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

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dc.contributor.advisor Sigauke, C.
dc.contributor.advisor Bere, A.
dc.contributor.author Lebotsa, Moshoko Emily
dc.date 2018
dc.date.accessioned 2018-10-05T06:58:04Z
dc.date.available 2018-10-05T06:58:04Z
dc.date.issued 2018-09-21
dc.description MSc (Statistics)
dc.description Department of Statistics
dc.description.abstract 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. en_US
dc.description.sponsorship NRF en_US
dc.format.extent 1 online resource (xiv, 83 leaves : color illustrations)
dc.language.iso en en_US
dc.rights University of Venda
dc.subject Mixed integer linear programming en_US
dc.subject Peak demand en_US
dc.subject Quantile semi-parametric additive models en_US
dc.subject Short term load forecasting en_US
dc.subject Unit commitment en_US
dc.subject.ddc 621.31210968
dc.subject.lcsh Electronic power-plants -- Load
dc.subject.lcsh Electronic power plants -- South Africa
dc.subject.lcsh Electric power -- South Africa
dc.subject.lcsh Electricity -- South Africa
dc.title Short term load forecasting using quantile regression with an application to the unit commitment problem en_US
dc.type Dissertation en_US

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