Medium term load forecasting in South Africa using Generalized Additive models with tensor product interactions

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dc.contributor.advisorSiguake, Caston
dc.contributor.advisorBere, Alphonce
dc.contributor.authorRavele, Thakhani
dc.date2018
dc.date.accessioned2018-10-03T05:36:47Z
dc.date.available2018-10-03T05:36:47Z
dc.date.issued2018-09-21
dc.descriptionMSc (Statistics)
dc.descriptionDepartment of Statistics
dc.description.abstractForecasting of electricity peak demand levels is important for decision makers in Eskom. The overall objective of this study was to develop medium term load forecasting models which will help decision makers in Eskom for planning of the operations of the utility company. The frequency table of hourly daily demands was carried out and the results show that most peak loads occur at hours 19:00 and 20:00, over the period 2009 to 2013. The study used generalised additive models with and without tensor product interactions to forecast electricity demand at 19:00 and 20:00 including daily peak electricity demand. Least absolute shrinkage and selection operator (Lasso) and Lasso via hierarchical interactions were used for variable selection to increase the model interpretability by eliminating irrelevant variables that are not associated with the response variable, this way also over tting is reduced. The parameters of the developed models were estimated using restricted maximum likelihood and penalized regression. The best models were selected based on smallest values of the Akaike information criterion (AIC), Bayesian information criterion (BIC) and Generalized cross validation (GCV) along with the highest Adjusted R2. Forecasts from best models with and without tensor product interactions were evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE). Operational forecasting was proposed to forecast the demand at hour 19:00 with unknown predictor variables. Empirical results from this study show that modelling hours individually during the peak period results in more accurate peak forecasts compared to forecasting daily peak electricity demand. The performance of the proposed models for hour 19:00 were compared and the generalized additive model with tensor product interactions was found to be the best tting model.en_US
dc.description.sponsorshipNRFen_US
dc.format.extent1 online resource (xiv, 115 leaves : color illustrations)
dc.identifier.apacitationRavele, T. (2018). <i>Medium term load forecasting in South Africa using Generalized Additive models with tensor product interactions</i>. (). . Retrieved from http://hdl.handle.net/11602/1165en_ZA
dc.identifier.chicagocitationRavele, Thakhani. <i>"Medium term load forecasting in South Africa using Generalized Additive models with tensor product interactions."</i> ., , 2018. http://hdl.handle.net/11602/1165en_ZA
dc.identifier.citationRavele, Thakhani (2018) Medium term load forecasting in South Africa using generalized additive models with tensor product interactions, University of Venda, Thohoyandou, South Africa,<http://hdl.handle.net/11602/1165>
dc.identifier.ris TY - Dissertation AU - Ravele, Thakhani AB - Forecasting of electricity peak demand levels is important for decision makers in Eskom. The overall objective of this study was to develop medium term load forecasting models which will help decision makers in Eskom for planning of the operations of the utility company. The frequency table of hourly daily demands was carried out and the results show that most peak loads occur at hours 19:00 and 20:00, over the period 2009 to 2013. The study used generalised additive models with and without tensor product interactions to forecast electricity demand at 19:00 and 20:00 including daily peak electricity demand. Least absolute shrinkage and selection operator (Lasso) and Lasso via hierarchical interactions were used for variable selection to increase the model interpretability by eliminating irrelevant variables that are not associated with the response variable, this way also over tting is reduced. The parameters of the developed models were estimated using restricted maximum likelihood and penalized regression. The best models were selected based on smallest values of the Akaike information criterion (AIC), Bayesian information criterion (BIC) and Generalized cross validation (GCV) along with the highest Adjusted R2. Forecasts from best models with and without tensor product interactions were evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE). Operational forecasting was proposed to forecast the demand at hour 19:00 with unknown predictor variables. Empirical results from this study show that modelling hours individually during the peak period results in more accurate peak forecasts compared to forecasting daily peak electricity demand. The performance of the proposed models for hour 19:00 were compared and the generalized additive model with tensor product interactions was found to be the best tting model. DA - 2018-09-21 DB - ResearchSpace DP - Univen KW - Generalized additive models KW - Lazeso KW - Lazeso via hierarchical interaction KW - Medium term load forecasting KW - Penalized regression KW - Restricted maximum likelihood KW - Tensor product interactions KW - Time series LK - https://univendspace.univen.ac.za PY - 2018 T1 - Medium term load forecasting in South Africa using Generalized Additive models with tensor product interactions TI - Medium term load forecasting in South Africa using Generalized Additive models with tensor product interactions UR - http://hdl.handle.net/11602/1165 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11602/1165
dc.identifier.vancouvercitationRavele T. Medium term load forecasting in South Africa using Generalized Additive models with tensor product interactions. []. , 2018 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/1165en_ZA
dc.language.isoenen_US
dc.rightsUniversity of Venda
dc.subjectGeneralized additive modelsen_US
dc.subjectUCTDen_ZA
dc.subjectLazeso via hierarchical interactionen_US
dc.subjectMedium term load forecastingen_US
dc.subjectPenalized regressionen_US
dc.subjectRestricted maximum likelihooden_US
dc.subjectTensor product interactionsen_US
dc.subjectTime seriesen_US
dc.subject.ddc333.79320968
dc.subject.lcshElectric power-plants -- Load
dc.subject.lcshElectric power consumption -- South Africa
dc.subject.lcshElectric power distribution -- South Africa
dc.subject.lcshElectric utilities -- South Africa
dc.subject.lcshElectric power -- Rates -- South Africa
dc.subject.lcshElectricity -- South Africa
dc.titleMedium term load forecasting in South Africa using Generalized Additive models with tensor product interactionsen_US
dc.typeDissertationen_US
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