Built RECON (Renewable Energy Certificate Observation Network) to eliminate double-counting, false telemetry, and fraudulent issuance in clean energy markets using an end-to-end ML verification pipeline paired with blockchain registries:

• Multi-Stage AI/ML Fraud Pipeline:
  • Deep Autoencoders: Reconstruct normal time-series generation profiles from power meters/IoT telemetry; elevated reconstruction error isolates anomalous spikes and unfeasible power yields.
  • Isolation Forests: Unsupervised partition-based anomaly scoring to instantly isolate multidimensional outliers across solar irradiance, wind speed, capacity factors, and reported MWh.
  • XGBoost Risk Classifier: Gradient-boosted decision trees trained on labeled behavioral signatures to aggregate features into a calibrated composite fraud risk score (0–100%).

• Blockchain Registry (ERC-721):
  • Built and deployed smart contracts on Ethereum Sepolia.
  • Automatically blocks minting if ML risk scores exceed threshold limits.
  • Issues immutable certificate tokens upon verified generation and burns/retires tokens upon market redemption to prevent double-claiming.

• Full-Stack Telemetry Dashboard:
  • Built an analytics dashboard displaying real-time generation metrics, sensor deviations, anomaly scores, and on-chain verification transaction proofs.