Office occupancy sensor solution

Types of occupancy sensors — pros and cons

• Passive Infrared (PIR): Good for detecting motion and low cost; struggles with stationary occupants and can give false negatives for seated workers.
• Ultrasonic and microwave: Detect motion through sound/echoes; better coverage but can be noisy and prone to false positives.
• Image-based sensors (cameras/vision): High accuracy for counting but raises privacy, storage, and compliance complexity.
• Thermal, camera-free (privacy-first): Detects body heat and movement with identity-agnostic output; balances accuracy and privacy for offices.

For modern workplaces where trust and compliance matter, thermal camera-free sensing often hits the best balance between actionable data and privacy protections. Butlr’s Heatic family is an example of a commercially mature thermal sensor designed for workplace scale.

Evaluation framework: Choosing the right office occupancy sensor solution

Accuracy & modes

• Can the sensor detect presence and count reliably in small and large spaces?
• Does the device support presence mode (real-time) and traffic mode (in/out counts)?

Privacy & compliance

• Is the sensing identity-agnostic (no RGB/video) and designed to avoid PII?
• Are data retention and anonymization controls available?

Integration & interoperability

• Does the platform provide APIs or standard connectors to BMS, CAFM, and analytics tools?
• Can occupancy data feed HVAC schedules, lighting, and desk/room booking systems?

Deployment & cost

• What is per-sensor cost and expected coverage density?
• How complex is installation (battery vs. wired; magnet/mounting)?

ROI & outcomes

• Can the vendor model expected energy savings and space cost reductions?
• Are baseline surveys and before/after metrics included in pilots?

Butlr’s platform emphasizes API-first integration, wireless installation, and privacy-by-design—features that shorten pilots and reduce stakeholder friction.

Real-world examples and outcomes

Example: mid-sized office (case summary)

A mid-sized firm deployed a thermal, camera-free occupancy sensing platform to solve rising HVAC and real estate costs while preserving privacy. After a baseline period, the organization automated HVAC schedules and optimized meeting-room allocations. Results included reduced HVAC runtime, fewer unnecessary heating/cooling cycles, and clearer desk/room utilization data that led to lease negotiations and space consolidation. The deployment emphasized transparent employee communications and privacy-by-design.

Example: portfolio-level decisions

Occupancy analytics aggregated across buildings help portfolio managers identify consistently underused floors or buildings and justify subleasing or lease renegotiations. When peak-day vs. average attendance diverges, a data-driven policy for flexible seating and coworking alternatives can lead to substantial savings across large portfolios. Industry reporting shows persistent gaps between average and peak utilization—insights that occupancy sensors make visible and actionable.

Deployment best practices

• Start with a short pilot (4–8 weeks) that includes baseline occupancy, energy consumption, and a defined ROI target.
• Place sensors to capture entrances, collaboration hubs, and representative desks; avoid blind spots and direct HVAC registers.
• Integrate occupancy outputs with BMS and lighting controls using testable automation rules (e.g., only allow HVAC setbacks when multiple connected sensors confirm vacancy).
• Use dashboards and regular stakeholder briefings to translate data into policy (cleaning schedules, desk hoteling rules, HVAC setbacks).
• Communicate transparently with employees about what data is captured, how it is used, and the privacy protections in place.

Butlr’s wireless Heatic sensors simplify pilot logistics with battery operation and magnetic installs, reducing labor friction during initial rollouts.

Metrics to track for success

• Energy: HVAC runtime hours, lighting kWh, and utility cost per square foot.
• Utilization: average daily attendance %, peak-day occupancy, and desk/room occupancy rates.
• Operational: cleaning frequency/cost, maintenance tickets per space, and HVAC setback compliance.
• Financial: projected vs. realized savings, payback period, and lease cost avoided through consolidation.

Quantify before-and-after changes. For example, occupancy-triggered lighting controls can reduce lighting consumption by 10–90% depending on room type; realistic facility-level savings often come from combined lighting+HVAC optimizations.

Comparison: camera vs. thermal approaches

• Accuracy: Cameras can count precisely; thermal sensors can accurately detect presence and approximate counts for space planning.
• Privacy: Cameras capture imagery and require strong governance; thermal sensors output anonymous heat patterns and are inherently less identifiable.
• Deployment complexity: Camera systems often need power, networking, and higher bandwidth; thermal wireless sensors can be faster to install and scale.
• Compliance: Thermal reduces legal risk in jurisdictions with strict worker surveillance rules.

Choosing between these depends on use case. For pure headcounts and detailed occupant behavior modeling, cameras may be chosen with governance. For broad space optimization, privacy, and workforce acceptance, thermal camera-free sensors are often preferred.

Conclusion

An office occupancy sensor solution that balances accuracy, interoperability, and privacy is a practical lever for cutting energy costs, improving real-estate decisions, and enhancing employee trust. Begin with a focused pilot, measure the right KPIs, and scale with clear governance—privacy-first thermal approaches offer an effective path to measurable outcomes with less resistance than camera-based alternatives. For teams weighing vendors, prioritize API access, transparent privacy controls, and documented pilot results to build a defensible, high-impact program.