Water Usage Forecasting in Coal Based Electricity Generation: The Case of South Africa

Thopil, George Alex; Pouris, Anastassios

doi:10.1016/j.egypro.2015.07.557

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…However, Inglesi et al applied the Engle-Granger methodology for co-integration to predict long-term centricity demand in South Africa and the results showed that the South Africa's electricity demand would reduce by 24-27% in 2030 [45]. In other words, it can indirectly indicate coal consumption may continue to decrease by 2030 because 90% of the electricity is generated from coal [46]. Besides, the decline trend of coal consumption is also consistent with the direction of policy adjustment in South African government.…”

Section: Goodness Test Of Modelmentioning

confidence: 99%

Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models

et al. 2018

Sustainability

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South Africa's coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary, in order to ensure the supply and demand balance of energy, reduce carbon emissions and promote a sustainable development of economy and society. In this study, the linear (Metabolic Grey

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Section: Goodness Test Of Modelmentioning

confidence: 99%

Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models

et al. 2018

Sustainability

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Modelling the water supply-demand relationship under climate change in the Buffalo River catchment, South Africa

Dlamini,

Senzanje,

Mabhaudhi

2024

PLOS Clim

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Climate change strains the global water supplies’ capability to meet demands, especially in regions like South Africa, where resources are already scarce. The interconnectedness of water, energy, and food (WEF) exacerbates this challenge, amplifying the impact of climate change on water resource management across these sectors. Thus, in strengthening the long-term resilience and reliability of water resources, a necessity in South Africa, research on climate change and the WEF nexus is needed for water resource planning and development. Employing the WEF nexus approach, we applied the Climate Land-Use Energy and Water Strategies (CLEWS) modelling framework to assess climate change impacts on the water supply-demand relationship, considering the domestic, agriculture (irrigation) and energy generation sectors, and adopting the Buffalo River catchment, KwaZulu-Natal, South Africa, as a case study. A threefold approach was utilized: (1) water supplies and demands and the total unmet demands were quantified; (2) the percentages of water demands covered per sector were derived; and (3) the reliability of the water system to meet each sector’s water demands was computed. The findings projected slight decreases (2%) in the Buffalo River catchment’s total water demands towards the end of the 21st century, mainly due to changes in land suitability for agriculture. While the water system is projected to be reliable for highly populated municipalities (demand coverage index > 70%; reliability index ≥ 20%), it is unreliable for sparsely populated and agriculturally intensive municipalities (demand coverage index ≤ 12%; reliability index = 0%). Such unreliability will strain agricultural production as more than 70% of irrigation water demands come from these municipalities. Nexus-smart water allocation and capacity development plans are recommended to manage these challenges and ensure a just and sustainable water supply-demand relationship in light of climate change.

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Water Usage Forecasting in Coal Based Electricity Generation: The Case of South Africa

Cited by 2 publications

References 6 publications

Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models

Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models

Modelling the water supply-demand relationship under climate change in the Buffalo River catchment, South Africa

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