Combined General Vector Machine for Single Point Electricity Load Forecast

General Vector Machine (GVM) is a newly proposed machine learning model, which is applicable to small samples forecast scenarios. In this paper, the GVM is applied into electricity load forecast based on single point modeling method. Meanwhile, traditional time series forecast models, including back propagation neural network (BPNN), Support Vector Machine (SVM) and Autoregressive Integrated Moving Average Model (ARIMA), are also experimented for single point electricity load forecast. Further, the combined model based on GVM, BPNN, SVM and ARIMA are proposed and verified. Results show that GVM performs better than these traditional models, and the combined model outperforms any other single models for single point electricity load forecast