IDEAS home Printed from https://ideas.repec.org/r/eee/renene/v80y2015icp338-347.html
   My bibliography  Save this item

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

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. 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.
  2. 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).
  3. 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.
  4. 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.
  5. 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.
  6. Bazine, Hasnaa & Mabrouki, Mustapha, 2019. "Chaotic dynamics applied in time prediction of photovoltaic production," Renewable Energy, Elsevier, vol. 136(C), pages 1255-1265.
  7. 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.
  8. 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.
  9. Emeksiz, Cem & Tan, Mustafa, 2022. "Multi-step wind speed forecasting and Hurst analysis using novel hybrid secondary decomposition approach," Energy, Elsevier, vol. 238(PA).
  10. 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.
  11. 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.
  12. 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).
  13. 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.
  14. 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.
  15. 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).
  16. 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.
  17. 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 1-24, March.
  18. 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.
  19. 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.
  20. 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.
  21. 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).
  22. 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.
  23. Heinermann, Justin & Kramer, Oliver, 2016. "Machine learning ensembles for wind power prediction," Renewable Energy, Elsevier, vol. 89(C), pages 671-679.
  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. 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.
  26. 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.
  27. 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.
  28. 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).
  29. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
  30. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.