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Monitoring of wind farms’ power curves using machine learning techniques

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  1. Chen, Yuntian & Jiang, Su & Zhang, Dongxiao & Liu, Chaoyang, 2017. "An adsorbed gas estimation model for shale gas reservoirs via statistical learning," Applied Energy, Elsevier, vol. 197(C), pages 327-341.
  2. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  3. Bilal, Boudy & Adjallah, Kondo Hloindo & Sava, Alexandre & Yetilmezsoy, Kaan & Ouassaid, Mohammed, 2023. "Wind turbine output power prediction and optimization based on a novel adaptive neuro-fuzzy inference system with the moving window," Energy, Elsevier, vol. 263(PE).
  4. Hsu-Hao Yang & Mei-Ling Huang & Shih-Wei Yang, 2015. "Integrating Auto-Associative Neural Networks with Hotelling T 2 Control Charts for Wind Turbine Fault Detection," Energies, MDPI, vol. 8(10), pages 1-16, October.
  5. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
  6. Wolf-Gerrit Früh, 2023. "Assessing the Performance of Small Wind Energy Systems Using Regional Weather Data," Energies, MDPI, vol. 16(8), pages 1-21, April.
  7. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
  8. Jin, Yuqing & Ju, Ping & Rehtanz, Christian & Wu, Feng & Pan, Xueping, 2018. "Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm," Applied Energy, Elsevier, vol. 222(C), pages 485-496.
  9. Raymond Byrne & Davide Astolfi & Francesco Castellani & Neil J. Hewitt, 2020. "A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
  10. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
  11. Taslimi-Renani, Ehsan & Modiri-Delshad, Mostafa & Elias, Mohamad Fathi Mohamad & Rahim, Nasrudin Abd., 2016. "Development of an enhanced parametric model for wind turbine power curve," Applied Energy, Elsevier, vol. 177(C), pages 544-552.
  12. Sasser, Christiana & Yu, Meilin & Delgado, Ruben, 2022. "Improvement of wind power prediction from meteorological characterization with machine learning models," Renewable Energy, Elsevier, vol. 183(C), pages 491-501.
  13. Xu, Keyi & Yan, Jie & Zhang, Hao & Zhang, Haoran & Han, Shuang & Liu, Yongqian, 2021. "Quantile based probabilistic wind turbine power curve model," Applied Energy, Elsevier, vol. 296(C).
  14. Yang, Hsu-Hao & Huang, Mei-Ling & Lai, Chun-Mei & Jin, Jhih-Rong, 2018. "An approach combining data mining and control charts-based model for fault detection in wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 808-816.
  15. Rogers, T.J. & Gardner, P. & Dervilis, N. & Worden, K. & Maguire, A.E. & Papatheou, E. & Cross, E.J., 2020. "Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression," Renewable Energy, Elsevier, vol. 148(C), pages 1124-1136.
  16. Badihi, Hamed & Zhang, Youmin & Hong, Henry, 2017. "Fault-tolerant cooperative control in an offshore wind farm using model-free and model-based fault detection and diagnosis approaches," Applied Energy, Elsevier, vol. 201(C), pages 284-307.
  17. Pere Marti-Puig & Alejandro Blanco-M & Juan José Cárdenas & Jordi Cusidó & Jordi Solé-Casals, 2019. "Feature Selection Algorithms for Wind Turbine Failure Prediction," Energies, MDPI, vol. 12(3), pages 1-18, January.
  18. Marčiukaitis, Mantas & Žutautaitė, Inga & Martišauskas, Linas & Jokšas, Benas & Gecevičius, Giedrius & Sfetsos, Athanasios, 2017. "Non-linear regression model for wind turbine power curve," Renewable Energy, Elsevier, vol. 113(C), pages 732-741.
  19. Yu, Ruiguo & Liu, Zhiqiang & Li, Xuewei & Lu, Wenhuan & Ma, Degang & Yu, Mei & Wang, Jianrong & Li, Bin, 2019. "Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space," Applied Energy, Elsevier, vol. 238(C), pages 249-257.
  20. Montaser Abdelsattar & Mohamed A. Ismeil & Karim Menoufi & Ahmed AbdelMoety & Ahmed Emad-Eldeen, 2025. "Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-31, January.
  21. Castellani, Francesco & Astolfi, Davide & Sdringola, Paolo & Proietti, Stefania & Terzi, Ludovico, 2017. "Analyzing wind turbine directional behavior: SCADA data mining techniques for efficiency and power assessment," Applied Energy, Elsevier, vol. 185(P2), pages 1076-1086.
  22. Nasery, Praanjal & Aziz Ezzat, Ahmed, 2023. "Yaw-adjusted wind power curve modeling: A local regression approach," Renewable Energy, Elsevier, vol. 202(C), pages 1368-1376.
  23. Cui, Bodi & Weng, Yang & Zhang, Ning, 2022. "A feature extraction and machine learning framework for bearing fault diagnosis," Renewable Energy, Elsevier, vol. 191(C), pages 987-997.
  24. Seo, Seokho & Oh, Si-Doek & Kwak, Ho-Young, 2019. "Wind turbine power curve modeling using maximum likelihood estimation method," Renewable Energy, Elsevier, vol. 136(C), pages 1164-1169.
  25. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Dominic T. J. O’Sullivan, 2019. "Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses," Energies, MDPI, vol. 12(2), pages 1-22, January.
  26. Marino Marrocu & Luca Massidda, 2017. "A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast," Energies, MDPI, vol. 10(12), pages 1-14, November.
  27. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2021. "Toward hybrid approaches for wind turbine power curve modeling with balanced loss functions and local weighting schemes," Energy, Elsevier, vol. 218(C).
  28. Bertašienė, Agnė & Azzopardi, Brian, 2015. "Synergies of Wind Turbine control techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 336-342.
  29. Long, Huan & Zhang, Zijun & Su, Yan, 2014. "Analysis of daily solar power prediction with data-driven approaches," Applied Energy, Elsevier, vol. 126(C), pages 29-37.
  30. Yang, Mao & Shi, Chaoyu & Liu, Huiyu, 2021. "Day-ahead wind power forecasting based on the clustering of equivalent power curves," Energy, Elsevier, vol. 218(C).
  31. Mario Collotta & Antonio Messineo & Giuseppina Nicolosi & Giovanni Pau, 2014. "A Dynamic Fuzzy Controller to Meet Thermal Comfort by Using Neural Network Forecasted Parameters as the Input," Energies, MDPI, vol. 7(8), pages 1-30, July.
  32. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2020. "Wind turbine power curve modeling for reliable power prediction using monotonic regression," Renewable Energy, Elsevier, vol. 147(P1), pages 214-222.
  33. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
  34. Wenna Zhang & Xiandong Ma, 2016. "Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines," Energies, MDPI, vol. 9(4), pages 1-15, April.
  35. Chen, Hao, 2022. "Cluster-based ensemble learning for wind power modeling from meteorological wind data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  36. Li, Yanting & Liu, Shujun & Shu, Lianjie, 2019. "Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data," Renewable Energy, Elsevier, vol. 134(C), pages 357-366.
  37. Karamichailidou, Despina & Kaloutsa, Vasiliki & Alexandridis, Alex, 2021. "Wind turbine power curve modeling using radial basis function neural networks and tabu search," Renewable Energy, Elsevier, vol. 163(C), pages 2137-2152.
  38. Ana Rita Nunes & Hugo Morais & Alberto Sardinha, 2021. "Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review," Energies, MDPI, vol. 14(21), pages 1-22, November.
  39. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
  40. Zou, Runmin & Yang, Jiaxin & Wang, Yun & Liu, Fang & Essaaidi, Mohamed & Srinivasan, Dipti, 2021. "Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer," Applied Energy, Elsevier, vol. 304(C).
  41. Maurizio Volpe & Carmelo D'Anna & Simona Messineo & Roberto Volpe & Antonio Messineo, 2014. "Sustainable Production of Bio-Combustibles from Pyrolysis of Agro-Industrial Wastes," Sustainability, MDPI, vol. 6(11), pages 1-17, November.
  42. Díaz, Santiago & Carta, José A. & Castañeda, Alberto, 2020. "Influence of the variation of meteorological and operational parameters on estimation of the power output of a wind farm with active power control," Renewable Energy, Elsevier, vol. 159(C), pages 812-826.
  43. Marvuglia, Antonino & Havinga, Lisanne & Heidrich, Oliver & Fonseca, Jimeno & Gaitani, Niki & Reckien, Diana, 2020. "Advances and challenges in assessing urban sustainability: an advanced bibliometric review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
  44. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
  45. Saeedreza Jadidi & Hamed Badihi & Youmin Zhang, 2021. "Fault-Tolerant Cooperative Control of Large-Scale Wind Farms and Wind Farm Clusters," Energies, MDPI, vol. 14(21), pages 1-29, November.
  46. Rashmi P. Shetty & A. Sathyabhama & Srinivasa Pai P., 2019. "Efficient Modelling and Simulation Of Wind Power Using Online Sequential Learning Algorithm For Feed Forward Networks," Journal of Mechanical Engineering Research & Developments (JMERD), Zibeline International Publishing, vol. 42(1), pages 109-115, March.
  47. Xu, Qifa & Fan, Zhenhua & Jia, Weiyin & Jiang, Cuixia, 2020. "Fault detection of wind turbines via multivariate process monitoring based on vine copulas," Renewable Energy, Elsevier, vol. 161(C), pages 939-955.
  48. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
  49. Yan, Jie & Zhang, Hao & Liu, Yongqian & Han, Shuang & Li, Li, 2019. "Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modelling," Applied Energy, Elsevier, vol. 239(C), pages 1356-1370.
  50. 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.
  51. Lydia, M. & Kumar, S. Suresh & Selvakumar, A. Immanuel & Prem Kumar, G. Edwin, 2014. "A comprehensive review on wind turbine power curve modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 452-460.
  52. Ouyang, Tinghui & Kusiak, Andrew & He, Yusen, 2017. "Modeling wind-turbine power curve: A data partitioning and mining approach," Renewable Energy, Elsevier, vol. 102(PA), pages 1-8.
  53. Astolfi, Davide & Castellani, Francesco & Garinei, Alberto & Terzi, Ludovico, 2015. "Data mining techniques for performance analysis of onshore wind farms," Applied Energy, Elsevier, vol. 148(C), pages 220-233.
  54. Mérigaud, Alexis & Ringwood, John V., 2016. "Condition-based maintenance methods for marine renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 53-78.
  55. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
  56. Han, Qinkai & Ma, Sai & Wang, Tianyang & Chu, Fulei, 2019. "Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
  57. Gonzalez, Elena & Stephen, Bruce & Infield, David & Melero, Julio J., 2019. "Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study," Renewable Energy, Elsevier, vol. 131(C), pages 841-853.
  58. Riccardo Squatrito & Filippo Sgroi & Salvatore Tudisca & Anna Maria Di Trapani & Riccardo Testa, 2014. "Post Feed-in Scheme Photovoltaic System Feasibility Evaluation in Italy: Sicilian Case Studies," Energies, MDPI, vol. 7(11), pages 1-19, November.
  59. Tugce Demirdelen & Pırıl Tekin & Inayet Ozge Aksu & Firat Ekinci, 2019. "The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence," Sustainability, MDPI, vol. 11(17), pages 1-18, September.
  60. Carta, José A. & Díaz, Santiago & Castañeda, Alberto, 2020. "A global sensitivity analysis method applied to wind farm power output estimation models," Applied Energy, Elsevier, vol. 280(C).
  61. Meyer, Angela, 2021. "Multi-target normal behaviour models for wind farm condition monitoring," Applied Energy, Elsevier, vol. 300(C).
  62. Sergio Velázquez Medina & José A. Carta & Ulises Portero Ajenjo, 2019. "Performance Sensitivity of a Wind Farm Power Curve Model to Different Signals of the Input Layer of ANNs: Case Studies in the Canary Islands," Complexity, Hindawi, vol. 2019, pages 1-11, March.
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