Trend-based time series data clustering for wind speed forecasting

Abstract

Wind forecasting is a time series problem, can aide in estimating the annual energy production of potential wind farms. Seasonality and trend are the two significant components that characterize the wind time series data. Variability in trend and seasonal component affects the performance of most of the forecasting methods. Therefore, to simplify the wind forecasting technique, generally, nonlinear seasonal and trend components are eliminated from wind time series data. Accuracy depends on the application function that is applicable to eliminate the trend and seasonality. In this article, a hybrid approach for time series forecasting has been proposed. A clustering technique has been developed, which finds the clusters of time series data showing identical trend components. After finding the proper clusters of similar trend components, statistical methods, namely, autoregressive integrated moving average and generalized autoregressive score techniques, are applied to the individual cluster. In the end, resulting components are aggregated. The experiment shows that the cluster-based forecasting technique gives better performance as compared with existing statistical models.