Hour-ahead demand forecasting in smart grid using support vector regression (SVR)

Sajjad Fattaheian-Dehkordi, Alireza Fereidunian*, Hamid Gholami-Dehkordi, Hamid Lesani

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

38 Citations (Scopus)


Demand forecasting plays an important role as a decision support tool in power system management, especially in smart grid and liberalized power market. In this paper, a demand forecasting method is presented by using support vector regression (SVR). The proposed method is applied to practical hourly data of the Greater Tehran Electricity Distribution Company. The SVR parameters are selected by using a grid optimization process and an investigation on different kernel functions. Moreover, correlation analysis is used to find exogenous variables. Acceptable accuracy of load prediction is shown by comparing the result of SVR model to that of the artificial neural networks and the actual data, concluding that the method is applicable to day-ahead spot pricing of electricity in the liberalized power market.

Original languageEnglish
Pages (from-to)1650-1663
Number of pages14
JournalInternational Transactions on Electrical Energy Systems
Issue number12
Publication statusPublished - 1 Dec 2014
MoE publication typeNot Eligible


  • Demand forecasting
  • Demand responsiveness
  • Short-term load forecasting
  • Support vector regression
  • SVR


Dive into the research topics of 'Hour-ahead demand forecasting in smart grid using support vector regression (SVR)'. Together they form a unique fingerprint.

Cite this