Optimal sampling plan for clean development mechanism energy efficiency lighting projects

Xianming Ye, Xiaohua Xia, Jiangfeng Zhang

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Clean development mechanism (CDM) project developers are always interested in achieving required measurement accuracies with the least metering cost. In this paper, a metering cost minimisation model is proposed for the sampling plan of a specific CDM energy efficiency lighting project. The problem arises from the particular CDM sampling requirement of 90% confidence and 10% precision for the small-scale CDM energy efficiency projects, which is known as the 90/10 criterion. The 90/10 criterion can be met through solving the metering cost minimisation problem. All the lights in the project are classified into different groups according to uncertainties of the lighting energy consumption, which are characterised by their statistical coefficient of variance (CV). Samples from each group are randomly selected to install power meters. These meters include less expensive ones with less functionality and more expensive ones with greater functionality. The metering cost minimisation model will minimise the total metering cost through the determination of the optimal sample size at each group. The 90/10 criterion is formulated as constraints to the metering cost objective. The optimal solution to the minimisation problem will therefore minimise the metering cost whilst meeting the 90/10 criterion, and this is verified by a case study. Relationships between the optimal metering cost and the population sizes of the groups, CV values and the meter equipment cost are further explored in three simulations. The metering cost minimisation model proposed for lighting systems is applicable to other CDM projects as well.
LanguageEnglish
Number of pages10
JournalApplied Energy
Early online date27 Jun 2013
DOIs
Publication statusPublished - 2013

Fingerprint

clean development mechanism
energy efficiency
Energy efficiency
Lighting
Sampling
sampling
cost
Costs
plan
lighting
project
population size
Energy utilization

Keywords

  • optimal sampling plan
  • clean development mechanism
  • energy efficiency
  • lighting projects
  • lighting
  • CDM
  • sample size determination

Cite this

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abstract = "Clean development mechanism (CDM) project developers are always interested in achieving required measurement accuracies with the least metering cost. In this paper, a metering cost minimisation model is proposed for the sampling plan of a specific CDM energy efficiency lighting project. The problem arises from the particular CDM sampling requirement of 90{\%} confidence and 10{\%} precision for the small-scale CDM energy efficiency projects, which is known as the 90/10 criterion. The 90/10 criterion can be met through solving the metering cost minimisation problem. All the lights in the project are classified into different groups according to uncertainties of the lighting energy consumption, which are characterised by their statistical coefficient of variance (CV). Samples from each group are randomly selected to install power meters. These meters include less expensive ones with less functionality and more expensive ones with greater functionality. The metering cost minimisation model will minimise the total metering cost through the determination of the optimal sample size at each group. The 90/10 criterion is formulated as constraints to the metering cost objective. The optimal solution to the minimisation problem will therefore minimise the metering cost whilst meeting the 90/10 criterion, and this is verified by a case study. Relationships between the optimal metering cost and the population sizes of the groups, CV values and the meter equipment cost are further explored in three simulations. The metering cost minimisation model proposed for lighting systems is applicable to other CDM projects as well.",
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Optimal sampling plan for clean development mechanism energy efficiency lighting projects. / Ye, Xianming; Xia, Xiaohua; Zhang, Jiangfeng.

In: Applied Energy, 2013.

Research output: Contribution to journalArticle

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