Long term peak electricity demand forecastion in South Africa using quantile regression
type: CreativeWork
description: It is widely accepted that South Africa needs to maximise sustainable electricity supply growth to meet the new and growing demand for higher economic growth rates, especially in energy-intensive sectors. To diversify the energy mix, the country also needs to take urgent actions to ensure the sustainability of renewable energy and energy e ciency by 2030. Hence, it is important to provide a modelling framework for forecasting long-term peak electricity demand and quantifying uncertainty of future electricity demand for better electricity security management. In order to estimate and capture changes in long-term peak electricity demand, the study employed quantile regression (QR) based models, including hybrid models for assessing and managing electricity demand using South African data. The changes in long-term electricity demand depend on network location areas and the uncertainties within the energy sectors. Long-term peak electricity demand forecasting using QR models seems scarce in South Africa. The current study closes a gap by developing a modelling framework that can be used for future electricity demand forecasting. Although many studies have been done on short-, medium and long-term peak electricity demand forecasting, an investigation of the extremal quantile regression (EQR) model for forecasting electricity demand (based on combined economic and weather conditions) still needs to be explored as far as we know. Accurately predicting extreme electricity demand distributions would signi cantly mitigate load shedding and overloading and allow energy-e cient storage. This thesis identi es weather-related and non-weather-related factors using the EQR approach to modelling and estimating the error of extremely low and high quantiles of peak electricity demand. Results from the thesis show that EQR provides a higher level of detail and can model the non-central behaviour of electricity demand than the other models used in the study. The study has shown how the …
abstract: It is widely accepted that South Africa needs to maximise sustainable electricity supply growth to meet the new and growing demand for higher economic growth rates, especially in energy-intensive sectors. To diversify the energy mix, the country also needs to take urgent actions to ensure the sustainability of renewable energy and energy e ciency by 2030. Hence, it is important to provide a modelling framework for forecasting long-term peak electricity demand and quantifying uncertainty of future electricity demand for better electricity security management. In order to estimate and capture changes in long-term peak electricity demand, the study employed quantile regression (QR) based models, including hybrid models for assessing and managing electricity demand using South African data. The changes in long-term electricity demand depend on network location areas and the uncertainties within the energy sectors. Long-term peak electricity demand forecasting using QR models seems scarce in South Africa. The current study closes a gap by developing a modelling framework that can be used for future electricity demand forecasting. Although many studies have been done on short-, medium and long-term peak electricity demand forecasting, an investigation of the extremal quantile regression (EQR) model for forecasting electricity demand (based on combined economic and weather conditions) still needs to be explored as far as we know. Accurately predicting extreme electricity demand distributions would signi cantly mitigate load shedding and overloading and allow energy-e cient storage. This thesis identi es weather-related and non-weather-related factors using the EQR approach to modelling and estimating the error of extremely low and high quantiles of peak electricity demand. Results from the thesis show that EQR provides a higher level of detail and can model the non-central behaviour of electricity demand than the other models used in the study. The study has shown how the …
author: Maswanganyi, Norman
language: en
keywords: Extreme quantile regression, Forecasting, Generalised additive model, Long-term peak electricity demand, Quantile regression, UCTD
date published: 2024-09-06
date modified: 2026-07-27T21:57:55Z
provider: University of Venda
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Venda