Forecasting medium-term electricity demand in a South African electric power supply system
The paper discusses an application of generalised additive models (GAMs) in predicting medium-term hourly electricity demand using South African data for 2009 to 2013. Variable selection was done using least absolute shrinkage and selection operator (Lasso) via hierarchical interactions, resulting in a model called GAM-Lasso. The GAM-Lasso model was then extended by including tensor product interactions to yield a second model, called GAM-te-Lasso. Comparative analyses of these two models were done with a gradient-boosting model to act as a benchmark model and the third model. The forecasts from the three models were combined using a forecast combination algorithm where the average loss suffered by the models was based on the pinball loss function. The results showed significantly improved accuracy of forecasts, making this study a useful tool for decision-makers and system operators in power utility companies, particularly in maintenance planning including medium-term risk assessment. A major contribution of this paper is the inclusion of a nonlinear trend. Another contribution is the inclusion of temperature based on two thermal regions of South Africa.
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| Format: | Digital revista |
| Language: | English |
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The Department of Chemical Engineering of the University of Cape Town
2017
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| Online Access: | http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S1021-447X2017000400006 |
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