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. The Canadian Journal of Statistics 47: 65–89; 2019 © 2018 Statistical Society of Canada

Modeling Energy Demand—A Systematic Literature Review

“…Historic energy demand [33,34,37,39,68,71,82,89,90,101,107,108,111,131,147,163,175,178,229,236,263,285,331,340,346,349,356,361,365,396,398,425,442,449,478] Weather data [37,39,68,82,89,101,107,147,163,175,183,229,263,340,349,356,396,...…”