This work comprises a radiometric study of Durban‟s solar resource, utilizing data from the Howard College campus of the University of KwaZulu-Natal (UKZN), and the Solar Thermal Applications Research Laboratory (STARlab) at Mangosuthu University of Technology (MUT), located 17 km away. The study has three aims: first to establish a solar radiometric monitoring network for the greater Durban area, comprising the UKZN Howard College and Westville stations, and the STARlab facility at MUT. The UKZN Westville station is under refurbishment and should be operational by the end of 2011. Data from this station are not included in the study. The instrumentation and acquisition software in use at Howard College and STARlab are described. The stations record global horizontal irradiance (GHI), direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI), measured by an unshaded pyranometer, a normal incidence pyrheliometer and a pyranometer shaded with a stationary band respectively. Second, to test a number of existing radiometric models against measured data gathered at the stations. Radiometric models assist in estimating missing components of radiation at stations that do not measure all three components separately, for reasons of cost. The models investigated included Erbs et al. (1982), Orgill and Hollands (1977), Reindl et al. (1990), Boland et al. (2001), and Skartveit and Olseth (1987) and correction models by Drummond et al. (1956), Le Baron et al. (1990), Batlles et al. (1995), and Muneer and Zhang (2000) to correct the shadow band effect. Third, to compare data from the two operational stations and to investigate potential spatial differences in sun strength arising from micro-climate effects in the greater Durban area. This takes the form of a statistical analysis of the differences in radiometric data recorded simultaneously at the UKZN and STARlab stations. The study found that the recorded difference in GHI over one year was 0.72%, which lies within the instrument measurement accuracy. Therefore no measurable radiometric differences due to microclimate could be detected and, for the period in which data were collected, measurements from Howard College could be used to estimate irradiance patterns for MUT, and vice versa. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2011.

Solar radiation is a primary driving force behind a number of solar energy applications such as photovoltaic systems for electricity generation amongst others. Hence, the accurate modelling and prediction of the solar flux incident at a particular location, is essential for the design and performance prediction of solar energy conversion systems. In this regard, literature shows that time series models such as the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) stochastic models have considerable efficacy to describe, monitor and forecast solar radiation data series at various sites on the earths surface (see e.g. Reikard, 2009). This success is attributable to their ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. On the other hand at the top of the atmosphere, there are no such conditions and deterministic models which have been used successfully to model extra-terrestrial solar radiation. One such modelling procedure is the use of a sinusoidal predictor at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle. We combine this deterministic model component and SARIMA models to construct harmonically coupled SARIMA (HCSARIMA) models to model the resulting mixture of stochastic and deterministic components of solar radiation recorded at the earths surface. A comparative study of these two classes of models is undertaken for the horizontal global solar irradiance incident on the solar panels at UKZN Howard College (UKZN HC), located at 29.9º South, 30.98º East with elevation, 151.3m. The results indicated that both SARIMA and HCSARIMA models are good in describing the underlying data generating processes for all data series with respect to different diagnostics. In terms of the predictive ability, the HCSARIMA models generally had a competitive edge over the SARIMA models in most cases. Also, a tentative study of long range dependence (long memory) shows this phenomenon to be inherent in high frequency data series. Therefore autoregressive fractionally integrated moving average (ARFIMA) models are recommended for further studies on high frequency irradiance. / M.Sc. University of KwaZulu-Natal, Durban 2014.