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Panel-based stratified cluster sampling and analysis for photovoltaic outdoor measurements

Pepelyshev, Andrey, Sovetkin, Evgenii and Steland, Ansgar 2017. Panel-based stratified cluster sampling and analysis for photovoltaic outdoor measurements. Applied Stochastic Models in Business and Industry 33 (1) , pp. 35-53. 10.1002/asmb.2217

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Abstract

We study a stratified multisite cluster-sampling panel time series approach in order to analyse and evaluate the quality and reliability of produced items, motivated by the problem to sample and analyse multisite outdoor measurements from photovoltaic systems. The specific stratified sampling in spatial clusters reduces sampling costs and allows for heterogeneity as well as for the analysis of spatial correlations due to defects and damages that tend to occur in clusters. The analysis is based on weighted least squares using data-dependent weights. We show that this does not affect consistency and asymptotic normality of the least squares estimator under the proposed sampling design under general conditions. The estimation of the relevant variance–covariance matrices is discussed in detail for various models including nested designs and random effects. The strata corresponding to damages or manufacturers are modelled via a quality feature by means of a threshold approach. The analysis of outdoor electroluminescence images shows that spatial correlations and local clusters may arise in such photovoltaic data. Further, relevant statistics such as the mean pixel intensity cannot be assumed to follow a Gaussian law. We investigate the proposed inferential tools in detail by simulations in order to assess the influence of spatial cluster correlations and serial correlations on the test's size and power.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Publisher: Wiley-Blackwell
ISSN: 1524-1904
Date of First Compliant Deposit: 1 December 2016
Date of Acceptance: 31 October 2016
Last Modified: 15 Jun 2017 08:41
URI: http://orca-mwe.cf.ac.uk/id/eprint/96574

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