Univariate Modelling of Electricity Consumption in South Africa: Neural Networks and Neuro-fuzzy Systems

This paper presents an experiment that consists of constructing neural networks and neuro-fuzzy models with historical electricity consumption time series data to create models that can be used to forecast consumption in the future. The data was sampled on a monthly basis from January 1985 to December 2011. A 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. Different input sizes were used to construct 10 different one step ahead forecasting models for each technique. The results of the two techniques were compared and the results show that neuro-fuzzy models outperformed the neural network models in terms of accuracy.