What happens when occupancy changes?

Example load curves show occupancy changes have different effects on different buildings.

Whenever a building experiences occupancy changes, such as a tenant move out, many building operators expect their energy use to do go down. While this is typically true, it’s not a straight line. We’ve written before about the factors in play here (the type of tenant, the mechanical system design, the building’s management practices) and we recently dug deeper into the data for a portfolio of buildings to quantify the elasticity of occupancy on kWh. A summary of that analysis deserves its own blog post, but here’s a hint… it’s fairly inelastic at 0.21.

This doesn’t mean occupancy changes won’t show up in your building’s load curve. Let’s look at two examples.

Occupancy changes drive drop in baseload

One Gridium building – clocking in at 25,000 SF – had a tenant representing approximately 30% of its occupancy move out, and its baseload dropped nearly 50%:

baseload occupancy changes

Snapmeter’s machine learning algorithms spot this change compared to the expected load curve at 50kW, and then quickly adapt and readjust to this new normal baseload at the lower level of 30kW. Such a massive drop in <a href="http://gridium cialis 100mg pills.com/this-one-number-is-all-you-need-to-know-about-energy-in-buildings/” target=”_blank”>the building’s most important number is an outlier of the occupancy changes most building operators should expect. Notably, even with a new lower baseload, there are still additional savings opportunities to reduce peak demand or adjust this building’s Monday morning hard start. After all, there is always room to improve.

Occupancy changes drive lower daytime use

A different Gridium building – measuring 149,000 SF – experienced occupancy changes recently when it saw 6 of its 25 floors vacate. No change this time in baseload, but rather in daytime energy use. Compared to the same month last year, this building’s operations-based energy costs (which are different from calendar-driven, weather-driven, and rate-driven costs) dropped 11%:

daytime use occupancy changes

The Chief Engineer at this building explains it further:

I have shut off HVAC systems on those floors and found that my fans operate at about 45% capacity as opposed to 60% which is typical.

Many building operators have questions about occupancy changes and what to expect when they occur. Calculating the impact of these changes is a key feature of Billcast, and a hot topic for scientists in the research community. Stay tuned for a more detailed summary of our elasticity analysis.

About Millen Paschich

Millen began his career at Cambridge Associates, trained in finance at SMU, and has an MBA from UCLA. Talk to him about bicycling, business, and green chile burritos.

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