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

fYear

2014

Firstpage

29

Lastpage

34

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Computers and Artificial Intelligence (ECAI), 2014 6th International Conference on

Print_ISBN

978-1-4799-5478-0

Type

conf

DOI

10.1109/ECAI.2014.7090206

Filename

7090206