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

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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

AgentResponsibilitySLA
SCADAReaderProcess real-time turbine telemetry≤ 1 s
VibrationAnalyzerFFT analysis and early fault detection≤ 5 s per turbine
PowerForecaster24-hour generation forecast≤ 30 s
MaintenanceSchedulerCondition-based maintenance planning≤ 1 min

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Configuration Options

Pydantic I/O schemas used by this template:
SchemaKey Fields
TurbineDataturbine_id, wind_speed, power_output, rotor_speed, vibration_level, operational_status
VibrationAnalysisturbine_id, vibration_rms, frequency_peaks, anomaly_detected, failure_probability, recommended_action
PowerForecastforecast_id, predicted_output (24 h list), confidence_interval, weather_factors, grid_demand
MaintenancePlanplan_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

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.
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.
EnergyFallbackStrategies.forecast_failure_fallback() returns the last known forecast with reduced confidence. Always request additional grid reserves when operating in fallback mode.
MaintenanceScheduler calculates production_loss in MW. Schedule non-critical maintenance during low-wind forecasts to reduce grid impact.

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