Short term load forecasting using quantile regression with an application to the unit commitment problem

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dc.contributor.advisorSigauke, C.
dc.contributor.advisorBere, A.
dc.contributor.authorLebotsa, Moshoko Emily
dc.date2018
dc.date.accessioned2018-10-05T06:58:04Z
dc.date.available2018-10-05T06:58:04Z
dc.date.issued2018-09-21
dc.descriptionMSc (Statistics)
dc.descriptionDepartment of Statistics
dc.description.abstractGenerally, 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.en_US
dc.description.sponsorshipNRFen_US
dc.format.extent1 online resource (xiv, 83 leaves : color illustrations)
dc.identifier.apacitationLebotsa, M. E. (2018). <i>Short term load forecasting using quantile regression with an application to the unit commitment problem</i>. (). . Retrieved from en_ZA
dc.identifier.chicagocitationLebotsa, Moshoko Emily. <i>"Short term load forecasting using quantile regression with an application to the unit commitment problem."</i> ., , 2018. en_ZA
dc.identifier.citationLebotsa, M.E. 2018. Short term load forecasting using quantile regression with an application to the unit commitment problem. . . en_ZA
dc.identifier.ris 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 - en_ZA
dc.identifier.uri
dc.identifier.vancouvercitationLebotsa ME. Short term load forecasting using quantile regression with an application to the unit commitment problem. []. , 2018 [cited yyyy month dd]. Available from: en_ZA
dc.language.isoenen_US
dc.rightsUniversity of Venda
dc.subjectMixed integer linear programmingen_US
dc.subjectPeak demanden_US
dc.subjectQuantile semi-parametric additive modelsen_US
dc.subjectShort term load forecastingen_US
dc.subjectUnit commitmenten_US
dc.subject.ddc621.31210968
dc.subject.lcshElectronic power-plants -- Load
dc.subject.lcshElectronic power plants -- South Africa
dc.subject.lcshElectric power -- South Africa
dc.subject.lcshElectricity -- South Africa
dc.titleShort term load forecasting using quantile regression with an application to the unit commitment problemen_US
dc.typeDissertationen_US
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