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

Author

Marwala, Lufuno ; Twala, Bhekisipho

Author_Institution

Sch. of Electr. & Electron. Eng., Univ. of Johannesburg, Johannesburg, South Africa

fYear

2014

fDate

6-11 July 2014

Firstpage

3049

Lastpage

3055

Abstract

This paper presents an experiment that consists of constructing auto-regressive moving average (ARMA), 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. 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 neuro-fuzzy models outperformed the neural network and ARMA models in terms of accuracy.

Keywords

autoregressive moving average processes; backpropagation; energy consumption; load forecasting; multilayer perceptrons; power engineering computing; time series; ARMA models; South Africa; Takagi-Sugeno fuzzy models; auto-regressive moving average; back propagation; electricity consumption forecasting; historical electricity consumption time series data; multilayer perceptron neural network; neuro-fuzzy modelling technique; Autoregressive processes; Computational modeling; Data models; Electricity; Forecasting; Neural networks; Predictive models; Takagi-Sugeno; auto-regressive moving average; electrictity consumption; forecasting; neural networks; neuro-fuzzy;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), 2014 International Joint Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-6627-1

Type

conf

DOI

10.1109/IJCNN.2014.6889898

Filename

6889898