Method for estimating at least one power consumed by a subset

Abstract

Method for estimating at least one power consumed by a subset Method for determining an electrical power consumed by a subset of a group (G) comprising at least one subset, each subset of the group (G) comprising at least one electrical equipment of a set (E) of electrical equipment, during a period comprising a plurality of elementary periods, the method being implemented by computer and comprising: estimating the electrical power consumed by the subset using: individual elementary operating times of each subset of the group (G) during the respective elementary periods and, total elementary energy consumptions of the set (E) during the respective elementary periods. Figure for abstract: Fig.2

Description

Title of the invention: Method for estimating at least one power consumed by a sub-assembly Field of invention

[0001] The field of the invention is that of monitoring the power consumed by a set of equipment. One aim of this type of monitoring is to identify power drifts in order to be able to act on these drifts to improve the energy efficiency of the set of equipment.

[0002] The invention applies to any industrial installation comprising several energy-consuming equipment and whose share of energy consumption is comparable between equipment. For example, the invention can be applied to drinking water production units, waste sorting, recovery and transformation units and purification stations also called STEP in the present patent application.

[0003] For example, at present, most wastewater treatment plants are equipped with electricity meters allowing energy consumption to be monitored globally at the plant level, i.e. for all the plant's equipment.

[0004] In order to identify the most consuming equipment and to detect power drifts in the respective equipment, it is necessary to be able to monitor the electrical consumption at the level of each of the equipment.

[0005] However, installing an electricity meter on each piece of equipment in a WWTP is a considerable investment. Monitoring the individual consumption of a WWTP's equipment requires an average of 20 electricity meters (between 5 and 80 per WWTP), and installing an electricity meter costs an average of 1,000 euros. State of the art

[0006] Solutions are known for estimating the power consumed by the respective equipment solely from the nominal powers of this equipment which are given by the suppliers, by providing a margin on these powers, just as proposed in the document “Energy consumption of intensive wastewater treatment in France: state of play and variation factors” - Sticker et Al, December 2017, IRSTEA.

[0007] However, these solutions do not allow the identification of drifts in power consumed by the different equipment.

[0008] One aim of the invention is to limit at least one of the aforementioned drawbacks. Summary of the invention

[0009] To this end, the invention relates to a method for estimating an electrical power consumed by a subset of a group of at least one subset, each subset of the group comprising at least one electrical equipment item from a set of electrical equipment items, during a period comprising a plurality of elementary periods, the method being implemented by computer and comprising: - estimate the electrical power consumed by the sub-assembly using: - individual elementary operating times of each sub-assembly of the group during the respective elementary periods and, - the total elementary energy consumption of the whole during the respective elementary periods.

[0010] Advantageously, the method comprises: - determine the individual elementary operating times of each subset of the group during the respective elementary periods, - determine the total elementary energy consumption of the whole during the respective elementary periods.

[0011] Each of these steps may consist of receiving the total elementary energy consumption values ​​and / or individual elementary operating times and / or calculating the total elementary energy consumption values ​​and / or individual elementary operating times from received values ​​and / or received measurements.

[0012] Advantageously, the step of estimating the electrical power comprises: - determining, from the total elementary energy consumptions and the individual elementary operating durations, a set of at least one parameter of a parametric model modeling: - a distribution, over the elementary periods, of values ​​of individual elementary powers consumed, during the respective elementary periods, of each subset of the group, - for each elementary period, the total elementary energy consumption during the elementary period, by a sum of a possible constant and the individual elementary energy consumption of each subset of the group during the elementary period.

[0013] Advantageously, at least when a number of activations / deactivations of at least one subset of the group during all of the elementary periods is greater than a main threshold, the model is a probabilistic graphical model.

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] Advantageously, the parametric model models values ​​of the individual elementary powers consumed, during the respective elementary periods: - of each subset of a first subgroup of the group by a multimodal distribution and / or - of each subset of a second subgroup of the group by a unimodal distribution. Advantageously, the method comprises the following steps: - grouping subsets of the group so that the first subgroup consists of each subset of the group having a number of activations / deactivations during all the elementary periods greater than a predetermined main threshold and the second subgroup consists of each subset having a number of activations / deactivations during all the elementary periods less than or equal to the main threshold. Advantageously, the method comprises the following step: - determine a number of subsets of the first subgroup such that the model is a probabilistic graphical model only when the first subgroup is non-empty. Advantageously, at least when no subset of the group has an activation / deactivation number during all the elementary periods greater than a predetermined main threshold, the parametric model is a linear model. Advantageously, the method comprises the following step: - determine a number of subsets having an activation / deactivation number during the set of elementary periods greater than the main threshold, so that the parametric model is linear only when no subset of the group has an activation / deactivation number during the set of elementary periods greater than the main threshold. Advantageously, the method comprises a step of prior grouping of the equipment of the set according to a correlation criterion between states of the equipment, using state information of the respective equipment, so as to form said group of at least one subset. Advantageously, the method comprises a step of initial estimation of the energy consumption of another set of at least one other piece of equipment from a nominal power of the other piece of equipment and status information of the other piece of equipment. Advantageously, the method comprises an initial verification step implemented work for each piece of equipment in a global set comprising each piece of equipment in the set, consisting of checking whether the equipment complies with a set of at least one importance criterion using a predetermined nominal power of the equipment and / or equipment status information, the other set of at least one other piece of equipment being made up of each piece of equipment in the set not complying with the set of at least one importance criterion.

[0022] Advantageously, the method comprises, prior to the estimation step, a step of identifying each piece of equipment in the overall assembly having a total individual operating duration less than or equal to a predetermined maximum portion or greater than or equal to a predetermined minimum portion of the duration T, and a step of removing each piece of equipment identified during the identification step from the overall assembly.

[0023] The invention also relates to a computer program product stored in a non-transitory computer-readable medium, said computer program product comprising code instructions which, when the program is executed by a computer, cause the computer to execute the method according to the invention.

[0024] The invention also relates to a data processing device comprising a processor configured to execute a method according to the invention.

[0025] The invention also relates to an industrial system comprising: - a set of equipment comprising a group of at least one sub-set, each sub-set of the group comprising at least one electrical equipment from a set of electrical equipment, - an electric meter, - an electrical equipment control unit capable of activating and deactivating each piece of equipment in the set, - a calculation unit configured to implement the method according to the invention, to estimate an electrical power consumed by a subset of the group,

[0026] the total elementary energy consumptions of the individual elementary operating times of each subset of the group during the elementary periods being derived from energy measurements delivered by the electricity meter and the individual elementary operating times of each subset of the group during the respective elementary periods being derived from the control unit. Brief description of the figures

[0027] [Fig.l] schematically represents the equipment of a purification station according to an example of realization,

[0028] [Fig.2] schematically represents an example of an industrial system according to the invention,

[0029] [Fig. 3] represents a flowchart of the steps of a method according to an exemplary embodiment of the invention,

[0030] [Fig.4] schematically represents an example of a Bayesian network modeling elementary energy consumption and individual elementary powers individually and relative to each other,

[0031] [Fig.5] represents a flowchart of steps of a stochastic approximation algorithm of expectation-maximization according to an exemplary embodiment. Detailed description

[0032] The present invention relates to a computer-implemented method for estimating the electrical consumption of equipment in a global set EG of equipment during a predetermined monitoring period denoted T.

[0033] Power is understood to mean electrical power in this patent application. Industrial equipment and system

[0034] The global set EG of equipment EQm with m being an integer and m between 1 and M comprises, for example, equipment of an industrial system, for example a STEP or a subset of equipment of a STEP.

[0035] A wastewater treatment plant (WWTP) comprises several pieces of equipment and functional units for treating municipal and / or industrial wastewater by intensifying natural processes. These pieces of equipment vary depending on the size of the treatment plant, the level of treatment required and the specific technologies used. The overall EG system performs this function.

[0036] A WWTP typically comprises a UPT pretreatment unit which aims to remove solid debris, coarse matter and undesirable elements from the raw water before proceeding to the main treatment steps. The pretreatment steps and equipment may vary depending on the specific design of the treatment plant, for example: a pump Ei ensuring via a lifting station the supply of equipment E2 comprising, for example, a screen to remove the different types of debris of different sizes and / or a sand / oil separator to separate the sand from the floating matter (oils, greases and / or hydrocarbons) and / or a sand washer and / or a biological grease treatment device.

[0037] A WWTP advantageously, but not necessarily, comprises a primary treatment unit UTP. This stage conventionally makes it possible to eliminate finer suspended matter by flocculation / coagulation / decantation processes and flotation - with the injection of chemical reagents depending on the desired degree of treatment using, for example, a decanter.

[0038] A WWTP typically comprises a secondary treatment unit UTS used to carry out wastewater treatment by biological and / or physicochemical means, exploiting the capacity of certain bacteria or biomass to eliminate pollutants dissolved in the water obtained at the outlet of the primary treatment unit, such as carbon pollution and / or nitrogen compounds and / or phosphorus compounds.

[0039] The secondary treatment unit UTS comprises, for example, at least one source of pressurized air, for example a booster E3 configured to inject air into one of the biological treatment basins of the secondary unit UTS to adapt the oxygen supplies to variations in pollution loads, and to the needs of the biomass for the treatment of pollution.

[0040] The biomasses are thus implemented in one or more biological treatment basins, under different conditions (anoxic, aerobic, anaerobic, etc.) suitable for their metabolism. The biomasses develop in the form of free biological sludge (activated sludge process) or in the form of biofilm (biomass supported by biofilm supports or self-supported, i.e. granular sludge). The secondary treatment unit UTS also comprises at least one agitator E4 for mixing the water and, for example, several brush aerators E5, E6 for the sludge.

[0041] The tertiary treatment unit UTT implements a step of separating the mixture from the biological and / or physicochemical treatment basin(s). It thus makes it possible to separate sludge on the one hand, and purified water on the other. It includes an E7 separator which can take the form of a flotation system, a filter, a membrane integrated into the biological treatment basin, or a decanter external to the biological treatment basin.

[0042] The purified water can undergo post-treatment (for example filtration, UV, etc.) or be used directly at the outlet of the separator, or even discharged into the natural environment.

[0043] The WWTP typically comprises a sludge treatment unit UB configured to thicken the sludge extracted from the units (possibly UTP), UTS, UTT by increasing their concentration. The sludge thickener may be static (gravity) or dynamic. This thickener may, for example, be in the form of a drainage table, a centrifugation system, a gravity thickener, a drum or even a flotation unit.

[0044] The thickened sludge from the sludge treatment unit UB can be treated for example using means in the form of a thermal hydrolyzer, a digester, dewatering equipment such as centrifuges, belt filters, filter presses, followed or not by chemical (liming) and / or biological (composting) stabilization and / or thermal drying and / or incineration.

[0045] The STEP advantageously comprises a unit for treating odors UTO coming from the secondary treatment unit UTS and sludge UB. This unit for treating odors UTO comprises, for example, at least one fan E8 and / or a deodorization tower E9 and / or a reagent station Ei0.

[0046] The STEP may also include an air conditioning unit UC to regulate the temperature of the premises of the STEP, for example offices.

[0047] The STEP includes UE lighting equipment, making it possible to illuminate premises of the STEP.

[0048] Alternatively, the STEP comprises part of the aforementioned equipment.

[0049] The STEP may include other equipment.

[0050] The invention applies to equipment in other types of factories. In a non-limiting manner, the factory may be an energy recovery unit. An energy recovery unit is a factory supplying a heat network or producing electricity through the incineration of waste.

[0051] Such a unit comprises one or more waste combustion furnaces. Each furnace comprises a burner to ensure the temperature of the combustion chamber rises to incineration conditions. The furnace may be gas or electric and, in the latter case, consume electrical energy when the furnace is started.

[0052] The heat of the gases and fumes resulting from combustion (temperature between 850°C and 1000°C) is advantageously transferred, via at least one transfer equipment comprising for example a fan, to a heat transfer fluid (generally water) circulated, via a circulation system, in a recovery unit, comprising for example at least one boiler and / or a turbine to recover the heat energy and recover it, for example in the form of electricity or heat.

[0053] The energy recovery unit advantageously comprises a unit for treating the fumes from the incineration unit to capture the pollutants included in the fumes before releasing the treated fumes into the atmosphere.

[0054] Each of these units advantageously comprises at least one fan and / or at least one pump for circulating a fluid in the energy recovery unit. This equipment consumes electrical energy.

[0055] The energy recovery unit advantageously comprises a unit for recovering clinker, solid residues from the combustion of waste. This unit conventionally comprises mechanical transfer equipment for this clinker, for example conveyor belts which consume electrical energy.

[0056] Just like the STEPs, an energy recovery unit typically includes an air conditioning unit to regulate the temperature of the unit's premises. energy recovery, for example offices and a lighting unit, allowing the lighting of premises in the recovery unit.

[0057] In [Fig.2], an example of a global set EG of equipment EQm (m integer with m = 1 to M) of an industrial system SL is shown.

[0058] This overall assembly EG comprises, for example, equipment of a STEP, for example the equipment of the secondary processing unit UTS, the equipment EQ i, EQ2, EQ3, EQ4 corresponding, for example, respectively to the equipment E3, E4, E5, E6 of [Fig.l] and the lighting unit UE of [Fig.l] noted EQ5 in [Fig.2].

[0059] In the global set EG of equipment EQm with m = 1 to M, we consider a set E of equipment EQ; with i = 1 to N, N being less than or equal to M, taken from the equipment EQm of the global set EG.

[0060] In the non-limiting example of [Fig.2], M is equal to 5 and N is equal to 4.

[0061] In the method according to the invention, a group G of at least one sub- set SEj, with j integer and j = 1 to J, J being greater than or equal to 1 and less than or equal to N.

[0062] Each subset SEj comprises at least one piece of equipment from the set E.

[0063] Each equipment EQ; belongs to only one of the subsets SEj. In other words, the subsets SEj are disjoint.

[0064] In the non-limiting example of [Fig.2], J = 2.

[0065] Furthermore, in the non-limiting example of [Fig.2], the EQm equipment is the EQ equipment; for m= 1 to 4.

[0066] The industrial system SI comprises, for example, an electric meter CE, a control unit UCO and a calculation unit UCA.

[0067] The electric meter CE is capable of making measurements ME(k) of an energy representative of the total energy consumption during different elementary periods Tk of a period T.

[0068] The period, noted T, comprises K elementary periods noted Tk of the same elementary duration Is, with K being an integer greater than 1.

[0069] Advantageously, the period T is made up of the K elementary periods Tk.

[0070] More generally, the period T comprises K elementary periods Tk.

[0071] In a variant, the duration of the period T is greater than the sum of the durations of the elementary periods.

[0072] For example, at least two elementary periods of the period T are temporally spaced from each other.

[0073] K may depend on the period of provision of the information used in the method according to the invention.

[0074] In a non-limiting example, the period T is 1 month and the elementary duration is 10 minutes.

[0075] The elementary duration is adapted by the person skilled in the art to the equipment of the overall assembly and in particular to the average frequency of change of state (i.e. activation / deactivation) of this equipment.

[0076] The control unit UCO is capable of controlling each piece of equipment in the set E to switch it from an activated state (i.e. running) to a deactivated state (i.e. stopped), and vice versa. This control unit UCO is configured to deliver activation / deactivation commands for the respective pieces of equipment or, at least, subsets of equipment in the set E so as to individually activate / deactivate the respective pieces of equipment.

[0077] Advantageously, the control unit UCO is capable of adjusting the operating level of the equipment of the assembly E, for example the speed of a motor, or the power level of a booster (maximum power or not).

[0078] Advantageously, the calculation unit UCA has individual elementary operating durations aj(k) of the respective equipment subsets SEj over the respective elementary periods Tk.

[0079] These elementary operating times aj(k) can be calculated by the calculation unit UCA from status information making it possible to calculate these individual elementary operating times aj(k) received by the calculation unit UCA. This status information is, for example, transmitted to the calculation unit UCA from the control unit UCO.

[0080] This state information is for example activation / deactivation times {tcm} of the equipment EQm or the activation / deactivation times {tCj} of the respective subsets SEj of the set E are transmitted to the calculation unit UCA from the control unit UCO and the calculation unit UCA is configured to calculate the elementary operating times tkj from the activation / deactivation times considered.

[0081] By activation times of a piece of equipment or a subset of equipment, we mean the times when the equipment changes from its inactive state to its active state and by deactivation times of the equipment, we mean the times when the equipment changes from its active state to its inactive state. By activation / deactivation times of a piece of equipment, we mean the activation times and the deactivation times of the equipment. This also applies to subsets of equipment.

[0082] In another example, the state (active or inactive) of each equipment or of each subset of equipment SEj of the set E is transmitted at regular time intervals (shorter than the elementary periods Tk to the calculation unit UCA during the entire duration T and constitutes the state information. The calculation unit UCA calculates the individual elementary operating durations aj(k) from these states.

[0083] Alternatively, the UCA calculation unit directly receives the elementary durations of operation aj(k) of the equipment subsets SEj, over the elementary period Tk.

[0084] The calculation unit UCA is configured to implement the method according to the invention from total elementary energy consumptions CET(k) and individual elementary operating durations aj(k) of the respective subsets SEj.

[0085] Advantageously, the individual elementary operating durations aj(k) come from the UCO control unit.

[0086] Advantageously, the total elementary energy consumptions CET(k) are the elementary measurements ME(k) or are determined, for example by the calculation unit UCA, from respective elementary measurements ME(k) taken by the electricity meter CE during the respective elementary periods Tk.

[0087] For example, the calculation unit UCA is configured to estimate the total elementary energy consumptions CET(k) from the respective elementary measurements ME(k) delivered by the meter CE. These estimates are then the total elementary energy consumptions CET(k) used in the method according to the invention. Method according to the invention

[0088] As visible in [Fig.3], the method according to the invention comprises a step EPIND of estimating at least one power consumed by at least one subset SEj of the group G, from: - individual elementary operating durations aj(k) of each subset SEj of group G during respective elementary periods Tk of period T, - and total elementary energy consumption CET(k) during the respective elementary periods Tk.

[0089] This method does not require the use of a frequency behavior of a voltage measured at the terminals of the assembly E nor of a current flowing through this assembly E.

[0090] In other words, this method uses individual elementary operating durations aj(k) and elementary total energy consumptions CET(k), for example elementary energy consumption measurements, without using frequency behavior of a voltage at the terminals of the set of equipment or of a current flowing through this set of equipment to determine individual powers consumed by subsets SEj of equipment making up this set E. An advantage is that it makes it possible to distinguish the powers consumed by different subsets of equipment or equipment having similar operating principles (compressor, motor, pump) at the origin of similar frequency signatures.

[0091] This method is precise, inexpensive, and particularly suitable for sets of equipment such as factory equipment, such as purification plants.

[0092] Indeed, this method only requires the presence of an electric meter CE making it possible to generate measurements ME(k) representative of the total elementary energy consumptions CET(k) of the set E during the respective elementary periods Tk or allowing the calculation unit UCA to estimate the total elementary energy consumptions CET(k) of the set E during the respective elementary periods Tk and the arrangement, by the calculation unit UCA of the individual elementary operating durations aj(k) of the subsets SEj of equipment.

[0093] The invention makes use of the activation / deactivation times of the different equipment in the set, allowing the calculation of the individual elementary operating times aj(k) conventionally available in a factory in which the equipment is controlled automatically, to estimate the elementary powers of the equipment or subsets of equipment.

[0094] Another advantage of the method is that the EPIND estimation step of the method can be implemented without knowing the nominal power of each subset.

[0095] In other words, in a particular embodiment, the EPIND estimation step advantageously does not use nominal powers of equipment or subsets of equipment of the set E.

[0096] This invention also makes it possible to detect a deterioration in the energy efficiency of the equipment subassemblies.

[0097] In the remainder of the description, it is considered that the set E comprises several sub-sets, each comprising at least one piece of equipment, which will be called sub-sets of equipment even if each of them may comprise a single piece of equipment. The invention also applies when the set E comprises a single sub-set and / or at least one sub-set comprises a single piece of equipment.

[0098] The method advantageously, but not necessarily, comprises a step of calculating at least one individual elementary energy consumption of at least one subset SEj during an elementary period Tk, from the individual power consumed during the elementary period and the individual elementary operating duration aj(k) of the subset SEj during an elementary period Tk.

[0099] Advantageously, this step comprises, for each subset SEj, the calculation of the individual elementary energy consumptions of this subset during the respective elementary periods Tk. Disaggregation of consumed energy

[0100] Advantageously, during the EPIND estimation step, a para model is used metric modeling the total elementary energy consumption CET(k) during each elementary period Tk in a linear combination of the elementary energy consumptions of the respective equipment subsets SEj during the elementary period Tk considered.

[0101] For example, the value CET(k) of the total elementary energy consumption during each elementary period Tk is modeled by a sum of a possible constant CST and the individual elementary energy consumptions CEj(k) of the respective subsets SEj, during the elementary period Tk.

[0102] The model models the individual elementary energy consumption CEj(k) of a subset SEj by a product between an elementary power consumed by the subset SEj during the period Tk and the individual elementary operating duration aj(k) of the subset during the elementary period Tk.

[0103] In other words, the model models the individual elementary energy consumption CEj(k) as follows:

[0104] CEjk) =

[0105] By individual elementary power Pj(k) consumed by a subset of equipment SEj, we mean the electrical power consumed by the subset of equipment SEj during the elementary period duration Tk.

[0106] This is, for example, the electrical power absorbed by this sub-assembly during this elementary period Tk.

[0107] This individual elementary power consumed is considered fixed during the elementary period Tk

[0108] The constant CST is, for example, the energy generated by equipment whose state remains substantially the same over the entire duration of the period T.

[0109] In order to distribute the total elementary energy consumption CET(k) over the different subsets SEj, we advantageously make, during the EPIND step, an a priori on the distributions of the individual elementary powers consumed Pj(k), over the elementary periods Tk, by the subsets SEj.

[0110] In other words, the parametric model models the distributions of the individual elementary powers consumed Pj(k), over the elementary periods Tk, by the respective subsets SEj.

[0111] The determination of the elementary powers consumed Pj(k) by the respective subsets of equipment SEj therefore includes the determination of the parameters of the parametric model which optimizes, according to a predetermined criterion, the distribution of the total elementary energy consumptions CET(k) during the elementary periods Tk.

[0112] In summary, we distribute each elementary total energy consumption CET(k) between the different subsets SEj using the elementary durations of func tioning aj(k) of the different subsets SEj and determining the parameters of a parametric model modeling: - the distributions of the individual elementary powers Pj(k) of the subsets SEj by at least one parameter of the model, - and each total elementary energy consumption CET(k) by a sum of a possible constant CST and the individual elementary energy consumptions CEj(k) of the respective subsets SEj, during the elementary period Tk.

[0113] This step will be described more precisely in the rest of the text.

[0114] Modeling of total energy consumption

[0115] The model models the total elementary energy consumption CET(k) during each elementary period Tk by a sum of a possible constant CST and the individual elementary energy consumptions CEj(k) of the respective subsets SEj, during the elementary period Tk.

[0116] In other words, the total elementary energy consumption CET(k) is defined as follows: [°117] CET(k) = Y^Pjik) *aj(k) + CST = L^CE^k) + CST

[0118] The value of the total elementary energy consumption CET(k) during the elementary period Tk is an input data. It is, for example, measured by the electricity meter CE or estimated based on a measurement from the meter CE as explained previously. Initial check

[0119] A non-limiting example of implementation of the method according to the invention is shown in [Fig.3].

[0120] The method advantageously comprises an initial verification step VERI, implemented for each equipment EQm of the global set EG for m integer ranging from 1 to M, consisting of verifying compliance with a set of at least one importance criterion in terms of power and / or number of activations / deactivations and, if at least one criterion of the entire equipment is not respected by said equipment EQm, an initial estimation step EST1 of the energy consumption CEm of the equipment EQm.

[0121] The initial verification VERI of compliance with all of at least one importance criterion uses a predetermined nominal power PNm of the equipment EQm and / or status information of the equipment EQm during the period T or the elementary periods Tk. The nominal power PNm is conventionally provided by the manufacturer or accessible in the supplier documentation.

[0122] In the non-limiting example of [Fig.2], the initial verification VERI comprises an initial VERIa verification of compliance with a first criterion of importance in terms of power based on the nominal power PNm of the EQm equipment.

[0123] The criterion of importance in terms of power, noted Cia in the present application, is, for example, respected only when the nominal power PNm of the equipment EQm is greater than or equal to a predetermined power threshold SP.

[0124] Advantageously, the predetermined power threshold, noted SP in the present patent application, is between 1% and 2% of the average nominal power of the equipment EQm of the overall set EG. This makes it possible to exclude from the set E and therefore from precise power calculations, equipment which has little impact on energy consumption drifts due to the low nominal power.

[0125] For example, in the example of [Fig.2], the lighting unit is not very powerful by compared to other equipment in the overall EG set and therefore does not meet this Cia criterion.

[0126] In a non-limiting exemplary embodiment, the threshold is set at 2 kW.

[0127] In the non-limiting example of [Fig.2], the initial verification VERI comprises, only when the first importance criterion is met, a second verification VERIb of compliance with an importance criterion in terms of the number of state changes, based on state information, for example the activation / deactivation times of the equipment EQm during the period T or during the elementary periods Tk.

[0128] The importance criterion in terms of the number of activations / deactivations of states Clb is, for example, respected only when a number of changes of state, i.e. the number of activations / deactivations, of the equipment EQm during the period T or during all the periods Tk, is greater than or equal to a predetermined initial threshold SI.

[0129] The number of activations / deactivations of a device during a period corresponds to the number of changes of state between the activated state and the deactivated state during the entire duration of the same period. It is the sum of the number of activations and the number of deactivations obtained during the entire duration of the same period.

[0130] The number of activations / deactivations of a piece of equipment during all the elementary periods Tk is the sum of the activation / deactivation numbers corresponding to the different elementary periods Tk.

[0131] Advantageously, the initial threshold SI is between 5 and 10. This makes it possible to avoid integrating into the set E equipment for which the number of state changes is not sufficient to guarantee good performance of the statistical calculations.

[0132] Alternatively, the initial verification VERI comprises only one of the two verifications VERIa or VERIb. Alternatively, the order of the two verification steps is reversed.

[0133] It is of course possible to consider, as a variant or in addition, other criteria depending on the information available.

[0134] Alternatively, the method is devoid of the initial VERI verification. Initial estimate

[0135] The initial estimate EST1 is implemented for each Em equipment not respecting all of at least one importance criterion.

[0136] These pieces of equipment form a set called remaining and noted ER of MR residual pieces of equipment, MR being an integer between 0 and Ml.

[0137] In the example of [Fig.2], the residual assembly ER consists of the lighting unit UE.

[0138] This step is therefore, for example, implemented only when the remaining set ER is not empty.

[0139] The initial estimate EST1 of the energy consumption CEm of an item of equipment EQ m uses the nominal power PNm of the item of equipment EQm and a total operating time Tm of the item of equipment EQm during which the item of equipment considered is in the active state during all the elementary periods during all the elementary periods Tk.

[0140] The total operating time is, for example, calculated from the activation / deactivation times of the equipment EQm during the period T or from elementary operating times of the equipment during the different elementary periods Tk.

[0141] The total operating time Tm of an EQm device is, for example, given by the following formula:

[0142] T _ y*

[0143] where att{k) is the elementary operating duration during which the equipment EQm is in the active state, during the kth elementary period Tk, and Is is the elementary duration of each elementary period Tk.

[0144] The initial estimate EST1 therefore comprises the calculation CD of the total individual operating time T m of the equipment EQm from state information, for example activation / deactivation instants, relating to the equipment EQm.

[0145] The initial estimate EST1 then comprises a CPI calculation of a product of a predetermined correction factor FC, the nominal power PNm of the equipment EQm and the operating time Tm of the equipment EQm during the period T or the set of elementary periods Tk. In other words, the energy consumption of the equipment CEm during the period T or during the set of elementary periods Tk is, for example, estimated during step EST1 using the following formula:

[0146] CEm = FC*PNm*Tm

[0147] The correction factor FC is a constant between 0.5 and 1 corresponding to a factor between a power actually consumed and the nominal power.

[0148] The correction factor is, for example, equal to 0.85. Alternatively and / or additionally, the estimation step comprises the estimation of the individual elementary energy consumption CEm(k) of the equipment EQm during at least one elementary period Tk.

[0149] The following formula is advantageously used:

[0150] CEm(k) = FC^PN^a^k)

[0151] The initial estimate EST1 has the advantage of being inexpensive in terms of calculation and fast.

[0152] Furthermore, this initial estimate makes it possible to take into account the consumption of equipment which consumes low power compared to the other equipment in the overall set and whose estimate of power consumed by a method of solving a linear or probabilistic regression model is not very precise. Indeed, when the variation in the power generated by one piece of equipment is less than the random variation in the value generated by another piece of equipment, these models are not able to differentiate between these two variations.

[0153] Advantageously, the initial estimate EST1 uses the nominal power of each piece of equipment.

[0154] Advantageously, the method then comprises a step EST_CET of estimating the total elementary energy consumptions CET(k) by the set E from the measurements ME(k) and the individual elementary energy consumptions CE^k) of the equipment EQm of the remaining set ER.

[0155] The total elementary energy consumption CET(k) by the set E during the elementary period Tk is then the energy consumption measured during this elementary period Tk from which we subtract the sum of the elementary energy consumptions of the equipment of the remaining set ER, that is to say:

[0156] CET(k\ = -ZMR, CEdk] Set E

[0157] The set E of equipment EQ; with i = 1 to N, N being less than or equal to M, advantageously comprises, when the first verification step VERI is implemented, only the equipment of the global set EG respecting the set of at least one importance criterion in terms of power and / or number of activation / deactivation Cia, Clb.

[0158] Thus, the set E only includes equipment presenting a significant power in view of the set of equipment and / or which presents characteristics state change characteristics, and more precisely the dynamics, necessary for a good estimation of their power from their state change information.

[0159] In the non-limiting example of [Fig.2], the assembly E does not include the lighting unit UE.

[0160] Alternatively, the set E comprises all the equipment EQm, with m = 1 to M. In other words, the set E is the set EG. For example, the method is devoid of the initial verification step VERI. REGI prior grouping

[0161] Advantageously, the method comprises, as represented in the non-limiting example of [Fig.3], a prior grouping REGI of the equipment EQ; of the set E according to a predetermined prior grouping criterion so as to form the group G composed of at least one subset SEj with j being an integer ranging from 1 to J, J being less than or equal to N.

[0162] Each subset SEj of group G comprises at least one item of equipment EQ; of set E.

[0163] This preliminary grouping REGI is carried out from the status information of the EQ equipment; of the set E.

[0164] The prior grouping criterion is advantageously a correlation criterion between the states of the equipment EQ; of the set E during the entire duration of the set of elementary periods Tk or of the period T.

[0165] For example, for each pair of equipment in the set E, we calculate a correlation coefficient r(X, Y) given by: [° 166 ] LJ CMH \ ' /

[0167] where X and Y are the lists of states Xt, and respectively Yt, of a first equipment and respectively of a second equipment during the period T or the set of elementary periods Tk.

[0168] These n states Xt and Yt of index t, with t being an integer ranging from 1 to n, are, for example, taken at regular time intervals. The time interval is less than the duration of the elementary periods. Each state Xt or Yt takes the value 0 when the equipment is inactive and the value 1 when the equipment is active.

[0169] One advantage is that it allows the estimation of an individual elementary power consumed and therefore the identification of power drifts of a set of equipment operating almost simultaneously and therefore difficult to distinguish as we will see later.

[0170] In the non-limiting example of [Fig.2], at the end of the grouping step REGI prerequisite applied to the overall set E composed of equipment EQi to EQ4, we obtain two subsets SEi and SE2 composed respectively of equipment EQi to EQ3 and equipment EQ4 corresponding for example respectively to equipment E3 to E5 and E6 of a secondary processing unit UTS. Status information of an SE subset

[0171] According to an exemplary embodiment, the state information of a subset SEj is the state information of a piece of equipment of the subset.

[0172] For example, the number of changes of state of a subset SEj during the duration T or during an elementary period Tk is the number of changes of state of a piece of equipment of the subset during the period considered.

[0173] Alternatively, the status information of a subset SEj is obtained from status information of several devices of the subset SEj, for example of all the devices of the subset SEj.

[0174] For example, the averages of the state information of the equipment of the subset SEj.

[0175] The same applies for each elementary operating duration of a subset SEj during an elementary period Tk which can be, in a non-limiting manner, an average of the operating durations of the equipment of the subset during the same elementary period Tk or the elementary operating duration of one of the equipment of the subset SEj during the elementary period.

[0176] The total individual operating time Tj of a piece of equipment EQj is, for example, given by the following formula: [0177> 7-.,.=

[0178] where a / k) is the elementary operating duration during which the subset of equipment SEj is in the active state, during the kth elementary period Tk.

[0179] The same applies to the number of activations / deactivations of each subset SEj during a given period which may be, in a non-limiting manner, an average of the numbers of activations / deactivations of the equipment of the subsets of equipment which compose it during this period or a number of activations / deactivations of one of the equipment which it comprises during this period.

[0180] The method advantageously comprises a step of determining a number of activations / deactivations, during the period T or during all the periods Tk, of at least one piece of equipment from the status information of this equipment.

[0181] The method advantageously comprises a step of determining a number of activations / deactivations, during the period T or during all the periods Tk, of at least one subset of equipment from the status information of at least one of its equipment.

[0182] Distribution of individual elementary powers

[0183] The parametric model comprises a set 6 of parameters comprising a first set of parameters modeling the respective distributions of the values ​​of the individual elementary powers Pj(k) of the respective subsets SEj.

[0184] The EPIND estimation step includes determining the parameters of the parametric model.

[0185] The probabilistic graphical model has the advantage of not requiring, in order to obtain precise estimates of the different individual powers, a strict a priori on the distribution of the values ​​of individual elementary powers in the sense that it allows the use of general distributions such as unimodal or multimodal distributions.

[0186] In particular, the probabilistic model does not require considering that the individual elementary powers of each of the devices are fixed, that is to say are the same over the entire period T. It therefore makes it possible to detect drifts occurring over shorter durations than over a period T. The powers of the subsets SEj are hidden variables (that is to say variables unknown to the probabilistic graphical model).

[0187] The model is also defined by the fact that when the subset SEj is deactivated for the entire duration of the period T or of the set of elementary periods Tk, the individual elementary power Pj(k) of the corresponding subset SEj is equal to zero.

[0188] By a subset SEj deactivated for the entire duration of the period T, we mean a subset SEj whose total individual operating duration Dj is zero.

[0189] When the subset SEj is activated for at least part of the period T or of the set of elementary periods, that is to say when its operating duration is non-zero over this period or set of periods, the distribution of the individual elementary power values ​​Pj(k) of this subset SEj is modeled by a parametric graphical model for which the parameter values ​​are sought to be determined.

[0190] The distributions of the values ​​of the individual elementary powers P j(k) of the respective subsets SEj can be multimodal or unimodal.

[0191] The unimodal distribution is defined by a set of so-called unimodal parameters.

[0192] An example of a unimodal distribution of the values ​​of the individual powers of a subset of equipment SEj is a Gaussian or normal law mP, a Y Vf P

[0193] where the unimodal parameters are Pj which is a mean of the normal distribution and which is a standard deviation of the normal distribution.

[0194] Another example of unimodal distribution of individual power values of a subset of equipment SEj is a gamma distribution or gamma law r(a,j, fij) defined by the following unimodal parameters: fi.

[0195] is the shape parameter of the gamma distribution associated with the subset SEj

[0196] fij is the intensity parameter of the gamma distribution associated with the subset SEj.

[0197] An example of a multimodal distribution defined by so-called multimodal parameters is a mixture of gamma laws j tV « i where l is an integer and L is an integer greater than 1 and corresponds to the number of modes of the mixture of gamma laws. In other words, in general, the multimodal distribution is a mixture of distributions of different modes where l is an integer with ? ranging from 1 to L.

[0198] The mixture of gamma laws is, for example, defined by a set of the following so-called multimodal parameters: alJ, ffij.

[0199] al,j is the shape parameter of the distribution of the mode / , that is to say of the mode of order l, of the distribution associated with the subset SEj.

[0200] fiij is the intensity parameter of the distribution of the order l mode of the distribution associated with the subset SEj.

[0201] is the weight parameter of the order mode / in the distribution associated with the subset SEj. Distribution of total elementary energies

[0202] Advantageously, the set 6 of parameters also comprises a second set of at least one parameter defining a distribution of the total elementary energy consumptions CET(k) for k = 1 to K by one or more parameters and at least one parameter of this distribution is advantageously defined during the step of determining the parameters of the model.

[0203] This distribution is, for example, a Gaussian defined by the following two parameters: a standard deviation o and an average of the elementary energy consumption W

[0204] The values ​​of the total elementary energy consumption CET(k) are therefore defined by a normal law;y ^E(k) rr-j depending on these two parameters.

[0205] A parameter modeling the distribution of the values ​​of the total elementary energy consumption CET(k) can be predetermined, for example while the other parameter(s) are estimated during the estimation of the model parameters or all the parameters can be estimated during the estimation of the model parameters.

[0206] Distribution types other than Gaussians can of course be included- faces.

[0207] For example, we can define a gamma distribution or a mixture of gammas. Probabilistic graphical model

[0208] At least when a number of activation / deactivation of at least one subset SEj during the set of elementary periods Tk is greater than a predetermined main threshold, the parametric model is a probabilistic graphical model.

[0209] It should be noted that when the period T is made up of the elementary periods Tk, the number of activations / deactivations of the subset SEj during the set of elementary periods Tk is the number of activations / deactivations during the period T, which is not the case when the period T comprises a set of elementary periods whose duration is less than the total duration (for example when they are disjoint or when they do not cover the entire duration T).

[0210] In the embodiment of [Fig.3], the parametric model is a probabilistic graphical model only when a number of activation / deactivation of at least one subset SEj during the set of elementary periods Tk is greater than a main threshold.

[0211] Advantageously, the main threshold is higher than the initial threshold as we will see later.

[0212] Alternatively, the main threshold is the initial threshold.

[0213] Alternatively, the main threshold is zero. In other words, a graphical model is used probabilistic, whatever the activation / deactivation numbers of the different subsets SEj.

[0214] The probabilistic graphical model is, for example, a Bayesian network as represented in Figure 4, in which the hidden variables Pj(k ), the set of parameters, as well as the individual elementary operating times and the total elementary energy consumption are the nodes NO of the network. These nodes NO are connected two by two by arcs A as represented in [Fig.4] so as to define the parametric model described previously.

[0215] Advantageously, the EPIND estimation step comprises a step denoted AL for implementing a stochastic approximation algorithm for maximizing expectation, also called SAEM, an acronym for the English expression “Stochastic Approximation of Expectation-Maximization”.

[0216] This type of algorithm is known to those skilled in the art and will be described briefly in the patent application.

[0217] The implementation of the SAEM algorithm includes the following step: - initialize IN the set 0 of parameters, i.e. the values ​​of the model parameters and the individual elementary powers consumed For k= 1 to K and for j= 1 to J by the subsets of equipment SEj, i.e. the values ​​of the hidden variables.

[0218] The value of each individual elementary power P ,(k) for k= 1 to K of a given subset SEj is, for example, initialized to a sum of a constant and the nominal powers of the equipment constituting the subset SEj.

[0219] The constant can be zero.

[0220] Alternatively, the values ​​of the individual elementary powers are initialized to values ​​within a predetermined power range, for example to the same power value. This initialization is, for example, carried out randomly.

[0221] The following parameters can, in a non-limiting manner, be initialized in the following manner, during the initialization step IN.

[0222] For each subset SEj whose distribution of individual elementary power values ​​is modeled by a gamma mixture defined by the following parameters: ,, al,j, we initialize these parameters using the equations following:

[0223] H

[0224] .2-2¾ “ / V

[0225] where ^ij is the expectation of the distribution of the mode / of the subset SEj and whose value is initialized to the nominal power of the subset SEj by multiplying by a variable having a value included in a predetermined interval.

[0226] The predetermined interval extends, for example, from 0.7 to 1.3.

[0227] The value is, for example, set randomly within the interval.

[0228] is the standard deviation of the Z mode distribution of the subset SEj and is initialized to a constant, for example 1.

[0229] . = 1 / Nmode where Nmode is the number of modes of the gamma mixture.

[0230] and P} j are calculated using the above equations and the initial values ​​of and

[0231] For each subset SEj whose distribution of elementary individual power values ​​is modeled by a Gaussian distribution: ai , ^j.

[0232] is initialized to a value À*PNomj where 2 is a predetermined constant, for example 0.1 and where PNoïîlj is the nominal power of the subset SEj.

[0233] The nominal power of SEj is either known or calculated from the nominal powers of the equipment constituting SEj. It is, for example, the sum of these nominal powers.

[0234] For the distribution of the total elementary energy consumption values: cr2 is, for example, initialized to 1.

[0235] The value of is, for example, initialized to the sum of the individual elementary consumptions estimated by the subsets SEj during the elementary period Tk, calculated from the initial values ​​of these individual elementary powers P / k)- [02361 êw =

[0237] The constant CST can be initialized to a predetermined percentile of an elementary energy consumption CET(k).

[0238] The constant CST is, for example, fixed at the 5th percentile of the energy consumption CET(k).

[0239] The step AL of implementing the stochastic approximation algorithm of expectation maximization comprises the set of steps following EE repeated, as long as a stopping criterion is not respected by the current values ​​of the parameters: - generate GEN a set P of values ​​of the hidden variables distributed, over the set of elementary periods Tk, according to the distributions defined by the current values ​​of the parameters, - calculate CAE the expected likelihood of the model defined by the current values ​​of the set 0 of parameters, from these samples, - implement EMA a maximum likelihood estimator of the parameters so as to update the current values ​​of the parameters.

[0240] When the set of EE steps is first implemented, the current values ​​of the parameters are the initial values ​​of the parameters defined during the initialization step.

[0241] The set P generated during the generation step GEN comprises for each subset SEj, a set of K values ​​of individual elementary powers P / (k) distributed according to the distribution defined by the current values ​​of the parameters CET(k), , al,j, Pij and Pj'(k) for all j' * j and being an integer between 1 and J.

[0242] This step is, for example, implemented by a Markov chain Monte Carlo algorithm also called MCMC (acronym for the English expression “Markov chain Monte Carlo”), for example a Hamiltonian Monte Carlo algorithm, also known as Hybrid Monte Carlo, to generate samples of the distributed hidden variables, over the set of elementary periods Tk, according to the distributions defined by the values ​​of the current parameters.

[0243] In an exemplary embodiment, during the GEN step, an MCMC algorithm is implemented Q times (where Q is an integer greater than zero) so as to generate Q sets P of values ​​of the hidden variables and the set P generated during the GEN step is an average of the Q sets generated.

[0244] The likelihood of the model L(ff) defined by the parameter set 9 is given by the following formula: 102451 =n* ^4(4)444 l 4)4-44 / 144 - 44)

[0246] where J is the number of equipment subsets SEj.

[0247] K is the number of elementary periods.

[0248] is the set of distribution parameters that model the distribution of individual elementary powers of SEj.

[0249] / 4 / 4) is an a priori probability of the set of parameters 4-

[0250] j^P^k) | 6*4) is a posteriori probability of a set of powers knowing the parameters 9j of the distributions of the subset SEj. ^4(0 is an a posteriori probability of an elementary power consumed during the elementary period Tk knowing the elementary powers P / tk) of the different equipment during the elementary period Tk.

[0251] The expected likelihood E((L((9)) is, for example, calculated using the following formula:

[0253] where Q is the number of implementations of the MCMC algorithm, is the likelihood calculated during the qth implementation of the MCMC algorithm to generate the powers with q integer ranging from 1 to Q.

[0254] At the end of the EMA step, the current values ​​of the parameters are those which maximize the likelihood, according to the estimator implemented. In other words, the current values ​​of the parameters are those which make it possible to obtain the highest value of the likelihood by implementing the estimator.

[0255] It should be noted that the implementation of the AL algorithm is both a step of determining the values ​​of the parameters of the model, that is to say a step of parameterization of the model, and the values ​​of the hidden variables.

[0256] These model parameter values ​​are the last current parameter values ​​generated during the expectation maximization algorithm. In other words, these are the parameter values ​​obtained during the last implementation of the EMA step.

[0257] The values ​​of the individual elementary powers Pj{k} are also the last current values ​​of these powers, that is to say the values ​​obtained during the last implementation of the GEN step.

[0258] The stopping criterion is, for example, respected when the set EE of steps has been implemented a number of times equal to a predetermined maximum number, this number being an integer greater than 1.

[0259] In a variant, the stopping criterion is a criterion of difference between the two likelihoods calculated during the last implementation of the set of steps and during the previous implementation, less than or equal to a predetermined threshold.

[0260] In a variant, the stopping criterion is a criterion of difference between the two likelihoods calculated during the last implementation of the set of steps and during the previous implementation less than or equal to a predetermined threshold, for a minimum number of successive implementations of the set of steps.

[0261] The minimum number is advantageously greater than or equal to 2. REG2 Grouping

[0262] In a particular embodiment, the step EPIND comprises a secondary grouping REG2 of the subsets SEj of the group G: - in a first subgroup G1 consisting of each subset having an activation / deactivation number, during all the elementary periods Tk, greater than a main threshold, - and in a second subgroup G2 consisting of each subset having an activation / deactivation number, during all the elementary periods Tk, which is less than or equal to the main threshold.

[0263] This step REG2 is carried out using the activation / deactivation numbers, during all the elementary periods Tk, of the respective subsets SEj of the group G.

[0264] Each of these groups Gl, G2 can be empty or made up of all the subsets SEj of the group G.

[0265] Advantageously, the distribution of the values ​​of the individual elementary powers consumed by each subset of the first group Gl is modeled by a multimodal distribution and the distribution of the values ​​of the power of each subset of the second group G2 is modeled by a unimodal distribution.

[0266] These distributions are, for example, used in the model and therefore during the implementation of the expectation maximization algorithm to determine the values ​​of the parameters and hidden variables of the model.

[0267] Alternatively, the method is devoid of secondary grouping. In the latter case, the distributions, used in the model, of the values ​​of the individual elementary powers of all the equipment can be unimodal or multimodal. Linear regression model REGL

[0268] In the non-limiting embodiment of [Fig.2], the method comprises a step DNB of determining the number of subsets SEj having an activation / deactivation number during all the elementary periods Tk, greater than the main threshold.

[0269] For example, we determine the number of subsets included in the first group Gl.

[0270] When no subset SEj of the group G has a number of activations / deactivations during the set of elementary periods Tk during which the number is greater than the main threshold, i.e. when the first group Gl is empty, the parametric model is a linear regression model.

[0271] The EPIND estimation step comprises the REGL implementation of an algorithm for estimating parameters of the linear regression model.

[0272] The use of the linear model makes it possible to estimate the individual elementary powers even in the case where the number of information (number of activation / deactivation) of the respective subsets is limited.

[0273] Furthermore, the fact of using this linear model only when no subset meets the criterion of the number of changes of state has the advantage of not degrading the estimation of the powers of the multimodal subsets. Furthermore, the parameter estimation algorithms of a linear regression model are relatively easy to implement, which makes it possible to limit the cost in terms of calculations and makes it possible to obtain substantially the same results as the use of an algorithm to determine the parameters of a probabilistic graphic model when the equipment is unimodal.

[0274] In this type of parametric model, it is assumed that the individual elementary powers are considered fixed over the duration T, that is to say that the individual elementary powers of each subset are the same during all the elementary periods, that is to say whatever K. In other words, each subset SEj has an individual power Pj fixed over all the elementary periods or during the entire duration T.

[0275] In an exemplary embodiment, the linear regression model models the total energy consumed during the elementary period Tk by a sum of a constant and the energies consumed:

[0276] CET = ) + CST

[0277] where CST is a constant, and a^k) is the operating time of the subset SEj during the elementary period Tk

[0278] The parameters of the linear model are P j and CST.

[0279] The algorithm for estimating the parameters of the linear regression model is, by

[0280]

[0281]

[0282]

[0283]

[0284]

[0285]

[0286]

[0287]

[0288]

[0289]

[0290]

[0291]

[0292]

[0293]

[0294] example, an algorithm for solving the linear regression model by a least squares method or a least squares algorithm. This algorithm is an optimization algorithm which calculates, for the subsets SEj an estimate of their individual powers Pj, which minimize the following quantity Gj: Gj^JCETik) - L^Pfajtk)} The method is, for example, a non-negative least squares method, also called "Non-negative least squares" or NNLS in Anglo-Saxon terminology. In this method, the value of the individual power Pj is constrained to be a positive value. Alternatively, the value of the individual power Pj is constrained to lie within a predetermined interval: Pnomj < Pj < n2*Pnomj Advantageously, the least squares method is underconstrained. This method is also called "constraint least squares" in English terminology. This makes it possible to limit the solution space. Pnom, is a predetermined nominal power of the SE subset. ni and n2 are positive predetermined parameters such that n2 is greater than ni. ni is positive and less than or equal to 1 and n2 is positive and greater than 1. The values ​​are set based on the ability of each sub-assembly to operate at a power far from its nominal power. For example ni is equal to 0.5 and n2 is equal to 2. The EPIND step advantageously, but not necessarily, includes a calculation step ESTE of the elementary energies Ej consumed by the respective subsets SEj from the individual powers Pj and the total individual operating times Dj of the subsets SEj during the period T. The elementary energy Ej consumed by each subset SEj, during the period T is given by the following formula: SEj= PfDj Alternatively, the method is devoid of the DNB verification step and comprises the REGL implementation of the linear regression algorithm to estimate the individual powers of the subsets SEj of the group G and possibly the ESTE step described above. However, the previously described embodiment is more advantageous or a disadvantage of the linear model lies in the assumption of unimodality of the equipment subsets for equipment which may have several operating modes or whose power is intended to vary continuously over a range of values. Variant

[0295] Alternatively, the method comprises a step of determining the parameters of a linear regression model modeling, for each elementary period, the total elementary energy, by a sum of a constant CST and the individual elementary energies consumed by the subsets of equipment of the second group. Each individual elementary energy corresponds to a product of a fixed individual power and an elementary duration of operation of the equipment over the elementary period.

[0296] The EPIND estimate includes an estimate of the total energy consumed by the subsets of the second group G2 from the elementary energies Ej of the subsets of the second group G2.

[0297] The EPIND estimation then comprises a step of correcting each value of CET(k) by subtracting the estimate of the total energy consumed by the subsets of the second group G2 from the corresponding measurement of energy consumption.

[0298] The EPIND estimation then comprises a step of determining a set of parameters of a probabilistic graphical model modeling the distributions of the values ​​of the corrected total elementary energy consumption CET(k) and the distributions of the values ​​of the individual elementary powers of the respective subsets SEj of the first group G1 during the respective elementary periods Tk by multimodal distributions.

[0299] The method according to the invention is independent of the values ​​of the energy consumption and the operating times of the equipment during at least one period prior to period T. Only measurements of energy and activation / deactivation times of the equipment obtained during period T are used.

[0300] Such a method is more generic, that is to say, applicable to sets E or global sets EG of equipment which are very different from each other.

[0301] Detection of devices being almost always in the same state

[0302] Advantageously, the method comprises a step of identifying the equipment, which, in the global set EG, has a substantially identical state (activated or deactivated) throughout the period T.

[0303] For this purpose, for example, each piece of equipment is identified having a total individual operating time (i.e. a total operating time during the period T) less than or equal to a predetermined maximum portion or greater than or equal to a predetermined minimum portion of the duration T.

[0304] The maximum portion is, for example, between 0.1% and 1% and the minimum portion is, for example, between 99% and 99.9%.

[0305] The method also advantageously comprises a step of removing each identified piece of equipment from the overall set EG.

[0306] These steps are advantageously implemented before the EPIND estimation step. This makes it possible to remove, from this step, equipment likely to disturb the model. The energy consumed by this equipment is found in the CST constant.

[0307] These steps are, for example, implemented in addition to or instead of steps VERI, EST1 and EST_CET. Material

[0308] From a hardware point of view, the UCA calculation unit can be seen as a calculator interacting with a computer program product.

[0309] The computing unit comprises at least one computer, for example, a microcomputer, a computer network, an electronic component, a tablet, a smartphone or a personal digital assistant (PDA). The computing unit comprises a memory assembly in which the computer program is stored.

[0310] The memory assembly comprises, for example, a computer-readable medium. The computer-readable medium is a tangible device readable by the reader of the computing unit, capable of storing electronic instructions and of being coupled to communication means of the computing unit.

[0311] In other words, the computer-readable medium is a tangible medium. In other words, it is not a transient signal per se, such as radio waves or other freely propagating electromagnetic waves, such as light pulses or electronic signals. Such a computer-readable storage medium is, for example, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0312] For example, the readable medium is an optical disk, a magneto-optical disk, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a random access memory (RAM), a magnetic card or an optical card. The memory assembly may include an operating system and load the programs according to the invention. It includes registers adapted to record parameter variables created and modified during the execution of the aforementioned programs. A computer program comprising software instructions is then stored on the readable medium.

[0313] The calculation unit comprises a data processing unit comprising at least one electronic circuit designed to manipulate and / or transform data re presented by electronic or physical quantities in registers of the evaluation system and / or memories into other similar data corresponding to physical data in the memories of registers or other types of display devices, transmission devices or storage devices.

[0314] The data processing unit comprises, for example, memories, for storing data, operatively coupled to the data processing circuit and a reader adapted to read the computer-readable medium.

[0315] Each function of the computing unit is performed by causing the data processing unit to read a predetermined program from hardware such as the memory assembly such that the data processing circuitry performs calculations, controls communications, and reads and / or writes data to the memory assembly.

[0316] The method is executed on a single computer or on a system distributed between several computers (in particular via the use of cloud computing).

[0317] The data processing unit comprises at least one of the elements listed below: a set of one or more processors (for example a central processing unit (CPU), a graphics processing unit (GPU), a microcontroller and a digital signal processor (DSP) capable of interpreting instructions in the form of a computer program a programmable logic circuit, such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a programmable logic device (PLD) programmable logic arrays (PLA), a system-on-chip (SOC), and / or an electronic card in which steps of the method according to the invention are implemented in hardware elements.

[0318] The program product may include the computer-readable recording medium.

[0319] Alternatively, the program instructions are obtained from an external source and downloaded via a network. This is particularly the case for applications. In this case, the computer program product comprises a computer-readable data carrier on which the program instructions are stored or a data carrier signal on which the program instructions are encoded.

[0320] The invention relates to a computer program product comprising the computer-readable medium containing instructions which, when executed by the processing circuit, cause the computing unit UCA to implement the steps of the method according to the invention.

[0321] The form of the program instructions is, for example, a source code form, a computer-executable form, or any intermediate form between a source code and a computer-executable form, such as the form resulting from the conversion of the source code via an interpreter, an assembler, a compiler, a linker, or a locator. Alternatively, program instructions are microcode, firmware instructions, state definition data, integrated circuit configuration data (e.g., VHDL), or object code. Program instructions are written in any combination of one or more programming languages, e.g., an object-oriented programming language (C++, JAVA, Python), a procedural programming language (e.g., C).

[0322] The communication means allow communication between the elements of the computing unit and possibly between at least one element of the computing unit and a device external to this computing unit, for example the UCO control unit. The communication means can establish a physical link between elements of the system and / or between an element of the system and a device external to the system and / or a remote communication link (wireless) between elements of the system and / or between an element of the system and a device external to the system.

[0323] The communication means advantageously allow the transmission of data between the control unit UCO and / or the electricity meter CE and the calculation unit UCA.

Claims

1.

2.

3. Claims Method for estimating an electrical power consumed by a subset (SEj) of a group (G) comprising at least one subset (SEj), each subset (SEj) of the group (G) comprising at least one electrical equipment of a set (E) of electrical equipment, during a period (T) comprising a plurality of elementary periods (Tk), the method being implemented by computer and comprising: estimate (EPIND) the electrical power consumed by the subset (SEj) using: - individual elementary operating durations (aj(k)) of each subset (SEj) of the group (G) during the respective elementary periods (Tk) and, - the total elementary energy consumption (CET(k)) of the whole (E) during the respective elementary periods (Tk). Method according to the preceding claim, comprising: determine the individual elementary operating durations (aj(k)) of each subset (SEj) of the group (G) during the respective elementary periods (Tk), determine the total elementary energy consumptions (CET(k)) of the set (E) during the respective elementary periods (Tk). Method according to any one of the preceding claims, in which estimating (EPIND) the electrical power (Pj(k)) comprises: determine, from the total elementary energy consumption and the individual elementary operating times (aj(k)), a set of at least one parameter of a parametric model modeling: a distribution, over the elementary periods (Tk), of values ​​of individual elementary powers consumed, during the respective elementary periods (Tk), of each subset (SEj) of the group (G), - for each elementary period (Tk), the total elementary energy consumption during the elementary period (Tk), by a sum of a possible constant (CST) and the individual elementary energy consumption (CEj(k)) of each subset (SEj) of the group (G) during the elementary period (Tk).

4. Method according to the preceding claim, in which at least when a number of activation / deactivation of at least one subset (SEj) of the group (G) during the set of elementary periods (Tk) is greater than a main threshold, the model is a probabilistic graphical model.

5. Method according to claim 4, in which the parametric model models values ​​of the individual elementary powers consumed, during the respective elementary periods (Tk): - of each subset of a first subgroup (Gl) of the group (G) by a multimodal distribution, and / or - of each subset of a second subgroup (G2) of the group (G) by a unimodal distribution.

6. Method according to claim 5, comprising: - grouping (REG) subsets (SEj) of the group (G) so that the first subgroup (Gl) consists of each subset of the group (G) having a number of activations / deactivations during all the elementary periods (Tk) greater than a predetermined main threshold and the second subgroup (G2) consists of each subset having a number of activations / deactivations during all the elementary periods (Tk) less than or equal to the main threshold.

7. Method according to claim 5, comprising: - determining a number of subsets (SEj) of the first subgroup (Gl) so that the model is a model probabilistic graph only when the first subgroup (Gl) is non-empty.

8. Method according to any one of claims 3 to 7, in which, at least when no subset of the group (G) has a number of activation / deactivation during the set of elementary periods (Tk) greater than a predetermined main threshold, the parametric model is a linear model.

9. Method according to claim 8, comprising: - determining a number of subsets (SEj) having a number of activation / deactivation during the set of elementary periods (Tk) greater than the main threshold, so that the parametric model is linear only when no subset of the group (G) has a number of activation / deactivation, during the set of elementary periods (Tk), greater than the main threshold.

10. Method according to any one of the preceding claims, comprising a step of prior grouping (REGI) of the equipment of the set (E) according to a correlation criterion between states of the equipment (EQ;), using state information of the respective equipment, so as to form said group (G) of at least one subset (SEj).

11. Method according to any one of the preceding claims, comprising a step of initial estimation (EST1) of the energy consumption of another set of at least one other equipment from a nominal power of the other equipment and status information of the other equipment.

12. Method according to the preceding claim, comprising an initial verification step (VERI) implemented for each equipment of a global set (EG) comprising each equipment of the set (E), consisting of verifying whether the equipment complies with a set of at least one importance criterion (Cia, Clb) using a predetermined nominal power (P;) of the equipment and / or state information of the equipment, the other set of at least one other equipment being made up of each equipment of the set (E) not complying the set of at least one importance criterion.

13. Method according to any one of the preceding claims comprising, prior to the estimation step, a step of identifying each piece of equipment in the global set (EG) having a total individual operating time less than or equal to a predetermined maximum portion or greater than or equal to a predetermined minimum portion of the duration T, and a step of removing each piece of equipment identified during the identification step from the global set (EG).

14. A computer program product stored in a non-transitory computer-readable medium, said computer program product comprising code instructions which, when the program is executed by a computer, cause the computer to perform the method of any preceding claim.

15. A data processing device comprising a processor configured to execute a method according to any one of claims 1 to

16. 13. Industrial system comprising: - a set (E) of electrical equipment (EQ;) comprising a group (G) comprising at least one subset (SEj), each subset (SEj) of the group (G) comprising at least one electrical equipment of the set (E) of electrical equipment, - an electrical meter (CE), - a control unit (UCO) of the electrical equipment capable of activating and deactivating each equipment of the set (E), - a calculation unit (UCA) configured to implement the method according to any one of claims 1 to 13a, to estimate an electrical power consumed by a subset (SEj) of the group (G), the total elementary energy consumptions of the individual elementary operating durations (aj(k)) of each subset (SEj) of the group (G) during the elementary periods (Tk) being derived from energy measurements delivered by the electrical meter (CE) and the individual elementary operating durations (aj(k)) of each subset (SEj) of the group (G) during the respective elementary periods (Tk) coming from the control unit (UCO).

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