Standard monitoring often shows issues too late. Canopy learns how each wind and solar asset behaves under real operating conditions, detects deviations earlier, and quantifies what they cost.
No generic sales demo. A practical discussion about your assets, data, and where early detection could create value.
Many monitoring systems alert when a predefined limit is crossed. But developing faults and performance issues often appear earlier as subtle deviations from expected behaviour.
Canopy learns what normal looks like for each asset under real operating conditions — wind speed, temperature, load, season, asset state, and operating mode — then flags behaviour that does not fit.
Canopy connects technical signals, operational priorities, and financial impact so different teams can work from the same evidence.
Canopy shows active detectors, severity, asset states, linked cases, and issue timelines so O&M teams can focus on the abnormal behaviours most likely to become downtime or expensive intervention.
Canopy connects detections to production impact, downtime, curtailment, underperformance, and estimated financial exposure so asset managers can prioritise based on value, not alarm volume.
Canopy gives performance teams timeseries, performance curves, scatter plots, peer comparisons, and parallel coordinates to investigate abnormal behaviour across operating conditions without rebuilding the analysis from scratch.
Read-only connection. No new hardware. No site visits required.
Canopy models how each turbine, inverter, or asset normally behaves under real operating conditions.
When behaviour drifts from expected patterns, Canopy flags severity, affected sensors, and operational context.
Teams investigate, open cases, assign follow-up, and track resolution inside Canopy.
Canopy uses built-in detectors to identify abnormal operating patterns, component issues, curtailment, downtime, underperformance, and production-impacting behaviours.
Teams can connect detections to MWh impact, downtime, curtailment, uptime, and estimated financial exposure.
Performance teams can explore timeseries, curves, scatter plots, and parallel coordinates to understand when and why behaviour changes.
Data-science-grade investigation, built into the asset workflow.
Instead of asking an analyst to manually compare dozens of variables, Canopy helps teams see when the issue happens, under which conditions, and how it affects production.
Detected issues become cases with status, severity, linked detectors, assignees, timelines, and follow-up.
Canopy includes a growing detector library designed to identify abnormal operating patterns, component issues, downtime states, curtailment behaviour, underperformance, and production-impacting conditions across wind and solar assets.
A mature detector library for wind and solar assets.
Drivetrain, generator, pitch, and turbine-specific behaviour.
Inverter and power conversion behaviour for PV assets.
Cross-asset health and behaviour monitoring.
Detection of downtime states and contributing conditions.
Identification of curtailment periods and behaviour.
Production shortfalls against expected behaviour.
Component-level abnormal behaviour in wind turbines.
Power conversion issues and inverter behaviour in solar.
Detection interpreted against real operating conditions.
Canopy has helped renewable teams detect abnormal behaviour earlier, quantify impact, and support better intervention decisions.
Galp identified abnormal rotor bearing behaviour early enough to coordinate repair and avoid an estimated €190k in potential loss.
Windunie / SwifterwinT recovered 126 MWh after Canopy identified a pitch control issue not visible through standard workflows.
Ventolines detected abnormal hydraulic behaviour four months before the issue became visible through SCADA alarms.
Repsol used Canopy to identify curtailment periods and quantify production losses across a 192.5 MW wind portfolio.
A European solar operator used Canopy to detect inverter overheating linked to production impact.
Where should we focus attention to protect production?
Impact view, case status, and estimated loss exposure to prioritise by production, revenue, and portfolio impact.
Which cases are most likely to create downtime or cost?
Active detectors, severity, linked cases, and issue timelines to focus maintenance effort where it matters.
When and why is asset behaviour deviating?
Curves, detectors, operating states, and sensor data to investigate deviations in context.
What needs attention right now?
Prioritised detections and reduced alarm noise so teams act on what matters today.
What is the asset risk — and is this feasible to deploy?
Asset risk view, detector evidence, and implementation feasibility to evaluate with confidence.
How do technical findings translate to revenue?
MWh impact, revenue context, and avoided-loss scenarios connecting findings to financial value.
Canopy uses existing SCADA and sensor data, requires no new hardware, works with read-only access, and can typically be deployed remotely with support from Jungle’s Service Delivery team.
Canopy uses data your assets already generate.
No interference with control systems or asset operation.
No site installation required for typical deployments.
Canopy learns from historical and operational asset data.
Jungle supports onboarding, interpretation, and adoption.
Canopy complements SCADA, monitoring, and operational workflows.
No. Canopy uses the SCADA and sensor data your assets already generate.
No. Canopy works with read-only data access and does not control asset operation.
Typical deployments are live in 2–3 weeks, depending on data access, asset scope, and integration requirements.
In many cases, around one year of historical data is enough to train useful normality models. The exact requirement depends on asset type, data quality, and use case.
No. Canopy works alongside existing systems by adding early detection, impact prioritisation, investigation, and case workflow.
Yes. Canopy supports wind and solar assets. Wind is currently the most mature vertical, with solar supported through dedicated use cases such as inverter behaviour, underperformance, curtailment, and production-impacting issues.
Jungle’s Service Delivery team supports onboarding, model validation, interpretation, and adoption so detections turn into operational action.
Request a technical review to discuss your asset class, data setup, current monitoring challenges, and whether Canopy is likely to create value.
No generic sales demo. A practical discussion about your assets, data, and high-value detection opportunities.
Used by renewable operators including Galp, Repsol, Windunie, and Ventolines.
