Vireco provides a site‑specific digital twin that models how an airport’s energy systems behave, using existing telemetry and operating context to forecast responses to control changes. By learning asset reactions to setpoint adjustments, HVAC schedules, EV charging, and storage dispatch, the platform lets operators predict cost, reliability, and demand impacts before implementing decisions.
Funding
Funding not disclosed
Founders
Product
Problem
Airport energy systems are becoming increasingly complex as they integrate electrified vehicle fleets, EV charging stations, expanded terminal loads, and diverse control platforms. Operators must understand how adjustments to HVAC, charging, storage, and other controllable loads will impact overall cost, reliability, and demand, but existing BMS, SCADA, DCS, and PLC tools provide limited predictive insight.
Solution
Vireco creates a site‑specific digital twin that models the behavior of an airport’s energy infrastructure using existing telemetry and contextual data. The platform operates above legacy control systems, allowing real‑time safety and protection logic to remain unchanged while providing a predictive layer for operators. By automatically learning how assets respond to set‑point changes, schedules, and dispatch decisions, Vireco can forecast the resulting cost, reliability, and demand outcomes before implementation. The system highlights inefficiencies, peak exposure, and operational drift, then generates actionable supervisory recommendations that operators can verify and apply. This physics‑informed forecasting enables more informed decision‑making without requiring additional hardware installations.
Target Audience
Primary users are energy operators and facility managers at airports who oversee HVAC, EV charging, storage, and other controllable loads, as well as engineering teams responsible for integrating multiple control platforms.
Features
- Automated learning of asset response curves from live telemetry and operating context
- Physics‑informed forecasting engine that simulates outcomes of set‑point, schedule, or dispatch changes
- Integration layer that sits above BMS, SCADA, DCS, and PLC systems without disrupting existing safety logic
- Inefficiency detection that identifies avoidable energy use, peak demand exposure, and operational drift
- Actionable supervisory recommendations presented in a verification‑ready format for operators