Articles | Volume 10, issue 4
https://doi.org/10.5194/wes-10-779-2025
© Author(s) 2025. 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-10-779-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Apr 2025
Research article |
| 28 Apr 2025
| 28 Apr 2025
Modular deep learning approach for wind farm power forecasting and wake loss prediction
Artificial Intelligence Lab Brussels, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Elsene, Belgium
Faculty of Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium
Timothy Verstraeten
Artificial Intelligence Lab Brussels, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Elsene, Belgium
Faculty of Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium
Pieter-Jan Daems
Faculty of Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium
Ann Nowé
Artificial Intelligence Lab Brussels, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Elsene, Belgium
Jan Helsen
Faculty of Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium
Flanders Make@VUB, Pleinlaan 2, 1050 Elsene, Belgium
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Diederik van Binsbergen, Pieter-Jan Daems, Timothy Verstraeten, Konstantinos Vratsinis, Amir Nejad, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-120, https://doi.org/10.5194/wes-2026-120, 2026
Preprint under review for WES
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A key challenge for offshore wind farms is to quantify wakes reliably from turbine measurements. This study develops a statistical approach that learns from measured turbine data how these wakes vary between wind farms and transfers that knowledge to new sites with quantified uncertainty. It also assesses how atmospheric conditions affect wake recovery. The results show that stable conditions lead to slower recovery and larger predicted losses, while unstable conditions support faster recovery.
Rebeca Marini, Konstantinos Vratsinis, Pieter-Jan Daems, Timothy Verstraeten, Etienne Cheynet, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-117, https://doi.org/10.5194/wes-2026-117, 2026
Preprint under review for WES
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We studied which environmental conditions affect the power produced by a single wind turbine without interference from nearby turbines. Using measured turbine and weather data together with machine learning, we found that the difference between air and sea temperature has a meaningful influence but is not included in commonly used power corrections. This could improve power predictions and wind resource assessments, and the method can be extended to larger groups of turbines.
Faras Jamil, Cedric Peeters, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-83, https://doi.org/10.5194/wes-2026-83, 2026
Preprint under review for WES
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Maintenance costs are a major challenge for the wind energy industry. This research introduces an automated artificial intelligence method to monitor drivetrain health. A model trained on healthy data analyses vibration patterns to identify deviations signalling potential faults. It is validated on offshore farms and has detected all 19 confirmed fault cases. It provides a single health indicator to help operators plan efficient maintenance strategies, reducing the Levelised Cost of Energy.
Kayacan Kestel, Xavier Chesterman, Donatella Zappalá, Simon Watson, Mingxin Li, Edward Hart, James Carroll, Yolanda Vidal, Amir R. Nejad, Shawn Sheng, Yi Guo, Matthias Stammler, Florian Wirsing, Ahmed Saleh, Nico Gregarek, Thao Baszenski, Thomas Decker, Martin Knops, Georg Jacobs, Benjamin Lehmann, Florian König, Ines Pereira, Pieter-Jan Daems, Cédric Peeters, and Jan Helsen
Wind Energ. Sci., 11, 2103–2155, https://doi.org/10.5194/wes-11-2103-2026, https://doi.org/10.5194/wes-11-2103-2026, 2026
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Wind energy use has been rapidly expanding worldwide in recent years. Driven by global decarbonization goals and energy security concerns, this growth is expected to continue. To achieve these targets, production costs must decrease, with operation and maintenance being major contributors. This paper reviews current and emerging technologies for monitoring wind turbine drivetrains, and highlights key academic and industrial challenges that may hinder progress.
Konstantinos Vratsinis, Rebeca Marini, Pieter-Jan Daems, Lukas Pauscher, Jeroen van Beeck, and Jan Helsen
Wind Energ. Sci., 11, 1803–1820, https://doi.org/10.5194/wes-11-1803-2026, https://doi.org/10.5194/wes-11-1803-2026, 2026
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Using data collected over 13 months at an offshore wind farm, our study shows that a wind turbine’s position within the farm influences its energy output at a given nacelle-measured wind speed. Front-row turbines respond differently to similar wind speeds and turbulence than those further back. This finding suggests that current methods for characterizing inflow conditions may not fully capture actual wind behavior, underscoring the need for improved performance analysis techniques.
Matthias Stammler, Faras Jamil, Xinrun Liu, Jens Jo Matthys, Nikhil Sudhakaran, Cédric Peeters, Asger Bech Abrahamsen, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-81, https://doi.org/10.5194/wes-2026-81, 2026
Preprint under review for WES
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This study compares traditional and artificial intelligence supported methods for detecting damage in rotating machine parts. Bearings were tested under controlled conditions to see how well each method identified developing problems. The artificial intelligence supported method detected damage as early as, or earlier than, established techniques while needing far less expert attention. This supports automated monitoring of wind turbines, enabling earlier maintenance and lower operating costs.
Xavier Chesterman, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 11, 1163–1183, https://doi.org/10.5194/wes-11-1163-2026, https://doi.org/10.5194/wes-11-1163-2026, 2026
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This paper presents a sensor-error-robust methodology for failure prediction in wind turbines. Sensor malfunctions pose a significant challenge for data-driven prognostic approaches. The proposed method employs a masked autoencoder that enables selective deactivation of signals. Evaluation is done using data from several operational offshore wind farms. Results demonstrate that the model effectively mitigates the impact of sensor errors while maintaining high accuracy in failure prediction.
Jakob Gebel, Pieter-Jan Daems, Diederik van Binsbergen, Veronica Liverud Krathe, Amir R. Nejad, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-13, https://doi.org/10.5194/wes-2026-13, 2026
Preprint under review for WES
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The study examines two critical turbine components: the blades and the main bearings. Computer simulations are used to model how turbines respond to disturbed wind from nearby turbines. The analysis is carried out in two stages: first, different turbine spacings and alignments are tested, and second, the method is applied to a real offshore wind farm in the Belgian North Sea.
Ivo Vervlimmeren, Xavier Chesterman, Timothy Verstraeten, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 10, 2615–2637, https://doi.org/10.5194/wes-10-2615-2025, https://doi.org/10.5194/wes-10-2615-2025, 2025
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We introduce a new method to refine failure prediction for wind turbines, leading to better and more efficient alarming. We do this by filtering detected anomalies based on the anomalies from the whole fleet. We compare submethods and find one that removes up to 65 % of detected anomalies while leaving the failure-predicting ones. We also detail how we trained the model that generated these anomalies and discuss the construction of the scalable pipeline that was used to deploy such models.
Faras Jamil, Cédric Peeters, Timothy Verstraeten, and Jan Helsen
Wind Energ. Sci., 10, 1963–1978, https://doi.org/10.5194/wes-10-1963-2025, https://doi.org/10.5194/wes-10-1963-2025, 2025
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A hybrid fault detection method is proposed, which combines physical domain knowledge with machine learning models to automatically detect mechanical faults in wind turbine drivetrain components. It offers detailed insights for experts while giving operators a high-level overview of the machine's health to assist in planning effective maintenance strategies. It was validated on multiple years of wind farm data and the potential faults were accurately predicted, which was confirmed by experts.
Simon Daenens, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 10, 1137–1152, https://doi.org/10.5194/wes-10-1137-2025, https://doi.org/10.5194/wes-10-1137-2025, 2025
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This study presents a novel model for predicting wind turbine power output at a high temporal resolution in wind farms using a hybrid graph neural network (GNN) and long short-term memory (LSTM) architecture. By modeling the wind farm as a graph, the model captures both spatial and temporal dynamics, outperforming traditional power curve methods. Integrated with a normal behavior model (NBM) framework, the model effectively identifies and analyzes power loss events.
Jakob Gebel, Ashkan Rezaei, Adithya Vemuri, Veronica Liverud Krathe, Pieter-Jan Daems, Jens Jo Matthys, Jonathan Sterckx, Konstantinos Vratsinis, Kayacan Kestel, Amir R. Nejad, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-173, https://doi.org/10.5194/wes-2024-173, 2025
Preprint under review for WES
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A simulation model of a deployed offshore wind turbine was developed using real-world measurement data. The method shows how to obtain, update and validate a simulation model and allows to improve the efficiency and longevity of offshore wind turbines and support operation and maintenance decisions. Simulations were conducted to analyze the effects of turbulence and wind patterns on turbine lifespan, providing insights to improve maintenance planning and reduce operational costs.
Rebeca Marini, Konstantinos Vratsinis, Kayacan Kestel, Jonathan Sterckx, Jens Matthys, Pieter-Jan Daems, Timothy Verstraeten, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-9, https://doi.org/10.5194/wes-2025-9, 2025
Revised manuscript not accepted
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This work evaluated the wind profile in a Belgian offshore zone. The estimated wind profile was made using measurements that allow for reconstruction at heights along the rotor area. The IEC standard defines these profiles as a 1/7th power law, which is proven not to occur 100 % of the time. It is also possible to infer that there will be differences when using different wind profiles for load assessment, as more realistic profiles can lead to a better assessment of the wind turbine's lifetime.
Diederik van Binsbergen, Pieter-Jan Daems, Timothy Verstraeten, Amir R. Nejad, and Jan Helsen
Wind Energ. Sci., 9, 1507–1526, https://doi.org/10.5194/wes-9-1507-2024, https://doi.org/10.5194/wes-9-1507-2024, 2024
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Wind farm yield assessment often relies on analytical wake models. Calibrating these models can be challenging due to the stochastic nature of wind. We developed a calibration framework that performs a multi-phase optimization on the tuning parameters using time series SCADA data. This yields a parameter distribution that more accurately reflects reality than a single value. Results revealed notable variation in resultant parameter values, influenced by nearby wind farms and coastal effects.
Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 8, 893–924, https://doi.org/10.5194/wes-8-893-2023, https://doi.org/10.5194/wes-8-893-2023, 2023
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This paper reviews and implements several techniques that can be used for condition monitoring and failure prediction for wind turbines using SCADA data. The focus lies on techniques that respond to requirements of the industry, e.g., robustness, transparency, computational efficiency, and maintainability. The end result of this research is a pipeline that can accurately detect three types of failures, i.e., generator bearing failures, generator fan failures, and generator stator failures.
Adithya Vemuri, Sophia Buckingham, Wim Munters, Jan Helsen, and Jeroen van Beeck
Wind Energ. Sci., 7, 1869–1888, https://doi.org/10.5194/wes-7-1869-2022, https://doi.org/10.5194/wes-7-1869-2022, 2022
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The sensitivity of the WRF mesoscale modeling framework in accurately representing and predicting wind-farm-level environmental variables for three extreme weather events over the Belgian North Sea is investigated in this study. The overall results indicate highly sensitive simulation results to the type and combination of physics parameterizations and the type of the weather phenomena, with indications that scale-aware physics parameterizations better reproduce wind-related variables.
Amir R. Nejad, Jonathan Keller, Yi Guo, Shawn Sheng, Henk Polinder, Simon Watson, Jianning Dong, Zian Qin, Amir Ebrahimi, Ralf Schelenz, Francisco Gutiérrez Guzmán, Daniel Cornel, Reza Golafshan, Georg Jacobs, Bart Blockmans, Jelle Bosmans, Bert Pluymers, James Carroll, Sofia Koukoura, Edward Hart, Alasdair McDonald, Anand Natarajan, Jone Torsvik, Farid K. Moghadam, Pieter-Jan Daems, Timothy Verstraeten, Cédric Peeters, and Jan Helsen
Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, https://doi.org/10.5194/wes-7-387-2022, 2022
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This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the energy conversion systems transferring the kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps.
Cited articles
Becker, M., Allaerts, D., and van Wingerden, J. W.: FLORIDyn – A dynamic and flexible framework for real-time wind farm control, J. Phys. Conf. Ser., 2265, 032103, https://doi.org/10.1088/1742-6596/2265/3/032103, 2022. a
Bleeg, J., Purcell, M., Ruisi, R., and Traiger, E.: Wind farm blockage and the consequences of neglecting its impact on energy production, Energies, 11, 1609, https://doi.org/10.3390/en11061609, 2018. a
Boersma, S., Doekemeijer, B. M., Gebraad, P. M., Fleming, P. A., Annoni, J., Scholbrock, A. K., Frederik, J. A., and van Wingerden, J.-W.: A tutorial on control-oriented modeling and control of wind farms, in: 2017 American control conference (ACC), Seattle, WA, USA, 24–26 May 2017, IEEE, 1–18, https://doi.org/10.23919/ACC.2017.7962923, 2017. a, b
Bossanyi, E. and Ruisi, R.: Axial induction controller field test at Sedini wind farm, Wind Energ. Sci., 6, 389–408, https://doi.org/10.5194/wes-6-389-2021, 2021. a
Braun, M., Gruhl, C., Hans, C. A., Härtel, P., Scholz, C., Sick, B., Siefert, M., Steinke, F., Stursberg, O., and von Berg, S. W.: Predictions and Decision Making for Resilient Intelligent Sustainable Energy Systems, arXiv [preprint], https://doi.org/10.48550/arXiv.2407.03021, 2024. a
Daenens, S., Vervlimmeren, I., Verstraeten, T., Daems, P.-J., Nowé, A., and Helsen, J.: Power prediction using high-resolution SCADA data with a farm-wide deep neural network approach, J. Phys. Conf. Ser., 2767, 092014, https://doi.org/10.1088/1742-6596/2767/9/092014, 2024. a
Fleming, P., King, J., Simley, E., Roadman, J., Scholbrock, A., Murphy, P., Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort, R., Jager, D., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.: Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2, Wind Energ. Sci., 5, 945–958, https://doi.org/10.5194/wes-5-945-2020, 2020. a
Fleming, P. A., Ning, A., Gebraad, P. M., and Dykes, K.: Wind plant system engineering through optimization of layout and yaw control, Wind Energy, 19, 329–344, 2016. a
Foloppe, B., Munters, W., Buckingham, S., Vandevelde, L., and van Beeck, J.: Coupling of a dynamic wake model with WRF: a case study of the Belgian wind farms, in: 18th EAWE PhD Seminar, Bruges, Belgium,, 2 to 4 November 2022, 30–31, 2022. a
Gal, Y. and Ghahramani, Z.: Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, in: Proceedings of The 33rd International Conference on Machine Learning, edited by: Balcan, M. F. and Weinberger, K. Q., vol. 48 of Proceedings of Machine Learning Research, PMLR, New York, New York, USA, 1050–1059, https://proceedings.mlr.press/v48/gal16.html (last access: 25 July 2024), 2016. a
Gebraad, P. M. and Van Wingerden, J.: A control-oriented dynamic model for wakes in wind plants, J. Phys. Conf. Ser., 524, 012186, https://doi.org/10.1088/1742-6596/524/1/012186, 2014. a
IEA: Renewables 2022 – Analysis and forecast to 2027, https://iea.blob.core.windows.net/assets/ada7af90-e280-46c4-a577-df2e4fb44254/Renewables2022.pdf (last access: 25 July 2024), 2022. a
Jensen, N. O.: A note on wind generator interaction, vol. 2411, Citeseer, https://backend.orbit.dtu.dk/ws/portalfiles/portal/55857682/ris_m_2411.pdf (last access: 25 July 2024), 1983. a
Kölle, K., Göçmen, T., Garcia-Rosa, P. B., Petrović, V., Eguinoa, I., Vrana, T. K., Long, Q., Pettas, V., Anand, A., Barlas, T. K., Cutululis, N., Manjock, A., Tande, J. O., Ruisi, R., and Bossanyi, E.: Towards integrated wind farm control: Interfacing farm flow and power plant controls, Advanced Control for Applications: Engineering and Industrial Systems, 4, e105, https://doi.org/10.1002/adc2.105, 2022. a
Larsen, G. C., Madsen, H. A., Bingöl, F., Mann, J., Ott, S., Sorensen, J. N., Okulov, V., Troldborg, N., Nielsen, M., Thomsen, K., Larsen, T. J., and Mikkelsen, R.: Dynamic wake meandering modeling, ISBN 978-87-550-3602-4, https://www.osti.gov/etdeweb/servlets/purl/20941220#page=1.00&gsr=0 (last access: 25 July 2024), 2007. a
Laves, M.-H., Ihler, S., Fast, J. F., Kahrs, L. A., and Ortmaier, T.: Well-calibrated regression uncertainty in medical imaging with deep learning, in: Medical Imaging with Deep Learning, PMLR, 393–412, https://proceedings.mlr.press/v121/laves20a/laves20a.pdf (last access: 25 July 2024), 2020. a
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444, 2015. a
Lee, J. C. Y. and Fields, M. J.: An overview of wind-energy-production prediction bias, losses, and uncertainties, Wind Energ. Sci., 6, 311–365, https://doi.org/10.5194/wes-6-311-2021, 2021. a
Lin, Z. and Liu, X.: Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network, Energy, 201, 117693, https://doi.org/10.1016/j.energy.2020.117693, 2020. a
Liu, T., Huang, Z., Tian, L., Zhu, Y., Wang, H., and Feng, S.: Enhancing wind turbine power forecast via convolutional neural network, Electronics, 10, 261, https://doi.org/10.3390/electronics10030261, 2021. a
Martínez-Tossas, L. A., Annoni, J., Fleming, P. A., and Churchfield, M. J.: The aerodynamics of the curled wake: a simplified model in view of flow control, Wind Energ. Sci., 4, 127–138, https://doi.org/10.5194/wes-4-127-2019, 2019. a
Martínez-Tossas, L. A., King, J., Quon, E., Bay, C. J., Mudafort, R., Hamilton, N., Howland, M. F., and Fleming, P. A.: The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows, Wind Energ. Sci., 6, 555–570, https://doi.org/10.5194/wes-6-555-2021, 2021. a
Meyers, J., Bottasso, C., Dykes, K., Fleming, P., Gebraad, P., Giebel, G., Göçmen, T., and van Wingerden, J.-W.: Wind farm flow control: prospects and challenges, Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, 2022. a
Nejad, A. R., Keller, J., Guo, Y., Sheng, S., Polinder, H., Watson, S., Dong, J., Qin, Z., Ebrahimi, A., Schelenz, R., Gutiérrez Guzmán, F., Cornel, D., Golafshan, R., Jacobs, G., Blockmans, B., Bosmans, J., Pluymers, B., Carroll, J., Koukoura, S., Hart, E., McDonald, A., Natarajan, A., Torsvik, J., Moghadam, F. K., Daems, P.-J., Verstraeten, T., Peeters, C., and Helsen, J.: Wind turbine drivetrains: state-of-the-art technologies and future development trends, Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, 2022. a
NREL: OpenFAST, https://github.com/OpenFAST/openfast, GitHub [code], https://github.com/OpenFAST/openfast (last access: 25 July 2024), 2025a. a
NREL: FLORIS Wake Modeling and Wind Farm Controls Software (FLORIS v3.4.1), GitHub [code], https://github.com/NREL/floris (last access: 25 July 2024), 2025b. a
NREL: Simulator fOr Wind Farm Applications (SOWFA), https://github.com/NREL/SOWFA, GitHub [code], https://github.com/NREL/SOWFA (last access: 25 July 2024), 2025c. a
Nygaard, N. G., Steen, S. T., Poulsen, L., and Pedersen, J. G.: Modelling cluster wakes and wind farm blockage, J. Phys. Conf. Ser., 1618, 062072, https://doi.org/10.1088/1742-6596/1618/6/062072, 2020. a
Park, J. and Park, J.: Physics-induced graph neural network: An application to wind-farm power estimation, Energy, 187, 115883, https://doi.org/10.1016/j.energy.2019.115883, 2019. a
Perez-Sanjines, F., Verstraeten, T., Nowé, A., and Helsen, J.: Deep ensemble with Neural Networks to model power curve uncertainty, J. Phys. Conf. Ser., 2362, 012029, https://doi.org/10.1088/1742-6596/2362/1/012029, 2022. a, b
Pettas, V., Kretschmer, M., Clifton, A., and Cheng, P. W.: On the effects of inter-farm interactions at the offshore wind farm Alpha Ventus, Wind Energ. Sci., 6, 1455–1472, https://doi.org/10.5194/wes-6-1455-2021, 2021. a
Piotrowski, P., Rutyna, I., Baczyński, D., and Kopyt, M.: Evaluation metrics for wind power forecasts: A comprehensive review and statistical analysis of errors, Energies, 15, 9657, https://doi.org/10.3390/en15249657, 2022. a
Pombo, D. V., Göçmen, T., Das, K., and Sørensen, P.: Multi-horizon data-driven wind power forecast: From nowcast to 2 days-ahead, in: 2021 International Conference on Smart Energy Systems and Technologies (SEST), 1–6, https://doi.org/10.1109/SEST50973.2021.9543173, 2021. a
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-turbine and wind-farm flows: A review, Boundary-Lay. Meteorol., 174, 1–59, https://doi.org/10.1007/s10546-019-00473-0, 2020. a, b
Schepers, J.: WakeFarm: nabij zog model en ongestoord wind snelheidsveld, Energieonderzoek Centrum Nederland, Technical Report, ECN-C-98-016, 1998. a
Sood, I., Simon, E., Vitsas, A., Blockmans, B., Larsen, G. C., and Meyers, J.: Comparison of large eddy simulations against measurements from the Lillgrund offshore wind farm, Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, 2022. a
Strickland, J. M., Gadde, S. N., and Stevens, R. J.: Wind farm blockage in a stable atmospheric boundary layer, Renewable Energy, 197, 50–58, https://doi.org/10.1016/j.renene.2022.07.108, 2022. a
University of Hannover: Parallelized Large-Eddy Simulation Model (PALM), GitLab [code], https://gitlab.palm-model.org/releases/palm_model_system (last access: 25 July 2024), 2021. a
Van Der Laan, M., Pena, A., Volker, P., Hansen, K. S., Sørensen, N. N., Ott, S., and Hasager, C. B.: Challenges in simulating coastal effects on an offshore wind farm, J. Phys. Conf. Ser., 854, 012046, https://doi.org/10.1088/1742-6596/854/1/012046, 2017. a
Verstraeten, T., Nowé, A., Keller, J., Guo, Y., Sheng, S., and Helsen, J.: Fleetwide data-enabled reliability improvement of wind turbines, Renew. Sust. Energ. Rev., 109, 428–437, 2019. a
Verstraeten, T., Daems, P.-J., Bargiacchi, E., Roijers, D. M., Libin, P. J., and Helsen, J.: Scalable optimization for wind farm control using coordination graphs, arXiv [preprint], https://doi.org/10.48550/arXiv.2101.07844, 2021. a
Wang, H.-Z., Li, G.-Q., Wang, G.-B., Peng, J.-C., Jiang, H., and Liu, Y.-T.: Deep learning based ensemble approach for probabilistic wind power forecasting, Appl. Energ., 188, 56–70, 2017. a
Wang, Y., Zou, R., Liu, F., Zhang, L., and Liu, Q.: A review of wind speed and wind power forecasting with deep neural networks, Appl. Energ., 304, 117766, https://doi.org/10.1016/j.apenergy.2021.117766, 2021. a
Yin, X. and Zhao, X.: Big data driven multi-objective predictions for offshore wind farm based on machine learning algorithms, Energy, 186, 115704, https://doi.org/10.1016/j.energy.2019.07.034, 2019. a
Zehtabiyan-Rezaie, N., Iosifidis, A., and Abkar, M.: Physics-guided machine learning for wind-farm power prediction: Toward interpretability and generalizability, PRX Energy, 2, 013009, https://doi.org/10.1103/PRXEnergy.2.013009, 2023. a, b
Short summary
Wind farms are crucial for a sustainable energy future. However, their power can fluctuate significantly due to changing weather conditions, which complexly affect their power generation. This paper presents a novel machine-learning-based method to enhance wind farm power predictions, enabling improved power scheduling, trading and grid balancing. This makes wind power more valuable and easier to integrate into the energy system.
Wind farms are crucial for a sustainable energy future. However, their power can fluctuate...
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