IEA Wind TCP

2025

Wang, H., He, S., Yan, J., Han, S., & Liu, Y. (2025). Deep reinforcement learning-driven wind farm flow control considering dynamic wind. Energy Conversion and Management, 337, 119888. DOI: 10.1016/j.enconman.2025.119888

Bjerregård, M. B., Browell, J., Zack, J., Møller, J. K., Madsen, H., Giebel, G., & Möhrlen, C. (2025). evalprob4cast: An R-package for evaluation of ensembles as probabilistic forecasts or event forecasts. arXiv preprint arXiv:2504.03544.

2024

Wöß, R., Hlavácková-Schindler, K., Schicker, I., Papazek, P., and Plant, C.: The Spatio-Temporal Visualization Tool HMMLVis in Renewable Energy Applications, EGUsphere [preprint], DOI: 10.5194/egusphere-2024-3126, 2024.

Schneider, M. J., Browell, J. and Rankin, R. (2024) Limiting extreme behavior in forecasting competitions. Foresight, 75(Q4), pp. 32-37. (https://eprints.gla.ac.uk/339378/2/339378.pdf)

Dennis van der Meer, Pierre Pinson, Simon Camal, Georges Kariniotakis, CRPS-based online learning for nonlinear probabilistic forecast combination, International Journal of Forecasting, Volume 40, Issue 4, 2024, Pages 1449-1466, ISSN 0169-2070, DOI: 10.1016/j.ijforecast.2023.12.005.

Stippel, L., Camal, S., & Kariniotakis, G. (2024). Multivariate federated tree-based forecasting combining resilience and privacy: Application to distributed energy resources. Electric Power Systems Research, 234, 110730. DOI: 10.1016/j.epsr.2024.110730

Han Wang, Jie Yan, Jiawei Zhang, Shihua Liu, Yongqian Liu, Shuang Han, Tonghui Qu, Short-term integrated forecasting method for wind power, solar power, and system load based on variable attention mechanism and multi-task learning, Energy, Volume 304, 2024, 132188, ISSN 0360-5442, DOI: 10.1016/j.energy.2024.132188.

Song, W., Yan, J., Han, S., Liu, S., Wang, H., Dai, Q., … & Liu, Y. (2024). A multi-task spatio-temporal fusion network for offshore wind power ramp events forecasting. Renewable Energy, 237, 121774. DOI: 10.1016/j.renene.2024.121774

Møller, J. K., Nystrup, P., Hjorth, P. G., & Madsen, H. (2024). Optimal Forecast Reconciliation with Uncertainty Quantification. arXiv preprint arXiv:2402.06480.

Dantas, G., & Browell, J. (2025). Seamless short-to mid-term probabilistic wind power forecasting. arXiv preprint arXiv:2502.11960.

Georges Kariniotakis, Renewable energy forecasting: State of the art & latest tendencies of research.. Energy, mathematics, and theoretical challenges Workshop, Sep 2024, Paris, France. ⟨hal-04945444⟩

2023

Rostami-Tabar, B., Browell, J., & Svetunkov, I. (2023). Probabilistic forecasting of hourly emergency department arrivals. Health Systems, 13(2), 133–149. DOI: 10.1080/20476965.2023.2200526

C. Möhrlen, J. Zack, M. B. Bjerregård and G. Giebel, “IEA wind recommended practice for the implementation of renewable forecasting solutions: hands-on examples for the use of the guideline,” 22nd Wind and Solar Integration Workshop (WIW 2023), Copenhagen, Denmark, 2023, pp. 83-94, doi: 10.1049/icp.2023.2722.

Hansen, ME, Nystrup, P, Møller, JK & Madsen, H 2023, ‘Reconciliation of wind power forecasts in spatial hierarchies‘, Wind Energy, vol. 26, no. 6, pp. 615-632. https://doi.org/10.1002/we.2819

Møller, J. K., Nystrup, P., & Madsen, H. (Accepted/In press). Likelihood-based inference in temporal hierarchies. International Journal of Forecasting.
https://doi.org/10.1016/j.ijforecast.2022.12.005

Wang, Qin, Tuohy, Aidan, Ortega-Vazquez, Miguel, Bello, Mobolaji, Ela, Erik, Kirk-Davidoff, Daniel, Hobbs, William B., Ault, David J., and Philbrick, Russ. Quantifying the value of probabilistic forecasting for power system operation planning. United Kingdom: N. p., 2023. Web. doi:10.1016/j.apenergy.2023.121254.

2022

Corinna Möhrlen, John Zack, Gregor Giebel. IEA Wind Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions. 370 pp. Elsevier Academic Press, November 11, 2022. ISBN: 9780443186813. DOI: 10.1016/C2021-0-03549-5 — Link to OpenAccess Book (Download)

Clifton, A., S. Barber, A. Stökl, H. Frank and T. Karlsson: Research challenges and needs for the deployment of wind energy in hilly and mountainous regions. Wind Energy Science 7(6), 2022.

Mikkel L. Sørensen, Peter Nystrup, Mathias B. Bjerregård, Jan K. Møller, Peder Bacher, Henrik Madsen,WIREs Energy Environment – 2022 – S rensen – Recent developments in multivariate wind and solar power forecasting.

Jie Yan, Corinna Möhrlen, Tuhfe Göçmen, Mark Kelly, Arne Wessel, and Gregor Giebel, Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain. Renewable and Sustainable Energy Reviews, 165:112519,23, DOI: 10.1016/j.rser.2022.112519, 2022. +++ OpenAccess Article +++

Möhrlen, C., Ó Foghlú, D., Power, S., Nolan, G., Conway, K., Lambert, E., Ging, J.: EirGrid’s met mast and alternatives study. IET Renew. Power Gener. 00, 1– 14 (2022). https://doi.org/10.1049/rpg2.12502  +++ OpenAccess Article +++

Joseph C. Y. Lee, Caroline Draxl, Larry K. Berg, Evaluating wind speed and power forecasts for wind-energy applications using an open-source and systematic validation framework, Renewable Energy, 200, 457-475, https://doi.org/10.1016/j.renene.2022.09.111,

S.-E. Haupt, M. Garcia Casado, M. Davidson, J. Dobschinski, P. Du, M. Lange, T. Miller, C. Möhrlen, A. Motley, R. Pestana, J.Zack, IEEE Power and Energy Magazine (Nov.-Dec. 2019) , The use of Probabilistic Forecasts – applying them in theory and practice, IEEE Power and Energy Magazine, vol. 17, no. 6, pp. 46-57

I. Würth, L. Valldecabres, E. Simon, C. Möhrlen, B. Uzunoğlu, C. Gilbert, G. Giebel, D. Schlipf, A. Kaifel (2019), Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36., PDF-version,  Energies 2019, 12, 712 – Special Issue Solar and Wind Energy Forecasting. Open Access Online: https://www.mdpi.com/1996-1073/12/4/712  PDF Version: https://www.mdpi.com/1996-1073/12/4/712/pdf