Title :
A comparative study of artificial neural network and ANFIS for short term load forecasting
Author :
Cevik, Hasan Huseyin ; Cunkas, Mehmet
Author_Institution :
Dept. of Electr. & Electron. Eng., Selcuk Univ., Konya, Turkey
Abstract :
Short term load forecast provides market participants the opportunity to balance their generation and/or consumption needs and contractual obligation one day in advance. It also helps to determine reference price for electricity energy and provide system operator a balanced system. This paper presents a comparative study of ANFIS and ANN methods for short term load forecast. Using the load, season and temperature data of Turkey between years of 2009-2011, the prediction is carried out for 2012. The mean absolute percentage errors for ANFIS and ANN models are found as 1.85 and 2.02, respectively in all days except holidays of 2012.
Keywords :
fuzzy neural nets; fuzzy reasoning; load forecasting; power engineering computing; ANFIS method; ANN method; Turkey; artificial neural network method; balanced system; consumption need; contractual obligation; electricity energy; generation need; market participants; mean absolute percentage errors; reference price; short-term load forecasting; system operator; temperature data; Artificial neural networks; Forecasting; Load forecasting; Load modeling; Predictive models; Statistical analysis; Temperature; ANFIS; artificial neural networks; short term load forecasting;
Conference_Titel :
Electronics, Computers and Artificial Intelligence (ECAI), 2014 6th International Conference on
Print_ISBN :
978-1-4799-5478-0
DOI :
10.1109/ECAI.2014.7090206
