Research on short-term gas load forecasting based on support vector machine model

  • Authors:

  • Chao Zhang;Yi Liu;Hu Zhang;Hong Huang;Wei Zhu

  • Affiliations:

  • Center for Public Safety Research, Department of Engineering Phyiscs, Tsinghua University, Beijing, China;Center for Public Safety Research, Department of Engineering Phyiscs, Tsinghua University, Beijing, China;Center for Public Safety Research, Department of Engineering Phyiscs, Tsinghua University, Beijing, China;Center for Public Safety Research, Department of Engineering Phyiscs, Tsinghua University, Beijing, China;Beijing Research Center of Urban System Engineering, Beijing, China

  • Venue:
  • LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

The short-term gas load forecasting model is developed based on the SVM regression. Gas supply data from a North-China city are taken for model validation. The forecasting error is less than 5% when the through-year data is used in the SVM model training. The un-update model which fits the realistic situation gives only slight error level increase. The data preprocessing, including the grouping and normalization, is effective for increasing accuracy of the regression analysis. In the normalization process, wide data range may be useful to get more accurate forecasting results. The SVM forecasting model developed in this paper may be effective for practical using, especially for the un-updated model.