Classifications

• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B15/00—Systems controlled by a computer
  • G05B15/02—Systems controlled by a computer electric
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
  • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
  • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
  • G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; 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
  • G01—MEASURING; TESTING
  • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
  • G01D2204/00—Indexing scheme relating to details of tariff-metering apparatus
  • G01D2204/20—Monitoring; Controlling
  • G01D2204/24—Identification of individual loads, e.g. by analysing current/voltage waveforms
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/10—Plc systems
  • G05B2219/16—Plc to applications
  • G05B2219/163—Domotique, domestic, home control, automation, smart, intelligent house
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
  • G06N20/20—Ensemble learning
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N5/00—Computing arrangements using knowledge-based models
  • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound

Definitions

• the field of the invention is that of monitoring the fluid consumption of a building to be monitored, for example the consumption of liquid water by a residential house, a building, a school, a hospital, a factory, etc.
• the invention relates more precisely to monitoring by classification, here by supervised learning, fluid consumption events, present in a measurement signal representative of the temporal evolution of the overall flow rate of consumption of the fluid of interest by the building to be monitored, and in particular by different consuming elements, such as for example taps , showers, toilet flushes in the case of a residential house.
• Classification amounts to determining the class (label, category, or label in English) of consumer element which is to be associated with each of the fluidic events.
• the invention makes it possible in particular to detect excessive consumption of consuming elements of one or other of the predefined classes, and then makes it possible to indicate to the user if consuming elements are overused or present a flow rate above 'a predefined reference.
• a building to be monitored can be a house, a building, a school, a hospital, a factory, a company hotel, etc.
• the fluid of interest can be, depending on the type of building to be monitored, a gas or liquid such as water, hydrogen, oxygen, nitrogen, etc.
• the building to be monitored comprises several devices connected by a fluid circuit to a fluid source and which consume the fluid of interest. Here we will call these devices “consumer elements”. Consumer elements can be classified into different predefined classes. For example, in the case of a residential house where the fluid of interest is liquid water, the consuming elements can fall into the class of faucets, that of showers, that of toilet flushes. toilet water, that of dishwashers, and even that of washing machines.
• the building to be monitored is usually equipped with a flow sensor adapted to measure a signal representative of the temporal evolution of the overall flow rate of the fluid consumption of interest by the building to be monitored, and more precisely by its consuming elements.
• This measurement signal includes fluid consumption events, which are defined as moments with non-zero flow.
• the invention aims to propose a method which makes it possible to classify with improved precision the fluidic events linked to the consumption of the fluid of interest by the consuming elements of the building to be monitored, this is that is to say, to determine the class of consumer elements to be associated with each of the fluidic events, this to monitor the fluidic consumption of the building to be monitored.
• the object of the invention is a method, implemented by computer, for monitoring the consumption of a fluid of interest by a building to be monitored BS, for classifying fluidic events ⁇ Ev ( ⁇ , ⁇ ) of consumption of the fluid of interest by EC consumer elements of the building to be monitored BS, among several predefined classes L ⁇ ⁇ ( ⁇ , ⁇ ) of EC consumer elements, the method comprising the following phases.
• the acquisition step during the generation phase is carried out by means of several fluidic measurement sensors each connected to a consumer element EC of the N real reference buildings. Furthermore, the acquisition step during the classification phase is carried out by means of at least one fluid measurement sensor connected to a fluid input of the building to be monitored, and preferably by means of a single sensor.
• the monitoring method comprises a phase of monitoring the building to be monitored, comprising the following steps: identifying a class of consumer elements, among at least part of the predicted classes, for which a monitoring parameter associated with the corresponding fluidic events presents a deviation from a reference value greater than a threshold deviation; then communicating to a user the identified class of consumer elements. [15]
• the temporality parameters P T(i) and P ⁇ )( ⁇ , ⁇ ) can also include, for each of the time slots, the cumulative volume of the fluid of interest flowed and the flow duration.
• the event parameters P Ev(i) and ⁇ P ⁇ *( ⁇ , ⁇ ) can include, for each fluidic event, the initial instant of the fluidic event, the flow duration, the elapsed volume and/or the average flow.
• the database and the input data of the prediction model may include so-called comparison parameters, denoted respectively P Ev
• the classification phase can be repeated, the measurement signals S ⁇ mes ⁇ ( (t) of the building to be monitored then being acquired over predefined monitoring periods [20]
• the average overall flow rate of all fluidic events ⁇ Ev ( ⁇ , ⁇ ) can take into account the average overall flow rate of previous iterations.
• the generation phase can include the following steps: - determination of a first database where each fluidic event is defined by at least said event parameters and by said temporality parameters; - determination of a second database where each fluidic event is defined by at least said event parameters, by said temporality parameters, and by so-called class comparison parameters defined, for each fluidic event of at least one class considered, as the ratio of an average flow rate of the fluidic event considered to an average overall flow rate of all the fluidic events of the class considered.
• the parameterization phase can then include the following steps: - parameterization of a first prediction mode by supervised automatic learning from the first database; - configuration of a second prediction mode by supervised automatic learning from the second database; [23]
• the classification phase can then include the following steps: - determination, for fluidic events present in the measurement signal S ⁇ mes ⁇ ( (t), of the event parameters and the corresponding temporality parameters; then - prediction by the first prediction model, for each of the fluidic events, which are defined by the event parameters and the temporality parameters which form input data of the first prediction model, of the corresponding consumer element class; then - determination, from the previously predicted classes, of corresponding comparison parameters per class; then - prediction by the second prediction model, for each of the fluidic events, which are defined by the event parameters, the temporality parameters, and the per-class comparison parameters which form input data to the second prediction model, of the corresponding consumer item class.
• the N real reference buildings can be connected to a determination device of at least one prediction model, comprising fluidic measurement sensors adapted to acquire the measurement signals for each of the consumer elements of the real reference building considered, and a computer connected to the fluidic measurement sensors of the reference building considered and adapted to carry out the configuration phase.
• the latter can be connected to a classification device comprising a fluidic measurement sensor adapted to acquire the signal measurement S ⁇ mes ⁇ ( (t), and a computer connected to the fluidic measurement sensor of the building to be monitored and adapted to carry out the classification phase.
• the method can include a correction step by the user so that the consumer elements of the identified class then present a value of the tracking parameter having a deviation less than the threshold deviation.
• Figure 1A schematically illustrates an example of a building to be monitored, here a residential house, equipped with a device for classifying fluidic events linked to the consumption of the fluid of interest by the building to be monitored
• Figure 1B illustrates an example of a measurement signal representative of the temporal evolution of the overall flow rate of consumption of the fluid of interest by the building to be monitored in Fig.
• Figure 2 illustrates a flowchart of a method for monitoring fluidic events linked to the consumption of the fluid of interest by the building to be monitored, according to one embodiment
• Figure 3A schematically illustrates an example of a real reference building, here a residential house, equipped with a device for determining a prediction model by supervised learning
• Figure 3B illustrates examples of measurement signals representative of the local consumption flow rate by each of the consumer elements EC of the real reference building of Fig.3A
• Figure 3C illustrates time slots located before and after the initial instant of a fluidic event, making it possible to define so-called temporality parameters representative of each of the fluidic events
• Figure 4 illustrates steps implemented during the database generation phase, from simulated signals for so-called simulated buildings
• Figure 5 illustrates a flowchart of a method for monitoring fluidic events linked to the consumption of the fluid of interest by the building to be monitored, according to another embodiment which implements two prediction models
• Figures 6A to 6C illustrate examples of cumulative distributions of flow rate, flow duration and elapse
• the monitoring method is carried out using a computer (processing unit) which integrates at least one prediction model f1, f2 by supervised automatic learning.
• the prediction model f1, f2 can be a decision tree model, boosting of weak classifiers, linear or quadratic discriminant analysis, forest of decision trees, k nearest neighbors method, among others.
• the monitoring method comprises a phase of learning the prediction model f1, f2 which is carried out from at least one database BD1, BD2 pretreated in a particular way.
• This database BD1, BD2 is not formed, as it could be according to a natural approach, by classified (labeled) flow measurement signals coming from each of the EC consumer elements of several real buildings reference Brief. Indeed, this approach cannot be sufficiently representative of the diversity of effective fluid consumption in real buildings.
• the database is formed from simulated and classified signals, representative of the flow rate of each of the consumer elements EC of simulated buildings Bsim, and preferably of a much greater number M of simulated buildings Bsim to the number N of real reference buildings
• the Pstat Bsim consumption profiles of the simulated buildings, from which the digital simulation was carried out, were determined from the classified flow measurement signals coming from the real reference buildings, so that the simulated signals remain consistent with the actual consumption of the real buildings.
• the database is formed from the different fluidic events Ev of the fluidic consumption of the consumer elements EC of the simulated buildings.
• Figure 1A illustrates an example of a building to be monitored BS comprising several consumer elements EC of different classes, and equipped with a classification device adapted to classify the fluidic events E ⁇ v linked to the consumption of the fluid of interest by the consumer elements EC, that is to say adapted to determine the class L ⁇ ⁇ * of each of the fluid consumption events by the building to be monitored BS.
• the building to be monitored BS is one or more structures which consume a fluid of interest through its consumer elements EC, and whose consumption must be monitored in order to detect possible anomalies such as excessive consumption. by EC consumer elements of a certain class.
• the user is able to know the consumption of each class of EC consumer elements, and can thus know, for example, if the average flow rate of a class of EC consumer elements is greater than a predefined value.
• consumption we mean that the building to be monitored BS receives the fluid of interest from a fluid source SF, and uses it (“consumes”) according to different uses, which may be personal and/or professional.
• a building to be monitored BS can be, for example, a house, an apartment, a building comprising several apartments, a school, a factory, a hospital, a campsite, or other, and generally speaking, is any type of structure or set for personal and/or professional use.
• the building to be monitored BS may include several distinct buildings, for example in the case of a factory.
• the fluid of interest can be a liquid or a gas, such as water, hydrogen, oxygen, nitrogen, helium, etc.
• the building monitor BS can be a factory which consumes, for example, hydrogen, oxygen or nitrogen, in liquid or gas phase. It can also be a training building (school) that uses liquid water. In the remainder of the description, the building to be monitored BS is a residential house, and the fluid of interest is liquid water.
• the building to be monitored BS includes several consumer elements EC which ensure the effective consumption of the fluid of interest.
• the EC consumer elements are of different classes L, for example a class R for taps (toilets, kitchen, bathroom, etc.), a class D for showers, a class C for toilet flushes, another class LV for dishwashers, and another class LL for washing machines.
• L ⁇ R, D, C, LV, LL ⁇ .
• the building to be monitored BS also includes a fluidic circuit which ensures the distribution of the fluid of interest from the fluidic source SF to the consumer elements EC. These are distribution conduits, possibly fitted with valves.
• the fluidic source SF of the fluid of interest may be a supply network, for example of the city, a reservoir, or other.
• the building to be monitored BS is equipped with a classification device, which includes a fluidic measurement sensor CM BS to acquire a measurement signal S ⁇ mes ⁇ ( (t) representative of the temporal evolution of the overall flow, at the entrance to the building to be monitored BS.
• the classification device also includes a processing unit UT (computer) to determine the classes L ⁇ ⁇ of the fluidic events ⁇ Ev linked to the consumption of the fluid of interest by the consumer elements EC and present in the measurement signal S ⁇ mes ⁇ ( (t).
• the processing unit UT integrates at least one prediction model f1 by classification whose parameterization has been carried out by supervised automatic learning from a database BD1 [41]
• the classification device is not able to know the temporal evolution of the flow rate of each of the consumer elements EC, but only to know the overall flow rate at the entrance to the building to be monitored BS. This is due to this absence of sensors dedicated to each of the EC consumer elements, and therefore to the fact that only the overall flow information is accessible, that the invention provides for using a prediction model, here by supervised learning, to estimate the class L ⁇ ⁇ of consumer element EC which is to be associated with such and such fluidic event E ⁇ v of overall flow.
• the fluidic measurement sensor CM BS is connected to the fluidic circuit, and is located between the fluidic source SF and the consumer elements EC. It is suitable for measuring a measurement signal S ⁇ mes ⁇ ( (t) representative of the overall flow rate of consumption of the fluid of interest by the building to be monitored BS.
• This fluidic measurement sensor CM BS can be a water meter with flow meter, a pulse water meter, among others.
• the fluidic measurement sensor CMBS is a water meter with turbine flow meter, which acquires a measurement signal S ⁇ mes ⁇ ( (t), whose data are representative of the different fluidic events ⁇ Ev.
• FIG. 1B illustrates an example of a measurement signal S ⁇ mes ⁇ ( (t), here in the form of a temporal evolution of the overall flow rate linked to the water consumption of a house to be monitored BS.
• the measurement signal S ⁇ mes ⁇ ( (t) presents several fluidic events ⁇ Ev ( ⁇ ) , where j is an increment which goes from 1 to NP, with NP the total number of fluidic events E ⁇ v over a monitoring duration ⁇ ts (for example 1 day ).
• the measured information can be transmitted in real time to the processing unit, for example at a predefined frequency or as soon as a fluidic event ⁇ Ev ( ⁇ ) is finished, in the form of one or more electronic messages each containing the parameters ⁇ P ⁇ *( ⁇ ) of the fluidic event ⁇ Ev ( ⁇ ) considered.
• the measurement signal S ⁇ mes ⁇ ( (t) can be a vector whose values correspond to the flow rate measured at regular frequency.
• the measurement signal S ⁇ mes ⁇ ( (t) can be a vector comprising only the instant when a pulse is emitted.
• the processing unit UT is connected to the fluidic measurement sensor CM BS in a wired or non-wired manner. It can be placed in the building to be monitored BS or can be located remotely from it.
• This is a computer which includes a calculator and at least one memory. It allows the implementation of the operations of the monitoring process making it possible to determine the class L ⁇ ⁇ ( ⁇ ) of the fluidic events E ⁇ v ( ⁇ ) present in the measurement signal S ⁇ mes ⁇ ( (t).
• the calculator comprises a programmable processor capable of executing instructions recorded on an information recording medium.
• the memory contains instructions for implementing the monitoring method.
• Figure 2 is a flowchart of a monitoring method, according to one embodiment, of the classes L ⁇ ⁇ ( ⁇ ) of consumer elements EC to be associated with each of the fluidic events ⁇ Ev ( ⁇ ) present in the measurement signal S ⁇ mes ⁇ ( (t).
• the method comprises a phase 100 of generating a database BD1, a phase 200 of learning, followed by a phase 300 of prediction.
• the building to be monitored BS is a residential house with several residents.
• the monitoring method is obviously not limited to this example.
• the building to be monitored BS includes EC consumer elements of different known L classes, namely here classes: R (faucets), C (toilet flushes), D (showers), LL (washing machines) and LV (washing machines). -dishes).
• R fastaucets
• C toilet flushes
• D showsers
• LL washing machines
• LV washing machines
• the BD1 database is generated on the basis of measurement signals Dmes ⁇ ( ⁇ ) ⁇ ⁇ 4, ⁇ ,5... (t) representative of the real flow (ie the measured flow and not simulated) of each of the EC consumer elements of N real reference buildings Brief (n) , with n ranging from 1 to N>1. These are therefore classified (ie labeled) measurement signals, given that we have a measurement signal for each of the EC consumer elements.
• the real reference building Brief(n) is identical or similar to the building to be monitored BS, in the sense that it has the same use (here a residential house) and includes EC consumer elements of the same class L (here: tap R, shower D, flush toilet C, washing machine LL, and dishwasher LV).
• Phase 100 of generating the database BD1 is illustrated in more detail in Figure 4.
• This phase 100 comprises the following steps: o a step 110 of acquisition, by experimental measurement, of measurement signals Dmes ⁇ ( ⁇ ) ⁇ ⁇ 4, ⁇ ,5... (t), representative of the actual flow rate of each of the consumer elements EC of the N houses reference reals Brief(n); o a step 120 of determining profiles, called statistics, of consumption Pstat ⁇ !#($) ⁇ ⁇ 4, ⁇ ,5... of each simulated building Bsim(m), with m ranging from 1 to M>N; o a step 130 of generating, by digital simulation, simulated signals (t), representative of the flow rate of each of the consumer elements EC of the M simulated houses Bsim (m) ; o a step 140 of determining, for each of the fluidic events Ev identified in the simulated signals, at least the event parameters PEV and the temporality parameters PT; o a step 150 of generating the database BD1.
• step 110 we acquire measurement signals Dmes ⁇ ( ⁇ ) ⁇ ⁇ 4, ⁇ ,5... (t) representative of the real flow rate of each of the consumer elements EC of the N real houses reference Brief (n) , over at least one monitoring duration ⁇ ts (here at least one day).
• the number N can be equal, for example, to a few units, or even to ten. It differs from the number M of simulated houses which can be equal to a few hundred, thousands, tens of thousands or even more.
• Figure 3A schematically and partially illustrates a real reference house, in short, identical or similar to the house to be monitored BS (here a residential house).
• the reference house Brief may or may not have a number of inhabitants identical to that of the house to be monitored BS.
• the N real reference houses Brief(n) are connected, directly or indirectly, to a device for determining at least one prediction model f1, f2. This device includes CM EC fluidic measurement sensors and a UT processing unit.
• the CM EC fluidic measurement sensors acquire measurement signals Dmes ⁇ ( ⁇ ) ⁇ ⁇ 4, ⁇ ,5... (t) which correspond to the temporal evolution of the flow rate of each of the consumer elements EC of the N real reference houses, and transmit them to the processing unit UT.
• each EC consumer element is equipped with a CMEC fluidic measurement sensor.
• the measurement signals Dmes ⁇ ( ⁇ ) ⁇ ⁇ 4, ⁇ ,5... (t) are here vectors indicating the value of the flow rate measured at a frequency predefined, for example every second.
• Dmes ⁇ ( ⁇ ) ( ⁇ ( ⁇ ) ⁇ ⁇ 4, ⁇ ,5... t) ⁇ Dmes 4 (t); Dmes ⁇ ( ⁇ ) ⁇ (t); Dmes ⁇ ( ⁇ ) 5 (t); Dmes ⁇ 7; Dmes ⁇ ( ⁇ ) 7 ⁇ (t) ⁇ .
• Figure 3B illustrates examples of measurement signals from a Dmes R faucet (t), a Dmes D shower (t), a Dmes C toilet flush (t), a washing machine -Dmes LL (t) laundry, and a DmesLV (t) dishwasher.
• a statistical profile of a building brings together information linked to this building and its occupants/users, such as the number of occupants , the number and class of the different EC consumer elements. It also includes statistical information linked to the fluid consumption habits of each occupant, such as the typical times of use of the different EC consumer elements, the flow durations, the breaks between each fluid event (in particular for showers). Finally, it includes statistical information linked to the EC consumer elements themselves: maximum flow rate, average flow rate, etc. This consumption information is called statistical insofar as it can include an average value and a variability (standard deviation) associated with a given distribution (normal, log-normal, exponential, bimodal, etc.).
• the shower flow duration will be long, for others short, etc.
• These simulated signals are said to be classified to the extent that a class of consumer element is associated with each of the signals.
• Each simulated signal Dsim ⁇ !# ⁇ ⁇ includes one or more fluidic events. It presents for example the format of a vector indicating the value of the flow at a frequency for example of one second over a period of one day, and preferably over a period of several days or even weeks.
• the simulated signals can thus take the form of the signals illustrated in fig.3B.
• the measurement signal S ⁇ mes ⁇ ( (t) is formed from a succession of fluidic events .P ⁇ ⁇ ( ⁇ ) / and more precisely from a succession of parameters representative of the fluidic events.
• a fluidic event Ev Bsim(m) of the simulated global flow signal Ssim Bsim(m) (t) can come from the at least partial juxtaposition of several fluidic events from the simulated signals Dsim ⁇ !#( $) ⁇ ⁇ ( t ) . Also, we can assign the new 'mixed' or 'mixture' class, denoted M, to each fluidic event Ev of the same simulated global flow signal Ssim Bsim(m) (t) and coming from at least two consumer elements.
• upstream Cav and downstream Cap time slots have the same duration in pairs, but they could have different durations.
• each of the simulated global flow signals Ssim Bsim(m) comprises, per fluidic event Ev, event parameters PEV representative of the fluidic event in question (initial instant, flow duration, etc.) as well as PT temporality parameters.
• the BD1 database as being the collection of all the fluidic events of all the simulated global flow signals Ssim Bsim(m) , and more precisely event parameters P Ev , temporality parameters P T , and here comparison parameters PEv
• the number NA can thus be very high, especially since it comes from the large number M of simulated houses Bsim (m) .
• the fluidic events Ev (i) are mixed, in the sense that they are no longer associated with this or that simulated house Bsim(m) nor with this and that day of measurement.
• temporal correlation information is present via the PT temporality parameters, which improves the prediction performance of the model.
• Phase 200 Configuration of the prediction model.
• Phase 200 then consists of learning the prediction model from the database BD1, that is to say, configuring the prediction model automatically so that from the data input which are here the parameters ⁇ P Ev(i) ; P T(i) ; P Ev
• Lev(i) f1( PEv(i); PT(i); PEV
• Phase 300 consists of predicting the class (classification) of fluidic events E ⁇ v ( ⁇ ) present in a measurement signal S ⁇ mes ⁇ ( (t) representative of the overall flow rate of the house to be monitored BS , this signal having been acquired by the fluidic measurement sensor CMBS (see fig.1A).
• the event parameters ⁇ P ⁇ *( ⁇ , ⁇ ) are identical to the event parameters P Ev of the BD1 database, and include in this example, the initial instant of the fluidic event, its flow duration and the elapsed volume.
• the measurement signal S ⁇ mes ⁇ ( (t) is acquired by the fluidic measurement sensor CM BS over a monitoring duration ⁇ t s(k) , for example over a day. When the monitoring duration k is over, the fluidic measurement sensor acquires a new measurement signal S ⁇ mes ⁇ ( (t) over the next monitoring duration ⁇ t s(k+1) , and so on.
• the additional parameters are determined, here the temporality parameters ⁇ P )( ⁇ , ⁇ ) and the comparison parameters P ⁇ ⁇ *
• the input data supplied to the prediction model f1 are .P ⁇ ⁇ *( ⁇ , ⁇ ) ; ⁇ P )( ⁇ , ⁇ ) ; ⁇ P ⁇ *
• ⁇ (( ⁇ , ⁇ " ⁇ ) /, and the output data are the estimated classes L ⁇ ⁇ *( ⁇ , ⁇ ) for each fluidic event ⁇ Ev ( ⁇ , ⁇ ) , so that we have: L ⁇ ⁇ *( ⁇ , ⁇ ) f1: .P ⁇ ⁇ *( ⁇ , ⁇ ) ; ⁇ P )( ⁇ , ⁇ ) ; ⁇ P ⁇ *
• the monitoring method is able to determine the class of consumer element L ⁇ ⁇ *( ⁇ , ⁇ ) for each fluidic event E ⁇ v ( ⁇ , ⁇ ) present in the measurement signal S ⁇ mes ⁇ ( (t) representative of the overall consumption flow of the fluid of interest of the building to be monitored BS.
• the input data includes the temporality parameters ⁇ P )( ⁇ , ⁇ ) , it appears that the model prediction has improved accuracy.
• the prediction step 330 may include constraints which block the attribution of one or the other of the classes to certain fluidic events ⁇ Ev ( ⁇ , ⁇ ) and assign a class called 'Other' when the estimated class is prohibited.
• the events of each class respect conditions, for example, on the flow duration, the average flow rate, the number of similar successive fluidic events, etc.
• the 'Shower' class could not be assigned to a fluid event whose flow duration would be less than 5 seconds or greater than 900 seconds.
• Phase 400 Monitoring
• the monitoring process can make it possible to detect unusual consumption (for example excessive or insufficient) of consuming elements of one or other of the predefined classes, to then communicate it to a user.
• the method includes a monitoring phase 400, which follows the classification phase 300.
• the monitoring parameter PS 7 ⁇ can come from the event parameters ⁇ P ⁇ of the fluidic events of the predicted class considered, such as for example an average or instantaneous flow rate, an average flow duration, etc.
• a difference E 7 ⁇ between the tracking parameter PS 7 ⁇ is a predefined reference value PS 7 ⁇ , ⁇ .
• This reference value is associated with the class considered. It can thus be a reference value of the average flow, in the case where we consider for example the class of taps (or showers, washing machines, etc.). It may be a predefined and constant value over time, or derived from a sliding value (average, minimum or maximum value, etc.) determined over a predefined time window (previous days, weeks, months, years, etc.). This value can come from consumption habits from the building to be monitored BS, or from real reference buildings. In short, from simulated buildings Bsim, or even from national statistical data, etc.
• the difference E 7 ⁇ can be the difference in absolute value (or the ratio) between the value of the monitoring parameter and its reference value: > PS 7 ⁇ ⁇ PS 7 ⁇ , ⁇ > .
• This information is communicated to a user of the monitoring method.
• This communication can take different forms, and can be a display on a monitoring screen.
• the method may also include a user correction step so that the consumer elements of the identified class then present a tracking parameter having a deviation less than the predefined threshold deviation. For example, this can result in replacing shower heads, or even by replacing the consuming elements in question with less consuming elements.
• FIG. 5 illustrates a flowchart of a monitoring method according to another embodiment, which differs from the method of fig.2 essentially in that it uses two prediction models f1 and f2 successively.
• the principle is to use the first prediction model f1 to carry out a first level of classification, and thus deduce additional parameters, which will then be added to the input data of the second prediction model f2 which will then carry out a second level of classification. classification. This makes it possible to further improve the performance of the monitoring process.
• the monitoring method comprises a phase 100 of generating two databases BD1 and BD2, then a phase 200 of learning the two prediction models f1 and f2, and finally a phase 300 of prediction at two levels.
• Step 150 consists of determining (sub-step 151) the event parameters P Ev , the temporality parameters PT (and here the comparison parameters PEv
• the BD1 database therefore includes these parameters PEv, PT and here PEv
• Step 150 also consists of determining (sub-step 152) additional parameters P Ev
• the BD2 database includes the event parameters PEv, the temporality parameters PT, the comparison parameters PEv
• the prediction model f1 is parameterized from the database BD1
• the prediction model f2 is parameterized from the database BD2.
• the acquisition steps 310 and preprocessing 320 of the measurement signal S ⁇ mes ⁇ ( (t) are carried out in an identical or similar manner to the steps described in connection with fig.2
• the input data provided to the model f1 are .
• ⁇ (( ⁇ , ⁇ " ⁇ ) /, and the output data are the estimated classes L ⁇ ⁇ *( ⁇ , ⁇ ) for each fluidic event ⁇ Ev ( ⁇ , ⁇ ) , so that [106]
• the comparison parameters are determined by class ⁇ P ⁇ *
• This step is possible to the extent that the previous fluidic events E ⁇ v ( ⁇ , ⁇ " ⁇ @ ⁇ ) were classified during the previous iterations, either by the f1 model or by the f2 model. It is then possible to determine the characteristic flow rate (e.g.
• step 333 we proceed to the second level of prediction of the fluidic events ⁇ Ev ( ⁇ , ⁇ ) present in a measurement signal S ⁇ mes ⁇ ( (t), at the help of the second model f2.
• the input data provided to the f2 model are .
• 7( ⁇ , ⁇ ) /, and the output data are the estimated classes L ⁇ ⁇ *( ⁇ , ⁇ ) for each event fluidic ⁇ Ev ( ⁇ , ⁇ ) , so that appears that the monitoring method according to this embodiment has improved performance.
• Figures 6A to 6C illustrate changes in the distribution function (also called cumulative distribution function, or Cumulative Distribution Function in English) of the flow rate (fig.6A), the flow duration (fig.6B) and the volume flowed (fig.6C) for the fluidic events resulting from the simulated global flow signal Ssim BSim including the statistical consumption profile Pstat ⁇ ⁇ ⁇ !# is identical to the Pstat profile ⁇ ⁇ ⁇ ⁇ of a real reference house. These curves are compared to those from the real house considered.
• This probability P(D ⁇ d) is equal to the proportion % ⁇ ts
• the distribution function CDFD(d) is between 0% and 100%, and the average flow D varies between zero and the maximum flow measured by the measurement signals DmesEC(t).

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• 2022
  • 2022-09-20 FR FR2209520A patent/FR3139918B1/fr active Active
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