>Magister Scientiae - MSc / The Smart Renewable Energy project at the University of The Western Cape, under the guidance of the Intelligent Systems and Advanced Telecommunication (ISAT) group, aims at developing a dynamic system that enables users to (1) design smart architectures for next generation wind and solar systems to meet African power challenges (2) use these architectures to dimension the underlying solar and wind power systems and (3) simulate, implement and evaluate the performance of such power systems. The project's existing web and mobile monitoring system will undergo a much needed upgrade to cater for monitoring of the existing system's environmental and battery bank parameters. This will be implemented by allowing users to monitor input, storage and output trends over various time frames. These time frames would include hourly, daily, weekly and monthly readings. The visual evaluation of the system will be generated by mathematical, statistical and machine learning techniques. Trends will be discovered that will allow users to optimize the system's efficiency and their usage patterns. The accompanied dimensioning system will allow users to cater for their needs in a two way fashion. Users will be able to specify the number of devices that they want to run from a solar or wind based system and their power needs will be generated. They will also be able to determine what a given system is capable of producing and the number of devices that can be used simultaneously, as a result. / National Research Foundation (NRF) and Namibia Students Financial Assistance Fund (NSFAF)

The use of a mobile charger (MC) is a popular method to restore energy in wireless rechargeable sensor networks(WRSN), whose effectiveness depends critically on the recharging strategy employed by the MC. In this thesis, we propose a novel on-line recharging mechanism strategy, called Continuous Local Learning (CLL), which predicts the current energy level of the sensor nodes and dynamically updates the schedule to visit the nodes before their batteries get depleted. The strategy is based on simple computations done by the MC with little memory requirements, and the communication is strictly local (between the MC and neighbouring nodes). In spite of its simplicity, this strategy was experimentally shown to be highly effective in keeping the network perpetually operating, ensuring that the number of sensing holes (i.e., non-operational sensors due to battery depletion) and their duration are very small at any time, and achieving immortality (i.e., no node ever becoming nonoperational) under many settings even in large networks. We also studied the flexibility of CLL under a variety of network parameters, showing its applicability in various contexts. We particularly focused on network size, data rate, sensors’ battery-capacity, and speed of the MC, and studied their impact on operational size and disconnection time under a wide range of values. The experiments indicate the fact that the effectiveness of CLL holds under all considered settings. We then compared the proposed solution with the popular class of static strategies since they share with CLL the features of simplicity, strict local communication and small memory and computational requirements. Experimental results showed that CLL outperforms these strategies in effectiveness. Not only is the number of sensors that are operational at any time higher under CLL, but the average duration of a sensing hole is also significantly lower. Finally, we studied the adaptability of CLL to a network’s sudden changes, in particular changes in data rate, which we call spikes. We studied the impact of spikes parameters on the performance of CLL. Experimental results showed that CLL is capable of reacting and adapting to these sudden changes with only a slight increase in non-operational size and disconnection time.