Short-Term Load Forecasting Method for AC/DC Distribution System Based on Ensemble Learning

There are more diverse types of loads in an AC/DC distribution system, so it is much more difficult to grasp the change rules. Accurate load forecasting is important for the scheduling of AC/DC distribution system. Aiming at the precision problem of traditional short-term load forecasting methods, such as neural network, grey theory and support vector machine, this study uses the ensemble learning to improve the traditional forecasting methods, and proposes a gradient boosting method based on shallow neural network (GBSNN) as a base learner. Meanwhile, by using the Huber function as the loss function, it is robust to abnormal load data and can reduce the generalization error. Through simulation results and comparison analysis, the proposed short-term load forecasting method based on GBSNN has higher precision than other methods and better performance in load forecasting of AC/DC distribution system.