Modelling and forecasting the costs of attending to electricity faults using univariate and multivariate time series forecasting models.
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Date
2018
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Abstract
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.
Description
Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.