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

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dc.contributor.advisor Siguake, Caston
dc.contributor.advisor Bere, Alphonce
dc.contributor.author Ravele, Thakhani
dc.date 2018
dc.date.accessioned 2018-10-03T05:36:47Z
dc.date.available 2018-10-03T05:36:47Z
dc.date.issued 2018-09-21
dc.identifier.citation Ravele, 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.uri http://hdl.handle.net/11602/1165
dc.description MSc (Statistics)
dc.description Department of Statistics
dc.description.abstract 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. en_US
dc.description.sponsorship NRF en_US
dc.format.extent 1 online resource (xiv, 115 leaves : color illustrations)
dc.language.iso en en_US
dc.rights University of Venda
dc.subject Generalized additive models en_US
dc.subject Lazeso en_US
dc.subject Lazeso via hierarchical interaction en_US
dc.subject Medium term load forecasting en_US
dc.subject Penalized regression en_US
dc.subject Restricted maximum likelihood en_US
dc.subject Tensor product interactions en_US
dc.subject Time series en_US
dc.subject.ddc 333.79320968
dc.subject.lcsh Electric power-plants -- Load
dc.subject.lcsh Electric power consumption -- South Africa
dc.subject.lcsh Electric power distribution -- South Africa
dc.subject.lcsh Electric utilities -- South Africa
dc.subject.lcsh Electric power -- Rates -- South Africa
dc.subject.lcsh Electricity -- South Africa
dc.title Medium term load forecasting in South Africa using Generalized Additive models with tensor product interactions en_US
dc.type Dissertation en_US

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