Wind speed forecasting using Singular Systems Analysis

Abstract

We report on a method to predict wind speeds up to 24 hours ahead using a technique originating in Dynamical Systems and Chaos theory using a signal processing technique known as Singular Systems Analysis. The method predicts wind speeds based on a set of previous measurements which were used to construct an attractor in an optimally defined phase space as a ‘training set’. Current wind measurements can then be projected to onto that phase space to find most similar previous measurements. By tracing the evolution of these similar previous data, it is possible not only to forecast the wind speed but also to obtain a measure of the expected forecasting uncertainty. The method was applied to a set of hourly wind speed data from a UK Meteorological Office weather station near Edinburgh. A comparison with a simple persistence prediction showed that the Singular Systems Analysis was both, consistently better at predicting wind speeds between 12 and 24 hours ahead than persistence, and also able to provide a meaningful forecasting uncertainty.