Short-Term Load Forecasting Model Based on Multi-label and BPNN

Abstract

With the rapid development of smart grid, the importance of power load forecast is more and more important. Short-term load forecasting (STLF) is important for ensuring efficient and reliable operations of smart grid. In order to improve the accuracy and reduce training time of STLF, this paper proposes a combined model, which is back-propagation neural network (BPNN) with multi-label algorithm based on K-nearest neighbor (K-NN) and K-means. Specific steps are as follows. Firstly, historical data set is clustered into $K$ clusters with the K-means clustering algorithm; Secondly, we get $N$ historical data points which are nearest to the forecasting data than others by the K-NN algorithm, and obtain the probability of the forecasting data points belonging to each cluster by the lazy multi-label algorithm; Thirdly, the BPNN model is built with clusters including one of $N$ historical data points and the respective forecasting load are given by the built models; Finally, the forecasted load of each cluster multiply the probability of each, and then sum them up as the final forecasting load value. In this paper, the test data which include daily temperature and power load of every half hour from a community compared with the results only using BPNN to forecast power load, it is concluded that the combined model can achieve high accuracy and reduce the running time.