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Models for monitoring wind farm power

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  1. Kabalci, Ersan, 2011. "Development of a feasibility prediction tool for solar power plant installation analyses," Applied Energy, Elsevier, vol. 88(11), pages 4078-4086.
  2. Lapira, Edzel & Brisset, Dustin & Davari Ardakani, Hossein & Siegel, David & Lee, Jay, 2012. "Wind turbine performance assessment using multi-regime modeling approach," Renewable Energy, Elsevier, vol. 45(C), pages 86-95.
  3. Morshedizadeh, Majid & Kordestani, Mojtaba & Carriveau, Rupp & Ting, David S.-K. & Saif, Mehrdad, 2017. "Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production," Energy, Elsevier, vol. 138(C), pages 394-404.
  4. Siyu Tao & Qingshan Xu & Andrés Feijóo & Stefanie Kuenzel & Neeraj Bokde, 2019. "Integrated Wind Farm Power Curve and Power Curve Distribution Function Considering the Wake Effect and Terrain Gradient," Energies, MDPI, vol. 12(13), pages 1-14, June.
  5. Pelletier, Francis & Masson, Christian & Tahan, Antoine, 2016. "Wind turbine power curve modelling using artificial neural network," Renewable Energy, Elsevier, vol. 89(C), pages 207-214.
  6. Wang, Yibo & Shao, Xinyao & Liu, Chuang & Cai, Guowei & Kou, Lei & Wu, Zhiqiang, 2019. "Analysis of wind farm output characteristics based on descriptive statistical analysis and envelope domain," Energy, Elsevier, vol. 170(C), pages 580-591.
  7. 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.
  8. Song, Zhe & Zhang, Zijun & Jiang, Yu & Zhu, Jin, 2018. "Wind turbine health state monitoring based on a Bayesian data-driven approach," Renewable Energy, Elsevier, vol. 125(C), pages 172-181.
  9. Zhang, Zijun & Kusiak, Andrew & Song, Zhe, 2013. "Scheduling electric power production at a wind farm," European Journal of Operational Research, Elsevier, vol. 224(1), pages 227-238.
  10. Mazhar Hussain Baloch & Dahaman Ishak & Sohaib Tahir Chaudary & Baqir Ali & Ali Asghar Memon & Touqeer Ahmed Jumani, 2019. "Wind Power Integration: An Experimental Investigation for Powering Local Communities," Energies, MDPI, vol. 12(4), pages 1-24, February.
  11. 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.
  12. 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.
  13. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
  14. 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.
  15. Pandit, Ravi Kumar & Infield, David, 2019. "Comparative analysis of Gaussian Process power curve models based on different stationary covariance functions for the purpose of improving model accuracy," Renewable Energy, Elsevier, vol. 140(C), pages 190-202.
  16. Ciulla, G. & D’Amico, A. & Di Dio, V. & Lo Brano, V., 2019. "Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks," Renewable Energy, Elsevier, vol. 140(C), pages 477-492.
  17. Marvuglia, Antonino & Messineo, Antonio, 2012. "Monitoring of wind farms’ power curves using machine learning techniques," Applied Energy, Elsevier, vol. 98(C), pages 574-583.
  18. Aziz, Usama & Charbonnier, Sylvie & Bérenguer, Christophe & Lebranchu, Alexis & Prevost, Frederic, 2021. "Critical comparison of power-based wind turbine fault-detection methods using a realistic framework for SCADA data simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
  19. Ruiz de la Hermosa González-Carrato, Raúl, 2018. "Wind farm monitoring using Mahalanobis distance and fuzzy clustering," Renewable Energy, Elsevier, vol. 123(C), pages 526-540.
  20. Cambron, P. & Lepvrier, R. & Masson, C. & Tahan, A. & Pelletier, F., 2016. "Power curve monitoring using weighted moving average control charts," Renewable Energy, Elsevier, vol. 94(C), pages 126-135.
  21. Liu, Hui & Chen, Chao, 2019. "Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction," Applied Energy, Elsevier, vol. 254(C).
  22. Hui Huang & Qiliang Zhu & Xueling Zhu & Jinhua Zhang, 2023. "An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation," Energies, MDPI, vol. 16(4), pages 1-20, February.
  23. 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.
  24. Jian Teng & Corey D. Markfort, 2020. "A Calibration Procedure for an Analytical Wake Model Using Wind Farm Operational Data," Energies, MDPI, vol. 13(14), pages 1-19, July.
  25. 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.
  26. Optis, Mike & Perr-Sauer, Jordan, 2019. "The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 27-41.
  27. 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.
  28. 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.
  29. Colak, Ilhami & Sagiroglu, Seref & Yesilbudak, Mehmet, 2012. "Data mining and wind power prediction: A literature review," Renewable Energy, Elsevier, vol. 46(C), pages 241-247.
  30. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
  31. 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.
  32. Kusiak, Andrew & Li, Mingyang & Zheng, Haiyang, 2010. "Virtual models of indoor-air-quality sensors," Applied Energy, Elsevier, vol. 87(6), pages 2087-2094, June.
  33. Kusiak, Andrew & Zheng, Haiyang, 2010. "Optimization of wind turbine energy and power factor with an evolutionary computation algorithm," Energy, Elsevier, vol. 35(3), pages 1324-1332.
  34. Saulo Custodio de Aquino Ferreira & Paula Medina Maçaira & Fernando Luiz Cyrino Oliveira, 2024. "Joint Modeling of Wind Speed and Power via a Nonparametric Approach," Energies, MDPI, vol. 17(14), pages 1-24, July.
  35. Wang, Yun & Duan, Xiaocong & Zou, Runmin & Zhang, Fan & Li, Yifen & Hu, Qinghua, 2023. "A novel data-driven deep learning approach for wind turbine power curve modeling," Energy, Elsevier, vol. 270(C).
  36. 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.
  37. 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.
  38. 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.
  39. Al-Khayat, Mohammad & AL-Rasheedi, Majed, 2024. "A new method for estimating the annual energy production of wind turbines in hot environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).
  40. Mingzhe Zou & Sasa Z. Djokic, 2020. "A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements," Energies, MDPI, vol. 13(16), pages 1-30, August.
  41. 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.
  42. He, Yaoyao & Zhu, Chuang & An, Xueli, 2023. "A trend-based method for the prediction of offshore wind power ramp," Renewable Energy, Elsevier, vol. 209(C), pages 248-261.
  43. Qunli Wu & Chenyang Peng, 2016. "A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction," Energies, MDPI, vol. 9(8), pages 1-20, July.
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