ENBIS-20 Online Conference

28 September – 1 October 2020; Online

Greenfield Challenge Session

30 September 2020, 17:00 – 17:30

Efficient sampling plans for utility meter surveillance

Katy Klauenberg
, Physikalisch-Technische Bundesanstalt (PTB), Germany

Inspections according to statistical sampling plans allow conclusions to be drawn about the reliability of a whole population of e.g. measurement devices. However, confirming high reliability levels requires large sample sizes and is thus expensive or even infeasible.

When reliability is judged by not exceeding a certain threshold, considerably more efficient attribute sampling plans can be implemented. Specifically for location-scale distributed continuous variables, we proved that if 100q% of a population meets a tighter threshold D, then at least 100p% of the same population meets threshold Dγ  (with 0<q<p<1, γ>1). Consequently, verifying the conformance of a smaller portion q of devices, requires smaller sample sizes and retains the simplicity of attribute sampling.

We communicated this and related research to verification authorities, testing laboratories and the wider legal metrology community. As a result, procedural instructions were published which enforce a new regulation in the German Measures and Verification Ordinance, affect millions of in-service utility meters in Germany and ensure 95% conforming utility meters – not only at testing but continuously.

This talk is based on joint work with Clemens Elster (PTB).


Katy Klauenberg is a statistician in the working group “Data analysis and measurement uncertainty” at Germany’s national metrology institute PTB. Her research focusses on statistics in the science of measurements and includes Bayesian methods, regression problems and sampling procedures. She organizes a biannual seminar and provides training and support for the evaluation of measurement uncertainty. Katy is a mathematician by training, received her PhD in the field of statistical image processing and was a postdoc in Sheffield’s Probability and Statistics department before joining PTB in 2009.

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