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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2603.18281 |
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| _version_ | 1866908900030152704 |
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| author | Brealy, Simon M. Bull, Lawrence A. Brennan, Daniel S. Beltrando, Pauline Sommer, Anders Dervilis, Nikolaos Worden, Keith |
| author_facet | Brealy, Simon M. Bull, Lawrence A. Brennan, Daniel S. Beltrando, Pauline Sommer, Anders Dervilis, Nikolaos Worden, Keith |
| contents | Population-based Structural Health Monitoring (PBSHM) aims to share information between similar machines or structures. This paper takes a population-level perspective, exploring the use of additive Gaussian processes to reveal variations in turbine-specific and farm-level power models over a collected wind farm dataset. The predictions illustrate patterns in wind farm power generation, which follow intuition and should enable more informed control and decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18281 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On Additive Gaussian Processes for Wind Farm Power Prediction Brealy, Simon M. Bull, Lawrence A. Brennan, Daniel S. Beltrando, Pauline Sommer, Anders Dervilis, Nikolaos Worden, Keith Machine Learning Population-based Structural Health Monitoring (PBSHM) aims to share information between similar machines or structures. This paper takes a population-level perspective, exploring the use of additive Gaussian processes to reveal variations in turbine-specific and farm-level power models over a collected wind farm dataset. The predictions illustrate patterns in wind farm power generation, which follow intuition and should enable more informed control and decision-making. |
| title | On Additive Gaussian Processes for Wind Farm Power Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.18281 |