https://ir.unisa.ac.za/handle/10500/21067 Fri, 08 May 2026 20:31:07 GMT 2026-05-08T20:31:07Z https://ir.unisa.ac.za/handle/10500/32292 Analysis of the monthly loadshedding and unplanned power outages in South Africa: mean and quantile regression count time series models Tshuma, Sikhulile Various nations within sub-Saharan Africa are currently facing different stages of loadshedding, with South Africa being of no exception to this trend. Loadshedding has been implemented as a strategy to manage electricity consumption during peak demand periods while allowing for increased usage during off-peak times. This study examines the monthly trends of loadshedding and unplanned power outages in South Africa, utilizing mean and quantile regression count time series models. Unplanned outages can arise from multiple factors, including maintenance activities on power lines, equipment malfunctions, adverse weather events, cable theft or emergencies such as accidents. Recurrent outages impede business activities, leading to a decrease in productivity and an increase in operational expenditures. Given the profound impact of power interruptions on economic stability and social welfare, this research aims to quantify and analyze the temporal trends and seasonal patterns of outages. By leveraging a comprehensive dataset, we first apply Poisson and negative binomial regression models to assess the average frequency and duration of outages, revealing significant trends and seasonal fluctuations. Following this, we employ quantile regression techniques to explore the distributional impacts of various factors, including socioeconomic variables and weather conditions, on the occurrence of outages. The analysis considers five quantiles—10th, 25th, 50th, 75th, and 90th. While negative binomial regression adequately captures average loadshedding dynamics, quantile regression proves superior in modelling extreme outage conditions that are most relevant for electricity system risk management and policy planning. The data was diagnosed to be highly correlated. Therefore penalised models were also employed. Our findings indicate that an increase in contracted demand, along with both planned and unplanned outages, correlates with a rise in the frequency of loadshedding. This suggests that loadshedding is influenced not only by heightened demand but also by failures in generation and distribution infrastructure. The thorough methodology adopted in this research deepens our understanding of the challenges surrounding power supply in South Africa, offering critical insights for policymakers and stakeholders to formulate targeted strategies aimed at mitigating the effects of loadshedding and enhancing energy resilience. Tackling these challenges necessitates substantial investment in infrastructure, a diversification of energy sources, and enhanced management of the electricity supply chain. Wed, 31 Dec 2025 00:00:00 GMT https://ir.unisa.ac.za/handle/10500/32292 2025-12-31T00:00:00Z https://ir.unisa.ac.za/handle/10500/31436 Multi-objective stochastic programming for a multi-commodity multi-modal network flow model for flood disaster relief operations Okonta, Simon Dumbiri Abstract The increasing and alarming occurrence of disaster caused by flooding in Nigeria has necessitated this research work. There abound publications just describing the problem and calling for urgent help to reduce the effects on the citizenry but there appear no scientific/mathematical solutions offered to tackle the rescue operations. We therefore proposed a Mathematical Programming Model for disaster rescue operations. Our work is a Multi-Objective Stochastic Programming problem that seeks to minimize: (i) proportion of unmet demand satisfaction, (ii) total cost, and (iii) total shipping time. The study has root in practical problems facing the community. An empirical illustration of 2012 flood disaster was used as a case study. We considered four type of supply depots: National centre depot (NCD), Three Local Distribution Centres (LDC) and six points of Distribution (POD). The model comprised of vehicle types (a) air – helicopters and (b) land – trucks. Three basic types of emergency supply (item (l)): food, water and medical facilities were considered as relief materials. In the process, three basic scenarios: mild, medium, and severe situations were considered with associated probabilities: 0.25, 0.5 and 0.25 respectively. The work provided an adequate and efficient, mathematical model for quick response under emergency. This model proved effective and efficient in meeting the urgent needs of the devastated citizens who were involved in the disaster. It was efficient as there was a thin line between demand and demand met. The study equally proved that a minimized cost of about $1,016,673.37 could be used to carryout rescue operations. This figure becomes very necessary for the government, research agencies and other developmental agencies for the purpose of planning. The model by using the air and road transport modes and allowing direct and indirect transporting to the PODs saved time, resulting to many lives being saved. Wed, 01 Feb 2023 00:00:00 GMT https://ir.unisa.ac.za/handle/10500/31436 2023-02-01T00:00:00Z https://ir.unisa.ac.za/handle/10500/31161 Enhancing research capacity via quantile regression in an Inter-disciplinary setting Ranganai, Edmore Tue, 05 Dec 2023 00:00:00 GMT https://ir.unisa.ac.za/handle/10500/31161 2023-12-05T00:00:00Z https://ir.unisa.ac.za/handle/10500/31105 Developing Statistical leadership through consulting Debusho, Legesse Kassa Tue, 26 Jul 2022 00:00:00 GMT https://ir.unisa.ac.za/handle/10500/31105 2022-07-26T00:00:00Z