Abstract
This paper presents an experiment that consists of constructing auto-regressive moving average (ARMA), neural networks and neuro-fuzzy models with historical electricity
consumptiontime series data to create models that can be used to forecastconsumption inthe future. The data was sampled on a monthly basis from January 1985 to December 2011. An ARMA,multilayer perceptron neural network with back propagation and neuro-fuzzy modelling technique which combines Takagi-Sugeno fuzzy models and neural networks were used to create the models for one step ahead forecasting. The results of the three techniques were compared and the results show that neurofuzzy
models outperformed the neural network and ARMA models in terms of accuracy.