TUBITAK Destekli Projelerden Çıkan Yayınlar Topluluğu
Published January 1, 2010 | Version v1
Conference paper Open

Forecasting Turkey's Short Term Hourly Load with Artificial Neural Networks

Creators

  • 1. Hacettepe Univ, Clean & Renewable Energies Div, Ankara, Turkey
  • 2. Middle East Tech Univ, Dept Mech Engn, Ankara, Turkey
  • 3. Hacettepe Univ, Dept Environm Engn, Ankara, Turkey

Description

Load forecasting is important necessity to provide economic, reliable, high grade energy. In this study, short term hourly load forecasting systems were developed for nine load distribution regions of Turkey using artificial neural networks (ANN) approach. ANN is the most commonly preferred approach for load forecasting. The mean average percent error (MAPE) of total hourly load forecast for Turkey is found as 1.81%.

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