Measurement uncertainty in energy monitoring: Present state of the art

Carstens, Herman; Xia, Xiaohua; Yadavalli, Venkata S. Sarma

doi:10.1016/j.rser.2017.10.006

Cited by 34 publications

(20 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In what seems to be a recurring theme, the Bayesian approach is becoming increasingly popular because of its ability to perform inference even in the presence of uncertainty. Although several techniques have been tested to quantify uncertainty, Bayesian methods are notable for their support in almost all related fields, from general metrology to decision support (Carstens, Xia, & Yadavalli, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

A Decision Support System for Multi-Criteria Assessment of Large Building Stocks

Carbonari

Corneli

Giuda

et al. 2019

Journal of Civil Engineering and Management

View full text Add to dashboard Cite

Both public administrations and private owners of large building stocks need to work out plans for the management of their property, while having to deal with yearly budget limitations. Particularly for the former, this is a rather critical challenge, since public administrations are given the responsibility of sticking to very strict budget distributions over the years. As a consequence, when planning the actions to be taken on their building stocks in order to comply with their current use and the legislation in-force, they need to classify refurbishment priorities. The aim of this paper is to develop a first tool based on Bayesian Networks that offers an effective decision support service for owners even in case some information is incomplete. This tool can be used to evaluate the compliance of existing buildings with the latest standards. The decision support platform proposed includes a multi-criteria evaluation approach combining several performance indicators, each of which related to a specific regulatory area. This tool can be applied to existing buildings, where the building with the lowest score shows the highest priority of intervention. Also, the platform performs an assessment of expected costs for required refurbishment or renovation actions.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Decision Support System for Multi-Criteria Assessment of Large Building Stocks

Carbonari

Corneli

Giuda

et al. 2019

Journal of Civil Engineering and Management

View full text Add to dashboard Cite

show abstract

“…Measurement uncertainty is recognised in the M&V literature; however, strict guidance on how to manage it is seldom provided [21]. Measurement uncertainty is an important consideration for EES quantification, since the accuracy of the measurand influences the reported savings.…”

Section: Measurement Uncertaintymentioning

confidence: 99%

Structuring Uncertainty Management for Energy Savings Calculations

Johnson

Hamer

Vosloo

2019

SAJIE

View full text Add to dashboard Cite

“…In others, incorrect assumptions may lead to erroneous results. Mismeasurement in M&V is treated more fully in previous work [31], and Carroll et al [34] and Gustafson [35] have written excellent textbooks on the problem.…”

Section: Developing a Low-cost Meter Calibration Algorithmmentioning

confidence: 99%

Low-cost energy meter calibration method for measurement and verification

2017

Self Cite

View full text Add to dashboard Cite

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 adequate accuracy for most M&V applications, at a significantly lower cost.

show abstract

Measurement uncertainty in energy monitoring: Present state of the art

Cited by 34 publications

References 102 publications

A Decision Support System for Multi-Criteria Assessment of Large Building Stocks

A Decision Support System for Multi-Criteria Assessment of Large Building Stocks

Structuring Uncertainty Management for Energy Savings Calculations

Low-cost energy meter calibration method for measurement and verification

Contact Info

Product

Resources

About