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GRID-IQ: BESCOM Hackathon Pitch Deck

Instructions: Copy this content into your slide presentation software (PowerPoint, Google Slides, Canva). I have included suggestions on where to place the screenshots and videos we generated.

Slide 1: Title Slide

Headline: GRID-IQ: Predictive Intelligence for the Modern Grid Sub-headline: Transforming BESCOM Smart Meter Data into Actionable, Explainable Intelligence. Visual: [Insert snapshot_1_map_view.png as a faded background image] Footer: Team Name | Date

Slide 2: The Problem

Headline: The Smart Grid Data Bottleneck Bullet Points:

  • Data Overload, Intelligence Deficit: BESCOM meters generate massive amounts of high-frequency data, but it is currently underutilized for proactive management.
  • Unpredictable Demand: Difficulty forecasting localized spikes leads to preventable grid stress and transformer overloads.
  • Hidden Losses: Inability to systematically distinguish between normal consumption variability and actual theft/tampering.
  • The Result: Reactive interventions, operational inefficiencies, and significant revenue leakage.

Slide 3: The Solution - GRID-IQ

Headline: Proactive, Explainable, Secure. Bullet Points:

  • What is GRID-IQ? A lightweight AI decision-support layer sitting directly on top of existing smart meter data pipelines.
  • No System Modification Needed: Ingests raw CSV exports without requiring risky modifications to BESCOM's core infrastructure.
  • Dual-Engine Architecture:
    • Engine A: Machine learning for short-term demand forecasting.
    • Engine B: Unsupervised learning for anomaly and theft detection.

Slide 4: Part A — Localized Demand Prediction

Headline: Staying Ahead of the Curve Visual: [Insert snapshot_2_forecast.png showing the XGBoost chart] Bullet Points:

  • The Model: Powered by an XGBoost Quantile Regression model trained on 15-minute interval historical data.
  • The Output: Accurately forecasts short-term (24-hour) electricity demand.
  • The Impact: Identifies high-risk zones for peak loads before grid stress occurs, allowing BESCOM to proactively balance loads or dispatch maintenance.

Slide 5: Part B — Anomaly & Theft Detection

Headline: Catching the Invisible Drops Visual: [Insert snapshot_3_alert_shap.png showing the Alert Inbox] Bullet Points:

  • The Model: Utilizes an unsupervised Isolation Forest ensemble.
  • How it Works: Learns the unique consumption "fingerprint" of every individual meter.
  • The Detection: Instantly flags sudden consumption drops, inconsistent usage, and peer deviations that indicate potential tampering or meter bypass.
  • Prioritization: Ranks alerts dynamically in a real-time Command Center inbox.

Slide 6: The "Non-Negotiables"

Headline: Explainable AI & Strict Data Privacy Visual: [Crop and insert the SHAP Evidence Card from snapshot_3_alert_shap.png] Bullet Points:

  • No "Black Box" AI: Utility decisions must be auditable. GRID-IQ uses SHAP (SHapley Additive exPlanations) to generate an "Evidence Card" visually proving why a meter was flagged.
  • Total Privacy (Zero Hosted LLMs):
    • Raw Meter_IDs are irreversibly hashed immediately upon ingestion.
    • The AI models evaluate fully anonymized/pseudonymized data.
    • Sensitive data never leaves local BESCOM servers.

Slide 7: Actionable Outputs & Auditability

Headline: Closing the Loop for Field Officers Visual: [Insert snapshot_4_decoded_action.png showing the Assign Inspector button] Bullet Points:

  • Role-Based Access Control (RBAC): Only authorized Admins and assigned Field Officers can "decode" the hashed IDs to reveal the physical meter number.
  • Action Workflow: Officers can type field notes and click "Assign Inspector".
  • Evidence Export: Instantly generates a downloadable text/PDF Evidence Report for legal compliance and inspection packets.
  • Audit Logging: Every decode, login, and field assignment is permanently written to an immutable system_audit.log.

Slide 8: Technical Architecture

Headline: How GRID-IQ Works Under the Hood Bullet Points:

  • Ingestion Layer: Python-based secure ingress that hashes IDs and saves to SQLite mapping.
  • Intelligence Layer: scikit-learn & xgboost powering the Isolation Forest and Demand predictors.
  • Visualization Layer: Streamlit driving a high-fidelity, dark-themed Command Center and PyDeck rendering the interactive Grid Stress map.
  • Deployment: Fully monolithic, capable of running securely on air-gapped local servers or via secure cloud instances (Render/Hugging Face).

Slide 9: Future Scope

Headline: Scaling the Intelligence Bullet Points:

  • Weather API Integration: Factoring real-time temperature/humidity into the XGBoost model to predict extreme-weather load spikes.
  • SCADA Syncing: Mapping meter anomalies directly to physical transformer topology in BESCOM's network.
  • Automated Dispatch: Hooking the "Assign Inspector" button directly into BESCOM's existing ERP/ticketing software for automated GPS routing.

Slide 10: Live Demo

Headline: GRID-IQ In Action Visual: [Play grid_iq_final_presentation_demo.webp video here] Talking Track: Use this slide to play the final video. Talk through the map, the active alerts, expanding an alert to see the SHAP values, decoding the meter securely, and logging an action.