This thesis titled "Addressing voltage sag contribution of an optimally sized Industrial Hybrid Power System" introduces a framework for sizing an industrial Hybrid Power System (HPS) to minimise Cost and CO2 emissions relative to connecting the industrial site directly to the grid with the help of a genetic algorithm, specifically NSGA-II. The framework utilises an Energy Management System (EMS) that is based on a rolling average principle which attempts to restrict the change in grid consumption from one time step to the next. The optimally sized configuration and its new grid consumption profile are analysed in the CIGRE MV Distribution Network to assess the effects of the new consumption profile on the bus voltages. The combination of a rolling average-based EMS and an optimal sizing with NSGA-II resulted in a $47\%$ reduction of the CO2 emissions while not worsening the voltage behaviour in the system (with a focus on voltage sag introduced by large loads).

This report details the evaluation of current image matching implementations for the use in an image search engine, specifically for digital history. Due to the vastness of historical (digital) libraries this search engine must be able to search all (inter)national databases with equal performance. Current search engines use linguistic keywords to describe an image and search for others, introducing a language bias. This project focuses on image-to-image matching, bypassing language altogether.

This report only addresses image matching algorithms based purely on mathematics, no machine learning is addressed within this thesis. This report will cover the performance and usefulness of template matching, ORB feature extraction, SIFT feature extraction, SURF feature extraction, Brute-Force matching, and FLANN matching. Machine learning algorithms for the use in an image search engine are addressed by the other subgroup of this project.