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Conditional Bias Robust Estimation of the Total of Curve Data by Sampling in a Finite Population: An Illustration on Electricity Load Curves

Authors :
Hervé Cardot
Anne De Moliner
Camelia Goga
Institut de Mathématiques de Bourgogne [Dijon] (IMB)
Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC)
Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université de Bourgogne (UB)
EDF Labs
Laboratoire de Mathématiques de Besançon (UMR 6623) (LMB)
Université de Bourgogne (UB)-Université de Franche-Comté (UFC)
Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS)
Source :
Journal of Survey Statistics and Methodology, Journal of Survey Statistics and Methodology, Oxford University Press, 2020, 8 (3), pp.453-482. ⟨10.1093/jssam/smz009⟩
Publication Year :
2020
Publisher :
HAL CCSD, 2020.

Abstract

For marketing or power grid management purposes, many studies based on the analysis of total electricity consumption curves of groups of customers are now carried out by electricity companies. Aggregated totals or mean load curves are estimated using individual curves measured at fine time grid and collected according to some sampling design. Due to the skewness of the distribution of electricity consumptions, these samples often contain outlying curves which may have an important impact on the usual estimation procedures. We introduce several robust estimators of the total consumption curve which are not sensitive to such outlying curves. These estimators are based on the conditional bias approach and robust functional methods. We also derive mean square error estimators of these robust estimators, and finally, we evaluate and compare the performance of the suggested estimators on Irish electricity data.

Details

Language :
English
ISSN :
23250992
Database :
OpenAIRE
Journal :
Journal of Survey Statistics and Methodology, Journal of Survey Statistics and Methodology, Oxford University Press, 2020, 8 (3), pp.453-482. ⟨10.1093/jssam/smz009⟩
Accession number :
edsair.doi.dedup.....2c4387979eab03f0caea3fdb2a70b7a8
Full Text :
https://doi.org/10.1093/jssam/smz009⟩