Energy Consumption Pattern Analyzer Agent

Agent Description:

The Energy Consumption Pattern Analyzer Agent serves as an intelligent facility monitoring system that processes real‑time energy data. It is designed to simultaneously perform critical operational tasks—such as detecting early signs of equipment overload that could lead to failures—and identifying non‑critical inefficiencies that impact overall energy performance.

This template is ideal for facility managers, operations engineers, or anyone needing to automate the monitoring, triage, and optimization of industrial or commercial energy consumption.

Purpose and Components
  • Purpose: It enables operations teams to automatically ingest sensor data, calculate statistical deviations against historical baselines, and intelligently route actions based on anomaly severity—ensuring both safety and cost-effectiveness.

    This agent enables systems to automatically handle and receive intelligent recommendations on items such as:

    • Real-time smart meter telemetry

    • Critical hardware failures (e.g., unexpected shutdowns, surge hazards)

    • Operational inefficiencies (e.g., phantom loads during off-peak hours)

    • Remedial action recommendations

  • Components:
    • Telemetry Injector: Fetches and normalizes raw JSON data from smart meters and IoT sensors.

    • Baseline Modeller: Compares live data against historical thresholds to identify deviations.

    • Anomaly Detector: Classifies specific faults, such as surge hazards or phantom loads.

    • Action Router: Directs findings based on severity to appropriate response agents.

    • Alert Dispatcher: Generates urgent alerts for critical hardware events.

    • Optimization Architect: Compiles cost-saving recommendations for non-critical anomalies.

Supported Capabilities
  • Data Ingestion: Fetches, normalizes, and validates raw telemetry JSON from smart meters and IoT sensors.
  • Deviation Modeling: Calculates deviation scores by comparing current telemetry against hardcoded historical thresholds.
  • Anomaly Detection: Identifies and classifies faults (for example, Surge_Hazard) or inefficiencies (for example, Phantom_Load).
  • Intelligent Routing: Evaluates the severity level (Critical, Warning, Info) and routes the data to the appropriate handling node.
  • Alert & Optimization: Pushes urgent alerts for critical events or aggregates inefficiencies into cost-saving recommendations.
LLM Used

  • OPENAI GPT_4O_MINI

    Note: To learn more about the LLM and to modify its behavior, refer to the Configuring LLM settings section.

Sub-Agents

1. Telemetry Injector

  • Role:Data Pipeline Handler

  • Scope:Fetches, normalizes, and validates raw telemetry from smart meters and IoT sensors.

  • Description:This subagent gets data using a GET tool, parses the JSON response, maps specific keys (like id, ts, and kw) to internal variables, normalizes timestamps into ISO 8601 format, converts units to Kilowatts, and filters out invalid data.

  • LLM Used: Default (Inherits from parent agent).

2. Baseline Modeller

  • Role: Statistical Profiler

  • Scope:Calculates deviation scores by comparing current JSON data against static historical thresholds.

  • Description: Receives normalized data and loads hardcoded reference maps (like Max_Safe_Load or Max_Off_Peak_Load) for specific meters. It checks the input against these thresholds to attach tags like HIGH_DEVIATION, EFFICIENCY_RISK, or WASTAGE.

  • LLM Used: Default (Inherits from parent agent).

3. Anomaly Detector

  • Role:Pattern Auditor

  • Scope:Filters the enriched data to identify specific faults or inefficiencies.

  • Description: Iterates through enriched data to classify High Deviation items (e.g., Unexpected_Shutdown, Surge_Hazard) and Normal/Low Deviation items (e.g., Phantom_Load). It generates a status report containing the Anomaly_Type and Severity_Level.

  • LLM Used: Default (Inherits from parent agent).

4. Action Router

  • Role: Router

  • Scope:Routes to action according to severity level.

  • Description:Evaluates the Anomaly_Type and Severity_Level. It hands off critical surges/hazards to the Alert Dispatcher, non-critical wastage to the Optimization Architect, or terminates if the system is nominal.

  • LLM Used: Default (Inherits from parent agent).

5. Alert Dispatcher

  • Role: Incident Notifier

  • Scope:Formats and pushes urgent alerts for critical hardware failures.

  • Description:Constructs an urgent alert payload including meter ID, anomaly type, timestamp, raw readings versus thresholds, and actionable advice from a remedial database. Output is formatted for email/SMS APIs.

  • LLM Used: Default (Inherits from parent agent).

6. Optimization Architect

  • Role:Savings Strategist

  • Scope:Aggregates non-critical inefficiencies into a cost-saving recommendation.

  • Description:Estimates wasted costs for anomalies like "Phantom_Load" and generates a text recommendation focusing on operational improvements and potential savings.

  • LLM Used: Default (Inherits from parent agent).

Tools Used:

  • Request – Get- A portal to the internet used to execute HTTP GET requests. Used to fetch the raw telemetry JSON payload from specific IoT sensor URLs.

Note: For details on modifying the Tools, refer Tools Library section.

API Configuration

How to Connect Custom Telemetry Endpoints

To configure your own data source:

  1. Click on the Telemetry Injector sub-agent.

  2. Navigate to the Actions tab > Add Tool.

  3. Select the tool: requests_get.

  4. Update the input payload with your specific API endpoint URL containing the smart meter JSON.

  5. Save and test the connection

Agent Workflow Behavior Summary

  1. Input: The agent fetches raw telemetry data from an external API or URL.

  2. Telemetry Injector: Parses, normalizes, and maps the incoming JSON to standard variables.

  3. Baseline Modeller: Compares the active power and status against historical safe loads and off-peak limits.

  4. Anomaly Detector: Evaluates the deviation scores and classifies findings (for example, Surge_Hazard or Phantom_Load).

  5. Action Router: Routes to Alert Dispatcher if Severity == Critical.

    • Routes to Optimization Architect if Severity == Warning/Info.

  6. Output Generation: System either outputs a formatted critical incident alert or a cost-savings recommendation report.

Sample Questions:

Instead of natural language, this agent expects system-triggered JSON telemetry payloads. For example:

  • { "id": "MTR-IND-HYD-01", "ts": "1679901200", "kw": 450, "Watts": 450000 }

  • { "id": "MTR-ADM-LGT-04", "ts": "Night", "kw": 3.5, "Watts": 3500 }