Utility Rebate for College: Leverage Butlr Smart-Building Data

Step-by-step: Use Butlr data to prepare a winning utility rebate application

1. Identify eligible rebate programs

Determine whether local utilities offer prescriptive or custom rebates for the measures you are considering and note submission deadlines, required documentation, and pre-approval rules.

2. Establish a baseline

Use at least 6–12 months of historical energy data where possible and deploy Butlr sensors in representative spaces to capture occupancy patterns. Combine meter data with occupancy logs to correlate consumption to actual usage.

3. Define the retrofit or control strategy

Specify measures (for example, HVAC setpoint adjustments, VAV tuning, lighting controls) and estimate projected energy savings using building models or engineering calculations informed by Butlr occupancy data.

4. Implement monitoring prior to and after the project

Capture continuous occupancy and environmental metrics pre- and post-installation and ensure timestamp synchronization between Butlr data and utility meters or BAS for accurate comparisons.

5. Perform M&V and compile evidence

Use Butlr-derived occupancy hours, peak timing, and runtime reductions as supporting data; produce charts showing baseline vs post-implementation energy use adjusted for weather and occupancy and include clear narratives explaining control changes and verification methods.

6. Submit the application and follow up

Provide the utility with the M&V report, raw data summaries, and a technical contact; be prepared to supply additional data or host a site visit if requested.

Key metrics to capture and report

Utilities and evaluators look for clear, quantitative evidence; capture metrics that directly support claimed savings and demand reductions.

• Occupancy hours: total occupied hours per space, per day, and per week.
• Peak occupancy timing: when the building or space reaches peak load.
• HVAC runtime: equipment run hours during occupied vs unoccupied periods.
• kWh savings: meter-based energy reductions adjusted for weather and occupancy.
• Peak demand reduction (kW): reductions during critical demand windows for demand-response incentives.
• Ventilation rates or cycles: inferred from occupancy-driven ventilation adjustments.

Present these as before-and-after comparisons with clear baselines and statistical confidence where possible.

Best practices and common pitfalls

Best practices

• Start measurement before making changes; many utilities require pre-installation data.
• Use a combination of meter-level and space-level data for stronger attribution.
• Keep sensor placement representative of typical use such as classrooms, study spaces, labs, and dining halls.
• Document calibration, timestamps, and data-processing methods for transparency.

Pitfalls to avoid

• Skipping pre-approval for custom rebates — utilities can deny retroactive claims.
• Using short data windows that miss seasonal or behavioral variation.
• Ignoring privacy concerns; use anonymous sensing and document safeguards to ease stakeholder concerns.
• Over-attributing savings to changes when other factors such as occupancy shifts or weather may drive consumption.

Example scenarios for colleges

• Classroom scheduling optimization: Butlr shows many classrooms are empty during advertised hours; by adjusting cleaning and HVAC schedules based on real occupancy the campus reduced conditioned hours and claimed kWh savings in a custom rebate.
• Demand-response participation: Butlr identified building-level peaks during certain events; coordinated pre-cooling and HVAC staging reduced peak kW and enabled participation in a utility demand-response program with performance-based incentives.
• Ventilation optimization with DCV: Where CO2 sensors aren’t feasible, occupancy data provided a proxy to modulate ventilation; utilities accepted the M&V package showing reduced fan energy and lower conditioned air volumes.

Checklist and suggested timeline

Checklist

• Identify rebate type and pre-approval requirements.
• Deploy Butlr sensors or confirm existing coverage.
• Collect baseline occupancy and energy data (ideally 3–12 months).
• Design control changes and estimate savings.
• Implement changes and continue monitoring.
• Compile M&V report and submit to utility.
• Respond to follow-up requests and secure rebate.

Suggested timeline

• 0–1 month: Program identification and sensor deployment planning.
• 1–3 months: Baseline data collection (minimum) and measure design.
• 3–6 months: Implementation and continuous monitoring.
• 6–9 months: Post-implementation data analysis, M&V report creation.
• 9–12 months: Rebate submission and resolution.

Times vary by utility and program complexity; start early.

Conclusion

Utility rebates can significantly reduce the cost of campus energy projects, but winning them requires rigorous, auditable evidence of savings. Butlr's anonymous, heat-based occupancy sensing and analytics provide the missing link between operational changes and documented energy reductions.

By capturing accurate occupancy patterns, enabling robust M&V, and supporting performance-based claims, colleges can turn smarter building data into real financial incentives; for facilities teams the path to rebates is systematic: measure first, optimize intelligently, and prove savings with clear, utility-grade documentation.

Resources

• Butlr — AI-driven occupancy sensing and analytics: https://www.butlr.com/