Optimal HVAC: Reduce Energy Costs with Occupancy-Based Control

Core Strategies for Optimal HVAC

Occupancy-Based Control (OBC)

• Shift terminal boxes, VAV setpoints, and ventilation rates based on real-time occupancy counts and presence detection.
• Implement setbacks and zero-flow standby in truly unoccupied zones.
• Combine with predictive scheduling for meeting-heavy spaces.

Demand-Controlled Ventilation (DCV)

• Adjust outdoor-air supply proportional to actual occupancy to meet IAQ targets while avoiding over-ventilation.
• Integrate CO2 or occupancy proxies where appropriate.

Smart Zoning and VAV Optimization

• Re-evaluate zoning assumptions; combine or split zones to reflect real usage.
• Use terminal reheat strategies sparingly—optimize for airflow first.

Thermal Sensing and Privacy-First People Sensing

• Leverage low-resolution thermal sensing to obtain occupancy counts and flow without cameras or PII collection.
• Use mesh wireless sensors to minimize installation cost and enable rapid pilot rollouts.

Continuous Commissioning and Analytics

• Move from one-off retrofits to continuous performance monitoring, anomaly detection, and automated fault reporting.

How Privacy-First Thermal Sensing Enables Optimal HVAC

Camera-free thermal sensing converts heat signatures into anonymized occupancy and activity signals. Butlr’s Heatic™ sensor and platform deliver identity-agnostic, low-resolution thermal detections and mesh communications that feed occupancy counts into building controls or analytics platforms. This approach preserves privacy while providing the high-fidelity, real-time inputs required for occupancy-based HVAC strategies and DCV.

Benefits of privacy-first thermal sensing for HVAC

• Accurate room-level presence and counts for VAV/terminal control.
• Fine-grained temporal resolution (minute-level), enabling fast transitions to standby or occupied modes.
• Non-intrusive deployment and reduced security/compliance overhead versus cameras.

Implementation Framework: From Pilot to Sitewide Rollout

1. Define Objectives and KPIs

Examples: HVAC energy reduction percentage, peak demand reduction (kW), comfort complaint rate, IAQ targets (CO2 ppm).

2. Baseline Measurement

Use historical utility bills, EMCS logs, and spot audits to establish energy and runtime baselines.

3. Pilot Design

Select high-variability zones (conference rooms, cafeterias, auditoriums) for pilot. Install privacy-first thermal sensors and integrate with BMS/EMS or a gateway platform.

4. Control Logic

Implement occupancy thresholds, setback schedules, and DCV algorithms. Coordinate with outside-air economizers and chiller/boiler control to avoid control conflicts.

5. Monitoring and Commissioning

Track key metrics: kWh, kW, hours in setback, occupant comfort reports, CO2. Tune algorithms for false positives/negatives.

6. Scale

Apply lessons from pilots, adjust sensor density, and roll out across similar asset classes.

Real-World Examples and Case Studies

Enterprise cafeteria optimization: Butlr’s platform has been used to provide real-time occupancy visibility in high-traffic foodservice spaces, improving staffing and reducing waste while enabling HVAC adjustments tied to peak and off-peak periods. These operational insights also create a direct path to HVAC energy savings when combined with control adjustments.

Office and classroom pilots: Research and pilot implementations using occupancy sensors and OBC have demonstrated ventilation load reductions and temperature setback savings—studies show up to 60% reduction in outdoor air load in heating season for some office cases and up to 30% heating/cooling energy savings for residential-type controls when occupancy is used to trigger setpoints.

Calculating ROI: Sample Framework

To estimate ROI, combine baseline HVAC energy cost (kWh and $/kWh), expected percent savings from OBC/DCV (conservative 10–20%; pilot-informed higher values possible), implementation costs (sensors, gateways, integration, engineering), and operational savings beyond energy (reduced maintenance, staffing optimization, lower tenant complaints).

Example: A 100,000 ft² office with $0.12/kWh baseline and 34% HVAC share—if HVAC uses 34% of a 1,000,000 kWh annual load = 340,000 kWh. A 15% reduction in HVAC yields 51,000 kWh saved = $6,120/year. If sensor + integration costs are $60,000, simple payback ~9.8 years; higher savings, demand-charge reductions, or incentives shorten payback materially.

Use measured pilot savings to refine numbers—don’t rely solely on simulations.

Evaluation Criteria: Choosing Sensors & Controls

• Accuracy: Occupancy detection vs. people counting—choose technology that matches the control objective.
• Privacy: For tenant acceptance and compliance, prioritize identity-agnostic sensing.
• Integration: APIs and BMS connectors (BACnet, Modbus, MQTT) speed deployment.
• Scalability & Maintenance: Battery life, mesh networking reliability, and ease of replacement.
• Analytics: Out-of-the-box dashboards or raw API access for custom analytics.

Butlr’s API-first architecture and Heatic™ sensor are built specifically for these evaluation criteria, delivering anonymized occupancy data suitable for direct integration with BMS and analytics tools.

Conclusion

Optimal HVAC is achievable and economically compelling when you combine accurate, privacy-respecting people sensing with occupancy-based and demand-driven controls. Start with pilot deployments in the most variable-use spaces, measure rigorously, build out control logic conservatively, and scale with continuous commissioning. Platforms like Butlr’s thermal, camera-free sensing make it practical to unlock the occupancy data that modern HVAC control strategies require—without compromising privacy.