Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

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.

On Additive Gaussian Processes for Wind Farm Power Prediction

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.
Paper Structure (13 sections, 7 equations, 4 figures)

This paper contains 13 sections, 7 equations, 4 figures.

Table of Contents

  1. Introduction
  2. An application: Wind power forecasting
  3. Contribution
  4. Data Exploration
  5. Preprocessing
  6. Modelling Approach
  7. Generalised Additive Models
  8. Gaussian Processes
  9. Additive GPs
  10. Method
  11. Results
  12. Conclusions and next steps
  13. Acknowledgements

Figures (4)

  • Figure 1: Normalised data showing the power curve relationships for wind farm Ciabatta. Plot (a) shows data at the individual turbine level, whilst plot (b) shows data at the aggregate wind farm level.
  • Figure 2: Filtered data showing the power curves for both individual turbines (a), and at the aggregate wind farm level (b)
  • Figure 3: Filtered data showing the zonal and meridional wind speeds, coloured by power output for both an turbine X (a), and at the aggregate wind farm level (b)
  • Figure 4: Freestream wind and summed directional component mean predictions for turbine X (\ref{['fig:4sub1']}), turbine Y (\ref{['fig:4sub2']}), and wind farm Ciabbata (\ref{['fig:4sub3']}). The colours show the relative impact on the predicted power (in the transformed space)