While human behavior undoubtedly drives building energy waste, this problem is rarely quantified -- and often ignored.
Similar to 'crash reports' that help fix software bugs, an app must be developed to generate vital behavioral and operational info during periods of anomalous energy use. This will create a compelling and urgent rationale for occupants and facility managers to disclose details about behavior, ease the transfer of this info, develop a wealth of 'behind the meter' data, and craft new energy efficiency solutions.
Operational energy waste is all around us: buildings are heated and cooled with no one in them; computers stay 'idle' instead of 'sleeping;' we forget to turn off the lights. Estimated behavioral savings range from 5-30%+, but, critically, the data is sparse. Little is known about the scale of the problem -- or the relationships between the countless variables.
Most efficiency efforts revolve around the easily identifiable, from sustainable design to efficient HVAC and lighting. That's not enough. Regardless of system design, efficiency, or ability to automate, human behavior remains highly impactful. The basic thermostat is illustrative: no matter how advanced, a human being decides the temperature set point; even if capable of intelligent scheduling, there’s always a 'manual hold' function.
Meter data is essential, but we're missing a key piece of the puzzle: What's going on behind the meter?
It's a familiar IT experience: Software crashes, restarts, and you're asked to 'send report' to help fix the bug. This data (about operating system, application specifics, etc.) is then evaluated, previously unknown issues are identified, and the problem is fixed.
The energy space needs a similar tool. With simple, voluntary, and potentially anonymous reports about behavior when energy consumption spikes -- and with basic regression analysis against Green Button and other available data -- patterns of energy waste will surface.
AppA: Aggregation of anonymous behavioral data. Potentially led by, or in partnership with, DOE.
AppB: Customer-focused approach with individual solutions. Led by private sector.
1. Flag unusual and out-of-bounds energy use.
2. Alert user, via mobile device and email, to send report. Examples of possible info: occupancy and space use (self-generated or automated through geolocation, calendar integration, etc.); temperature set point; occupant comfort (e.g. too cold/hot?); plug-load (e.g. computers, mobile chargers, etc.)
3. AppA: Combine anonymous behavioral info with Green Button, weather, and other data; aggregate and analyze; identify relationships and patterns. AppB: Uncover user-specific inefficiencies.
4. Pick up fruit on the ground.