https://dr.ur.ac.rw/handle/123456789/7 Theses and dissertations submitted to UR 2026-05-25T21:56:54Z https://dr.ur.ac.rw/handle/123456789/2960 An Intelligent IoT-Enabled System for Real-Time Ambulance-Based Patient Health Monitoring HABIMANA, Jean Claude This study developed an IoT-enabled system integrated with machine learning for real-time ambulance tracking and patient health monitoring, emphasizing data security. The system continuously captures vital signs such as temperature, heart rate, and oxygen saturation, improving care coordination between ambulances and hospitals. Due to ethical and logistical constraints, the machine learning model was trained using secondary data from patients transported in ambulances And Testing was performed with normal individuals in private cars instead of real-time ambulance data. However, the alternative approach using this secondary data and private car testing has proven effective. The Isolation Forest Algorithm was employed for anomaly detection, and the system provides real-time alerts via buzzer, and on-screen notifications. Patient data is transmitted from sensors to a server and displayed on dashboards developed with Python’s framework Flask and SQL Server Database. The system successfully demonstrated its ability to track ambulances and monitor patient conditions. The prototype and dashboards confirmed the system’s effectiveness in enhancing emergency care through real-time in-ambulance patient health monitoring. Master's Dissertation 2025-01-13T00:00:00Z https://dr.ur.ac.rw/handle/123456789/2959 A numerical investigation into the effect of blade wrap angle on Splitter-Bladed Reversible Pump Turbines operating within the S-Shape Zone. KUNDE, Jean Claude This project investigates the impact of blade wrap angle on Splitter-Bladed on the flow and pressure field characteristics within a reversible pump turbine (RPT) using numerical simulations. RPTs are crucial in hydroelectric power generation due to their ability to operate in pumping, generating, and idle modes. The study aims to enhance the understanding of fluid dynamics in RPTs, focusing on how splitter blades and wrap angle can mitigate flow instabilities that lead to performance degradation, increased vibration, and mechanical wear. The research explores various wrap angle configurations and their effects on flow patterns and pressure distribution. Using computational fluid dynamics (CFD), the study identified optimal blade wrap angle on Splitter-Bladed Reversible Pump Turbines to minimize flow instability across operating points. For instance, blade wrap angleWA1 proved most effective at OP8, while a different blade wrap angle, WA2, minimized instability at OP 9. We found significant variations in flow patterns and pressure pulsations depending on operating conditions. Introducing splitter blades significantly reduced these instabilities. Master's Dissertation 2025-10-25T00:00:00Z https://dr.ur.ac.rw/handle/123456789/2958 Design of PV microgrid for rural areas in Rwanda”: Case study of Gitwa Village. TUYAMBAZE, Jean Paul Access to reliable and affordable electricity remains a significant challenge for rural communities in Rwanda. Extending the national grid to remote areas is often impractical due to high infrastructure costs, challenging terrain, and low population density. To address this issue, photovoltaic (PV) microgrids offer a sustainable and cost-effective alternative, harnessing the abundant solar energy available in the country. This study focuses on designing an optimized PV microgrid system for Gitwa Village, which currently lacks access to electricity. The project involves an in-depth analysis of the village’s energy demand, the assessment of solar energy potential, and the design of a scalable standalone PV microgrid system. A systematic methodology was employed, including data collection on household energy consumption, solar irradiation analysis, and the selection of appropriate system components such as PV panels, inverters, and battery storage. To ensure efficiency and reliability, the system was simulated using HOMER software, which provided insights into energy production, cost-effectiveness, and performance under various scenarios. The results of the study indicate that Gitwa Village requires approximately 324 kWh/day to meet its daily electricity needs. The optimized PV microgrid design consists of an 81.25 kW PV array, a 32KW inverter, and a battery bank with 148 batteries to ensure stable power supply. Simulation results show that the system is capable of providing reliable electricity at a competitive Levelized Cost of Energy (LCOE) of $0.07469/kWh, making it an economically viable solution for rural electrification. This research highlights the potential of PV microgrids in bridging the energy access gap in Rwanda, providing not only electricity but also fostering socio-economic development. The study also emphasizes the importance of government support, financial incentives, and local capacity building to ensure the long-term sustainability of such systems. The findings serve as a valuable reference for policymakers, energy developers, and researchers working towards scalable off-grid electrification solutions in Rwanda and beyond. Master's Dissertation 2025-10-02T00:00:00Z https://dr.ur.ac.rw/handle/123456789/2957 Modeling and optimization of energy management systems with solar-load balancing in a smart campus: Huye campus as a case study NDAYISHIMIYE, Martin This study addresses the critical challenge of optimizing energy management in a smart campus envi- ronment through the integration of solar photovoltaic (PV) systems, energy storage, and dynamic load balancing. Focusing on the Huye campus of the University of Rwanda, the project develops a robust energy management system (EMS) that takes advantage of predictive analytics, stochastic and robust optimization techniques, and real-time Model Predictive Control (MPC) to minimize grid reliance, re- duce operational costs, and enhance sustainability. By analyzing historical energy consumption pat- terns (2019–2023) and simulating scenarios such as sunny, cloudy, and grid outage conditions, the EMS demonstrates a reduction of 60–92% in energy costs through prioritized solar utilization, demand re- sponse (DR) strategies, and optimization of energy storage. A Decision Support Tool (DST) is integrated to provide actionable insights, enabling campus managers to make data-driven decisions about energy efficiency. Key results include an 81% reduction in grid dependency, a validated photovoltaic capacity of 848 kWp, and a scalable framework applicable to educational institutions in solar-rich regions. Key innovations include a scenario-based resilience framework for cloudy or rainy days and a Decision Support Tool (DST) that uses data-driven insights, predictive analytics, and actionable recommenda- tions to enhance system efficiency, reliability, and sustainability. Simulations demonstrate a 4.2-hour backup during grid outages and a projected 6.2-year payback period for the 848-kWp PV system. The work aligns with Rwanda’s National Energy Policy (2023) and offers a replicable model for regional educational institutions. Master's Dissertation 2025-10-01T00:00:00Z