The only constant in buildings is that they change.
Occupancy changes, equipment upgrades, special events occur, and even space use fluctuates. If your team has an energy reduction target, how can you possibly get accurate performance feedback with all of these changes?
The secret is a learning baseline model. A learning baseline model model that adapts alongside your building and gives you feedback on changes, while simultaneously learning the new normal.
Here’s a recent example from a Gridium building. A medium sized commercial building experienced a drop in vacancy on January 15th, where a high tech (i.e. high intensity) occupant vacated their space:
An occupant move-out on January 15th clearly drops energy use, shown in shaded blue
At the same time, the team was experimenting with a variety of efforts to reduce energy expenses and wanted to closely monitor performance. How will they know if they are getting better or worse after such a dramatic change?
Gridium Snapmeter steps in and learns the new building profile, recalculating at the end of each utility billing period. Visually this is easy to see:
As the bill period closed ( Feb 7), the learning baseline in Snapmeter recalculates providing operating feedback that reflects the “new normal”
As you can see a learning baseline automatically “learns” about the occupancy change and reflects the next month’s usage against the new baseline.
What’s the best part? You don’t have to think about it. Much like a Nest Learning Thermostat adapts to a change in your household schedule, Snapmeter learns the new occupancy and delivers reliable operational feedback to your team, allowing you to get accurate feedback, even after changes in occupancy or after capital projects.
