  Commercial building heating, ventilation, and air conditioning (HVAC) systems
have been studied for providing ancillary services to power grids via demand
response (DR). One critical issue is to estimate the counterfactual baseline
power consumption that would have prevailed without DR. Baseline methods have
been developed based on whole building electric load profiles. New methods are
necessary to estimate the baseline power consumption of HVAC sub-components
(e.g., supply and return fans), which have different characteristics compared
to that of the whole building. Tensor completion can estimate the unobserved
entries of multi-dimensional tensors describing complex data sets. It exploits
high-dimensional data to capture granular insights into the problem. This paper
proposes to use it for baselining HVAC fan power, by utilizing its capability
of capturing dominant fan power patterns. The tensor completion method is
evaluated using HVAC fan power data from several buildings at the University of
Michigan, and compared with several existing methods. The tensor completion
method generally outperforms the benchmarks.
