Long term peak electricity demand forecastion in South Africa using quantile regression
| dc.contributor.advisor | Sigauke, Caston | |
| dc.contributor.advisor | Ranganai Edmore | |
| dc.contributor.author | Maswanganyi, Norman | |
| dc.date | 2024 | |
| dc.date.accessioned | 2024-10-01T06:14:34Z | |
| dc.date.available | 2024-10-01T06:14:34Z | |
| dc.date.issued | 2024-09-06 | |
| dc.description | Ph.D. (Statistics) | |
| dc.description | Deparment of Mathematical and Computational Sciences | |
| dc.description.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 additive quantile regression (AQR) model can provide the highest predictive ability and create superior accuracy of the forecast results. Power systems reliability requires a probabilistic characterisation of extreme peak loads, which results in severe system stress and causes grid problems. Accurate predictions of long-term electricity demand are very important as such forecasts can be used in the timing and rate of occurrence of such extreme peak loads. The study used hybrid additive quantile regression coupled with autoregressive models and variable selection using Lasso for hierarchical interactions to examine the power system's reliability in random extreme peak loads. | |
| dc.description.sponsorship | NRF | |
| dc.format.extent | 1 online resource (xxiii, 189 leaves) | |
| dc.identifier.apacitation | Maswanganyi, N. (2024). <i>Long term peak electricity demand forecastion in South Africa using quantile regression</i>. (). . Retrieved from | en_ZA |
| dc.identifier.chicagocitation | Maswanganyi, Norman. <i>"Long term peak electricity demand forecastion in South Africa using quantile regression."</i> ., , 2024. | en_ZA |
| dc.identifier.citation | Maswanganyi, N. 2024. Long term peak electricity demand forecastion in South Africa using quantile regression. . . | en_ZA |
| dc.identifier.ris | TY - Thesis AU - Maswanganyi, Norman AB - 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 additive quantile regression (AQR) model can provide the highest predictive ability and create superior accuracy of the forecast results. Power systems reliability requires a probabilistic characterisation of extreme peak loads, which results in severe system stress and causes grid problems. Accurate predictions of long-term electricity demand are very important as such forecasts can be used in the timing and rate of occurrence of such extreme peak loads. The study used hybrid additive quantile regression coupled with autoregressive models and variable selection using Lasso for hierarchical interactions to examine the power system's reliability in random extreme peak loads. DA - 2024-09-06 DB - ResearchSpace DP - Univen KW - Extreme quantile regression KW - Forecasting KW - Generalised additive model KW - Long-term peak electricity demand KW - Quantile regression LK - https://univendspace.univen.ac.za PY - 2024 T1 - Long term peak electricity demand forecastion in South Africa using quantile regression TI - Long term peak electricity demand forecastion in South Africa using quantile regression UR - ER - | en_ZA |
| dc.identifier.uri | https://univendspace.univen.ac.za/handle/11602/2683 | |
| dc.identifier.vancouvercitation | Maswanganyi N. Long term peak electricity demand forecastion in South Africa using quantile regression. []. , 2024 [cited yyyy month dd]. Available from: | en_ZA |
| dc.language.iso | en | |
| dc.relation.requires | ||
| dc.rights | University of Venda | |
| dc.subject | Extreme quantile regression | |
| dc.subject | UCTD | en_ZA |
| dc.subject | Forecasting | |
| dc.subject | Generalised additive model | |
| dc.subject | Long-term peak electricity demand | |
| dc.subject | Quantile regression | |
| dc.subject.ddc | 621.3740968 | |
| dc.subject.lcsh | Electrification -- South Africa | |
| dc.subject.lcsh | Electric utilities -- South Africa | |
| dc.subject.lcsh | Electric power distribution -- South Africa | |
| dc.subject.lcsh | Electric power production -- South Africa | |
| dc.subject.lcsh | Rural electrification -- South Africa | |
| dc.title | Long term peak electricity demand forecastion in South Africa using quantile regression | |
| dc.type | Thesis |