A computing system for predicting a curtailed consumption of fluid comprising: a module for collecting consumption data comprising information relating to an actual consumption of fluid of a plurality of consumers during a learning phase, a processing circuit for aggregating the consumption data collected by groups as a function of at least one determined descriptive variable associated with each consumer and contained in the consumption data, a processor for determining on the basis of the aggregated consumption data a curve of global load for each group, a computer for computing a model of extraction of a load curve, termed heating and/or air conditioning, on the basis of each global load curve and of meteorological data, and a predictor for computing a prediction of a curtailed consumption of fluid for each group during a forthcoming curtailment phase.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of predicting a curtailed fluid consumption, implemented by computer means, including the following steps: collecting consumption data including information relating to a real fluid consumption of a plurality of consumers during a learning phase, aggregating the collected consumption data on a group basis as a function of at least one particular descriptive variable associated with each consumer and contained in the consumption data, determining from the aggregated consumption data a global load curve for each group relating to the fluid consumption of each group during the learning phase, calculating an extraction model of a heating and/or air conditioning load curve relating to the fluid consumption for heating and/or air conditioning of each of the groups from each global load curve and weather data containing at least information relating to the weather conditions for each group during said learning phase, and predicting a curtailed fluid consumption for each group for a future curtailment phase as a function of each heating and/or air conditioning load curve estimated by the extraction model and a consumption data history. 2. The method as claimed in claim 1 including, before aggregating the consumption data, pre-processing during which a correction of the consumption data for at least one consumer is carried out if consumption data for said at least one consumer is missing. 3. The method as claimed in claim 2, wherein, if, for the same consumer, consumption data is missing over a period less than or equal to a predetermined threshold period, then the missing consumption data is estimated, at the time of the correction, by interpolation with other consumption data collected for that same consumer. 4. The method as claimed in claim 2, wherein, if, for the same consumer, consumption data is missing over a period greater than a predetermined threshold period, then the missing consumption data is estimated, at the time of the correction, by seeking in a consumption data history a consumption data sequence minimizing the distance from the collected consumption data. 5. The method as claimed in claim 2, wherein the particular threshold period is three hours. 6. The method as claimed in claim 2, wherein the fluid consumption data includes time information relating to the time at which the consumption of fluid by the consumer occurred and wherein the pre-processing includes synchronizing said data if it is not synchronized. 7. The method as claimed in claim 6, wherein the synchronization of the consumption data is effected by interpolation. 8. The method as claimed in claim 1, wherein the weather data contains information relating to the outside temperature for each consumer during the learning phase and wherein there is calculated for each group an average of the temperatures contained in the weather data weighted by the power demand of the consumers of said group. 9. The method as claimed in claim 1, wherein said at least one descriptive variable is selected from at least one of the following variables: the region, the accommodation type and area, the number of persons in the accommodation or the heating and/or air conditioning method. 10. The method as claimed in claim 1, wherein the calculation of the extraction module includes modeling a power consumption demand for heating by the same group at a time t by LASSO type linear regression in accordance with the following formula: β ^ dh  ( τ ) = argmin  (  P t - β 0 dh - ∑ h = 0 h = 23   ( Tc t - h  β h + 1 dh - Tn t  β 25 dh )  2 + τ   ∑ i = 0 i = 25   β i dh  1 ) in which: the variable dh corresponds to the half-hour step of which the aim is to model the power at the time t with dh iε[1,48]; Pt is the global power demand for a group at a time t; β0dh is the constant associated with the model; βh+1dh correspond to the parameters associated with the temperature variables Tct-h; β25dh corresponds to the parameter associated with the normal temperature; τ is a penalty constraint; and {circumflex over (β)}dh(τ) corresponds to the vector of the estimates of the parameters of the extraction model. 11. The method as claimed in claim 10, wherein the prediction of the curtailed fluid consumption at a time t for a prediction horizon k is estimated in accordance with the following formula: {circumflex over ({circumflex over (P)})}=Xt{circumflex over (Ω)}dh in which: Xt represents a matrix of explanatory variables of the prediction model; and {circumflex over (Ω)}dh(τ) corresponds to the vector of the estimates of the parameters of the prediction model. 12. The method as claimed in claim 10, including a step of orthogonalization of the matrix Xt of the explanatory variables of the prediction module using a PLS1 type algorithm to maximize the correlation between the components of said matrix Xt and the parameters of the prediction model. 13. The method as claimed in claim 1, including, before the collection of consumption data, stratification of the consumers during which the inter-stratum variance is maximized and the intra-stratum variance is minimized. 14. (canceled) 15. A computer-readable storage medium on which is stored a computer program including instructions for executing the steps of the method as claimed in claim 1. 16. A computer system for predicting a curtailed fluid consumption, including: a collecting module configured to collect consumption data including information relating to a real fluid consumption by a plurality of consumers during a learning phase, a processing circuit configured to aggregate the collected consumption data on a group basis as a function of at least one particular descriptive variable associated with each consumer and contained in the consumption data, a processor configured to determine from the aggregated consumption data a global load curve for each group, a computer configured to calculate an extraction model from a so-called heating or air conditioning load curve relating to the fluid consumption for heating and/or air conditioning of each of the groups from each global load curve and weather data containing at least information relating to the weather conditions for each group during said learning phase, and a predictor configured to calculate a curtailed fluid consumption for each group for a future curtailment phase as a function of each heating and/or air conditioning load curve estimated by the extraction model and a consumption data history. 17. (canceled)