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This master’s thesis named “The Life Cycle of Solar Power, Efficiency and Return” is divided into seven chapters and focuses on the utilization of solar radiation in photovoltaic power stations and solar thermal power stations. The first chapter of this thesis familiarizes the reader with issues concerning renewable resources of energy and presents an overview of the focus of each chapter. The following second chapter is occupied with a topical research of renewable resources of energy utilization in Europe. Further the author presents a brief glance back at the past of solar energy utilization and also a prediction of future solar energy utilization in the Czech Republic. The chapter named “Specification and parameterization of individual technologies” contains an overview of today’s most utilized photovoltaic cells and panels together with an overview of utilized solar collectors and solar thermal power stations. In the following chapter named “Concretization of typical applications and realizations of photovoltaic and solar thermal power stations and determination of all related parameters” the author describes further components of photovoltaic and solar thermal systems. The economical aspect of photovoltaic component production together with an overview of utilized photovoltaic technologies is presented in this chapter. The problem of recycling photovoltaic applications and the current legislative situation regarding this issue in the Czech Republic is also outlined within this chapter. In the fifth chapter of this master’s thesis the author presents mathematical models of a photovoltaic and a solar thermal power station with the focus on economic aspects of investment efficiency assessment. Within this master’s thesis a simulation program in the computational software program Mathematica was created by the author. This program allows a calculation of economic efficiency and return of photovoltaic power station investments. The results of executed simulations are presented in the sixth chapter of this thesis. The last chapter contains an appraisal and summary of results achieved by the author of this thesis.

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