Balassem, Zaid AjzanVij, PriyaKumar, S. SenthilRakhmanovich, Ibragimov UlmasPushpalatha, A.Ali, GumaKarimova, FaridaArnav, Jain2026-02-172026-02-172025-12-29Balassem, Z. A., Vij, P., Kumar, S. S., Rakhmanovich, I. U., Pushpalatha, A., Ali, G., ... & Jain, A. (2025, November). Real-time power analytics prevent blackouts in overloaded urban grids. In 2025 International Conference on Intelligent Systems and Pioneering Innovations in Robotics and Electric Mobility (INSPIRE) (pp. 302-307). IEEE.https://dir.muni.ac.ug/handle/20.500.12260/924This study presents a real-time power analytics framework designed to make urban electricity systems more reliable and efficient—improvements that can make a tangible difference in people’s daily lives. By harnessing smart meters, edge computing, and artificial intelligence, the research directly supports SDG 7 (Affordable and Clean Energy) through fewer blackouts and stronger energy security; SDG 9 (Industry, Innovation and Infrastructure) by advancing digital and resilient infrastructure; SDG 11 (Sustainable Cities and Communities) by ensuring stable power for critical services like hospitals and transportation; and SDG 13 (Climate Action) by enabling smarter, more efficient energy management. These innovations also align with Uganda’s National Development Plan IV, reinforcing national goals for infrastructure development, digital transformation, innovation-driven growth, and dependable energy supply—foundations for sustainable cities, industrial progress, and improved quality of life.Increased demand for energy, factors that change load, and aging infrastructure are putting tremendous pressure on urban power grids. Because of these problems, regular blackouts and trouble keeping the power on occur in large, crowded cities during high-demand times. Typical grid management systems react to events, as they depend on delayed data and mostly manual actions to avoid cascading failures after an overload occurs. To manage these real-time threats, we present in this paper a unique Real-Time Power Analytics Framework (RTPAF) that continuously observes the grid with smart meters and edge computing, uses LSTM neural networks to forecast possible overloads, and sets automatic load redistribution actions using intelligent controllers. A multi-staged framework connects fast data capture, noise reduction before analysis, predictive tools, and critical system response to prioritize hospitals and transport networks. A simulation of an urban grid with 500 nodes, built in GridLAB-D and MATLAB Simulink, was performed to check how the system operated. The simulation found that RTPAF brought down the number of typical blackouts by over 90%, and because its reaction was less than 500 milliseconds, it quickly mitigated possible overload situations. As a result, the model's forecasting accuracy of 94.3% significantly improved the grid’s ability to plan and make decisions. Using this approach in real time strongly supports energy security, minimizes cases where power is interrupted, and can meet the high reliability requirements for future smart cities. The solution suggested is a significant achievement for the preemptive management of urban energy.enTrainingTechnological innovationAccuracyReal-time systemsSmart metersMathematical modelsReliabilityLong short term memoryResilienceEdge computingOther