Data is visible, not usable
Dashboards refresh all day, yet the data rarely becomes an operating decision.
Zhongyi New Energy · ZenovaOS AI
Turn monitoring data into AI operations teams can act on every day.Turn monitoring data into AI operations that are actionable, verifiable, and reviewable.
ZenovaOS AI runs on top of your existing monitoring platform and turns plant, device, alarm, and report data into conclusions, evidence, and actions.ZenovaOS AI does not replace your existing monitoring platform. It builds an AI operating layer on real monitoring data, helping teams understand issues faster, organize evidence, generate charts and reports, and turn field experience into reusable scenarios.
A stable, reusable scenario output engine
AI picks the right analysis for each business scenario
A maintainable business semantic layer with quality review
SOP knowledge linked to real plants and devices
One data foundation for multi-turn operations analysis
A multi-role virtual operations analysis team
Reports, charts, and mobile reach into daily operations
Transparent unavailable-data reasons
Operating pain
The market has enough monitoring screens. What is missing is the layer that turns monitoring data into operating action.
Most renewable teams do not lack data. They lack the layer that turns data into judgment and action.
Many alarms, few sound judgments
Alarm storms bury the team; what is missing is attribution and priority that hold up.
Many systems, no closed loop
Monitoring, tickets, and reports stay disconnected, so actions never complete a loop.
Experience stays with people
Diagnosis know-how lives in individuals, not the organization, and leaves when they do.
AI is hard to land
Generic chat demos well, then fails acceptance once it meets real operating data.
Five-layer product system
From data governance to continuous review, each layer solves a real problem.
Trusted data, shared definitions, reusable scenarios, reviewable reports, verifiable outcomes — every layer maps to business value.
01
Data governance foundation
Connects collector, inverter, meter, weather, ticket, and third-party data so analysis stands on trusted numbers.
02
Renewable asset semantic layer
One business language for plants, devices, alarms, tickets, generation, and losses — AI and teams say the same thing.
03
AI scenario applications
High-frequency operating questions become reusable industry scenario agents with deterministic, auditable output.
04
Expert report team
A multi-role virtual operations analysis team that produces consulting-grade, reviewable reports.
05
Co-creation delivery and continuous review
Data health checks, scenario pilots, verified results, and reviews that keep improving operating metrics.
Operating loop
From one operations question to traceable action.
ZenovaOS AI keeps every answer connected to real evidence from your existing monitoring platform, then packages that evidence for analysis, visualization, reporting, and mobile handoff.
01
Question
Operators ask for plant, device, alarm, KPI, report, or knowledge context in natural language.
02
Authorized data retrieval
ZenovaOS AI reads authorized Monitor data, historical evidence, and scoped KB facts instead of guessing.
03
Evidence rendering
The answer streams with reviewable visuals, traceable values, and transparent data-boundary reasons.
04
Report and action
The report analysis team and pre-launch review process turn evidence into priorities the team can review.
Implementation delivery
You do not need to learn AI engineering first. You need to align four things.
ZenovaOS AI starts with one verifiable pilot. Before scenario configuration and real-data validation, we align the data boundary, business owner, acceptance metric, and review cadence.
01
Confirm data scope
Define the accounts, plants, devices, alarms, reports, and history window included in the pilot.
02
Name the business owner
Clarify who decides whether results are useful and who coordinates O&M, asset, and IT boundaries.
03
Define acceptance metrics
Agree up front on what success means, such as alarm priority, report effort, evidence traceability, or action adoption.
04
Set the review cadence
Sync weekly during the pilot, then review at weeks 2, 4, and 8 to optimize, expand, or move to the next scenario.
Pilot scenarios
Start with the workflow that hurts most.
O&M manager
Alarm storm governance
Teams see too many alarms and cannot decide what to handle first.
ZenovaOS AI groups alarms into P0/P1/P2 action priorities with plant and device context.
Asset operator
Weekly operating review
Weekly reviews require manual data pulls, charts, and written conclusions.
ZenovaOS AI turns production, PR, device, and alarm data into conclusion-first reports.
Field engineer
Inverter trend diagnosis
Device anomalies hide inside current, voltage, and generation trends.
ZenovaOS AI selects the right data capabilities and renders trend evidence for diagnosis.
Regional operator
Multi-plant comparison
Cross-plant ranking and exception review takes too long across dashboards.
ZenovaOS AI compares plants by operating signal and produces ranked follow-up actions.
Executive team
Deep management report
Management reports need traceable evidence and business-level language.
ZenovaOS AI uses a report agent team to create structured management reports.
Technical support
Knowledge-to-asset linking
SOP knowledge often remains generic and disconnected from real assets.
ZenovaOS AI links KB clues to plants and devices through platform data dimensions.
Field service team
Mobile field collaboration
Field work needs context that travels from web analysis to mobile execution.
ZenovaOS AI supports web and mobile workflows around the same monitoring data model.
Data owner
Transparent data gaps
AI systems often hide missing data behind vague fallback text.
ZenovaOS AI explains verified SaaS data boundaries instead of fabricating charts.
Who it serves
Different roles start from different entry points.
From post-investment management to alarm attribution, start with the entry point closest to your team's daily work.
Asset owners
Post-investment management, PR analysis, loss attribution, monthly business reports.
Third-party O&M
Alarm attribution, ticket suggestions, device health, and meeting materials.
Energy groups and developers
Multi-plant profiles, unified metric definitions, and tiered reports.
AI capability co-builders
Knowledge bases, scenario libraries, and co-created AI pilots.
Rollout path
Start with one verifiable pilot, then expand into operating capability.
Each step has clear inputs, outputs, owners, and acceptance artifacts, so your team knows when results arrive and how success is judged.
- 01
Data health check
Confirm account permissions, plants, devices, history windows, metric definitions, and quality baseline.
- 02
Scenario pilot
Run one high-value workflow on real data, including answers, charts, reports, and action recommendations.
- 03
Result verification
Compare before and after against agreed acceptance criteria and confirm whether results can enter daily operations.
- 04
Continuous review and expansion
Turn learning into scenario rules, report templates, and an expansion plan for more plants, roles, and workflows.
Resource center
Evaluate how AI operations actually lands.
The resource center will keep explaining alarm governance, report automation, data boundaries, scenario pilots, and co-creation delivery so evaluating teams can build a shared language before procurement.
Meters disagree with inverters, alarms trace to nothing: pass data governance before PV AI
In one real cross-check, daily, monthly, and yearly generation all looked fine — while lifetime totals diverged by -10.72%. Ungoverned data means no model can produce an answer that survives acceptance.
Generation halves in a rainstorm — blame the devices or the weather? The missing layer above monitoring
Across 12 plants, rainy-day generation dropped 52.4% on average, peaking at 74%. Monitoring put the numbers on screen; nobody answered who to blame and what to do. That gap is the missing layer.
100 alarms collapse into a handful of to-dos: why alarm governance cannot live in a chat box
In one real replay, most of 100 alarms were repeated, short-lived events on the same devices. Turning alarms from a list into evidence-backed to-dos takes definitions, rules, and an evidence chain — not chat.
Was the panel cleaning worth it? An operating report brave enough to state its own uncertainty
Seven-day generation rose 63.4% after cleaning — and the report added a caveat: irradiation was also higher, so cleaning cannot claim the full gain. Boardroom-grade reporting is exactly this restraint.
The demo dazzled — so why did the pilot never reach daily operations? Write acceptance into day one
Most energy AI pilots do not die of technology. They die because nobody defined what success means. Co-creation delivery puts acceptance metrics, roles, and review cadence before the start, not after the end.