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论文类型：期刊论文

发表时间：2019-09-01

发表刊物：CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS

收录刊物：EI、SCIE

卷号：22

页面范围：12589-12597

ISSN号：1386-7857

关键字：Load forecasting; Neural network with random weights; Clustering; Multivariate time series

摘要：Forecasting short-term load is a basic but indispensable problem for power system operations. This paper treats the forecasting problem as a multivariate time series forecasting problem. The electricity load and the corresponding temperature data are analyzed as correlative time series, and are reconstructed to the multivariate phase space. A neural network with random weights and kernels, which combines the advantages of the neural network and support vector machine including simple training and good generalization performance, is used as the forecasting model. Then, in order to further improve the forecasting performance, different weights are applied to the input data in the phase space according to the predictive value, and the resulting model is called weighted neural network with random weights and kernels. Simulation results based on the real world data set from the EUNITE competition show the effectiveness of the proposed method.