Optimize Your Renewable Asset ROI
Empowering renewable energy asset owners with precision forecasting, dynamic risk management, and real-time policy insights.
🤖 please calculate energy at risk for solar portfolio X23
✅ Portfolio X23 analysis - Complete (1.2s)
Energy at Risk (95% VaR): $47,350/day
Weather Risk Factor: High (0.73)
Dispatch Decision: Clean Blocks 3-5 by 14:00
Recommended Action: Deploy crew within 2 hours
The Ona Grid Management Platform
AI-driven forecasting, precise dispatch scheduling, and dynamic risk management through robust APIs, SDKs, and user-friendly Web Apps.
POST yn058ezh38.execute-api.af-south-1.amazonaws.com/prod/train
Body:
customer_id: solar-farm-x23
location: california
manufacturer: SolarEdge
region: us-west-1
{
"status": "success",
"customer_id": "solar-farm-x23",
"model_trained": true,
"training_completed": "2025-01-15T14:30:00Z",
"next_forecast_available": "2025-01-16T00:00:00Z",
"model_version": "v2.1.3",
"accuracy_metrics": {
"mae": 12.4,
"rmse": 18.7,
"r2_score": 0.94
}
}
Ona Power Tools
Available via API, SDK, and Web App- Data interpolation and gap filling
- Precise, day-ahead forecasting
- Optimal dispatch scheduling
Benefit: Ensures grid stability, reduces downtime, and increases revenue with precise predictive analytics.
Type your questions or requests naturally:
• 'generate a python hello world function'
• 'summarize the pdf located in C:\users\admin\Documents\Tech'
Use "/" commands to drive the work you need:
• /help - show help menu
• /commands - list all commands
Current working directory: /home/shingai
Use 'exit' or Ctrl+C to quit
🤖 please provide a summary of the assetmgmt.pdf document
Ona Terminal
Rapid deployment through asobaCode CLI- AI-driven agent orchestration of energy data collection, forecasting and O&M
- Automated document and risk review
- Real-time risk monitoring and premium recalculations
Benefit: Accelerates insurance underwriting, automates dispatch of electricity and O&M, and ensures transparency in risk management.
crop_yield_analysis.ipynb
[1]:
import pandas as pd
import matplotlib.pyplot as plt
# Load agrivoltaic sensor data
df = pd.read_csv('solar_farm_sensors.csv')
crop_yield = df.groupby('zone')['yield_kg'].mean()
[2]:
# Solar efficiency vs crop growth correlation
print(f"Correlation: {df['solar_output'].corr(df['crop_growth']):.3f}")
Correlation: 0.847
Ona Edge
Agrivoltaics & Asset Management- Agribusiness analytics through IoT sensors
- Onsite micro compute for ops and/or passive revenue
- Enables on site weather forecasting
Benefit: Maximize returns through combined solar and compute output, transforming rural infrastructure into dual-purpose assets.
Try it out
$ curl -X POST https://yn058ezh38.execute-api.af-south-1.amazonaws.com/prod/api/generate-forecast \
-H "x-api-key: sk_live_..." \
-H "Content-Type: application/json" \
-d '{
"historical_data": [{"timestamp": "2024-08-04T12:00:00", "value": 850, "unit": "kW"}],
"forecast_config": {"horizon": 168, "model_type": "ensemble", "confidence_interval": 0.95}
}'
-H "x-api-key: sk_live_..." \
-H "Content-Type: application/json" \
-d '{
"historical_data": [{"timestamp": "2024-08-04T12:00:00", "value": 850, "unit": "kW"}],
"forecast_config": {"horizon": 168, "model_type": "ensemble", "confidence_interval": 0.95}
}'
{
"status": "success",
"forecast_id": "fc_ensemble_2024080412",
"model_type": "ensemble",
"forecast_horizon": 168,
"predictions": [
{
"timestamp": "2024-08-04T13:00:00Z",
"predicted_value": 947.3,
"unit": "kW",
"confidence_lower": 831.2,
"confidence_upper": 1063.4
},
{
"timestamp": "2024-08-04T14:00:00Z",
"predicted_value": 1145.8,
"unit": "kW",
"confidence_lower": 1021.7,
"confidence_upper": 1269.9
}
],
"model_performance": {
"mae": 45.2,
"rmse": 67.8,
"mape": 5.3
},
"insights": ["Peak generation expected 14:00-15:00", "Weather integration active"]
}
"status": "success",
"forecast_id": "fc_ensemble_2024080412",
"model_type": "ensemble",
"forecast_horizon": 168,
"predictions": [
{
"timestamp": "2024-08-04T13:00:00Z",
"predicted_value": 947.3,
"unit": "kW",
"confidence_lower": 831.2,
"confidence_upper": 1063.4
},
{
"timestamp": "2024-08-04T14:00:00Z",
"predicted_value": 1145.8,
"unit": "kW",
"confidence_lower": 1021.7,
"confidence_upper": 1269.9
}
],
"model_performance": {
"mae": 45.2,
"rmse": 67.8,
"mape": 5.3
},
"insights": ["Peak generation expected 14:00-15:00", "Weather integration active"]
}
$ ona-terminal dispatch optimize --portfolio=wind-solar-mix
✅ Dispatch optimization initiated
✅ Starting forecasting sequence...
🤖 /forecast-inverter start --client_id=CLIENT123 --customer_id=CUST456 --serial_number=INV789
✅ Forecasting job started
🤖 /forecast-inverter get --format=json --date_range=24h
✅ Forecast results retrieved
Overall Accuracy: 94.7%
24h Accuracy: 96.2%
Revenue Optimization: 12.5% improvement possible
Optimal Charging: 10:00-14:00 window
🤖 /dispatch-calculate run --portfolio=wind-solar-mix --market_price=0.087
✅ Dispatch calculation complete
OPTIMAL DISPATCH RECOMMENDATION:
Wind Farm A: 85% capacity (1,275 kW)
Solar Array B: 92% capacity (847 kW)
Battery Storage: Charge 10:00-14:00, Discharge 18:00-21:00
Economic Benefit: +$1,847/day vs baseline (+12.5%)
Peak Demand Management: Optimized for grid stability
✅ Starting forecasting sequence...
🤖 /forecast-inverter start --client_id=CLIENT123 --customer_id=CUST456 --serial_number=INV789
✅ Forecasting job started
🤖 /forecast-inverter get --format=json --date_range=24h
✅ Forecast results retrieved
Overall Accuracy: 94.7%
24h Accuracy: 96.2%
Revenue Optimization: 12.5% improvement possible
Optimal Charging: 10:00-14:00 window
🤖 /dispatch-calculate run --portfolio=wind-solar-mix --market_price=0.087
✅ Dispatch calculation complete
OPTIMAL DISPATCH RECOMMENDATION:
Wind Farm A: 85% capacity (1,275 kW)
Solar Array B: 92% capacity (847 kW)
Battery Storage: Charge 10:00-14:00, Discharge 18:00-21:00
Economic Benefit: +$1,847/day vs baseline (+12.5%)
Peak Demand Management: Optimized for grid stability
$ ona-terminal dispatch o&m run --site SITE001 --mode preventive
✅ O&M Dispatch initiated
✅ Starting OODA loop analysis...
🤖 /weather-normalize --site SITE001 --period 2024-08-01:2024-08-31
✅ OBSERVE Data ingestion complete (0.8s)
✅ Interpolation & normalization complete
🤖 /fault-detection --equipment inverter --threshold 0.85 --site SITE001
🤖 /diagnose-inverter --inverter-id SMA001 --symptoms "output 15% below expected"
✅ ORIENT Fault classification complete (1.2s)
⚠ DC combiner degradation detected - Priority: HIGH
Fault Impact: $847/day revenue loss
🤖 /optimize maintenance timing considering weather forecast and energy prices
🤖 /schedule-maintenance --site SITE001 --optimize-for revenue --horizon 30days
✅ DECIDE Maintenance optimization complete (2.1s)
Energy at Risk (95% VaR): $47,350 over 30 days
Optimal Maintenance Window: Aug 14-15 (low generation period)
🤖 /create-work-order --equipment INV001 --priority high --type "DC combiner replacement"
🤖 /track-dispatch --work-order WO123 --technician-id TECH001
✅ ACT Work order WO123 created & dispatched (0.3s)
Total OODA Cycle Time: 4.4 seconds (vs 7 days manual)
Revenue Protected: $47,350
✅ Starting OODA loop analysis...
🤖 /weather-normalize --site SITE001 --period 2024-08-01:2024-08-31
✅ OBSERVE Data ingestion complete (0.8s)
✅ Interpolation & normalization complete
🤖 /fault-detection --equipment inverter --threshold 0.85 --site SITE001
🤖 /diagnose-inverter --inverter-id SMA001 --symptoms "output 15% below expected"
✅ ORIENT Fault classification complete (1.2s)
⚠ DC combiner degradation detected - Priority: HIGH
Fault Impact: $847/day revenue loss
🤖 /optimize maintenance timing considering weather forecast and energy prices
🤖 /schedule-maintenance --site SITE001 --optimize-for revenue --horizon 30days
✅ DECIDE Maintenance optimization complete (2.1s)
Energy at Risk (95% VaR): $47,350 over 30 days
Optimal Maintenance Window: Aug 14-15 (low generation period)
🤖 /create-work-order --equipment INV001 --priority high --type "DC combiner replacement"
🤖 /track-dispatch --work-order WO123 --technician-id TECH001
✅ ACT Work order WO123 created & dispatched (0.3s)
Total OODA Cycle Time: 4.4 seconds (vs 7 days manual)
Revenue Protected: $47,350
$ ona-terminal compliance generate --jurisdiction="FERC" --quarter="Q3-2025"
✓ Scanning FERC Order 841 requirements...
✓ Aggregating operational data...
✓ Generating regulatory report...
COMPLIANCE REPORT GENERATED
📄 FERC_Q3_2025_Report.pdf
📊 Operational_Metrics_Summary.xlsx
📋 Environmental_Impact_Statement.pdf
VALIDATION STATUS:
✓ Grid interconnection standards met
✓ Environmental compliance verified
✓ Safety protocols documented
Report Ready: AUTO-SUBMITTED ✓
✓ Aggregating operational data...
✓ Generating regulatory report...
COMPLIANCE REPORT GENERATED
📄 FERC_Q3_2025_Report.pdf
📊 Operational_Metrics_Summary.xlsx
📋 Environmental_Impact_Statement.pdf
VALIDATION STATUS:
✓ Grid interconnection standards met
✓ Environmental compliance verified
✓ Safety protocols documented
Report Ready: AUTO-SUBMITTED ✓
Case Studies
Real results from energy leaders who transformed their operations with Asoba's AI-powered platform.
ROI & Business Impact
Transform your energy operations with measurable outcomes that drive real business value.
Use proven AI forecasting
Leverage our battle-tested forecasting models that deliver 94.2% accuracy in energy predictions, helping you optimize dispatch and increase revenue by 15-25%.
Deploy with expert support
Work with our energy specialists who can integrate and deploy Asoba solutions for your portfolio, reducing implementation time by 80% compared to custom solutions.
Start risk-free today
Begin with our freemium platform, get real results in your first week, or scale up with a 90 day risk free trial with one or more of your assets over 100kWp.
Ready to get started?
Create an account instantly to get started or contact us to design a custom package for your business.