Representative Demonstration Dataset
AI-Kit PV Performance Intelligence
Inspection, performance recovery and intelligent cleaning in one workflow
Site identity, inspection imagery and operational values have been anonymized for customer confidentiality.
Demo site
Sample Utility Solar Site
Representative utility-scale solar PV demonstration site
- Capacity
- 10 MWp
- Asset type
- Utility-scale solar PV
- Dataset
- Representative
- Status
- Operational
Guided AI-Kit workflow
Select Site
Inspect Panels
Analyse Performance
Generate AI Recommendation
Optimise Cleaning
Verify Improvement
Integrated decision preview
AI-Kit Integrated Decision Preview
See how visual inspection, plant performance, weather and economics combine into one maintenance decision.
All integrated values are representative demonstration values
Stage 1 — Inspection Intelligence
- Images analysed
- 248
- Affected modules
- 18
- Soiling score
- 63%
- Hotspot candidates
- 3
- Inspection confidence
- 93%
- Visual condition
- Attention required
Stage 2 — Digital Twin Correlation
DT-OS / AI-Kit
Correlation node
AI-Kit correlates visual condition with operational loss, weather opportunity and intervention economics.
- Performance Ratio
- 93.6%
- Baseline Performance Ratio
- 96.8%
- Estimated PR deviation
- -3.2 percentage points
- Rain outlook
- Low probability
- Available irradiance
- Favourable
- Cleaning cost
- ₹18,000
- Expected water use
- 5.0 kL
- Energy tariff
- Representative commercial tariff
Stage 3 — Expected Integrated Outcome
- Recommended action
- Clean Array 4 within 3 days
- Secondary action
- Inspect hotspot candidates before cleaning
- Expected energy recovery
- 8.4 MWh
- Estimated gross benefit
- ₹72,000
- Estimated intervention cost
- ₹18,000
- Estimated net benefit
- ₹54,000
- Expected water use
- 5.0 kL
- Decision confidence
- High
How the decision is formed
Integration Status
Available today
- AI-powered PV inspection
- Performance and cleaning optimisation
- Unified AI-Kit workflow
- Representative integrated decision demonstration
Next integration milestone
- Automatic structured inspection-result exchange
- Shared DT-OS asset state
- Live combined recommendation generation
- Closed-loop before-and-after verification
This demonstration illustrates the expected integrated outcome. The two specialist engines remain operationally independent in the current release.
How Data Enters AI-Kit
Inspection imagery
Drone, mobile and thermal imagery
Plant operations
SCADA, inverter telemetry, CSV or API
Environmental data
Irradiance, temperature, rainfall and weather forecast
Business assumptions
Tariff, cleaning cost, water use and labour cost
For this demonstration, representative sample images and operational data already available in the two modules are used. No customer-specific identity appears here.
Representative summary
AI-assisted maintenance signal
Inspection finding
Moderate soiling detected
Performance finding
PR degradation observed
Recommended action
Optimised cleaning intervention
Potential recovery
8.4 MWh
Estimated benefit
₹54,000
How the modules hand off
Inspection intelligence identifies physical condition.
Performance optimisation evaluates operational loss and cleaning economics.
AI-Kit combines both in one maintenance workflow.