In order to predict Cooling Load, an Empirical Mode Decomposition(EMD), Phase Space Reconstruction(PSR) based on

Radial Basis Function Neural Network(RBFNN) ensemble learning paradigm is proposed. The original Cooling Load series are decomposed into a finite number of independent intrinsic mode functions with different frequencies, and then grouped by component method into various sub-components of the high-frequency, low-frequency component of the total, the remainder. Then different RBFNN models are used to model based on PSR, forecast the all sub-series, according to the intrinsic characteristic time scales. All fore casting results are combined to output the ultimate result. This model is applied to Cooling Load tendency forecasting. The results

prove that the finally forecasting performance outperforms the RBFNN, SVM, LSSVM, SVM based on EMD and SVM based on

EMD ahead forecasting.