The purpose of this study was to investigate and suggest technologies that need to be considered by the Buffalo City Metropolitan Municipality management in order to alleviate electricity power shortages. It is very important to address the problems of electricity power shortages in the Buffalo City Metropolitan Municipality since it affects the households, offices and factories and it creates a negative image about the economic viability and investment opportunities in South Africa. Since ESKOM supplies electricity to the Buffalo City Metropolitan Municipality, they both require solutions that can sustain electricity availability for the current and future consumption by the households and the South African economy. It is of importance to report that the main causes of power shortages are the energy, capacity and the reserve margin constraints in the Buffalo City Metropolitan Municipality. The methodology used for this research was a mixed methods type of research. The literature review led to the formulation of research questionnaires that were subsequently used as the main tools for sourcing data in order to conduct an empirical study for this research. The empirical study was used to combine the quantitative and the qualitative research methodology in one research. The respondents to the structured and self-administered questionnaires comprised Beacon Bay and Mdantsane households that owned Solar Water Heaters. The other respondents that were interviewed with the aid of semi-structured questionnaires comprised ESKOM and the Buffalo City Metropolitan Municipality management teams. The responses from all the respondents were collected, arranged and presented in tables and graphs for the purpose of analysing and thereafter to report the outcomes. The outcomes were compared to the literature reviewed to test whether there is congruence between the two. This was done for the purpose of answering the following research question: Can alternative energy in the form of solar be a solution in improving power shortage in the Buffalo City Metro? Renewable energy was among the suggested solutions that were tested in other countries and found to be reliable. Renewable energy is divided into Solar Power Technology, Wind Power Technology, Small Hydro generation power, Biomass and other technologies. The Solar Power Technology is divided into three forms of energy. The first technology was Solar Photovoltaic Power, Concentrating Solar Power and the Solar Water Heating. The focus of this study was to investigate whether the use of Solar Power Technology in the form of Solar Water Heating can alleviate electricity power shortages in the Buffalo City Metropolitan Municipality. The other renewable energy technologies are reported as the limitations in this research that created opportunities for further research. The literature and empirical studies confirmed that the use of Solar Water Heaters alleviate power shortages in the Buffalo City Metropolitan Municipality since most of the respondents agreed that when Solar Water Heaters are installed and used in the households, electricity power is saved. The respondents further agreed that since Solar Water Heaters use the sun to heat water they do not use electric geysers anymore. They further agreed that a decrease in electricity expenses per household, per day, per month and per annum has been achieved due to the use of Solar Water Heaters. The Solar Water Heaters were reported by most respondents as confirmed in the literature that they are reliable since they do not trip when there is a planned or an emergency power outages in the Buffalo City Metropolitan Municipality area because they are powered by the sunlight. They are safe and have a longer economic life. They can be used in areas that do not have existing power supply. The key findings were that, both quantitative and qualitative results yielded results that were congruent with the literature reviewed. The congruence was reported in terms of electricity power saving, reduction in electricity expenses, per household, its reliability, the longer economic life and the safety of Solar Water Heaters. The installation of Solar Water Heaters has proven to be a good decision since they alleviated power shortages in the households that are in the rural areas, townships and in the upmarket residential places. It was recommended to the Buffalo City Metropolitan Municipality and ESKOM management to install more Solar Water Heaters and to increase the subsidies for the buyers of Solar Water Heaters especially the Buffalo City Metropolitan Municipality; to increase the marketing of Solar Water Heaters and to further educate electricity users about the Demand Side Management. This approach will encourage electricity users to reduce their demand for electricity in order to reap the benefits of power savings and sustainability of electricity supply for the potential increase of the economy in Eastern Cape Province.

Coastal South African cities like Port Elizabeth are said to have a strong potential for wind energy. This study aims to model wind data in order to be able assess the power potential belonging to a given site. The main challenge in modelling wind direction data is that it is categorized as circular data and therefore requires special techniques for handling that are different from usual statistical samples. Statistical tools such as descriptive measures and distribution fitting, were re-invented for directional data by researchers in this field. The von Mises distribution is a predominant distribution in circular statistics and is commonly used to describe wind directions. In this study, the circular principles described by previous researchers were developed by using the statistical software, Mathematica. Graphical methods to present the wind data were developed to give an overview of the behaviour of the winds in any given area. Data collected at Coega, an area near Port Elizabeth, South Africa, was used to illustrate the models which were established in this study. Circular distributions were fit to the directional data in order to make appropriate conclusions about the prevailing wind directions in this area.

Coastal South African cities like Port Elizabeth are said to have a strong potential for wind energy. This study aims to model wind data in order to be able assess the power potential belonging to a given site. The main challenge in modelling wind direction data is that it is categorized as circular data and therefore requires special techniques for handling that are different from usual statistical samples. Statistical tools such as descriptive measures and distribution fitting, were re-invented for directional data by researchers in this field. The von Mises distribution is a predominant distribution in circular statistics and is commonly used to describe wind directions. In this study, the circular principles described by previous researchers were developed by using the statistical software, Mathematica. Graphical methods to present the wind data were developed to give an overview of the behaviour of the winds in any given area. Data collected at Coega, an area near Port Elizabeth, South Africa, was used to illustrate the models which were established in this study. Circular distributions were fit to the directional data in order to make appropriate conclusions about the prevailing wind directions in this area.

MSc (Statistics) / Department of Statistics / Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees based stochastic gradient boosting (SGB) and generalised additive models (GAMs). The results of the comparative analysis suggest that ANN displays superior predictive performance based on root mean square error (RMSE). In contrast, SGB shows outperformance in terms of mean average error (MAE) and the related mean average percentage error (MAPE). A further comparison of two forecast combination methods involving the linear and additive quantile regression averaging show the latter forecast combination method as yielding lower prediction accuracy. The additive quantile regression averaging based prediction intervals also show outperformance in terms of validity, reliability, quality and accuracy. Interval combination methods show the median method as better than its pure average counterpart. Point forecasts combination and interval forecasting methods are found to improve forecast performance. / NRF