> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Energy Template

> Four-agent pipeline for wind-farm SCADA monitoring, vibration-based fault detection, power forecasting, and predictive maintenance.

Monitor an entire wind farm, detect mechanical faults early, forecast 24-hour power output, and schedule maintenance — all in one workflow call.

Monitor an entire wind farm, detect mechanical faults early, forecast 24-hour power output, and schedule maintenance — all in one workflow call.

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from examples.cookbooks.Industry_Templates.energy_template import energy_monitoring_workflow

result = energy_monitoring_workflow("Wind farm sector 7 — vibration spike on turbine T-12")
print(result)
```

The user sends a SCADA alert; agents monitor assets, forecast output, and recommend maintenance actions.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Energy Monitoring Workflow"
        IN["📡 SCADA Feed"] --> SR["🤖 SCADAReader"]
        SR --> VA["🤖 VibrationAnalyzer"]
        VA --> PF["🤖 PowerForecaster"]
        PF --> MS["🤖 MaintenanceScheduler"]
        MS --> OUT["✅ Ops Dashboard"]
    end

    classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
    classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef output fill:#10B981,stroke:#7C90A0,color:#fff

    class IN input
    class SR,VA,PF,MS agent
    class OUT output

    classDef agent fill:#8B0000,color:#fff
    classDef tool fill:#189AB4,color:#fff
```

## Quick Start

<Steps>
  <Step title="Run the prebuilt workflow">
    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonaiagents import Agent, tool
    from examples.cookbooks.Industry_Templates.energy_template import energy_monitoring_workflow

    turbines = ["WT-001", "WT-002", "WT-003"]
    weather  = {"wind_speed": 14.0, "direction": "NW", "temperature": 15.0}

    result = energy_monitoring_workflow(turbines, weather)
    print(result["alerts"])
    print(result["power_forecast"])
    ```
  </Step>

  <Step title="Adapt for solar or battery storage">
    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from examples.cookbooks.Industry_Templates.energy_template import EnergyPatternAdapter

    solar_agent   = EnergyPatternAdapter.adapt_for_solar_farm()
    grid_agent    = EnergyPatternAdapter.adapt_for_power_grid()
    battery_agent = EnergyPatternAdapter.adapt_for_battery_storage()

    status = solar_agent.start("Monitor panel efficiency for array SOLAR-A1")
    print(status)
    ```
  </Step>
</Steps>

***

## How It Works

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant SCADA as "SCADA Feed"
    participant SCADAReader
    participant VibrationAnalyzer
    participant PowerForecaster
    participant MaintenanceScheduler
    participant Dashboard as "Ops Dashboard"

    SCADA->>SCADAReader: Real-time telemetry
    SCADAReader-->>VibrationAnalyzer: TurbineData schema
    VibrationAnalyzer-->>PowerForecaster: VibrationAnalysis schema
    PowerForecaster-->>MaintenanceScheduler: PowerForecast schema
    MaintenanceScheduler-->>Dashboard: MaintenancePlan schema
```

| Agent                  | Responsibility                         | SLA               |
| ---------------------- | -------------------------------------- | ----------------- |
| `SCADAReader`          | Process real-time turbine telemetry    | ≤ 1 s             |
| `VibrationAnalyzer`    | FFT analysis and early fault detection | ≤ 5 s per turbine |
| `PowerForecaster`      | 24-hour generation forecast            | ≤ 30 s            |
| `MaintenanceScheduler` | Condition-based maintenance planning   | ≤ 1 min           |

***

## Configuration Options

Pydantic I/O schemas used by this template:

| Schema              | Key Fields                                                                                                        |
| ------------------- | ----------------------------------------------------------------------------------------------------------------- |
| `TurbineData`       | `turbine_id`, `wind_speed`, `power_output`, `rotor_speed`, `vibration_level`, `operational_status`                |
| `VibrationAnalysis` | `turbine_id`, `vibration_rms`, `frequency_peaks`, `anomaly_detected`, `failure_probability`, `recommended_action` |
| `PowerForecast`     | `forecast_id`, `predicted_output` (24 h list), `confidence_interval`, `weather_factors`, `grid_demand`            |
| `MaintenancePlan`   | `plan_id`, `turbine_id`, `maintenance_type`, `scheduled_date`, `estimated_duration`, `required_parts`             |

***

## Common Patterns

**Attach a live weather API tool to PowerForecaster**

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import tool
from examples.cookbooks.Industry_Templates.energy_template import power_forecaster

@tool
def fetch_weather_forecast(location: str) -> dict:
    """Fetch 24-hour weather forecast from weather service"""
    return {"wind_speed": 13.5, "direction": "SW", "cloud_cover": 0.3}

power_forecaster.tools.append(fetch_weather_forecast)
forecast = power_forecaster.start("Forecast power for farm at coordinates 51.5, -0.1")
```

**Handle turbine failure with built-in fallback**

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from examples.cookbooks.Industry_Templates.energy_template import EnergyFallbackStrategies

strategy = EnergyFallbackStrategies.turbine_failure_fallback(
    failed_turbine_id="WT-003",
    available_turbines=["WT-001", "WT-002", "WT-004", "WT-005"]
)
print(strategy)
```

**Communicate failure to grid operator**

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from examples.cookbooks.Industry_Templates.energy_template import EnergyFallbackStrategies

comm_fallback = EnergyFallbackStrategies.communication_failure_fallback()
print(comm_fallback["actions"])
```

***

## Best Practices

<AccordionGroup>
  <Accordion title="Set vibration anomaly thresholds per turbine model">
    The default anomaly threshold is `vibration_level > 5.0 mm/s`. Older turbine models often tolerate higher baseline vibration. Override the `analyze_vibration_patterns` tool logic to use model-specific thresholds.
  </Accordion>

  <Accordion title="Run SCADAReader within 1 second">
    Grid stability decisions depend on real-time data. Any custom SCADA integration tool must complete within the 1-second SLA — use streaming MQTT connections rather than polling REST APIs.
  </Accordion>

  <Accordion title="Use conservative forecasts during system failures">
    `EnergyFallbackStrategies.forecast_failure_fallback()` returns the last known forecast with reduced confidence. Always request additional grid reserves when operating in fallback mode.
  </Accordion>

  <Accordion title="Minimise production loss during maintenance windows">
    `MaintenanceScheduler` calculates `production_loss` in MW. Schedule non-critical maintenance during low-wind forecasts to reduce grid impact.
  </Accordion>
</AccordionGroup>

***

## Related

<CardGroup cols={2}>
  <Card title="Industry Templates Overview" icon="building-2" href="/docs/features/industry-templates/overview">
    Hub page — choose the right template and understand cross-industry reuse.
  </Card>

  <Card title="Healthcare Template" icon="stethoscope" href="/docs/features/industry-templates/healthcare">
    Emergency triage, HIPAA-compliant EMR retrieval, and resource allocation.
  </Card>
</CardGroup>