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Probabilistic gradient boosting machines for GEFCom2014 wind forecasting

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Cited by:

  1. Montero-Manso, Pablo & Hyndman, Rob J., 2021. "Principles and algorithms for forecasting groups of time series: Locality and globality," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1632-1653.
  2. Dumas, Jonathan & Wehenkel, Antoine & Lanaspeze, Damien & Cornélusse, Bertrand & Sutera, Antonio, 2022. "A deep generative model for probabilistic energy forecasting in power systems: normalizing flows," Applied Energy, Elsevier, vol. 305(C).
  3. Ricardo J. Bessa & Corinna Möhrlen & Vanessa Fundel & Malte Siefert & Jethro Browell & Sebastian Haglund El Gaidi & Bri-Mathias Hodge & Umit Cali & George Kariniotakis, 2017. "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry," Energies, MDPI, vol. 10(9), pages 1-48, September.
  4. Liao, Qishu & Cao, Di & Chen, Zhe & Blaabjerg, Frede & Hu, Weihao, 2023. "Probabilistic wind power forecasting for newly-built wind farms based on multi-task Gaussian process method," Renewable Energy, Elsevier, vol. 217(C).
  5. Tartakovsky, Alexandre M. & Ma, Tong & Barajas-Solano, David A. & Tipireddy, Ramakrishna, 2023. "Physics-informed Gaussian process regression for states estimation and forecasting in power grids," International Journal of Forecasting, Elsevier, vol. 39(2), pages 967-980.
  6. Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
  7. Yang, Mao & Huang, Yutong & Xu, Chuanyu & Liu, Chenyu & Dai, Bozhi, 2025. "Review of several key processes in wind power forecasting: Mathematical formulations, scientific problems, and logical relations," Applied Energy, Elsevier, vol. 377(PC).
  8. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha & Wenying Wen, 2019. "Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods," Energies, MDPI, vol. 12(9), pages 1-17, May.
  9. Wen, Honglin, 2024. "Probabilistic wind power forecasting resilient to missing values: An adaptive quantile regression approach," Energy, Elsevier, vol. 300(C).
  10. Chen, Yuejiang & Xiao, Jiang-Wen & Wang, Yan-Wu & Luo, Yunfeng, 2025. "Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation," Applied Energy, Elsevier, vol. 377(PA).
  11. Gilbert, Ciaran & Browell, Jethro & McMillan, David, 2021. "Probabilistic access forecasting for improved offshore operations," International Journal of Forecasting, Elsevier, vol. 37(1), pages 134-150.
  12. Lu, Shixiang & Xu, Qifa & Jiang, Cuixia & Liu, Yezheng & Kusiak, Andrew, 2022. "Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network," Energy, Elsevier, vol. 242(C).
  13. Conor Sweeney & Ricardo J. Bessa & Jethro Browell & Pierre Pinson, 2020. "The future of forecasting for renewable energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(2), March.
  14. Croonenbroeck, Carsten & Stadtmann, Georg, 2019. "Renewable generation forecast studies – Review and good practice guidance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 312-322.
  15. Long Cai & Jie Gu & Jinghuan Ma & Zhijian Jin, 2019. "Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees," Energies, MDPI, vol. 12(1), pages 1-19, January.
  16. Wen, Honglin & Pinson, Pierre & Gu, Jie & Jin, Zhijian, 2024. "Wind energy forecasting with missing values within a fully conditional specification framework," International Journal of Forecasting, Elsevier, vol. 40(1), pages 77-95.
  17. Juan Manuel González Sopeña & Vikram Pakrashi & Bidisha Ghosh, 2022. "A Spiking Neural Network Based Wind Power Forecasting Model for Neuromorphic Devices," Energies, MDPI, vol. 15(19), pages 1-24, October.
  18. Liu, Yin & Davanloo Tajbakhsh, Sam & Conejo, Antonio J., 2021. "Spatiotemporal wind forecasting by learning a hierarchically sparse inverse covariance matrix using wind directions," International Journal of Forecasting, Elsevier, vol. 37(2), pages 812-824.
  19. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
  20. V, Gayathry & Deepa, K. & Sangeetha, S.V. Tresa & T, Porselvi & Ramprabhakar, J. & Gowtham, N., 2025. "Uncertainty analysis of different forecast models for wind speed forecasting," Renewable Energy, Elsevier, vol. 241(C).
  21. Li, Jiajia & Yang, Shiyu & Li, Jun & Li, Houjian, 2024. "Targeting SDG7: Identifying heterogeneous energy dilemmas for socially disadvantaged groups in India using machine learning," Energy Economics, Elsevier, vol. 138(C).
  22. Zhang, Wenjie & Quan, Hao & Srinivasan, Dipti, 2018. "Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination," Energy, Elsevier, vol. 160(C), pages 810-819.
  23. Xinxin He & Jungang Luo & Peng Li & Ganggang Zuo & Jiancang Xie, 2020. "A Hybrid Model Based on Variational Mode Decomposition and Gradient Boosting Regression Tree for Monthly Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 865-884, January.
  24. Wang, Yun & Zhang, Fan & Kou, Hongbo & Zou, Runmin & Hu, Qinghua & Wang, Jianzhou & Srinivasan, Dipti, 2025. "A review of predictive uncertainty modeling techniques and evaluation metrics in probabilistic wind speed and wind power forecasting," Applied Energy, Elsevier, vol. 396(C).
  25. Sun, Mucun & Feng, Cong & Chartan, Erol Kevin & Hodge, Bri-Mathias & Zhang, Jie, 2019. "A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization," Applied Energy, Elsevier, vol. 238(C), pages 1497-1505.
  26. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
  27. Paula Medina Maçaira & Yasmin Monteiro Cyrillo & Fernando Luiz Cyrino Oliveira & Reinaldo Castro Souza, 2019. "Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning," Energies, MDPI, vol. 12(5), pages 1-20, March.
  28. Ambach, Daniel & Schmid, Wolfgang, 2017. "A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting," Energy, Elsevier, vol. 135(C), pages 833-850.
  29. Jethro Browell, 2017. "Risk Constrained Trading Strategies for Stochastic Generation with a Single-Price Balancing Market," Papers 1708.02625, arXiv.org.
  30. Jethro Browell, 2018. "Risk Constrained Trading Strategies for Stochastic Generation with a Single-Price Balancing Market," Energies, MDPI, vol. 11(6), pages 1-17, May.
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