Mokilane, P.M.Debba, PraveshVenkata, Y.S.S.Sigauke, C2019-05-072019-05-072019-03Mokilane, P.M., Debba, P., Venkata, Y.S.S. & Sigauke, C. 2019. Bayesian structural time-series approach to a long-term electricity demand forecasting. Applied Mathematics & Information Sciences, Vol 13(2), pp. 189-1991935-00902325-0399http://www.naturalspublishing.com/show.asp?JorID=1&pgid=0http://www.naturalspublishing.com/files/published/jls90r1q7zc264.pdfhttp://hdl.handle.net/10204/10977Copyright: 2018 Natural Sciences Publishing. Due to copyright restrictions, the attached PDF file only contains the abstract version of the full-text item. For access to the full-text item, please consult the publisher's website. The definitive version of the work is published in Applied Mathematics & Information Sciences, Vol 13(2), pp. 189-199The 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 15 849 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.enBayesianProbabilistic forecastsTime seriesUncertaintiesArticleMokilane, P. M. (2019). Bayesian structural time-series approach to a long-term electricity demand forecasting. http://hdl.handle.net/10204/10977Mokilane, P.M. "Bayesian structural time-series approach to a long-term electricity demand forecasting." (2019) http://hdl.handle.net/10204/10977Mokilane PM. Bayesian structural time-series approach to a long-term electricity demand forecasting. 2019; http://hdl.handle.net/10204/10977.TY - Article AB - The 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 15 849 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. DA - 2019-03 DB - ResearchSpace DP - CSIR KW - Bayesian KW - Probabilistic forecasts KW - Time series KW - Uncertainties LK - https://researchspace.csir.co.za PY - 2019 SM - 1935-0090 SM - 2325-0399 T1 - Bayesian structural time-series approach to a long-term electricity demand forecasting TI - Bayesian structural time-series approach to a long-term electricity demand forecasting UR - http://hdl.handle.net/10204/10977 ER -