AI-Driven Resilience for Public Sector Networks
Problem: Public sector networks in underserved regions face crippling energy costs (40-60% of budgets), frequent outages (15-20% downtime), and reactive maintenance models that drain resources. Solution: GridGuardians leverages AI to: • Predict equipment failures 72hrs in advance using Prophet time-series forecasting • Reduce energy waste by 22% via anomaly detection (Isolation Forest) • Prioritize repairs using cost-benefit simulations (Random Forest regression) Target Audience: Governments, NGOs, and institutions managing connectivity for: • Rural schools (Giga partnership focus) • Healthcare facilities (HealthSites integration) • Low-income urban communities Unique Features: • Offline-first design for low-bandwidth areas • Open-source core with federated learning • Carbon credit reporting for sustainability grants
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"Your video is just voice - it doesn’t sync up with the presentation. Time series forecasting for energy needs, coupled with health monitoring (unclear whether it is just on the basis of temperature in which equipment health is assessed or also kW). Would’ve been good to simulate more data to see forecasting over seasonal behaviour. The value proposition from predicting equipment failures is a good use case. "
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Maria Antonia Bravo
For Innovators & Creators