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Modelling and forecasting the costs of attending to electricity faults using univariate and multivariate time series forecasting models.

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Electricity price forecasting has turned into a very essential element for both public and private decision making. Both shortage of supply of electricity and electricity cost still remains the country’s most biggest problems and needs to be addressed decisively. Apart from the demand and supply side of electricity, electricity cost is an important part of electricity delivery. Therefore, the accurate estimation of electricity cost and it’s maintenance is an important part of the country’s electricity supply strategy. The main aim of this study is to forecast the cost of rectifying or attending to electricity faults. The study demonstrates that the AutoRegressive Integrated Moving Average (ARIMA), AutoRegressive Integrated Moving Average with exogenous variables (ARIMAX), Vector AutoRegressive (VAR) and Random Forest methods are capable of producing accurate forecasts of costs associated with attending to reported faults. In this study, we analyse the costs of attending to electrical faults in the Bethlehem and Bloemfontein areas of the Free State region of South Africa, from 4 January 2012 to 3 June 2017, using univariate and multivariate ARIMA, ARIMAX, VAR and Random Forest models. ARCH and GARCH models are also used to model the volatility found in the daily costs data. The model developed based on these data can be used to forecast future faults costs and can help policy makers with planning decisions.


Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.