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Wind speed prediction in the mountainous region of India using an artificial neural network model

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  1. Richmond, M. & Sobey, A. & Pandit, R. & Kolios, A., 2020. "Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning," Renewable Energy, Elsevier, vol. 161(C), pages 650-661.
  2. Athraa Ali Kadhem & Noor Izzri Abdul Wahab & Ishak Aris & Jasronita Jasni & Ahmed N. Abdalla, 2017. "Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network," Energies, MDPI, vol. 10(11), pages 1-17, October.
  3. Dongxiao Niu & Yi Liang & Wei-Chiang Hong, 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA," Energies, MDPI, vol. 10(12), pages 1-18, December.
  4. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
  5. Konstantinos Konstas & Panos T. Chountalas & Eleni A. Didaskalou & Dimitrios A. Georgakellos, 2023. "A Pragmatic Framework for Data-Driven Decision-Making Process in the Energy Sector: Insights from a Wind Farm Case Study," Energies, MDPI, vol. 16(17), pages 1-26, August.
  6. Sameer Al-Dahidi & Piero Baraldi & Enrico Zio & Lorenzo Montelatici, 2021. "Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production," Sustainability, MDPI, vol. 13(11), pages 1-19, June.
  7. Saira Al-Zadjali & Ahmed Al Maashri & Amer Al-Hinai & Rashid Al Abri & Swaroop Gajare & Sultan Al Yahyai & Mostafa Bakhtvar, 2021. "A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems," Energies, MDPI, vol. 14(23), pages 1-20, November.
  8. Shamshirband, Shahaboddin & Keivani, Afram & Mohammadi, Kasra & Lee, Malrey & Hamid, Siti Hafizah Abd & Petkovic, Dalibor, 2016. "Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: Case study of Aligoodarz, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 429-435.
  9. Liu, Tianhong & Qi, Shengli & Qiao, Xianzhu & Liu, Sixing, 2024. "A hybrid short-term wind power point-interval prediction model based on combination of improved preprocessing methods and entropy weighted GRU quantile regression network," Energy, Elsevier, vol. 288(C).
  10. Khaled, Mohamed & Ibrahim, Mostafa M. & Abdel Hamed, Hesham E. & AbdelGwad, Ahmed F., 2019. "Investigation of a small Horizontal–Axis wind turbine performance with and without winglet," Energy, Elsevier, vol. 187(C).
  11. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
  12. Agrawal, Alok & Sandhu, Kanwarjit Singh, 2016. "Most influential parametrical and data needs for realistic wind speed prediction," Renewable Energy, Elsevier, vol. 94(C), pages 452-465.
  13. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
  14. Balkissoon, Sarah & Fox, Neil & Lupo, Anthony & Haupt, Sue Ellen & Penny, Stephen G., 2023. "Classification of tall tower meteorological variables and forecasting wind speeds in Columbia, Missouri," Renewable Energy, Elsevier, vol. 217(C).
  15. Bazine, Hasnaa & Mabrouki, Mustapha, 2019. "Chaotic dynamics applied in time prediction of photovoltaic production," Renewable Energy, Elsevier, vol. 136(C), pages 1255-1265.
  16. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
  17. Heinermann, Justin & Kramer, Oliver, 2016. "Machine learning ensembles for wind power prediction," Renewable Energy, Elsevier, vol. 89(C), pages 671-679.
  18. He, Qingqing & Wang, Jianzhou & Lu, Haiyan, 2018. "A hybrid system for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 226(C), pages 756-771.
  19. Xin Zhao & Haikun Wei & Chenxi Li & Kanjian Zhang, 2020. "A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed," Energies, MDPI, vol. 13(7), pages 1-15, April.
  20. Emeksiz, Cem & Tan, Mustafa, 2022. "Multi-step wind speed forecasting and Hurst analysis using novel hybrid secondary decomposition approach," Energy, Elsevier, vol. 238(PA).
  21. Fabio Famoso & Ludovica Maria Oliveri & Sebastian Brusca & Ferdinando Chiacchio, 2024. "A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant," Energies, MDPI, vol. 17(7), pages 1-25, March.
  22. Hannah Jessie Rani R. & Aruldoss Albert Victoire T., 2018. "Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-35, May.
  23. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
  24. Sareen, Karan & Panigrahi, Bijaya Ketan & Shikhola, Tushar & Sharma, Rajneesh, 2023. "An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction," Energy, Elsevier, vol. 278(C).
  25. López, Germánico & Arboleya, Pablo, 2022. "Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador," Renewable Energy, Elsevier, vol. 183(C), pages 351-368.
  26. Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
  27. Han, Shuang & Qiao, Yanhui & Yan, Ping & Yan, Jie & Liu, Yongqian & Li, Li, 2020. "Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles," Renewable Energy, Elsevier, vol. 157(C), pages 190-203.
  28. Sun, Na & Zhou, Jianzhong & Chen, Lu & Jia, Benjun & Tayyab, Muhammad & Peng, Tian, 2018. "An adaptive dynamic short-term wind speed forecasting model using secondary decomposition and an improved regularized extreme learning machine," Energy, Elsevier, vol. 165(PB), pages 939-957.
  29. Ana Lagos & Joaquín E. Caicedo & Gustavo Coria & Andrés Romero Quete & Maximiliano Martínez & Gastón Suvire & Jesús Riquelme, 2022. "State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems," Energies, MDPI, vol. 15(18), pages 1-40, September.
  30. Shin, Heesoo & Rüttgers, Mario & Lee, Sangseung, 2023. "Effects of spatiotemporal correlations in wind data on neural network-based wind predictions," Energy, Elsevier, vol. 279(C).
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