One model of consumption
Meters are mapped to zones, buildings, tenants, and cost centers so the numbers are comparable and usable.
Utility Intelligence
Meter data becomes useful when consumption can be read in context.
Utility and energy-system data often already exists, but it is scattered across remote metering, heat-pump and ventilation controllers, photovoltaic and storage platforms, weather feeds, exports, and separate portals. BAAP organizes it into one operating model for energy optimization, leak detection, abnormal behavior tracking, and measurable cost reduction.
What this layer covers
metering and consumption structure
anomaly and leak detection
energy waste and savings evidence
Utility work starts with structure before it starts with AI.
Profiles and trends
Daily, weekly, and seasonal profiles make it easier to recognize what is normal and what is drifting.
Confidence in data
Completeness, delays, gaps, and abnormal jumps are tracked so operational conclusions stay trustworthy.
Once the structure exists, BAAP can move from monitoring to interpretation.
Leak and persistent consumption detection
Spot continuous flow, out-of-hours usage, and suspicious water behavior before the cost grows.
Abnormal energy behavior
Catch spikes, drift, and increasing baseload that may indicate inefficiency or equipment issues.
Operational waste reduction
Show where schedules, settings, or overlooked patterns create avoidable cost and inefficiency.
Savings matter only when they can be measured after the change.
BAAP is designed to measure the effect of corrective work. That can mean checking whether schedule changes, service actions, or setting corrections actually improved the operating picture after the intervention.
This moves utility work away from guesswork and closer to a traceable operational process.
Utilities rarely explain the whole building on their own.
The next layer is broader building context: BMS signals, heat pumps, ventilation, alarms, service history, and technical structure.