Title :
Forecasting Turkey´s short term hourly load with artificial neural networks
Author :
Bilgic, M. ; Girep, C.P. ; Aslanoglu, S.Y. ; Aydinalp-Koksal, M.
Author_Institution :
Clean & Renewable Energies Div., Hacettepe Univ., Ankara, Turkey
Abstract :
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%.
Keywords :
Artificial neural networks; Calendars; Economic forecasting; Electronic mail; Fuzzy logic; Load forecasting; Power generation economics; Predictive models; Statistical analysis; Temperature; Short term load forecasting; artificial neural networks;
Conference_Titel :
Transmission and Distribution Conference and Exposition, 2010 IEEE PES
Conference_Location :
New Orleans, LA, USA
Print_ISBN :
978-1-4244-6546-0
DOI :
10.1109/TDC.2010.5484442
