Abstract
The risk of missing data and subsequent incomplete data records at wind farms increases with the number of turbines and sensors. We propose here an imputation method that blends data-driven concepts with expert knowledge, by using the geometry of the wind farm in order to provide better estimates when performing Nearest Neighbor imputation. Our method relies on learning Laplacian eigenmaps out of the graph of the wind farm through spectral graph theory. These learned representations can be based on the wind farm layout only, or additionally account for information provided by collected data. The related weighted graph is allowed to change with time and can be tracked in an online fashion. Application to the Westermost Rough offshore wind farm shows significant improvement over approaches that do not account for the wind farm
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| Original language | English |
|---|---|
| Article number | e2962 |
| Journal | Wind Energy |
| Volume | 28 |
| Issue number | 1 |
| Early online date | 9 Dec 2024 |
| DOIs | |
| Publication status | Published - 31 Jan 2025 |
Data Availability Statement
The data that support the findings of this study are available from Ørsted. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from the authors with the permission of Ørsted.Acknowledgements
The authors gratefully acknowledge Ørsted for providing the data for the Westermost Rough offshore wind farm.Funding
The research leading to this work was carried out as a part of the Smart4RES project (European Union's Horizon 2020, No. 864337).
| Funders | Funder number |
|---|---|
| European Commission | 864337 |
Keywords
- Laplacian eigenmaps
- Nadaraya-Watson estimators
- missing data
- time series
- wind power forecasting
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
