Wind forecasting using Principal Component Analysis

We present a new statistical wind forecasting tool based on Principal Component Analysis (PCA), which is trained on past data to predict the wind speed using an ensemble of dynamically similar past events. At the same time the method provides a prediction of the likely forecasting error. The method is applied to Meteorological Office wind speed and direction data from a site in Edinburgh. For the training period, the years 2008–2009 were used, and the wind forecasting was tested for the data from 2010 for that site. Different parameter values were also used in the PCA analysis to explore the sensitivity analysis of the results. The forecasting results demonstrated that the technique can be used to forecast the wind up to 24 h ahead with a consistent improvement over persistence for forecasting more than 10 h ahead. The comparison of the forecasting error with the uncertainty estimated from the error growth in the ensemble forecast showed that the forecasting error could be well predicted. •A new wind forecasting tool based on Principal Component Analysis (PCA) is presented.•It can predict wind speed using an ensemble of dynamically similar past events.•It can discriminate dynamically useful information from noise.•Used to forecast up to 24 h ahead and improves over persistence for more than 10 h.•PCA also predicts forecasting error well.