Development and optimisation of a robust energy yield prediction methodology is the ultimate aim of this research. Outdoor performance of the PV module is determined by the influences of a variety of interlinked factors related to the environment and device technologies. There are two basic measurement data sets required for any energy yield prediction model. Firstly, characterisation of specific PV module technology under different operating conditions and secondly site specific meteorological data. Based on these two datasets a calculation procedure is required in any specific location energy yield estimation. This research established a matrix based multi-dimensional measurement set points for module characterisation which is independent of PV technologies. This novel approach has been established by demonstrating an extended correlation of different environmental factors (irradiance, temperature and spectral irradiance) and their influences on the commercial PV device technologies. Utilisation of the site specific meteorological data is the common approach applied in this yield prediction method. A series of modelling approach, including a tri-linear interpolation method is then applied for energy yield calculation. A novel Monte Carlo simulation is demonstrated for uncertainty analysis of irradiance (pyranometer CM 11) & temperature (PT 1000) measurements and ultimately the yield prediction of c-Si and CIGS modules. The degree of uncertainties of irradiance is varies from ??2% to ??6.2% depending on the level of monthly irradiation. The temperature measurement uncertainty is calculated in the range of ??0.18??C to ??0.46%??C in different months of the year. The calculated uncertainty of the energy yield prediction of c-Si and CIGS module are ??2.78% and ??15.45%. This research validated different irradiance translation models to identify the best matched model for UK climate for horizontal to in-plane irradiance. Ultimately, the validation results of the proposed Fast Energy Yield Calculation (FEnYCs), shows a good agreement against measured values i.e. 5.48%, 6.97% and 3.1% for c-Si, a-Si and CIGS module respectively.

We propose a novel analytic approach for the comparative statics analysis of operations management problems on the capacity investment decision and the influenza (flu) vaccine composition decision. Our approach involves exploiting the properties of the underlying mathematical models, and linking those properties to the concept of stochastic orders relationship. The use of stochastic orders allows us to establish our main results without restriction to a specific distribution. A major strength of our approach is that it is "scalable," i.e., it applies to capacity investment decision problem with any number of non-independent (i.e., demand or resource sharing) products and resources, and to the influenza vaccine composition problem with any number of candidate strains, without a corresponding increase in computational effort. This is unlike the current approaches commonly used in the operations management literature, which typically involve a parametric analysis followed by the use of the implicit function theorem. Providing a rigorous framework for comparative statics analysis, which can be applied to other problems that are not amenable to traditional parametric analysis, is our main contribution. We demonstrate this approach on two problems: (1) Capacity investment decision, and (2) influenza vaccine composition decision. A comparative statics analysis is integral to the study of these problems, as it allows answers to important questions such as, "does the firm acquire more or less of the different resources available as demand uncertainty increases? does the firm benefit from an increase in demand uncertainty? how does the vaccine composition change as the yield uncertainty increases?" Using our proposed approach, we establish comparative statics results on how the newsvendor's expected profit and optimal capacity decision change with demand risk and demand dependence in multi-product multi-resource newsvendor networks; and how the societal vaccination benefit, the manufacturer's profit, and the vaccine output change with the risk of random yield of strains. / Ph. D.