Conditional Bias Robust Estimation of the Total of Curve Data by Sampling in a Finite Population: An Illustration on Electricity Load Curves - Université de Bourgogne Accéder directement au contenu
Article Dans Une Revue Journal of Survey Statistics and Methodology Année : 2020

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

Résumé

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.
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Dates et versions

hal-03035958 , version 1 (30-01-2024)

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Hervé Cardot, Anne de Moliner, Camelia Goga. Conditional Bias Robust Estimation of the Total of Curve Data by Sampling in a Finite Population: An Illustration on Electricity Load Curves. Journal of Survey Statistics and Methodology, 2020, 8 (3), pp.453-482. ⟨10.1093/jssam/smz009⟩. ⟨hal-03035958⟩
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