Low-Cost Energy Meter Calibration Method for Measurement and Verification

TL;DR

This paper introduces a low-cost in-situ calibration method for energy meters using machine learning, significantly improving accuracy and reducing costs for measurement and verification in building energy projects.

Contribution

The study presents a novel hybrid calibration technique combining SIMEX and Bayesian regression, enabling accurate in-situ calibration with low-cost meters.

Findings

Significant improvement in calibration accuracy over traditional methods

Effective mitigation of measurement bias due to mismeasurement

Cost-effective calibration suitable for M&V applications

Abstract

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.