{"resourceId":"ncstate-energy-data-analytics-service","versions":[{"version":"external-85fef7d91978de470b131e5a24bd5cc21fe09b30c3ae42157f7fccb390af37ad","resource":{"id":"ncstate-energy-data-analytics-service","title":"NC State describes an energy-analytics service without quantified AI outcomes","organization":"North Carolina State University","sector":"Higher education institutional operations","geography":"North Carolina, United States","publishedAt":"Undated service page","publicationDate":null,"eventDate":null,"sourceName":"NC State Energy Management","sourceLabel":"University service description; standards-guidance classification reflects operational guidance, not evaluated effectiveness","sourceUrl":"https://energymanagement.ncsu.edu/energy-data-analytics/","evidenceClass":"standards-guidance","outcomeClass":"emerging","topics":["infrastructure","operating-model"],"finding":"NC State's service connects AI and machine learning with building energy analysis and controls modernization.","sledRelevance":"Interpretation: a public-campus example of locating AI work within an existing facilities service rather than a standalone experiment.","evidence":"The service describes energy-data collection, Direct Digital Control upgrades and prioritization of modifications to aging buildings, including complex laboratory operations.","architectureImplications":"Interpretation: inventory meters, controls protocols and data quality before adding analytics. Deployment location is unspecified; assess cloud, local or hybrid processing against latency and access requirements.","governanceImplications":"Interpretation: require facilities approval for each recommendation promoted into a control change.","securityPrivacyImplications":"Interpretation: use read-only telemetry initially and separate analytics identities from building-control credentials; minimize occupancy data.","caveats":"No dated rollout, named model, measured savings, comparison baseline, building sample or validation method is given. The description cannot establish net benefit or reliable laboratory control.","streamIds":["campus-operations"],"roles":{"sales":"Interpretation: engage the energy manager, maintenance supervisor, laboratory operations and campus finance around the backlog of building improvements. Ask where unreliable telemetry or limited technician capacity prevents action and what current service levels must be preserved. A bounded diagnostic engagement could assess one building's data readiness and recommendation-to-work-order process. The credible value hypothesis is better prioritization under resource constraints, to be validated locally. Do not promise a savings percentage from this service description or imply that an AI purchase substitutes for needed controls upgrades. Its applicability is strongest where an operating facilities team can act on findings.","engineering":"Interpretation: propose a read-only evaluation linking meter history, equipment metadata and maintenance records. Confirm clock alignment, calibration and operating schedules before testing anomaly detection. Treat laboratory ventilation and safety systems as constrained dependencies requiring specialist approval. Compare recommendations against a controls engineer's adjudication and the existing alert process. Measure false alerts, missed known faults and technician review effort. Inspect network separation and service-account scope before ingestion. Neither the model nor deployment topology is specified by the source, so procurement should follow local validation rather than copying an assumed stack.","delivery":"Interpretation: assign Energy Management or its local equivalent to own triage, with maintenance responsible for confirmed actions. Implement an equipment inventory, data-quality checks, alert review and work-order closure feedback. Include technicians in training and reserve time to investigate false alarms. Governance gates should precede sensitive telemetry collection and any automatic control integration. Proposed acceptance requires each pilot recommendation to have a traceable disposition and the agreed data-completeness threshold to be met before effectiveness scoring. Risks include alert fatigue, unmaintained sensors and stalled recommendations. Measure useful completed work and service impact, not dashboard activity alone."},"retrievedAt":"2026-09-10T03:01:39Z","enrichedAt":"2026-09-10T03:03:45Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Interpretation: train technicians to challenge recommendations and incorporate accessible service-request and complaint channels.","procurementImplications":"Interpretation: make sensor calibration, data export, protocol compatibility and ongoing maintenance explicit requirements.","operatingModelImplications":"Interpretation: route analytics findings into existing maintenance triage with named decision and closure owners.","updateExplanation":"New URL in full-archive checks. Undated operational detail fills the facilities service-ownership gap; no recent launch or substantive page revision is claimed.","sourceVerification":{"openedUrl":"https://energymanagement.ncsu.edu/energy-data-analytics/","referenceExcerpt":"Upgrading building systems to modern standards, such as Direct Digital Control (DDC).","promptVersion":"sled-research-v3.1","model":null,"basis":"agent-reported inspection"}}}]}