On Additive Gaussian Processes for Wind Farm Power Prediction
This work addresses wind farm power prediction for improved operational efficiency, but it is incremental as it applies an existing method to a new dataset without major innovations.
The paper tackled the problem of predicting wind farm power generation by using additive Gaussian processes to model variations at both turbine-specific and farm levels, resulting in predictions that align with intuitive patterns and support better control and decision-making.
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.