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Powering Up With Space-Time Wind Forecasting

Citations

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  1. Ambach, Daniel & Schmid, Wolfgang, 2015. "Periodic and long range dependent models for high frequency wind speed data," Energy, Elsevier, vol. 82(C), pages 277-293.
  2. Yuan, Xiaohui & Tan, Qingxiong & Lei, Xiaohui & Yuan, Yanbin & Wu, Xiaotao, 2017. "Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine," Energy, Elsevier, vol. 129(C), pages 122-137.
  3. Aziz Ezzat, Ahmed, 2020. "Turbine-specific short-term wind speed forecasting considering within-farm wind field dependencies and fluctuations," Applied Energy, Elsevier, vol. 269(C).
  4. Croonenbroeck, Carsten & Ambach, Daniel, 2014. "Censored spatial wind power prediction with random effects," Discussion Papers 362, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
  5. Gallego, C. & Pinson, P. & Madsen, H. & Costa, A. & Cuerva, A., 2011. "Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting," Applied Energy, Elsevier, vol. 88(11), pages 4087-4096.
  6. Croonenbroeck, Carsten & Ambach, Daniel, 2015. "Censored spatial wind power prediction with random effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 613-622.
  7. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
  8. Daniel Ambach & Robert Garthoff, 2016. "Vorhersagen der Windgeschwindigkeit und Windenergie in Deutschland," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(1), pages 15-36, February.
  9. Ines Würth & Laura Valldecabres & Elliot Simon & Corinna Möhrlen & Bahri Uzunoğlu & Ciaran Gilbert & Gregor Giebel & David Schlipf & Anton Kaifel, 2019. "Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36," Energies, MDPI, vol. 12(4), pages 1-30, February.
  10. Kiplangat, Dennis C. & Asokan, K. & Kumar, K. Satheesh, 2016. "Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition," Renewable Energy, Elsevier, vol. 93(C), pages 38-44.
  11. Nir Y. Krakauer & Daniel S. Cohan, 2017. "Interannual Variability and Seasonal Predictability of Wind and Solar Resources," Resources, MDPI, vol. 6(3), pages 1-14, July.
  12. Daniela Castro-Camilo & Raphaël Huser & Håvard Rue, 2019. "A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 517-534, September.
  13. Amanda S. Hering & Karen Kazor & William Kleiber, 2015. "A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales," Resources, MDPI, vol. 4(1), pages 1-23, February.
  14. Jeon, Jooyoung & Panagiotelis, Anastasios & Petropoulos, Fotios, 2019. "Probabilistic forecast reconciliation with applications to wind power and electric load," European Journal of Operational Research, Elsevier, vol. 279(2), pages 364-379.
  15. Tansu Filik, 2016. "Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(3), pages 1-15, March.
  16. Messner, Jakob W. & Pinson, Pierre, 2019. "Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1485-1498.
  17. Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando L., 2021. "A generalized dynamical model for wind speed forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
  18. Taylor, James W., 2017. "Probabilistic forecasting of wind power ramp events using autoregressive logit models," European Journal of Operational Research, Elsevier, vol. 259(2), pages 703-712.
  19. Mercedes Fernandez Sau & Daniela Rodriguez, 2018. "Minimum distance method for directional data and outlier detection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 587-603, September.
  20. 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.
  21. Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando Luiz, 2023. "An overview of non-Gaussian state-space models for wind speed data," Energy, Elsevier, vol. 266(C).
  22. Sun, Can & Bie, Zhaohong & Xie, Min & Jiang, Jiangfeng, 2016. "Fuzzy copula model for wind speed correlation and its application in wind curtailment evaluation," Renewable Energy, Elsevier, vol. 93(C), pages 68-76.
  23. Graciela Boente & Daniela Rodriguez & Wenceslao González Manteiga, 2014. "Goodness-of-fit Test for Directional Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(1), pages 259-275, March.
  24. Croonenbroeck, Carsten & Stadtmann, Georg, 2015. "Minimizing asymmetric loss in medium-term wind power forecasting," Renewable Energy, Elsevier, vol. 81(C), pages 197-208.
  25. Yilan Luo & Deniz Sezer & David Wood & Mingkuan Wu & Hamid Zareipour, 2019. "Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada," Energies, MDPI, vol. 12(10), pages 1-29, May.
  26. Taylor, James W. & Jeon, Jooyoung, 2015. "Forecasting wind power quantiles using conditional kernel estimation," Renewable Energy, Elsevier, vol. 80(C), pages 370-379.
  27. Haque, Ashraf U. & Mandal, Paras & Kaye, Mary E. & Meng, Julian & Chang, Liuchen & Senjyu, Tomonobu, 2012. "A new strategy for predicting short-term wind speed using soft computing models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4563-4573.
  28. Giwhyun Lee & Yu Ding & Marc G. Genton & Le Xie, 2015. "Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 56-67, March.
  29. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
  30. Xinxin Zhu & Marc Genton & Yingzhong Gu & Le Xie, 2014. "Space-time wind speed forecasting for improved power system dispatch," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 1-25, March.
  31. Drisya, G.V. & Asokan, K. & Kumar, K. Satheesh, 2018. "Diverse dynamical characteristics across the frequency spectrum of wind speed fluctuations," Renewable Energy, Elsevier, vol. 119(C), pages 540-550.
  32. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
  33. Amanda Hering, 2014. "Comments on: Space-time wind speed forecasting for improved power system dispatch," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 34-44, March.
  34. Daniel Ambach & Robert Garthoff, 2016. "Vorhersagen der Windgeschwindigkeit und Windenergie in Deutschland [Predictions of wind speed and wind energy in Germany]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(1), pages 15-36, February.
  35. Hu, Jianming & Zhang, Liping & Tang, Jingwei & Liu, Zhi, 2023. "A novel transformer ordinal regression network with label diversity for wind power ramp events forecasting," Energy, Elsevier, vol. 280(C).
  36. Xinxin Zhu & Marc Genton & Yingzhong Gu & Le Xie, 2014. "Rejoinder on: Space-time wind speed forecasting for improved power system dispatch," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 45-50, March.
  37. Jooyoung Jeon & James W. Taylor, 2012. "Using Conditional Kernel Density Estimation for Wind Power Density Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 66-79, March.
  38. Geovanny Marulanda & Antonio Bello & Jenny Cifuentes & Javier Reneses, 2020. "Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets," Energies, MDPI, vol. 13(13), pages 1-19, July.
  39. 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.
  40. Duca, Victor E.L.A. & Fonseca, Thais C.O. & Cyrino Oliveira, Fernando Luiz, 2022. "Joint modelling wind speed and power via Bayesian Dynamical models," Energy, Elsevier, vol. 247(C).
  41. Baïle, Rachel & Muzy, Jean-François, 2023. "Leveraging data from nearby stations to improve short-term wind speed forecasts," Energy, Elsevier, vol. 263(PA).
  42. Lohmann, Timo & Hering, Amanda S. & Rebennack, Steffen, 2016. "Spatio-temporal hydro forecasting of multireservoir inflows for hydro-thermal scheduling," European Journal of Operational Research, Elsevier, vol. 255(1), pages 243-258.
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