Forecasting the total electricity production in South Africa: Comparative analysis to improve the predictive modelling accuracy

Emrah Gulay; Emrah Gulay

doi:10.3934/energy.2019.1.88

AIMS Energy

2019, Volume 7, Issue 1: 88-110. doi: 10.3934/energy.2019.1.88

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Research article

Forecasting the total electricity production in South Africa: Comparative analysis to improve the predictive modelling accuracy

Emrah Gulay ^,

School of Civil and Environmental Engineering, Nanyang Technological University, Singapore

Received: 24 December 2018 Accepted: 28 January 2019 Published: 31 January 2019

Electricity plays an important role in the South African economy with the industrial sector consuming the highest proportion followed by the residential and mining sector. Besides the fact that electricity is considered as an important energy sources, an adequate supply of electricity remains an important factor that affects the development and economic growth of a country. Therefore, it becomes even more important to forecast the total electricity production in South Africa. It turns out that the comparison of the predictive performance of different forecasting methods is inevitable. Hybrid forecasting approaches, such as artificial neural network (ANN) based seasonal Autoregressive Integrated Moving Average (sARIMA) model, ANN based multiplicative Holt-Winters (HW) model, ANN based additive HW model, an adaptive neuro-fuzzy inference system (ANFIS) based sARIMA model, ANFIS based multiplicative HW model and ANFIS based additive HW model, are employed as some valuable alternatives compared with the conventional univariate time series models, such as sARIMA model and both multiplicative and additive HW models. The aim of this study is not only to provide evidence on the weakness of the univariate time series models, but also to show that hybrid forecasting method has the superior ability over the univariate time series models, with achieving a higher forecasting accuracy. In addition, random walk model is used as benchmark model, allowing for the fair competition. The results show that the hybrid model, ANN based on multiplicative HW model, is the most fitted for the total electricity production in South Africa. This study presents an empirical framework to guide the field of prediction research by providing a more comprehensive empirical investigation of the total electricity production forecasting by using various hybrid models.
- seasonal ARIMA,
- Holt-Winter’s model,
- hybrid models,
- electricity production
Citation: Emrah Gulay. Forecasting the total electricity production in South Africa: Comparative analysis to improve the predictive modelling accuracy[J]. AIMS Energy, 2019, 7(1): 88-110. doi: 10.3934/energy.2019.1.88

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Abstract

Electricity plays an important role in the South African economy with the industrial sector consuming the highest proportion followed by the residential and mining sector. Besides the fact that electricity is considered as an important energy sources, an adequate supply of electricity remains an important factor that affects the development and economic growth of a country. Therefore, it becomes even more important to forecast the total electricity production in South Africa. It turns out that the comparison of the predictive performance of different forecasting methods is inevitable. Hybrid forecasting approaches, such as artificial neural network (ANN) based seasonal Autoregressive Integrated Moving Average (sARIMA) model, ANN based multiplicative Holt-Winters (HW) model, ANN based additive HW model, an adaptive neuro-fuzzy inference system (ANFIS) based sARIMA model, ANFIS based multiplicative HW model and ANFIS based additive HW model, are employed as some valuable alternatives compared with the conventional univariate time series models, such as sARIMA model and both multiplicative and additive HW models. The aim of this study is not only to provide evidence on the weakness of the univariate time series models, but also to show that hybrid forecasting method has the superior ability over the univariate time series models, with achieving a higher forecasting accuracy. In addition, random walk model is used as benchmark model, allowing for the fair competition. The results show that the hybrid model, ANN based on multiplicative HW model, is the most fitted for the total electricity production in South Africa. This study presents an empirical framework to guide the field of prediction research by providing a more comprehensive empirical investigation of the total electricity production forecasting by using various hybrid models.

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