Intelligent short-term load forecasting in Turkey

Topalli A. K., Erkmen I., Topalli I.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, vol.28, no.7, pp.437-447, 2006 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 7
  • Publication Date: 2006
  • Doi Number: 10.1016/j.ijepes.2006.02.004
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.437-447
  • Keywords: artificial intelligence, hybrid learning, neural networks, STLF, NEURAL-NETWORKS
  • Middle East Technical University Affiliated: Yes


A method is proposed to forecast Turkey's total electric load one day in advance by neural networks. A hybrid learning scheme that combines off-line learning with real-time forecasting is developed to use the available data for adapting the weights and to further adjust these connections according to changing conditions. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days, weekends and special holidays. A traditional ARMA model is constructed for the same data as a benchmark. Proposed method gives lower percent errors all the time, especially for holidays. The average error for year 2002 is obtained as 1.60%. (C) 2006 Elsevier Ltd. All rights reserved.