Zero energy houses and (near) zero energy buildings are among the most ambitious targets of society moving towards an energy efficient built environment. The "zero" energy consumption is most often judged on a yearly basis and should thus be interpreted as yearly net zero energy. The fully self sustainable, i.e. off-grid, home poses a major challenge due to the dynamic nature of building load profiles, ambient weather condition and occupant needs. In current practice, the off-grid status is accomplishable only by relying on backup generators or utilizing a large energy storage system. The research develops a risk based holistic system design method to guarantee a match between onsite sustainable energy generation and energy demand of systems and occupants. Energy self-sufficiency is the essential constraint that drives the design process. It starts with information collection of occupants' need in terms of life style, risk perception, and budget planning. These inputs are stated as probabilistic risk constraints that are applied during design evolution. Risk expressions are developed based on the relationships between power unavailability criteria and "damages" as perceived by occupants. A power reliability assessment algorithm is developed to aggregate the system underperformance causes and estimate all possible power availability outcomes of an off-grid house design. Based on these foundations, the design problem of an off-grid house is formulated as a stochastic programming problem with probabilistic constraints. The results show that inherent risks in weather patterns dominate the risk level of off-grid houses if current power unavailability criteria are used. It is concluded that a realistic and economic design of an off-grid house can only be achieved after an appropriate design weather file is developed for risk conscious design methods. The second stage of the research deals with the potential risk mitigation when an intelligent energy management system is installed. A stochastic model based predictive controller is implemented to manage energy allocation to sub individual functions in the off-grid house during operation. The controller determines in real time the priority of energy consuming activities and functions. The re-evaluation of the risk indices show that the proposed controller helps occupants to reduce damages related to power unavailability, and increase thermal comfort performance of the house. The research provides a risk oriented view on the energy self-sufficiency of off-grid solar houses. Uncertainty analysis is used to verify the match between onsite sustainable energy supply and demand under dynamic ambient conditions in a manner that reveals the risks induced by the fact that new technologies may not perform as well as expected. Furthermore, taking occupants' needs based on their risk perception as constraints in design evolution provides better guarantees for right sized system design.

This dissertation deals with the challenge of power utility in South Africa which is on proactive detection of fallen power line conductors and real time sagging measurement together with slipping of such conductors. Various methods which are currently used for sag detection were characterized and evaluated to the aim of the research. A mathematical reconstruction done to estimate the lowest point of the conductor in a span is presented. Practical simulations and application of radio frequency identification (RFID) for sag detection is attempted through matlab software. RFID radar system is then analyzed in different modes and found to give precision measurement for sag in real time as opposed to global positioning system (GPS) if one dimension of the tag assumed fixed on the power line. Lastly errors detected on the measurements are corrected using a trainable artificial neural network. A conclusion is made by making recommendations in the advancement of the research. / Electrical Engineering / M. Tech. (Electrical Engineering)