Application of differential evolution and least squares support vector machine method in daily water demand prediction

Abstract:

To find the optimal parameters of least squares vector machine(LSSVM), the daily water demand forecasting method based on self-adaptive differential evolution(SADE) and LSSVM was proposed. The main influencing factors of daily water consumption were determined using improved rough set algorithm, and the correlation analysis on daily water consumption series was conducted. SADE was applied to optimize the parameters of LSSVM to build SADELSSVM-based forecasting model. The case study shows that compared with self-adaptive GA(SAGA) and differential evolution(DE), SADE has stronger global search ability and faster evolution speed, and the proposed model has better prediction performance than SAGALSSVM-based model and DELSSVM-based model.