular spectrum analysis," Renewable Energy, Elsevier, vol. 184(C), pages 36-44.
  42. Alma Y. Alanis & Oscar D. Sanchez & Jesus G. Alvarez, 2021. "Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
  43. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  44. Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
  45. 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.
  46. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez & Víctor Alonso-Gómez, 2019. "Maintenance Models Applied to Wind Turbines. A Comprehensive Overview," Energies, MDPI, vol. 12(2), pages 1-41, January.
  47. Deo, Ravinesh C. & Ghorbani, Mohammad Ali & Samadianfard, Saeed & Maraseni, Tek & Bilgili, Mehmet & Biazar, Mustafa, 2018. "Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data," Renewable Energy, Elsevier, vol. 116(PA), pages 309-323.
  48. Rehman, Shafiqur, 2005. "Prospects of wind farm development in Saudi Arabia," Renewable Energy, Elsevier, vol. 30(3), pages 447-463.
  49. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
  50. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
  51. Bhowmik, Mrinal & Muthukumar, P. & Anandalakshmi, R., 2019. "Experimental based multilayer perceptron approach for prediction of evacuated solar collector performance in humid subtropical regions," Renewable Energy, Elsevier, vol. 143(C), pages 1566-1580.
  52. Douak, Fouzi & Melgani, Farid & Benoudjit, Nabil, 2013. "Kernel ridge regression with active learning for wind speed prediction," Applied Energy, Elsevier, vol. 103(C), pages 328-340.
  53. Riahy, G.H. & Abedi, M., 2008. "Short term wind speed forecasting for wind turbine applications using linear prediction method," Renewable Energy, Elsevier, vol. 33(1), pages 35-41.
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