Use Occupancy Data to Reduce HVAC Costs with Real-Time, Anonymous Sensing

Practical HVAC strategies enabled by occupancy data

1. Demand-Controlled Ventilation (DCV)

• What it does: Adjusts fresh air supply to occupied spaces.
• Benefits: Reduces heating and cooling load from over-ventilation, lowering fan energy and conditioning costs.
• Implementation tip: Tie DCV to CO2 and occupancy inputs; use occupancy as a primary trigger for immediate airflow reduction when spaces are empty.

2. Dynamic Setpoint Adjustment

• What it does: Slightly relaxes temperature targets in unoccupied or lightly occupied zones.
• Benefits: Small setpoint shifts (1–2°C) can yield significant energy savings without perceptible comfort loss when spaces are empty.
• Implementation tip: Establish minimum and maximum bounds for comfort compliance and occupant expectations.

3. Zone-Based Scheduling and Short-Term Overrides

• What it does: Activates only the systems serving occupied rooms or floors.
• Benefits: Prevents whole-building conditioning for a single occupied zone.
• Implementation tip: Use real-time presence to allow short-term overrides for meetings and transient occupancy, reducing manual adjustments.

4. Preconditioning and Predictive Control

• What it does: Starts conditioning just early enough to reach comfort at occupancy rather than keeping spaces on all day.
• Benefits: Minimizes runtime while ensuring comfort when people arrive.
• Implementation tip: Combine recent occupancy patterns with real-time triggers to predict occupancy windows (e.g., a conference room used most weekdays from 9–11).

5. Peak Shaving and Load Shifting

• What it does: Temporarily reduces nonessential conditioning during high-price or grid-stress times.
• Benefits: Lowers demand charges and leverages thermal inertia of the building to maintain comfort.
• Implementation tip: Use occupancy data to ensure critical occupied spaces remain prioritized during demand response events.

6. Fault Detection and Predictive Maintenance

• What it does: Detects anomalies when a conditioned space remains unoccupied but HVAC runtime is high.
• Benefits: Identifies controls faults, stuck dampers, or sensor mismatches that waste energy.
• Implementation tip: Set alerts for mismatches between occupancy and HVAC state to prompt rapid diagnosis.

Deployment best practices

• Start with a pilot: Choose representative zones (open-plan offices, conference rooms, lobbies) to validate assumptions and tune settings.
• Integrate with the BMS: Connect occupancy feeds to HVAC controllers or the BMS using standard protocols for smooth control logic.
• Sensor placement matters: Mount sensors to cover typical use areas and avoid false triggers from adjacent spaces.
• Tune analytics: Filter transient noise (e.g., passing occupants) and set dwell time thresholds to prevent excessive toggling of HVAC systems.
• Document control logic: Maintain clear rules for when and how occupancy drives HVAC decisions to support operations teams.
• Train operators and occupants: Explain privacy measures, expected behavior, and how to request overrides for comfort or special events.

Measuring and verifying energy savings

Quantifying results builds support and informs scale-up.

• Establish a baseline: Use historical energy and HVAC runtime data for comparable periods, accounting for weather and occupancy trends.
• Define KPIs: Track HVAC energy use, runtime hours, conditioned area percentage, peak demand, and occupant comfort complaints.
• Use short and long windows: Measure immediate runtime changes and seasonal impacts for a full view of savings.
• Conduct M&V (Measurement & Verification): Apply straightforward M&V protocols to validate claimed savings and payback.
• Report in accessible dashboards: Present occupancy-driven savings and comfort metrics to stakeholders regularly.

Privacy and trust: anonymous sensing as a foundation

Privacy is essential for occupant acceptance. Anonymous, camera-free thermal sensing supports trust by design.

• No PII collected: Sensors detect heat and motion rather than identity.
• Limited data retention: Store only aggregate counts or occupancy status needed for HVAC control.
• Transparent policies: Communicate what is measured, why it matters, and how data is used.
• Compliance-friendly: A privacy-first approach helps align with workplace privacy expectations and regulations.

This combination of technical design and clear communication is critical to avoid resistance and maximize adoption.

Typical savings and ROI expectations

Savings vary by climate, building type, and how conservative previous controls were. Typical outcomes include:

• HVAC energy reductions commonly in the range of 10–30% in spaces previously conditioned on schedules.
• Faster payback when combined with controls upgrades and when applied to large or highly variable-use spaces.
• Additional indirect savings from reduced maintenance, lower peak demand charges, and improved tenant satisfaction.

A pilot helps refine estimates tailored to a specific portfolio.

Getting started: a pragmatic checklist

• Identify high-opportunity zones (conference rooms, meeting areas, intermittently used offices).
• Run a short pilot with anonymous, camera-free sensors to validate occupancy patterns.
• Integrate occupancy feeds with your BMS or control platform and implement simple control rules (e.g., setback when unoccupied for X minutes).
• Monitor KPIs and tune thresholds to balance comfort and savings.
• Scale to additional zones and incorporate predictive strategies as confidence grows.

Conclusion

Real-time, anonymous occupancy sensing transforms HVAC from a fixed-cost utility into a responsive, efficient service. By using heat-based, camera-free sensing and clear control strategies—demand-controlled ventilation, dynamic setpoints, zone-based scheduling, and predictive controls—facilities teams can reduce energy use, lower costs, and maintain occupant comfort without sacrificing privacy. Start with a focused pilot, measure results transparently, and scale what works to realize substantial HVAC savings across your portfolio.

Butlr’s ambient intelligence platform is an example of technology that uses heat-based, camera-free sensing to provide anonymous, real-time occupancy and activity insights—enabling the practical, privacy-forward strategies described above.