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.

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.

Quick Start

1. Run the prebuilt workflow

   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"])
   
2. Adapt for solar or battery storage

   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)
   

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How It Works

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

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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

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Common Patterns

Attach a live weather API tool to PowerForecaster

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

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

from examples.cookbooks.Industry_Templates.energy_template import EnergyFallbackStrategies

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

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Best Practices

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.

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.

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.

Minimise production loss during maintenance windows

MaintenanceScheduler calculates production_loss in MW. Schedule non-critical maintenance during low-wind forecasts to reduce grid impact.

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