Estimation of total electricity consumption curves by sampling in a finite population when some trajectories are partially unobserved

Abstract : Millions of smart meters that are able to collect individual load curves, that is, electricity consumption time series, of residential and business customers at fine scale time grids are now deployed by electricity companies all around the world. It may be complex and costly to transmit and exploit such a large quantity of information, therefore it can be relevant to use survey sampling techniques to estimate mean load curves of specific groups of customers. Data collection, like every mass process, may undergo technical problems at every point of the metering and collection chain resulting in missing values. We consider imputation approaches (linear interpolation, kernel smoothing, nearest neighbours, principal analysis by conditional estimation) that take advantage of the specificities of the data, that is to say the strong relation between the consumption at different instants of time. The performances of these techniques are compared on a real example of Irish electricity load curves under various scenarios of missing data. A general variance approximation of total estimators is also given which encompasses nearest neighbours, kernel smoothers imputation and linear imputation methods.
Document type :
Journal articles
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01996996
Contributor : Imb - Université de Bourgogne <>
Submitted on : Monday, January 28, 2019 - 4:30:02 PM
Last modification on : Thursday, February 7, 2019 - 2:51:26 PM

Identifiers

Citation

Hervé Cardot, Anne de Moliner, Camelia Goga. Estimation of total electricity consumption curves by sampling in a finite population when some trajectories are partially unobserved. Canadian Journal of Statistics, Wiley, 2018, ⟨10.1002/cjs.11473⟩. ⟨hal-01996996⟩

Share

Metrics

Record views

25