Simon Daenens, Ivo Vervlimmeren, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowe, Jan Helsen
Accurate loss estimation methods with a high level of temporal granularity are necessary to enable the implementation of efficient and adaptable control strategies for wind farms. Predictive models for the power of wind turbines within a wind farm are investigated using high-resolution SCADA data and deep learning methodologies. Traditional physical models offer detailed insights but are computationally expensive. Statistical models face limitations in handling wind energy variability. In this study, deep learning models are explored to capture spatial and temporal dynamics, with four models developed: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and a hybrid CNN-LSTM model. SCADA data from an offshore wind farm is preprocessed, anomalies removed, and annotated based on operational regions. The models are trained, validated, and tested, demonstrating improved accuracy over baseline methods. The hybrid model, incorporating spatial and temporal information, yields the highest predictive performance, showcasing the significance of these dimensions in wind power prediction.
Daenens, S, Vervlimmeren, I, Verstraeten, T, Daems, P-J, Nowe, A & Helsen, J 2024, 'Power prediction using high-resolution SCADA data with a farm-wide deep neural network approach', Journal of Physics: Conference Series, vol. 2767, no. 9, 092014. https://doi.org/10.1088/1742-6596/2767/9/092014
Daenens, S., Vervlimmeren, I., Verstraeten, T., Daems, P.-J., Nowe, A., & Helsen, J. (2024). Power prediction using high-resolution SCADA data with a farm-wide deep neural network approach. Journal of Physics: Conference Series, 2767(9), Article 092014. https://doi.org/10.1088/1742-6596/2767/9/092014
@article{c83850bbdb30486e8d2c25eda66e009c,
title = "Power prediction using high-resolution SCADA data with a farm-wide deep neural network approach",
abstract = "Accurate loss estimation methods with a high level of temporal granularity are necessary to enable the implementation of efficient and adaptable control strategies for wind farms. Predictive models for the power of wind turbines within a wind farm are investigated using high-resolution SCADA data and deep learning methodologies. Traditional physical models offer detailed insights but are computationally expensive. Statistical models face limitations in handling wind energy variability. In this study, deep learning models are explored to capture spatial and temporal dynamics, with four models developed: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and a hybrid CNN-LSTM model. SCADA data from an offshore wind farm is preprocessed, anomalies removed, and annotated based on operational regions. The models are trained, validated, and tested, demonstrating improved accuracy over baseline methods. The hybrid model, incorporating spatial and temporal information, yields the highest predictive performance, showcasing the significance of these dimensions in wind power prediction.",
author = "Simon Daenens and Ivo Vervlimmeren and Timothy Verstraeten and Pieter-Jan Daems and Ann Nowe and Jan Helsen",
note = "Funding information: The authors would like to acknowledge the Energy Transition Funds for their support through the POSEIDON and BeFORECAST projects. This research was supported by funding from the Flemish Government under the âOnderzoeksprogramma Artifici{\" }ele Intelligentie (AI) Vlaanderenâ program. Publisher Copyright: {\textcopyright} Published under licence by IOP Publishing Ltd.",
year = "2024",
month = may,
day = "30",
doi = "10.1088/1742-6596/2767/9/092014",
language = "English",
volume = "2767",
journal = "Journal of Physics: Conference Series",
issn = "1742-6596",
publisher = "IOP Publishing Ltd.",
number = "9",
}