Time filter

Source Type

Chen C.,Kunming University | Huang G.-Y.,Kunming University | Huang G.-Y.,Yunnan Mineral Pipeline Transmission Engineering Technology Research Center | Fan Y.-G.,Kunming University | And 5 more authors.
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | Year: 2014

In order to improve the precision of short-term load forecasting and overcome the disadvantage of large amount of data and the influence of input variables, a new method based on the combination of discrete Fréchet distance and LS-SVM is presented. This paper analyzes and summarizes the historical load data provided by the East-Slovakia Power Distribution Company. Combining with the law of the region's electricity, by introducing discrete Fréchet distance, the shape-similar days which are similar to the reference day are selected by establishing the mathematical model of discrete curve similarity, and then the similar daily load data are used to train the LS-SVM forecasting model. Through simulation and comparison with the results obtained by the standard LS-SVM model, it is proved that the prediction methods significantly improve the prediction accuracy.

Discover hidden collaborations