Shortterm Load Forecasting Based on WLSSVM Method withParameter Optimization by QPSO

In order to solve the problem that load forecasting model is difficult to accurately establish because of the nonlinear characteristics of load, the weighted least squares support vector machine (WLSSVM) method based on quantumbehaved particle swarm optimization (QPSO) is introduced to build shortterm load forecasting models. Firstly, the quantum particle swarm optimization method is used to train the model to obtain the optimal hyper parameters. Then the WLSSVM regression model with good generalization performance is used to strengthen robustness. In addition, the forecasting results by this method are compared with that by general LSSVM model, which show that this method can significantly improve the prediction accuracy. In the end, the shortterm load forecasting of Heilongjiang power grid is simulated by using of this method and the results verify its validity.