On Additive Gaussian Processes for Wind Farm Power Prediction

Simon M. Brealy; Lawrence A. Bull; Daniel S. Brennan; Pauline Beltrando; Anders Sommer; Nikolaos Dervilis; Keith Worden

doi:10.1007/978-3-031-61425-5_58

paper

On Additive Gaussian Processes for Wind Farm Power Prediction

arXiv:2603.18281 · doi:10.1007/978-3-031-61425-5_58

Abstract

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.