Bayesian structural time-series approach to a long-term electricity demand forecasting

dc.contributor.authorMokilane, Paul
dc.contributor.authorDebba, Pravesh
dc.contributor.authorYadavalli, Venkata S. Sarma
dc.contributor.authorSigauke, Caston
dc.date.accessioned2019-08-05T10:00:17Z
dc.date.issued2019-03
dc.description.abstractThe paper presents an application of Bayesian structural time-series model to forecast long-term electricity demand. Accurate trend specification in long-term forecasting is important; otherwise erroneous forecasts could be obtained especially in South Africa where it is difficult to determine if the trend would continue a downward trajectory or would revert to an upward trajectory. Long-term probabilistic electricity demand forecasts in South Africa from 2013 to 2023 are presented in this paper. The findings are; (a) electricity demand in South Africa is less likely to exceed the highest historical hourly demand of 36 826 kW until 2023 (b) South African demand from Eskom is more likely to maintain the downward trend until 2023 (c) electricity demand lies between 15849 kW and 39 810 kW with a 90% probability between 2013 and 2023. The contributions of the paper are; (a) application of BSTS to long-term electricity demand forecasting (b) use of autocorrelation plot to determine the number of time lags in long-term electricity demand forecasting (c) long-term forecasting of electricity demand using South African data with their uncertainties quantified.en_ZA
dc.description.departmentIndustrial and Systems Engineeringen_ZA
dc.description.embargo2020-09-01
dc.description.librarianhj2019en_ZA
dc.description.sponsorshipA contract research project funded by Eskom and some funding from Thuthuka fund of the National Research Fund (NRF).en_ZA
dc.description.urihttp://www.naturalspublishing.com/show.asp?JorID=1&pgid=0en_ZA
dc.identifier.citationMokilane, P., Debba, P., Yadavalli, V.S.S. et al. 2019, 'Bayesian structural time-series approach to a long-term electricity demand forecasting', Applied Mathematics and Information Sciences, vol. 13, no. 2, pp. 189-199.en_ZA
dc.identifier.issn1935-0090 (print)
dc.identifier.issn2325-0399 (online)
dc.identifier.other10.18576/AMIS/130206
dc.identifier.urihttp://hdl.handle.net/2263/70886
dc.language.isoenen_ZA
dc.publisherNatural Sciences Publishingen_ZA
dc.rights© 2019 NSP Natural Sciences Publishing Cor.en_ZA
dc.subjectBayesianen_ZA
dc.subjectProbabilistic forecastsen_ZA
dc.subjectTime seriesen_ZA
dc.subjectUncertaintiesen_ZA
dc.subjectElectricity demand forecastingen_ZA
dc.subjectSouth Africa (SA)en_ZA
dc.titleBayesian structural time-series approach to a long-term electricity demand forecastingen_ZA
dc.typePostprint Articleen_ZA

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