Scopus
A comparative study of artificial neural network and ANFIS for short term load forecasting
Proceedings of the 2014 6th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2014 · Ocak 2014
Makale Bilgileri
DergiProceedings of the 2014 6th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2014
Yayın TarihiOcak 2014
Scopus ID2-s2.0-84988306373
Özet
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.
Yazarlar (2)
1
Hasan Hüseyin Çevik
2
Mehmet Çunkaş
Anahtar Kelimeler
ANFIS
artificial neural networks
short term load forecasting
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
11
Atıf
2
Yazar
3
Anahtar Kelime