SPARSE APPROXIMATION AND FIT OF INTRADAY LOAD CURVES IN A HIGH DIMENSIONAL FRAMEWORK

An important perspective in electric consumption obviously is the forecasting. A crucial step in the forecasting process is the modeling. It is commonly admitted that many variables are influential for the prediction in this context. On the other hand, a prediction, to be robust and efficient, has necessarily to rely on a small number of well chosen predictors. We are typically in a situation where sparse multidimensional modeling can bring an essential input, and this paper is an attempt to prove it. In this perspective, we shall address the question of providing a sparse representation of the intraday load curves, with good approximation properties. One difficulty is that we have here a large set of potential predictors among climate variables and shape "patterns", and even if high dimensional sparse methods have clearly as objective to select among a large number of covariates, they are especially efficient when the predictors are not too correlated. So our task is twofold: first we need to operate a p...