Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N5/00—Computing arrangements using knowledge-based models
  • G06N5/04—Inference or reasoning models
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
  • G06F17/10—Complex mathematical operations
  • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F30/00—Computer-aided design [CAD]
  • G06F30/20—Design optimisation, verification or simulation
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q10/00—Administration; Management
  • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q10/00—Administration; Management
  • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
  • G06Q10/063—Operations research, analysis or management
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
  • G06Q50/06—Energy or water supply
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F2111/00—Details relating to CAD techniques
  • G06F2111/10—Numerical modelling
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
  • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
  • Y02P90/80—Management or planning
  • Y02P90/84—Greenhouse gas [GHG] management systems

Definitions

• the subject of the present invention relates to the field of the management of the fluid consumption, and more particularly relates to the reduction of fluid consumption.
• One of the objectives of the present invention is to accurately predict at a given moment the amount of fluid erased for an erasure phase to come.
• the present invention thus finds many advantageous applications, in particular for energy operators, by enabling them to optimally manage their fluid production and to ensure a balance between fluid supply and demand, especially during peak consumption.
• the present invention also finds other advantageous applications in particular for adjustment operators by allowing them to accurately quantify the consumption of erased fluid during an erasure period, this for example in order to contractualize an erasure offer.
• any energy source such as for example electricity, water, or gas or fuel oil, likely to be consumed by equipment of an installation (domestic or industrial) in particular for its operation.
• Controlling the consumption of fluids has become a daily and growing challenge for both individuals and industry: the reasons for controlling this consumption are both economic (high financial costs) and ecological (pollution , greenhouse gas emissions, natural resources management).
• This peak consumption comes in particular from the fluid consumption called for heating and / or air conditioning. It is mainly a consumption of electrical energy.
• certain electrical uses such as heating may be voluntarily interrupted at times of high demand, for example for a period of two hours (preferably between 18 and 20 hours).
• this quantity of erased fluid also called “erasure”; this amount of erased fluid corresponds here to the difference between the amount of fluid actually consumed and the amount of fluid that would have been consumed if the customer had not erased (this theoretical amount is also called “baseline”).
• Predicting in advance this consumption of erased fluid makes it possible to quantify a quantity of energy fluid for example to indicate this quantity in a sales contract.
• the operator may undertake to supply a quantity of energy fluid for a given period.
• An operator can therefore decide to delete any number of clients from his portfolio at any time for a variable duration.
• the prediction of the erasure can not therefore be built solely on a limited history of data.
• erasures are useful about twenty times in winter and ten times in summer.
• the document EP 2 047 577 relates to the erasure and proposes a solution for regulating the energy consumption. More particularly, this document describes a method for managing and modulating in real time the power consumption of a set of consumers. In this document, to know the consumption in real time, the method provides for the installation of an electrical control unit at each consumer to send in "push" mode a periodic record of consumption measurements to a central server that collects this information. and establishes an individual estimate of consumption.
• the present invention aims to improve the situation described above.
• the present invention provides a statistical approach for effectively predicting the effected fluid consumption for an upcoming erasure phase.
• the subject of the present invention relates to a method for predicting an erased fluid consumption which is implemented by computer means; the prediction method firstly comprises a collection of consumption data.
• the consumption data comprise information relating to a real consumption of fluid of a plurality of consumers during a learning phase.
• the method according to the present invention comprises an aggregation of consumption data collected in groups.
• this aggregation by groups is carried out in particular according to one or more specific descriptive variables; this or these variables are associated with each consumer and are contained in the consumption data.
• these descriptive variables are selected from at least one of the following variables: the region, the type and the housing area, the number of people for housing or the heating mode and / or the air conditioning mode.
• the method according to the present invention also comprises a determination, from the aggregated consumption data, of an overall load curve for each group.
• This global load curve is the curve relating to the fluid consumption of each group during the learning phase.
• the method according to the present invention comprises a calculation of a model of extraction of a load curve, called heating load curve and / or air conditioning.
• This load heating and / or air conditioning curve is here the curve relating to the fluid consumption for heating and / or cooling of each of the groups.
• this extraction model is made from each global load curve and meteorological data; preferably, these meteorological data contain at least one meteorological information for each group during the learning phase.
• the method according to the present invention comprises a prediction of an erased fluid consumption for each group for an erasure phase to come.
• this prediction is calculated according to each heating load curve and / or air conditioning estimated by the extraction model and a history of consumption data. Thanks to this succession of technical steps, characteristic of the present invention, it is possible to construct during a learning phase a model for extracting a heating and / or air conditioning load curve from a curve. global load, then to predict at a given time, on the basis of a history of consumption data, a fluid consumption erased for an erasure phase to come.
• the present invention makes it possible to accurately predict a consumption of erased fluid on a set of consumers; this prediction makes it possible, for example, to manage the energy production plan in advance and / or to sell this non-consumed energy on the adjustment market.
• the method according to the present invention comprises, prior to the aggregation of the consumption data, a pretreatment.
• a correction of the consumption data for at least one consumer is performed when consumption data of said at least one consumer are missing.
• This correction makes it possible to have a continuous consumption data sequence on the learning phase, which makes it possible to minimize the errors during the prediction.
• This preliminary correction can take several forms.
• the missing consumption data are estimated, during the correction, by interpolation with the other consumption data. collected for that same consumer.
• the missing consumption data are estimated, during the correction, by searching in a consumption data history for a consumption period. a sequence of consumption data minimizing the distance with the collected consumption data.
• the threshold period determined is a period of 3 hours.
• other periods can also be envisaged within the scope of the present invention.
• the aggregate aggregate load curves obtained with the consumption data are of good quality.
• the consumption data must be synchronous with each other.
• the fluid consumption data comprise temporal information relating to the instant at which the consumption of fluid by the consumer has been achieved.
• the pretreatment advantageously comprises a synchronization of the data when these are desynchronized.
• this synchronization of the consumption data is performed by interpolation.
• the meteorological data contains information relating to the outside temperature for each consumer during the learning phase.
• the method comprises, for each group, the calculation of an average of the temperatures contained in the meteorological data weighted by the called power of the consumers of the group.
• the calculation of the extraction model comprises a modeling of a consumption power called for heating and / or cooling by the same group at a time t, by a linear regression of the LASSO type carried out according to the following formula: in which :
• variable dh corresponds to the half-hourly step whose power is sought to be modeled at time t with dh iij f, 48];
• P t is the global power called for a group at a time t;
• the LASSO algorithm may be less efficient in estimating model parameters.
• a PLS type algorithm is used, in particular to retrieve the coordinates of the components.
• the prediction of the fluid consumption erased at a time t for a forecast horizon k is estimated according to the following formula:
• X t represents a matrix of explanatory variables of the forecasting model
• the method further comprises a step of orthogonalizing the matrix f of the explanatory variables of the prediction model by using a PLS1 type algorithm for maximizing the correlation between the components of said matrix X t and the parameters of the prediction model.
• the method prior to the collection of consumption data, includes consumer stratification in which the inter-stratum variance is maximized and the intra-stratum variance is minimized.
• this stratification is carried out in particular on the basis of the descriptive variables above.
• This stratification makes it possible to avoid the installation of a backup device for each of the consumers.
• the subject of the present invention relates to a computer program which includes instructions adapted to the execution of the steps of the method as described above, this in particular when said computer program is executed by a computer or at least one processor.
• Such a computer program can use any programming language, and be in the form of a source code, an object code, or an intermediate code between a source code and an object code, such as in a partially compiled form, or in any other desirable form.
• the subject of the present invention relates to a computer-readable or processor-readable recording medium on which is recorded a computer program comprising instructions for the execution of the steps of the method as described below. above.
• the recording medium can be any entity or device capable of storing the program.
• the medium may comprise storage means, such as a ROM, for example a CD-ROM or a microelectronic circuit-type ROM, or a magnetic recording means, for example a diskette of the type " floppy says "or a hard drive.
• this recording medium can also be a transmissible medium such as an electrical or optical signal, such a signal can be routed via an electrical or optical cable, conventional radio or radio or self-directed laser beam or other means.
• the computer program according to the invention can in particular be downloaded to an Internet type network.
• the recording medium may be an integrated circuit in which the computer program is incorporated, the integrated circuit being adapted to execute or to be used in the execution of the method in question.
• the subject of the present invention also relates to a computer system for predicting a consumption of erased fluid.
• the computer system comprises:
• a collection module configured to collect consumption data comprising information relating to a real consumption of fluid of a plurality of consumers during a learning phase
• a processing circuit configured to aggregate the consumption data collected in groups as a function of at least one specific descriptive variable associated with each consumer and contained in the consumption data
• a processor configured to determine from the aggregated consumption data an overall load curve for each group; a calculator configured to calculate a model for extracting a load curve, referred to as heating and / or air conditioning, relating to the overall fluid consumption for the heating and / or cooling of each of the groups from each global load curve and meteorological data containing at least one meteorological information for each group during said learning phase, and
• the computer system also comprises computer means which are specifically configured for the implementation of the steps of the method as described above.
• the present invention makes it possible to predict in advance and accurately the amount of fluid consumption erased during an erasure phase.
• the present invention has the advantage of providing with good reliability erasable power without the need to observe the slightest erasure.
• FIGS. 1 and 5 illustrate an embodiment of this embodiment which is devoid of any limiting character and on which:
• Figure 1 shows a schematic view of a computer system according to an exemplary embodiment of the present invention
• FIG. 2 represents a graph illustrating the evolution of the power consumed as a function of the outside temperature
• FIG. 3 represents a graph illustrating the evolution of the temperature, of a heating and / or air conditioning curve and of an overall load curve as a function of time:
• FIG. 4 represents a graph illustrating the comparison between the actual erased power and the prediction of an erased power
• Fig. 5 is a flowchart illustrating the remote learning method according to an advantageous exemplary embodiment of the present invention.
• the largest erasure field is electric heating and / or air conditioning; the example described here thus relates to the electrical consumption related to the operation of the electric heating or heaters for a home, also called consumer. It will be understood by those skilled in the art that application of the present invention to other fluids and / or other types of consumption may be contemplated.
• the heating and / or cooling of the customers are controlled in ON / OFF, to predict the erasure, it is necessary to take into account the history of the load curve of the erased use (the heating and / or the air conditioner).
• the present invention overcomes these problems and offers a powerful alternative solution that saves hardware, installation costs and IT.
• One of the objectives of the present invention is to reduce the average cost of the solution per customer.
• the computer system 100 comprises a collection module 10 which is configured to collect during a collection step IF data of consumption D_CONSi, iu [l Jî
• l ? nj include information relating to a real electricity consumption of a plurality of consumers CONSi, iji .n
• each of these data also includes temporal information relating to the time at which the consumption of electricity by the consumer has been achieved. We talk about timestamping.
• this collection SI is performed with a collection step of 30 minutes, which corresponds to a half-hourly step.
• a collection step of 30 minutes which corresponds to a half-hourly step.
• another collection step for example a step of 20, 15 or 10 minutes.
• the step of 30 minutes allows to obtain results of good quality, this step corresponding to the official step of the adjustment mechanism.
• the consumption data can undergo a pretreatment S2 to improve the operation that follows.
• this pretreatment comprises firstly a synchronization S2_l consumption data D CONS; when they are out of sync.
• the consumption data D CONS are synchronized and arrive preferably every 30 minutes from midnight.
• a consumption data D CONSi for a consumer i having a time stamp corresponding to "00h10m30s" is reduced to "OOhlOmOOs" after synchronization. If alternatively consumption data D CONS; are desynchronized by more than 1 minute, the data is then interpolated to reset the time stamp.
• the pretreatment also comprises a correction S2_2 of the data.
• the missing consumption data is estimated by searching in a consumption data history a data sequence minimizing the distance with the consumption data. collected.
• the data sequence is transformed into a power curve.
• the curve is then cut daily.
• the consumption data D CONS are aggregated in groups, Gj, jO [Î, m].
• Gj the consumption data D CONS; are aggregated in groups, Gj, jO [Î, m].
• m groups where m is strictly less than or equal to n.
• This aggregation S3 is performed by a processing circuit 20 as a function of a plurality of specific descriptive variables associated with each consumer CONSi. This variable can be initially contained in the consumption data D CONS;
• these descriptive variables include information relating for example to the region, the type of housing, the heating and / or air conditioning means, the number of persons per dwelling, etc.
• the average global charge curve Cg j for each group G j is calculated from consumer consumption data belonging to said group. To each group G j therefore corresponds to a global average load curve Cg j .
• past erasures and associated load deflates can bias the history of the load curve overall. This may change the extracted heating load curve and alter the prediction of the erasable power potential.
• meteorological data D MET j are recovered, for example via the collection module 10 or by other means. These data contain weather information for each group G j during said learning phase J.
• these meteorological data are retrieved from the weather stations SM] to SM j directly by the relief devices DR] to DR n .
• the relief devices DR 1? DR 2 and DR 3 associated respectively with the consumers CONS 15 CONS 2 , and CONS 3 recover from the weather station SM] the meteorological data D_METi containing information such as, for example, the outside temperature T ⁇ .
• the DR n-1 and DR n polling devices associated respectively with consumers CONS n-1 and CONS n recover from meteorological station SM m meteorological data D_MET m containing information such that for example, the outside temperature Te m .
• this data coming from weather stations SM ! SM m associated with each consumer or each group of consumers, can be recovered directly by the collection module 10.
• the weather stations are geolocated as consumers; thus, to make the association between the consumer and the meteorological data, one looks for the weather station available closest to a consumer. For each group, an average of the curves of the weather stations weighted by the power demand of the customers belonging to the group is calculated so that the meteorological data is representative of the group.
• An overall load curve Cg j for each group G j is then determined by a processor 30 of the system 100, during a determination step S4.
• This overall load curve Cg j represents the electricity consumption of each group G j during the learning phase.
• the present invention seeks to extract the charge curve Cc j , called heating and / or air conditioning, relating to the electricity consumption for heating each of the groups G j from each global load curve.
• Cg j and meteorological data D MET j ; j is here a positive integer between 1 and m.
• This extraction requires the best modeling of the impact of temperature on the level of the overall load curve at each moment. This is done by a calculator 40 during a calculation step S5 during which an extraction model is calculated.
• the variation of the load curve related to heating and / or air conditioning depends on the outside temperature.
• the inertia of the buildings means that it is not the instantaneous gross external temperature that impacts the level of the load curve, but the whole of the past outside temperatures.
• this outside temperature impacts the load curve for a certain period; the impact of the outside temperature on this curve then reduces as and when.
• the so-called heating power is therefore a linear combination of past temperatures if they are below the threshold temperature Ts.
• the normal temperature variable must be introduced in order to model so-called "seasonal" uses.
• raw temperatures are used instead of the smoothed temperatures.
• the present invention therefore provides for an automatic adaptation of the perimeter changes.
• the underlying concept here consists in estimating the impact of each hourly temperature of the last 48 hours on the power demand of the dwelling at a given moment when these temperatures are below the threshold temperature of heating and / or air conditioning.
• the delayed temperatures being numerous and strongly correlated with each other, the extraction model is based on a so-called LASSO criterion.
• the advantage of the LASSO regression is to be able to take into account in the model many variables with a certain correlation between them, which is not possible with a classical linear regression (the estimation of the parameters becomes unstable).
• the selection of variables of a linear regression is a discrete process, the variable is either retained or eliminated.
• the LASSO regression is a more continuous selection and allows to keep more information.
• this temperature is the temperature below which the electric heating or the air conditioning is started.
• the impact of the temperature on the electrical consumption is significant only below a certain temperature, which is this threshold temperature.
• the present invention provides for regressing the called power at a time t on the temperature in a B-spline base of degree 1 with an inner node (corresponding to the threshold temperature).
• the position of the node is varied and the position of the node minimizing the mean squared error of the regression is chosen as optimum.
• the function to be minimized is therefore the mean squared error of the regression
• the raw temperature variable is converted into a "thresholded" temperature depending on whether we wish to extract the heating load curve or the air conditioning load curve.
• the computer system 100 comprises a computer 40 which is configured to model, during a step S5, the called power P £ at a time t by a LASSO regression taking into account the following variables :
• the computer 40 Since the reaction of the power demand at the temperature differs from half an hour to another, it is desirable to estimate a LASSO model in no time in a day (ie 48 models and therefore 48 sets of parameters in the half hour case); the computer 40 according to the present invention is therefore implemented to implement the following algorithm:
• dh corresponds to the "half-hour type" whose power is sought to be modeled at time t with ah E l, 48j
• the LASSO algorithm may be less efficient in estimating model parameters.
• Pc t is an estimate of the heating power demand at time t for the half-hour type dh.
• This history is then used as a learning history to develop the prediction model of erasable power.
• the load curve of the erasable power is in the example described here the load curve Cc j relative to the heating and / or air conditioning since it is the only use that we pilot.
• predicting the load curve of the erasable power is to predict the load curve of the power demand called heating and / or air conditioning.
• the heating load curve Cc j was extracted from the overall load curve Cg j .
• a predictor 50 which is configured to calculate a prediction of an erased power consumption for future erasure in a prediction step S6.
• the estimation of the heating and / or air conditioning load curve on the data history is used as a learning history to establish a prediction model of the load curve of heating and / or air conditioning.
• the variables retained in this model are as follows:
• the reaction of the power demand at the temperature differs from half an hour to another. Consequently, the prediction depends on the half-hour type of the instant t + k that one wishes to predict.
• Pc t + k is an estimate of the expected power in t + k where t is the time when the forecast is made and k the forecast horizon
• • is the link function between the predictable variable and the explanatory variables of the forecasting model, with dh the typical half-hour of the time t + k that we are trying to predict.
• the predictor 50 is thus configured to implement the following mathematical algorithm:
• Pc t + k is the erase field that we want to predict at time t + k
• X t represents the matrix of explanatory variables of the forecasting model where each of the columns corresponds to each of the variables previously listed above
• the LASSO algorithm may have a lower efficiency in estimating model parameters.
• the construction of the components must be done according to their link with the variable to explain (in our case the power to be provided at time t).
• T the constraint of penalization.
• the formula implemented on the predictor 50 is the following at a time t to obtain the expected erasable power at a horizon k:
• the client refuses to have some of his appliances piloted like a towel dryer in the bathroom. He wants to be able to keep the management of the device,
• the electrical panel does not clearly identify the devices to be cut, - part of the customers of the group cancels the deletion order.
• the heating or cooling load curve Ccj of the customers of the group j is greater than the erasable charge curve Cej on the customers of the group j, Ccj> Cej.
• the proposed solution is to conduct a survey.
• the backup devices are installed only for certain consumers and not for all consumers of the erasure portfolio.
• the energy operator holds information on the type of housing, the surface of the dwelling, the year of construction of the dwelling, the weather station closest to the customer's place of residence, the regular presence of 'a person during the day, the number of people in the dwelling.
• the data collection is done by stratified sampling.
• the strata consist of so-called homogeneous consumers. In other words, consumers in the same stratum must be as homogeneous as possible.
• the collection of consumption data in each stratum is done by a simple random survey without discount.
• the present invention makes it possible to integrate an erase deposit upstream in order to integrate it into an energy production plan.
• This allows for example a supplier to predict in the short term the amount of erasable energy on a set of customers (also called erasure deposit).
• the erasure field is explained by various explanatory variables which are notably the rhythm of life, the type of housing, the outside temperature.
• This outside temperature is the most significant variable, especially with regard to consumption for heating and / or electric air conditioning.
• the present invention proposes a mathematical and statistical approach to take into account all of these parameters and to be able to predict with accuracy this deposit.

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