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

Fattaheian-Dehkordi, S ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1002/etep.1791
  3. Abstract:
  4. 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
  5. Keywords:
  6. Demand forecasting ; Demand responsiveness ; Commerce ; Decision support systems ; Electric load forecasting ; Electric utilities ; Forecasting ; Neural networks ; Regression analysis ; Short term load forecasting ; Support vector regression (SVR) ; SVR ; Smart power grids
  7. Source: International Transactions on Electrical Energy Systems ; Vol. 24, issue. 12 , 2014 , p. 1650-1663
  8. URL: http://onlinelibrary.wiley.com/doi/10.1002/etep.1791/abstract