Optimal metering plan for measurement and verification on a lighting case study

Xianming Ye and Xiaohua Xia

Energy, 2016, vol. 95, issue C, 580-592

Abstract: M&V (Measurement and Verification) has become an indispensable process in various incentive EEDSM (energy efficiency and demand side management) programmes to accurately and reliably measure and verify the project performance in terms of energy and/or cost savings. Due to the uncertain nature of the unmeasurable savings, there is an inherent trade-off between the M&V accuracy and M&V cost. In order to achieve the required M&V accuracy cost-effectively, we propose a combined spatial and longitudinal MCM (metering cost minimisation) model to assist the design of optimal M&V metering plans, which minimises the metering cost whilst satisfying the required measurement and sampling accuracy of M&V. The objective function of the proposed MCM model is the M&V metering cost that covers the procurement, installation and maintenance of the metering system whereas the M&V accuracy requirements are formulated as the constraints. Optimal solutions to the proposed MCM model offer useful information in designing the optimal M&V metering plan. The advantages of the proposed MCM model are demonstrated by a case study of an EE lighting retrofit project and the model is widely applicable to other M&V lighting projects with different population sizes and sampling accuracy requirements.

Keywords: Energy efficiency; Lighting; M&V; Sampling (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544215016400
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:95:y:2016:i:c:p:580-592

DOI: 10.1016/j.energy.2015.11.077

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().