Articles | Volume 3, issue 2
https://doi.org/10.5194/wes-3-667-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-3-667-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Oct 2018
Research article |
| 09 Oct 2018
| 09 Oct 2018
Probabilistic forecasting of wind power production losses in cold climates: a case study
Jennie Molinder
CORRESPONDING AUTHOR
Department of Earth Sciences, Uppsala Universite, Uppsala, Sweden
Heiner Körnich
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
Esbjörn Olsson
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
Hans Bergström
Department of Earth Sciences, Uppsala Universite, Uppsala, Sweden
Anna Sjöblom
Department of Earth Sciences, Uppsala Universite, Uppsala, Sweden
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Total article views: 4,464 (including HTML, PDF, and XML)
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Cited
16 citations as recorded by crossref.
- Fabrication and application of icephobic silicone coatings on epoxy substrate Q. Zheng et al. 10.1016/j.porgcoat.2021.106483
- Convolutional neural network with dual inputs for time series ice prediction on rotor blades of wind turbines M. Kreutz et al. 10.1016/j.procir.2021.11.075
- Evaluating atmospheric icing forecasts with ground‐based ceilometer profiles K. Hämäläinen et al. 10.1002/met.1964
- A Lagrangian meshfree model for solidification of liquid thin-films A. Bharadwaj et al. 10.1016/j.compfluid.2024.106267
- Measurement of Atmospheric Icing and Droplets S. Rydblom & B. Thornberg 10.1109/TIM.2020.2966313
- Single Column Model Simulations of Icing Conditions in Northern Sweden: Sensitivity to Surface Model Land Use Representation E. Janzon et al. 10.3390/en13164258
- Communicating Properties of Changes in Lagged Weather Forecasts S. Jewson et al. 10.1175/WAF-D-21-0086.1
- Machine Learning-Based Prediction of Icing-Related Wind Power Production Loss S. Scher & J. Molinder 10.1109/ACCESS.2019.2939657
- Skill and Potential Economic Value of Forecasts of Ice Accretion on Wind Turbines L. Strauss et al. 10.1175/JAMC-D-20-0025.1
- Modeling In-Flight Ice Accretion Under Uncertain Conditions G. Gori et al. 10.2514/1.C036545
- The smoother the better? A comparison of six post-processing methods to improve short-term offshore wind power forecasts in the Baltic Sea C. Hallgren et al. 10.5194/wes-6-1205-2021
- Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning J. Chatterjee et al. 10.1017/eds.2023.9
- Ice prediction for wind turbine rotor blades with time series data and a deep learning approach M. Kreutz et al. 10.1016/j.coldregions.2022.103741
- Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests J. Molinder et al. 10.3390/en14010158
- The Use of Uncertainty Quantification for the Empirical Modeling of Wind Turbine Icing J. Molinder et al. 10.1175/JAMC-D-18-0160.1
- Short-range solar radiation forecasts over Sweden T. Landelius et al. 10.5194/asr-15-39-2018
15 citations as recorded by crossref.
- Fabrication and application of icephobic silicone coatings on epoxy substrate Q. Zheng et al. 10.1016/j.porgcoat.2021.106483
- Convolutional neural network with dual inputs for time series ice prediction on rotor blades of wind turbines M. Kreutz et al. 10.1016/j.procir.2021.11.075
- Evaluating atmospheric icing forecasts with ground‐based ceilometer profiles K. Hämäläinen et al. 10.1002/met.1964
- A Lagrangian meshfree model for solidification of liquid thin-films A. Bharadwaj et al. 10.1016/j.compfluid.2024.106267
- Measurement of Atmospheric Icing and Droplets S. Rydblom & B. Thornberg 10.1109/TIM.2020.2966313
- Single Column Model Simulations of Icing Conditions in Northern Sweden: Sensitivity to Surface Model Land Use Representation E. Janzon et al. 10.3390/en13164258
- Communicating Properties of Changes in Lagged Weather Forecasts S. Jewson et al. 10.1175/WAF-D-21-0086.1
- Machine Learning-Based Prediction of Icing-Related Wind Power Production Loss S. Scher & J. Molinder 10.1109/ACCESS.2019.2939657
- Skill and Potential Economic Value of Forecasts of Ice Accretion on Wind Turbines L. Strauss et al. 10.1175/JAMC-D-20-0025.1
- Modeling In-Flight Ice Accretion Under Uncertain Conditions G. Gori et al. 10.2514/1.C036545
- The smoother the better? A comparison of six post-processing methods to improve short-term offshore wind power forecasts in the Baltic Sea C. Hallgren et al. 10.5194/wes-6-1205-2021
- Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning J. Chatterjee et al. 10.1017/eds.2023.9
- Ice prediction for wind turbine rotor blades with time series data and a deep learning approach M. Kreutz et al. 10.1016/j.coldregions.2022.103741
- Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests J. Molinder et al. 10.3390/en14010158
- The Use of Uncertainty Quantification for the Empirical Modeling of Wind Turbine Icing J. Molinder et al. 10.1175/JAMC-D-18-0160.1
1 citations as recorded by crossref.
Latest update: 21 Feb 2025
Short summary
This study shows that using probabilistic forecasting can improve next-day production forecasts for wind energy in cold climates. Wind turbines can suffer from severe production losses due to icing on the turbine blades. Short-range forecasts including the icing-related production losses are therefore valuable when planning for next-day energy production. Probabilistic forecasting can also provide a likelihood for icing and icing-related production losses.
This study shows that using probabilistic forecasting can improve next-day production forecasts...
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