Forecasting electricity consumption in South Africa: ARMA, neural networks and neuro-fuzzy systems

Lufuno Marwala, Bhekisipho Twala
2014 2014 International Joint Conference on Neural Networks (IJCNN)  
This paper presents an experiment that consists of constructingauto-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
more » ... etworks 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.
doi:10.1109/ijcnn.2014.6889898 dblp:conf/ijcnn/MarwalaT14 fatcat:g5ijgmxrsffd3pwwo2rrk7jz2q