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AI Occupancy-Based HVAC Control

Real-time HVAC optimization based on occupancy patterns and predictions

The Problem

“AI Occupancy-Based HVAC Control for Real-Time Energy Optimization”

Organizations face these key challenges:

1

HVAC systems condition empty rooms due to fixed schedules

2

Occupancy is uncertain, sparse, or only partially observable from sensors

3

PV and battery assets are not integrated with HVAC control decisions

4

Weather variability and building thermal inertia make manual tuning ineffective

5

Comfort complaints increase when aggressive energy-saving rules are applied

6

Legacy thermostats and BMS platforms expose limited control interfaces

7

Energy managers lack room-level visibility into occupancy-driven waste

8

Control strategies often fail to adapt to holidays, hybrid work, and changing routines

Impact When Solved

Reduce HVAC energy consumption by 10% to 30% through occupancy-aware controlLower peak demand by shifting thermal loads to favorable tariff and PV production windowsIncrease rooftop PV self-consumption via pre-cooling and pre-heating strategiesImprove comfort consistency by reducing over-conditioning in unoccupied zonesCoordinate HVAC, battery, and PV assets in a single optimization loopProvide auditable control decisions and measurable savings for energy managers

The Shift

Before AI~85% Manual

Human Does

  • •Review occupancy assumptions, comfort complaints, and utility peaks by area
  • •Adjust HVAC schedules, setpoints, and ventilation rules based on observed usage
  • •Coordinate manual overrides and seasonal tuning through the building controls workflow
  • •Investigate persistent hot/cold zones and decide corrective actions

Automation

  • •Apply fixed schedules and simple occupancy or CO2 trigger rules
  • •Enable setback or ventilation changes only after threshold conditions are met
  • •Generate basic alarms when temperatures, runtime, or air quality exceed limits
With AI~75% Automated

Human Does

  • •Approve comfort, air quality, and energy cost priorities for control strategy
  • •Review recommended control actions for sensitive spaces, events, or policy exceptions
  • •Handle occupant complaints, safety exceptions, and manual override decisions

AI Handles

  • •Fuse occupancy signals and forecast near-term space usage by zone
  • •Predict thermal and ventilation needs and optimize setpoints, airflow, and equipment staging
  • •Precondition spaces and shift loads to reduce peak demand while maintaining comfort targets
  • •Continuously monitor occupancy, comfort, air quality, and energy performance for deviations

Operating Intelligence

How AI Occupancy-Based HVAC Control runs once it is live

AI runs the operating engine in real time.

Humans govern policy and overrides.

Measured outcomes feed the optimization loop.

Confidence95%
ArchetypeOptimize & Orchestrate
Shape6-step circular
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapecircular

Step 1

Sense

Step 2

Optimize

Step 3

Coordinate

Step 4

Govern

Step 5

Execute

Step 6

Measure

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI senses, optimizes, and coordinates in real time. Humans set policy and override when needed. Measurements close the loop.

The Loop

6 steps

1 operating angles mapped

Operational Depth

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