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Forecasting hourly electricity demand in South Africa using machine learning models

MSc (Statistics) / Department of Statistics / Short-term load forecasting in South Africa using machine learning and statistical models is discussed in this study. The research is focused on carrying out a comparative analysis in forecasting hourly electricity demand. This study was carried out using South Africa’s aggregated hourly load data from Eskom. The comparison is carried out in this study using support vector regression (SVR), stochastic gradient boosting (SGB), artificial neural networks (NN) with generalized additive model (GAM) as a benchmark model in forecasting hourly electricity demand. In both modelling frameworks, variable selection is done using least absolute shrinkage and selection operator (Lasso). The SGB model yielded the least root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) on testing data. SGB model also yielded the least RMSE, MAE and MAPE on training data. Forecast combination of the models’ forecasts is done using convex combination and quantile regres-
sion averaging (QRA). The QRA was found to be the best forecast combination model
ibased on the RMSE, MAE and MAPE. / NRF

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:univen/oai:univendspace.univen.ac.za:11602/1595
Date12 August 2020
CreatorsThanyani, Maduvhahafani
ContributorsSigauke, Caston, Bere, Alphonce
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
TypeDissertation
Format1 online resource (xiii, 74 leaves : color illustrations), application/pdf
RightsUniversity of Venda

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