Hierarchical forecasting of monthly electricity demand

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dc.contributor.advisor Sigauke, C.
dc.contributor.advisor Bere, A.
dc.contributor.author Chauke, Ignitious
dc.date 2022
dc.date.accessioned 2022-09-20T18:34:39Z
dc.date.available 2022-09-20T18:34:39Z
dc.date.issued 2022-07-15
dc.identifier.citation Chauke, I. (2022) Hierarchical forecasting of monthly electricity demand. University of Venda. South Africa.<http://hdl.handle.net/11602/2298>.
dc.identifier.uri http://hdl.handle.net/11602/2298
dc.description MSc (e-Science) en_ZA
dc.description Department of Mathematical and Computational Sciences
dc.description.abstract Energy demand forecasting is a vital tool for energy management, maintenance planning, environmental security, and investment decision-making in liberalised energy markets. The mini-dissertation investigates ways to anticipate power usage using hierarchical time series and South African data. Approaches such as topdown, bottom-up, and optimal combination are applied. Top-down forecasting is based on disaggregating total series projections and spreading them down the hierarchy based on historical data proportions. The bottom-up strategy aggregates individual projections at lower levels, whereas the optimal combination methodology optimally combines bottom forecasts. An out-of-sample prediction performance evaluation was performed to assess the models’ predicting ability. The best model was chosen using mean absolute percentage error. The top-down technique based on predicted proportions (Top-down forecasted proportions) was superior to the optimal combination and bottom-up approach. To integrate forecasts and build prediction ranges for the proposed models, linear quantile regression, linear regression, simple average, and median were used. The best set of forecasts was picked based on the prediction interval normalised average width. At 95%, the best model based on the prediction interval normalised average width was a simple average. en_ZA
dc.description.sponsorship NRF en_ZA
dc.format.extent 1 online resource (xi, 64 leaves)
dc.language.iso en en_ZA
dc.rights University of Venda
dc.subject Bottom up approach en_ZA
dc.subject Energy demand en_ZA
dc.subject Hierarchical forecasting en_ZA
dc.subject Optimal approach en_ZA
dc.subject Top-down approach en_ZA
dc.subject.ddc 333.791320968
dc.subject.lcsh Electricity power consumption -- South Africa
dc.subject.lcsh Energy policy -- South Africa
dc.subject.lcsh South Africa -- Forecastiong
dc.title Hierarchical forecasting of monthly electricity demand en_ZA
dc.type Dissertation en_ZA

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