  Energy meters need to be calibrated for use in Measurement and Verification
(M&V) projects. However, calibration can be prohibitively expensive and affect
project feasibility negatively. This study presents a novel low-cost in-situ
meter data calibration technique using a relatively low accuracy commercial
energy meter as a calibrator. Calibration is achieved by combining two machine
learning tools: the SIMulation EXtrapolation (SIMEX) Measurement Error Model,
and Bayesian regression. The model is trained or calibrated on half-hourly
building energy data for 24 hours. Measurements are then compared to the true
values over the following months to verify the method. Results show that the
hybrid method significantly improves parameter estimates and goodness of fit
when compared to Ordinary Least Squares regression or standard SIMEX. This
study also addresses the effect of mismeasurement in energy monitoring, and
implements a powerful technique for mitigating the bias that arises because of
it. Meters calibrated by the technique presented have satisfactory accuracy for
most M&V applications, at a significantly lower cost.
