Non-residential behavior change programs, like strategic energy management, are becoming increasingly popular. Not only do they move customers beyond widgetitis, but behavior programs can also result in larger, longer lasting savings. However, savings from behavior modification are difficult to explain and potentially more difficult to quantify. How can program managers make sure their behavior savings are above board? Here are a few pointers.
Documentation is a Must
A paper trail is necessary for behavior change programs. But it has to be more than a few emails. Customers need to provide detailed descriptions of what current behavior is, what modification is being made, and when it is occurring.
For example, a customer who is improving maintenance practices should describe what their current maintenance schedule is for key equipment, what the proposed maintenance schedule will be going forward, and when the changeover will happen. Be specific!
Accurate Billing Data
Savings for behavior modification are difficult to quantify using typical engineering equations. Behavior changes, individually, have small impacts on overall facility usage. Monthly bills don’t provide the granularity necessary to parse out those small changes.
When considering behavior modification programs, interval data is required to quantify savings accurately. Intervals should be at least every hour. Typical interval data is read every 15 minutes. Utilities with advanced metering (AMR or AMI) will be able to provide data at shorter intervals, which also works well.
Proper Analysis Tools
After getting solid documentation and gathering interval data, savings don’t just magically appear. Analysis tools that can parse data and cut through the noise are required. Most often, behavioral savings are calculated using regression analyses. A simple example is fitting data with a line (linear regression). As a reasonable program manager might expect, mathematical accuracy is paramount for these tools.
However, savings accuracy goes one step further than mathematical correctness. The data used in any regression tool needs to make logical and engineering sense. Correlating hourly energy usage to hourly weather data for a school – bingo! Correlating 15-minute energy usage data from a manufacturing plant to monthly production information – try again.
Program managers, implementers, and evaluators need to think critically about the data they are getting and its relationship to energy savings. Working through data requirements with customers may result in new metrics that they need to track to quantify savings properly. What better way to impact customer behavior than give them data that previously didn’t exist, and coach them on how to use it.