OCCUPANT-AWARE ENERGY MANAGEMENT: SIMULATED ENERGY SAVINGS ACHIEVABLE THROUGH APPLICATION OF TEMPERATURE SETPOINTS LEARNED THROUGH END-USER FEEDBACK
Parametric building energy simulations are performed
to quantify the range of annual HVAC energy savings
achievable through comfort-optimized adjustments to
conventional heating and cooling setpoints in office
buildings. Savings potential is examined in context with
occupant subjective feedback, using pilot data from
(N=45) real builing occupants collected with a novel
mobile sensing platform from 2-week pilot studies in
four (4) commerical buildings. Machine learning
techniques are used to generate probabilistic models of
thermal discomfort from physical and subjective
measures. Models are then interpreted to determine the
largest setpoint range achievable while maintaining
thermal conditions that are acceptable to 80 percent of
building occupants surveyed. Outcomes are
extrapolated across three (3) building vintages (pre1980,
post-1980, and ASHRAE 90.1-compliant) and
eight (8) California climate zones to determine the
potential range of energy savings achievable from
implementing customized setpoints learned through
occupant subjective feedback and concurrent thermal
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