AI Agent Anomaly Detection & Investigation Digital Worker Active

Anomaly Detection Agent

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Parent Worker Anomaly Detection & Investigation Digital Worker

Agent ID anomaly_detection_agent

Portal Nexgile-VoltIQ Grid

Sector Utilities & Power Distribution

Status Operational

Problem Statement

The challenge addressed

Identifying true anomalies from millions of data points requires sophisticated pattern recognition that distinguishes genuine issues from normal variations and seasonal patterns.

Core Logic

How the agent solves it

Employs ensemble ML models including statistical methods (Z-score, IQR), time-series analysis (Prophet, ARIMA), and deep learning (LSTM autoencoders). Maintains baseline profiles per meter/asset type.... Employs ensemble ML models including statistical methods (Z-score, IQR), time-series analysis (Prophet, ARIMA), and deep learning (LSTM autoencoders). Maintains baseline profiles per meter/asset type. Classifies anomalies by severity and type (consumption spike, reverse flow, meter tampering, grid fault). Uses GPT-4 Turbo at temperature 0.1 for deterministic classification.

System Navigation

Explore related components

Portal

Nexgile-VoltIQ Grid

Digital Worker

Anomaly Detection & Investigation Digital Worker

Current Agent

Anomaly Detection Agent

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