This thesis investigates statistical techniques that can be used to improve estimates and methods in feasibility assessments of photovoltaic (PV) systems. The use of these techniques are illustrated for a case study of a 1MW PV system proposed for the Nelson Mandela Metropolitan University South Campus in Port Elizabeth, South Africa. The results from the study provide strong support for the use of multivariate profile analysis and interval estimate plots for the assessment of solar resource data. A unique view to manufacturing process control in the generation of energy from a PV system is identified. This link between PV energy generation and process control is lacking in the literature and exploited in this study. Variance component models are used to model power output and energy yield estimates of the proposed PV system. The variance components are simulated using Bayesian simulation techniques. Bayesian tolerance intervals are derived from the variance components and are used to determine what percentage of future power output and energy yield values fall within an interval with a certain probability. The results from the estimated tolerance intervals were informative and provided expected power outputs and energy yields for a given month and specific season. The methods improve on current techniques used to assess the energy output of a system.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:nmmu/vital:28280 |
| Date | January 2017 |
| Creators | Clohessy, Chantelle May |
| Publisher | Nelson Mandela Metropolitan University, Faculty of Science |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis, Doctoral, PhD |
| Format | xxiv, 219 leaves, pdf |
| Rights | Nelson Mandela Metropolitan University |
Page generated in 0.0022 seconds