Hadi hah-Hosseini - Shahid Beheshti University, Dept. of Electrical and Computer Eng.,
C Lucas - University of Tehran, Dept. of Electrical and Computer Eng.,
A.R Koushki - Islamic Azad University Science and Research Branch,
M Nosrati Maralloo - Islamic Azad University Science and Research Branch,

Long-term forecasting of load demand is necessary for the correct operation of electric utilities. There is an on-going attention toward putting new approaches to the task. Recently, Neuro-fuzzy modeling has played a successful role in various applications over nonlinear time series prediction. This paper presents a neuro-fuzzy model for long-term load forecasting. This model is identified through Locally Liner Model Tree (LoLiMoT) learning algorithm. The model is compared to a multilayer perceptron and Radial Basis Function (RBF). The models are trained and assessed on load data extracted from a North- American electric utility.

long term load forecasting, Neuro- Fuzzy modeling, LoLiMoT, multilayer perceptron, Radial Basis Function