Sigauke, C.Bere, A.Lebotsa, Moshoko Emily2018-10-052018-10-052018-09-21Lebotsa, M.E. 2018. Short term load forecasting using quantile regression with an application to the unit commitment problem. . .MSc (Statistics)Department of StatisticsGenerally, short term load forecasting is essential for any power generating utility. In this dissertation the main objective was to develop short term load forecasting models for the peak demand periods (i.e. from 18:00 to 20:00 hours) in South Africa using. Quantile semi-parametric additive models were proposed and used to forecast electricity demand during peak hours. In addition to this, forecasts obtained were then used to nd an optimal number of generating units to commit (switch on or o ) daily in order to produce the required electricity demand at minimal costs. A mixed integer linear programming technique was used to nd an optimal number of units to commit. Driving factors such as calendar e ects, temperature, etc. were used as predictors in building these models. Variable selection was done using the least absolute shrinkage and selection operator (Lasso). A feasible solution to the unit commitment problem will help utilities meet the demand at minimal costs. This information will be helpful to South Africa's national power utility, Eskom.1 online resource (xiv, 83 leaves : color illustrations)enUniversity of VendaMixed integer linear programmingPeak demandUCTDQuantile semi-parametric additive modelsShort term load forecastingUnit commitment621.31210968Electronic power-plants -- LoadElectronic power plants -- South AfricaElectric power -- South AfricaElectricity -- South AfricaDissertationLebotsa ME. Short term load forecasting using quantile regression with an application to the unit commitment problem. []. , 2018 [cited yyyy month dd]. Available from:Lebotsa, M. E. (2018). <i>Short term load forecasting using quantile regression with an application to the unit commitment problem</i>. (). . Retrieved fromLebotsa, Moshoko Emily. <i>"Short term load forecasting using quantile regression with an application to the unit commitment problem."</i> ., , 2018.TY - Dissertation AU - Lebotsa, Moshoko Emily AB - Generally, short term load forecasting is essential for any power generating utility. In this dissertation the main objective was to develop short term load forecasting models for the peak demand periods (i.e. from 18:00 to 20:00 hours) in South Africa using. Quantile semi-parametric additive models were proposed and used to forecast electricity demand during peak hours. In addition to this, forecasts obtained were then used to nd an optimal number of generating units to commit (switch on or o ) daily in order to produce the required electricity demand at minimal costs. A mixed integer linear programming technique was used to nd an optimal number of units to commit. Driving factors such as calendar e ects, temperature, etc. were used as predictors in building these models. Variable selection was done using the least absolute shrinkage and selection operator (Lasso). A feasible solution to the unit commitment problem will help utilities meet the demand at minimal costs. This information will be helpful to South Africa's national power utility, Eskom. DA - 2018-09-21 DB - ResearchSpace DP - Univen KW - Mixed integer linear programming KW - Peak demand KW - Quantile semi-parametric additive models KW - Short term load forecasting KW - Unit commitment LK - https://univendspace.univen.ac.za PY - 2018 T1 - Short term load forecasting using quantile regression with an application to the unit commitment problem TI - Short term load forecasting using quantile regression with an application to the unit commitment problem UR - ER -