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Data mining and wind power prediction: A literature review

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  1. Bentsen, Lars Ødegaard & Warakagoda, Narada Dilp & Stenbro, Roy & Engelstad, Paal, 2023. "Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures," Applied Energy, Elsevier, vol. 333(C).
  2. Zhang, Lei & Chen, Zhiqiao & Su, Jing & Li, Jingfa, 2019. "Data mining new energy materials from structure databases," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 554-567.
  3. Ding, Jun-Wei & Chuang, Ming-Ju & Tseng, Jing-Siou & Hsieh, I-Yun Lisa, 2024. "Reanalysis and Ground Station data: Advanced data preprocessing in deep learning for wind power prediction," Applied Energy, Elsevier, vol. 375(C).
  4. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Yang, Qibo & Hsu, Yuan-Ming & Chen, Yudi & Lee, Jay, 2019. "A combined filtering strategy for short term and long term wind speed prediction with improved accuracy," Renewable Energy, Elsevier, vol. 136(C), pages 1082-1090.
  5. Shane Phelan & Paula Meehan & Stephen Daniels, 2013. "Using Atmospheric Pressure Tendency to Optimise Battery Charging in Off-Grid Hybrid Wind-Diesel Systems for Telecoms," Energies, MDPI, vol. 6(6), pages 1-20, June.
  6. Lorenzo Donadio & Jiannong Fang & Fernando Porté-Agel, 2021. "Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast," Energies, MDPI, vol. 14(2), pages 1-17, January.
  7. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
  8. 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.
  9. Urtasun, Andoni & Sanchis, Pablo & San Martín, Idoia & López, Jesús & Marroyo, Luis, 2013. "Modeling of small wind turbines based on PMSG with diode bridge for sensorless maximum power tracking," Renewable Energy, Elsevier, vol. 55(C), pages 138-149.
  10. Jin, Huaiping & Shi, Lixian & Chen, Xiangguang & Qian, Bin & Yang, Biao & Jin, Huaikang, 2021. "Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models," Renewable Energy, Elsevier, vol. 174(C), pages 1-18.
  11. 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.
  12. Osório, G.J. & Matias, J.C.O. & Catalão, J.P.S., 2015. "Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information," Renewable Energy, Elsevier, vol. 75(C), pages 301-307.
  13. Wiem Fekih Hassen & Maher Challouf, 2024. "Long Short-Term Renewable Energy Sources Prediction for Grid-Management Systems Based on Stacking Ensemble Model," Energies, MDPI, vol. 17(13), pages 1-19, June.
  14. Ma, Zhixiao & Xue, Bing & Geng, Yong & Ren, Wanxia & Fujita, Tsuyoshi & Zhang, Zilong & Puppim de Oliveira, Jose A. & Jacques, David A. & Xi, Fengming, 2013. "Co-benefits analysis on climate change and environmental effects of wind-power: A case study from Xinjiang, China," Renewable Energy, Elsevier, vol. 57(C), pages 35-42.
  15. Hirth, Lion & Ziegenhagen, Inka, 2015. "Balancing power and variable renewables: Three links," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1035-1051.
  16. Carlos Ruiz & Carlos M. Alaíz & José R. Dorronsoro, 2020. "Multitask Support Vector Regression for Solar and Wind Energy Prediction," Energies, MDPI, vol. 13(23), pages 1-21, November.
  17. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
  18. Joos, Michael & Staffell, Iain, 2018. "Short-term integration costs of variable renewable energy: Wind curtailment and balancing in Britain and Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 86(C), pages 45-65.
  19. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
  20. Meng, Anbo & Zhang, Haitao & Dai, Zhongfu & Xian, Zikang & Xiao, Liexi & Rong, Jiayu & Li, Chen & Zhu, Jianbin & Li, Hanhong & Yin, Yiding & Liu, Jiawei & Tang, Yanshu & Zhang, Bin & Yin, Hao, 2024. "An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division," Energy, Elsevier, vol. 299(C).
  21. Kirchner-Bossi, Nicolas & Kathari, Gabriel & Porté-Agel, Fernando, 2024. "A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space," Applied Energy, Elsevier, vol. 367(C).
  22. Gyeongmin Kim & Jin Hur, 2021. "A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations," Sustainability, MDPI, vol. 13(22), pages 1-12, November.
  23. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Li, Wenzhe & Hsu, Yuan-Ming & Lee, Jay, 2020. "Gaussian Process Regression for numerical wind speed prediction enhancement," Renewable Energy, Elsevier, vol. 146(C), pages 2112-2123.
  24. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
  25. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
  26. Chenhua Ni & Xiandong Ma, 2018. "Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs," Energies, MDPI, vol. 11(8), pages 1-18, August.
  27. 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.
  28. Wang, Mingyong & Wang, Zhi & Gong, Xuzhong & Guo, Zhancheng, 2014. "The intensification technologies to water electrolysis for hydrogen production – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 573-588.
  29. Vogel, E.E. & Saravia, G. & Kobe, S. & Schumann, R. & Schuster, R., 2018. "A novel method to optimize electricity generation from wind energy," Renewable Energy, Elsevier, vol. 126(C), pages 724-735.
  30. Gürdal Ertek & Lakshmi Kailas, 2021. "Analyzing a Decade of Wind Turbine Accident News with Topic Modeling," Sustainability, MDPI, vol. 13(22), pages 1-34, November.
  31. Yimei Wang & Yongqian Liu & Li Li & David Infield & Shuang Han, 2018. "Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method," Energies, MDPI, vol. 11(4), pages 1-19, April.
  32. Zhang, Guangchao & Zheng, Xiaoxiao & Liu, Shi & Chen, Minxin, 2022. "Three-dimensional wind field reconstruction using tucker decomposition with optimal sensor placement," Energy, Elsevier, vol. 260(C).
  33. 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.
  34. Gu, Bo & Zhang, Tianren & Meng, Hang & Zhang, Jinhua, 2021. "Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation," Renewable Energy, Elsevier, vol. 164(C), pages 687-708.
  35. González-Sopeña, J.M. & Pakrashi, V. & Ghosh, B., 2021. "An overview of performance evaluation metrics for short-term statistical wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  36. Lobato, E. & Doenges, K. & Egido, I. & Sigrist, L., 2020. "Limits to wind aggregation: Empirical assessment in the Spanish electricity system," Renewable Energy, Elsevier, vol. 147(P1), pages 1321-1330.
  37. Bigdeli, Nooshin & Afshar, Karim & Gazafroudi, Amin Shokri & Ramandi, Mostafa Yousefi, 2013. "A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 20-29.
  38. Zipeng Liang & Haoyong Chen & Xiaojuan Wang & Idris Ibn Idris & Bifei Tan & Cong Zhang, 2018. "An Extreme Scenario Method for Robust Transmission Expansion Planning with Wind Power Uncertainty," Energies, MDPI, vol. 11(8), pages 1-22, August.
  39. 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.
  40. Jianxiao Wang & Liudong Chen & Zhenfei Tan & Ershun Du & Nian Liu & Jing Ma & Mingyang Sun & Canbing Li & Jie Song & Xi Lu & Chin-Woo Tan & Guannan He, 2023. "Inherent spatiotemporal uncertainty of renewable power in China," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
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