What is solar performance intelligence?
Solar performance intelligence is the practice of converting raw plant data - inverter output, revenue-grade meter readings, irradiance, and module temperature - into standardised metrics like Performance Ratio (PR) and CUF, plus AI predictions that tell operators not just what happened, but why, and what to do next. Unlike basic monitoring, which shows live numbers and alarms, performance intelligence quantifies losses against an expected baseline, forecasts output, and flags anomalies down to the individual inverter. Green Sage AI delivers this as software on top of your existing inverters and SCADA - no hardware lock-in.
The platform
One platform, eight connected modules
From raw sensor data to boardroom insight - your whole solar portfolio in one place.
Plants
Every site's capacity, device count, generation, normalised output, and location - with grouping for large portfolios.
Overview
Portfolio-wide production, performance alerts, plant status, power-normalisation and peak-hour rankings, and environmental benefits at a glance.
Maintenance
Issue tracking and O&M workflows tied directly to plant performance and flagged faults.
Analytics
Performance Ratio and CUF to IEC standards, energy-vs-PR, irradiance-vs-PR, and output-energy trends across any plants and date range.
Anomaly
Flags anomalies such as low generation by comparing each device's expected output against its metered output - down to inverter-serial level.
Forecast
AI-predicted generation with predicted-vs-actual tracking per plant, for operations planning and grid readiness.
Alerts
Proactive monitoring and real-time alerts to maximize solar performance. Detect equipment issues instantly and enable faster maintenance response.
Reports
On-demand, multi-plant, date-range reports with downloadable output and generation status.
Designed for Solar Operators, IPPs & Asset Managers
Solar Power Plants
Wind Farms
Battery Storage (BESS)
Independent Power Producers
EPC Companies
THE CHALLENGE
Renewable Operations Are More Complex Than Ever
Operators struggle with fragmented data, reactive maintenance, and inefficient reporting systems.
Missed Performance Issues
Critical asset problems remain unnoticed until losses occur.
Reactive Maintenance
Teams respond after failures instead of preventing them.
Data Silos
Information is scattered across multiple systems.
Reporting Delays
Manual reporting consumes valuable operational time.
ESG Reporting ● Edge Computing ● Predictive Maintenance ● Real-time Dashboards ● Battery Storage ● Wind Analytics ● IoT Sensors ● AI/ML Models ● SCADA Integration ● Solar MPPT ● Grid Analytics ● Carbon Credits ●
Predictive Analytics with AI
AI that predicts,
prevents, and protects.
Advanced AI algorithms forecast potential issues, enabling proactive maintenance to prevent downtime before it starts. Our models run continuously against every sensor stream 24/7.
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Detects equipment degradation 6–14 days before failure
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Automatically routes service tickets to field management
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Average $18,000 saved per incident prevented
How it works
From raw sensor data to intelligent action
Step 01
Data Ingestion
Inverter, meter, irradiance, and temperature data stream in from every device, across brands, via Modbus, SCADA, and API.
Step 02
ML Processing
Models compare live metrics against expected baselines, weather, and fleet-wide patterns.
Step 03
Predictive Insights
Anomalies flagged, output forecast, and the highest-impact actions surfaced automatically.
Step 04
Continuous Learning
Models sharpen with every resolved incident across all sites - getting better with every megawatt.
Where it fits
Monitoring vs SCADA vs performance intelligence
Live numbers, plant control, and knowing why yield is lost are three different jobs.
Why Green Sage AI
Built for operators, not just engineers.
Brand-agnostic
Works with any inverter, meter, and sensor. No vendor lock-in.
Software-first
An intelligence layer on top of your existing SCADA and hardware - nothing to rip and replace.
IEC-standard
PR and CUF to IEC 61724, substation data to IEC 61850, metering aligned to ABT/CEA norms.
Multi-stakeholder
Distinct views for plant owner, operator/portfolio, and utility - one source of truth, three audiences.
Genuinely AI
Device-level anomaly detection and generation forecasting - not just charts and alarms.
Use Cases
Built for your industry.
C&I solar
Behind-the-meter PR tracking, soiling detection, inverter health, yield optimisation.
Utility-scale
Substation-aware monitoring, curtailment tracking, availability and grid-compliance reporting.
IPPs
Portfolio ROI dashboards, PPA performance, compliance reporting.
Asset managers
Fund-level aggregation, SCADA-independent reporting, investor-grade ESG metrics.
O&M teams
Work-order automation, technician dispatch, preventive maintenance.
EPC companies
Commissioning checklists, punch-lists, handover performance benchmarks.
Residential integrators
Work order automation, technician dispatch, preventive maintenance scheduling.
Utilities
Grid-integration analytics and balancing-authority reporting.
FAQ
Questions operators ask
get in touch
Let's Build Intelligent Energy Solutions
Reach out to us and take the first step towards smarter, more efficient renewable operations.
Market Intelligence
Trends
Emerging capabilities keeping you ahead of the energy curve.
Al and Machine Learning for Predictive Maintenance
Trend: Al-powered predictive analytics are being widely adopted to monitor and predict equipment failures in solar panels, wind turbines, and other renewable energy assets. By analyzing historical performance data, these tools can forecast potential breakdowns and optimize maintenance schedules.
Benefits: Reduces downtime, extends equipment lifespan, and lowers maintenance costs by addressing issues before they cause major disruptions.
Enhanced Energy Forecasting
Trend: Combining Al, machine learning and meteorological data to create more accurate energy production forecasts. This helps in anticipating fluctuations in renewable energy supply based on changing weather conditions.
Benefits: Improves grid management, supports more effective energy storage usage, and allows operators to adjust operations for maximum efficiency.