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Short term load forecasting of Iran national power system using artificial neural network

Barghinia, S ; Sharif University of Technology | 2001

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  1. Type of Document: Article
  2. DOI: 10.1109/PTC.2001.964937
  3. Publisher: 2001
  4. Abstract:
  5. One of the most important requirements for the operation and planning activities of an electrical utility is the prediction of load for the next hour to several days out, known as short term load forecasting (STLF). This paper presents STLF of Iran national power system (INPS) using artificial neural network (ANN). The developed program is based on a four-layered perceptron ANN building block. The optimum inputs were selected for the ANN considering historical data of the INPS. Instead of conventional back propagation (BP) methods, Levenberg-Marquardt BP (LMBP) method has been used for the ANN training to increase the speed of convergence. A data analyzer and a temperature forecaster are also included in the NRI STLF (NSTLF) package. The program has satisfactory results for one hour up to a week prediction of INPS load. © 2001 IEEE
  6. Keywords:
  7. ANN input selection ; Artificial neural network ; Levenberg-Marquardt method ; Multilayered perceptron ; Short term load forecasting ; Temperature forecasting
  8. Source: 2001 IEEE Porto Power Tech Conference, Porto, 10 September 2001 through 13 September 2001 ; Volume 3 , 2001 , Pages 361-365 ; 0780371399 (ISBN); 9780780371392 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/1047567