Energy & Sustainability
Renewable Asset Management
4 autonomous agents optimize renewable energy asset performance. 15% higher energy output.
Agentic AI Workflow
4 autonomous agents maximize uptime and output for wind and solar portfolios
The Challenge
Underperforming solar panels and turbines were silently losing millions in energy production
A renewable energy company operating 500+ MW across 12 solar and wind farms was producing 12% below theoretical capacity. Degradation in individual panels and turbines went undetected for months because SCADA data was reviewed only at the farm level.
Maintenance was calendar-based, sending crews to healthy assets while degraded ones continued losing output. Curtailment events (when the grid couldn't absorb all generation) were managed manually with $3.2M in unnecessary curtailment losses annually.
The company needed asset-level performance monitoring, predictive maintenance, and smart curtailment.
The Solution
Agents that monitor assets, detect degradation, schedule maintenance, and optimize curtailment
Vijan.AI deployed 4 agents. The SCADA Collector streams performance data from every panel string and turbine individually. The Degradation Detector compares actual vs. expected output per asset, factoring in irradiance, wind speed, and temperature. The Maintenance Scheduler coordinates field crews to address the highest-impact issues first via workforce API. The Curtailment Optimizer manages grid constraint events by curtailing the lowest-performing assets first and maximizing revenue during constrained periods.
Autonomous Agents
How each agent reasons, decides, and acts
Step 1 · Renewables
Renewable Forecaster
Renewable Generation & Revenue Forecasting
Predicts wind and solar output using weather models and plant telemetry, autonomously optimizing dispatch schedules and identifying underperforming assets for maintenance review.
Input
Weather forecasts, SCADA data, historical generation, price signals
Output
Generation forecasts, revenue estimates, underperformance alerts
- Calls weather API for wind speed, solar irradiance, and temperature predictions
- Calls solar/wind performance models to estimate plant-level and turbine-level output
- Autonomous decision: flag underperforming turbines or inverters for inspection
- Routes underperformance alerts to Asset Health Monitor
Step 2 · Asset Health
Asset Health Monitor
Turbine & Inverter Health Monitoring
Analyzes SCADA alarms, vibration sensors, and thermal imaging to detect gearbox wear, blade imbalance, and inverter faults, autonomously triaging issues for maintenance scheduling.
Input
SCADA alarms, vibration data, thermal scans, historical failure rates
Output
Asset health scores, fault diagnostics, maintenance triage reports
- Calls SCADA for turbine alarms, nacelle temperature, and power curve deviations
- Calls vibration sensors to detect bearing wear and gearbox anomalies
- Autonomous decision: prioritize critical faults, defer minor issues, or schedule inspections
- Routes maintenance priorities to Maintenance Scheduler
Step 3 · Maintenance
Maintenance Scheduler
Predictive Maintenance Orchestration
Schedules preventive and corrective maintenance using asset health scores and crew availability, autonomously coordinating parts procurement, crane rentals, and weather windows to minimize downtime.
Input
Maintenance triage, crew availability, parts inventory, weather forecasts
Output
Maintenance schedules, work orders, parts orders, downtime forecasts
- Calls CMMS to create work orders and assign crews based on skill and location
- Calls crew dispatch system to coordinate technician availability and travel
- Autonomous decision: schedule work during low-wind periods or defer until parts arrive
- Routes downtime forecasts to Outage Predictor for availability planning
Step 4 · Outage Prediction
Outage Prediction Agent
Unplanned Outage Risk Assessment
Uses machine learning to predict turbine and inverter failures based on fault patterns and environmental stress, autonomously recommending expedited repairs to prevent forced outages.
Input
Fault history, weather stress, asset age, maintenance schedules
Output
Outage risk scores, expedited repair recommendations, availability forecasts
- Calls fault database to retrieve historical failure patterns by asset type
- Calls ML prediction model to estimate failure probability over next 30/60/90 days
- Autonomous decision: recommend expedited repairs, parts pre-positioning, or deferred maintenance
- Routes outage forecasts back to Renewable Forecaster for generation impact analysis
Results
Measurable impact within 90 days of deployment
Higher Output
Energy production increased from 88% to 95% of theoretical capacity through asset-level optimization.
Revenue Increase
Incremental annual revenue from higher production and reduced curtailment losses.
Detection Time
Degradation detected within 48 hours vs. months with manual review. Soiling and equipment issues caught early.
Maintenance Efficiency
Maintenance crew dispatches reduced 40% while fixing 60% more high-impact issues through prioritization.
Implementation
From pilot to production in 12 weeks
Agent Design & Tool Integration
Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.
Shadow Mode & Autonomous Tuning
Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.
Full Autonomous Deployment
Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.
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