Application of neuro-fuzzy models in short term electricity load forecast

One of the important requirements for operational planning of electrical utilities is the prediction of hourly load up to several days, known as short term load forecasting (STLF). Considering the effect of its accuracy on system security and also economical aspects, 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 the application of short-term load forecasting. This model is identified through locally liner model tree (LoLiMoT) learning algorithm. The model is compared to a multilayer perceptron and Kohonen classification and intervention analysis. The models are trained and assessed on load data extracted from EUNITE network competition.