Short-term Load Forecasting of Power System Based on Adaptive Fusion of Mixed Kernel Function

Abstract

Neural network is an important tool to solve the problem of nonlinear system prediction and control. It has been widely concerned by scholars. However, the existing neural network cannot adaptively allocate the weight of mixed kernel function according to the sample characteristics when it is applied to electric load forecasting. Aiming at this problem, short-term load forecasting algorithm based on adaptive fusion of mixed kernel function is proposed. Firstly, kernel functions are selected from the standard local kernel function and the global kernel function library to form a mixed kernel function. The weight variables and parameters of the kernel function are combined to form a new parameter state vector. Then a nonlinear parameter estimation model is established. Based on this model, the high-order cubature Kalman filter is used to estimate the parameter state, so that the local kernel function and the global kernel function can be adaptively fused. Moreover, the trained neural network is used to predict the load. Finally, the experimental analysis is given based on the actual grid data, and the effectiveness of the adaptive fusion of mixed function algorithm is proved.