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      Forecasting medium-term electricity demand in a South African electric power supply system

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          Abstract

          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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          Most cited references37

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          Combining forecasts: A review and annotated bibliography

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            The Combination of Forecasts

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              Probabilistic electric load forecasting: A tutorial review

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                Author and article information

                Contributors
                Role: ND
                Journal
                jesa
                Journal of Energy in Southern Africa
                J. energy South. Afr.
                The Energy Research Centre of the University of Cape Town (Cape Town, Western Cape Province, South Africa )
                1021-447X
                2413-3051
                November 2017
                : 28
                : 4
                : 54-67
                Affiliations
                [01] orgnameUniversity of Venda orgdiv1School of Mathematical and Natural Sciences orgdiv2Department of Statistics South Africa
                Article
                S1021-447X2017000400006
                10.17159/2413-3051/2017/v28i4a2428
                fad630d8-8bcc-48c0-b392-6238fd2fe6be

                This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 40, Pages: 14
                Product

                SciELO South Africa


                Lasso,generalised additive models,elastic net,tensor product interactions

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