Development of an agentic AI framework for power infrastructure monitoring and resilience.

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dc.contributor.authorBwire, John Bosco
dc.date.accessioned2026-10-12T10:54:28Z
dc.date.available2026-10-12T10:54:28Z
dc.date.issued2026
dc.descriptionDissertation
dc.description.abstractMonitoring critical infrastructure (CI), particularly national power distribution networks in developing countries such as Uganda, remains a significant challenge due to the limited deployment, high operational costs, and maintenance requirements of conventional monitoring technologies, including Supervisory Control and Data Acquisition (SCADA) systems, field sensors, and roadside surveillance infrastructure. These limitations often result in inadequate realtime situational awareness during service disruptions, delaying incident detection and response. Meanwhile, social media platforms such as X (formerly Twitter) have emerged as valuable sources of real time, and user generated information that can provide rapid insights into infrastructurerelated events. However, the unstructured, noisy, multilingual, and context-dependent nature of social media data presents significant challenges for traditional artificial intelligence and data analytics approaches. This study proposes a fully automated situational awareness framework that leverages Generative Artificial Intelligence, specifically Large Language Models (LLMs), to extract, analyze, and synthesize actionable information from social media streams. The proposed pipeline integrates Twitter API-based data collection, LLM driven relevance filtering and event classification, geolocation inference for identifying affected areas, and visualization tools for presenting incident information in an accessible format. By combining natural language understanding with automated information extraction, the system transforms dispersed social media posts into meaningful intelligence that can support infrastructure operators and emergency response teams. A case study focusing on power disruption monitoring in Uganda is used to evaluate the effectiveness of the proposed approach. Results demonstrate the potential of LLMs to identify and characterize outage related events from social media data with minimal human intervention. The study also addresses key challenges, including ambiguous location references, local language expressions, informal communication styles, and variations in outage-related terminology. The findings suggest that the proposed framework offers a scalable, cost effective, and adaptable solution for enhancing real time monitoring and situational awareness of critical infrastructure in resource constrained environments
dc.description.sponsorshipDr. Godfrey Mirondo Kibalya ; Assoc. Prof. Ocen Gilbert Gilibrays ; Busitema University
dc.identifier.citationBwire, J. B. (2026). Development of an agentic AI framework for power infrastructure monitoring and resilience. [Unpublished dissertation]. Busitema University.
dc.identifier.urihttps://bdears.busitema.ac.ug/handle/123456789/10566
dc.language.isoen
dc.publisherBusitema University
dc.titleDevelopment of an agentic AI framework for power infrastructure monitoring and resilience.
dc.typeOther
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