A Comparative Study of Artificial Neural Network and ANFIS for Short Term Load Forecasting

Cevik, Hasan HuseyinCunkas, Mehmet2020-03-262020-03-262014978-1-4799-5479-72378-7147https://hdl.handle.net/20.500.12395/305286th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) -- OCT 23-25, 2014 -- Pitesti, ROMANIAShort 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.eninfo:eu-repo/semantics/closedAccessshort term load forecastingartificial neural networksANFISA Comparative Study of Artificial Neural Network and ANFIS for Short Term Load ForecastingConference ObjectN/AWOS:000380489500041N/A