Abstract: The original user side net load sequence of the power system is chaotic. In order to accurately predict the changes in user side load data of the power system, a heterogeneous computing based user side net load prediction algorithm is proposed. The user side net load data of the power system is analyzed with noise, the binary wavelet transform is expanded, and the user side net load data of the power system is preprocessed by setting threshold values and determining estimated signals. The empirical mode decomposition method is applied to decompose the user side net load of the power system. Two different algorithms, EKF(Extended Kalman Filter) and KELM(Kernel Extreme Learning Machine) are used to establish a power system user side net load prediction function based on EKF-KELM. The optimal parameters for IMF(Intrinsic Mode Function) components are calculated isomerically, and a kernel function is introduced to overlay all predicted values. The user side net load prediction results of the power system are obtained under heterogeneous computing. The experimental results show that the predicted value of the power system user side net load obtained by the proposed algorithm is basically consistent with the true value, with low root mean square error and average absolute error. This effectively reduces the time required for power system user side net load prediction and can obtain high-precision power system user side net load prediction results. 

Key words: heterogeneous computing, power system, user side, net load forecast