SD-LSSVR-Based Decomposition-and-Ensemble Methodology with Application to Hydropower Consumption Forecasting

Abstract

Due to the distinct seasonal characteristics of hydropower, this study tries to propose a seasonal decomposition (SD) based least squares support vector regression (LSSVR) ensemble learning model for hydropower consumption forecasting. In the SD-LSSVR-based decomposition and ensemble model, the original hydropower consumption series are first decomposed into trend cycle, seasonal factor and irregular component. Then the LSSVR is used to predict the three different components independently. Finally, these prediction results of the three components are combined with another LSSVR to formulate an ensemble result as the final prediction. Experimental results reveal that the proposed novel method is very promising for time series forecasting with seasonality and nonlinearity for that it outperforms all the other benchmark methods listed in our study in both level accuracy and directional accuracy.