A novel decomposition‐ensemble model for forecasting short‐term load‐time series with multiple seasonal patterns

Effective and stable load forecasting is necessary and of great importance in ensuring a reliable supply of electricity and the security of the power system. However, due to such factors as cyclicity and seasonality, electric load series show complex nonlinearity characteristics. As a result, obtaining the desired forecasting accuracy becomes highly difficult and challenging. To address this problem, based on the “divide and conquer” idea, we developed a novel decomposition‐ensemble model for short‐term load forecasting (STLF) by integrating singular spectrum analysis (SSA), a support vector machine, the Autoregressive Integrated Moving Average model and the cuckoo search algorithm. To effectively tackle nonlinearity characteristics and later improve forecasting performance, an SSA-based decomposition and reconstruction strategy was introduced into the proposed model and performed based on the pre-analysis of hidden characteristics of the data. Specifically, the decomposed modes that reflect the inner data characteristics were analyzed and selected to establish specific individual predictors. Finally, the cuckoo search algorithm was employed to generate the ensemble result. To verify the performance of the proposed model, half-hourly load data from New South Wales and hourly load data from Singapore were used as illustrative cases. The experimental results demonstrate that the proposed decomposition-ensemble model can provide more accurate electric power forecasting compared with the eight models discussed.