The use of Gaussian process regression for wind forecasting in the UK

Wind energy has experienced remarkable growth in recent years, both globally and in the UK. As a low carbon source of electricity this progress has been, and continues to be, encouraged through legally binding targets and government policy. However, wind energy is non-dispatchable and difficult to predict in advance. In order to support continued development in the wind industry, increasingly accurate prediction techniques are sought to provide forecasts of wind speed and power output. This thesis develops and tests a hybrid numerical weather prediction (NWP) and Gaussian process regression (GPR) model for the prediction of wind speed and power output from 3 hours to 72 hours in advance and considers the impact of incorporating atmospheric stability in the prediction model. In addition to this, the validity of the model as a probabilistic technique for wind power output forecasting is tested and the economic value of a forecast in the UK electricity market is discussed. To begin with, the hybrid NWP and GPR model is developed and tested for prediction of 10 m wind speeds at 15 sites across the UK and hub height wind speeds at 1 site. Atmospheric stability is incorporated in the prediction model first by subdividing input data by Pasquill-Gifford-Turner (PGT) stability class, and then by using the predicted Obukhov length stability parameter as an input in the model. The model is developed further to provide wind power output predictions, both for a single turbine and for 22 wind farms distributed across the UK. This shows that the hybrid NWP and GPR model provide good predictions for wind power output in comparison to other methods. The hybrid NWP and GPR model for the prediction of near-surface wind speeds leads to a reduction in mean absolute percentage error (MAPE) of approximately 2% in comparison to the Met office NWP model. Furthermore, the use of the Obukhov length stability parameter as an input reduces wind power prediction errors in comparison to the same model without this parameter for the single turbine and for offshore wind farms but not for onshore wind farms. The inclusion of the Obukhov length stability parameter in the hub height wind speed prediction model leads to a reduction in MAPE of between 2 and iv 5%, dependent on the forecast horizon, over the model where Obukhov length is omitted. For the prediction of wind power at offshore wind farms, the inclusion of the Obukhov length stability parameter in the hybrid NWP and GPR model leads to a reduction in normalised mean absolute error (NMAE) of between 0.5 and 2%. The performance of the hybrid NWP and GPR model is also evaluated from a probabilistic perspective, with a particular focus on the appropriate likelihood function for the GPR model. The results suggest that using a beta likelihood function in the hybrid model for wind power prediction leads to better probabilistic predictions than implementing the same model with a Gaussian likelihood function. The results suggest an improvement of approximately 1% in continuous ranked probability score (CRPS) when the beta likelihood function is used rather than the Gaussian likelihood function. After considering new techniques for the prediction of wind speed and power output, the final chapter in this thesis considers the economic benefit of implementing a forecast. The economic value of a wind power forecast is evaluated from the perspective of a wind generator participating in the UK electricity market. The impact of forecast accuracy and the change from a dual imbalance price to a single imbalance price is investigated. The results show that a reduction in random error in a wind power forecast does not have a large impact on the average price per MWh generated. However, it has a more significant impact on the variation in price received on an hourly basis. When the systematic bias in a forecast was zero, a forecast with NMAE of 20% of capacity results in less than £0.05 deviation in mean price per MWh in comparison with a perfect forecast. However, the same forecast leads to an increase in standard deviation of up to £21/MWh. This indicates that whilst a reduction in random error in a forecast might not lead to an improvement in mean price per MWh, it can lead to a more stable income stream. In addition to this, Chapter 6 considers the use of the probabilistic and deterministic forecasts developed throughout this thesis to choose an appropriate value to bid in the UK electricity market. This shows that using a probabilistic forecast can limit a generator’s exposure to variable prices and decrease the standard deviation in hourly prices.