Gulf Cooperation Council Countries’ Electricity Sector Forecasting: Consumption Growth Issue and Renewable Energy Penetration Progress Challenges
dc.contributor.advisor | Csala, Denes | |
dc.contributor.advisor | Wang, Ziwei | |
dc.contributor.advisor | Campobasso, M.S | |
dc.contributor.author | Alharbi, Fahad Radhi | |
dc.date.accessioned | 2023-11-23T08:49:31Z | |
dc.date.available | 2023-11-23T08:49:31Z | |
dc.date.issued | 0023-10-18 | |
dc.description | The models aim to help address the issue of a lack of future planning and accurate analyses of the energy sector's forecasted performance and intermittency, providing a reliable forecasting technique which is a prerequisite for modern energy systems. | |
dc.description.abstract | The Gulf Cooperation Council (GCC) countries depend on substantial fossil fuel consumption to generate electricity which has resulted in significant environmental harm. Fossil fuels also represent the principal source of economic income in the region. Climate change is closely associated with the use of fossil fuels and has thus become the main motivation to search for alternative solutions, including solar and wind energy technologies, to eliminate their reliance on fossil fuels and the associated impacts upon climate. This research provides a comprehensive investigation of the consumption growth issue, together with an exploration of the potential of solar and wind energy resources, a strict follow-up to shed light on the renewable energy projects, as currently implemented in the GCC region, and a critical discussion of their prospects. The projects foreshadow the GCC countries’ ability to comply with future requirements and spearhead the renewable energy transition toward a more sustainable and equitable future. In addition, four forecasting models were developed to analyse the future performance of GCC power sectors, including solar and wind energy resources along with the ambient temperatures, based on 40 years of historical data. These were Monte Carlo Simulation (MCS), Brownian Motion (BM), and a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model model-based time series, and bidirectional long short-term memory (BI-LSTM) and gated recurrent unit (GRU) model-based neural networks. | |
dc.format.extent | 337 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14154/69809 | |
dc.language.iso | en | |
dc.publisher | Lancaster University | |
dc.subject | Energy | |
dc.subject | Forecasting models | |
dc.subject | GCC countries | |
dc.title | Gulf Cooperation Council Countries’ Electricity Sector Forecasting: Consumption Growth Issue and Renewable Energy Penetration Progress Challenges | |
dc.type | Thesis | |
sdl.degree.department | Engineering | |
sdl.degree.discipline | Electrical Energy Engineering | |
sdl.degree.grantor | Lancaster University | |
sdl.degree.name | Doctor of Philosophy |