Research on Adaptive Selection Algorithm for Multi-model Load Forecasting Based on Adaboost

Abstract

With the development of energy Internet technology and artificial intelligence algorithms, smart grids have an increasing demand for the accuracy of load forecasting. This paper uses Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbors (KNN) regression models to forecast electricity consumption of local residents and wind power generation of selected enterprises respectively. The electricity load of residents is divided into workday load and weekend load for comparative analysis. Based on Adaptive Boosting (Adaboost) algorithm, this paper proposes an adaptive selection method which further improves the prediction accuracy of the regression model, by selecting appropriate number of sub-classifiers and setting training weights of training data and sub-classifiers to adjust the distribution of training samples. Simulation results demonstrate that even in scenarios of poor data quality, the prediction accuracy of the model with the proposed Adaboost-based algorithm can be enhanced by up to 10%.