Method and system for detecting and estimating the electric power consumption of a subscriber's facilities

0001Electric power is an essential element for industrial and economic activity, but also in everyone's daily life. More precisely, the consumption of this energy, the production of which has tripled during the last thirty years in France, breaks down by sectors where the most important sectors are those of industry and tertiary housing.

0002The large share of consumption in the two aforementioned sectors is accompanied by a growing interest from these two families of customers for a better understanding of their consumption of electrical energy. This is particularly true especially in the context of the opening of the electricity market and the multiplication of electrical uses (more and more electronic devices, for example).

0003Electricity producers are then faced with new challenges, which are the opening of the electricity market and the quality of the power supplied.

0004A need for customers to better understand the consumption of their electrical devices, in order to choose a set of services offered, to improve the management of their consumption appears. Electric power, or any other source of energy, is no longer just a source of energy. It has become a source of information, valuable both at the customer and supplier level.

0005Information extracted from electricity consumption (electrical energy) is a crucial component of any service offer to private and professional customers. In fact, it can be used to inform them of their consumption by use, as well as from the perspective of controlling energy demand (MDE or DSM: Demand Side Mangement). This information is also a useful tool for the supplier to:<ul id="ul0001" list-style="none" compact="compact"><li>■ better understand the profiles of its customers and offer them a range of targeted (personalized) services;</li><li>■ improve demand forecasting and the quality of the power supplied.</li></ul>

0006Among the determining factors in choosing a particular supplier are the price of electricity, the services offered and the quality of these services. It is obvious that the quality of services depends in particular on the quality of information extracted from electricity consumption.

0007The most reliable solution consists in having the consumption of each electrical device individually, which however implies an intrusion at the customer to instrument the devices of the installation. In addition to its intrusive nature, this solution is neither economically nor technically viable. This need has given rise to numerous studies in France and in other countries, with the common objective of implementing a tool whose main purpose is:<ul id="ul0002" list-style="none" compact="compact"><li>■ analyze and characterize the overall electricity consumption of each customer;</li><li>■ inform customers about the consumption of their electrical devices separately.</li></ul>

0008Recent studies exploit non-intrusive analysis of electricity consumption in other applications, in particular machine monitoring and fault detection.

0009A viable method must use the minimum of contextual information and be non-intrusive as much as possible.

0010The main methods identified in the literature are set out below, with their advantages and limits in the current context.

0011The aforementioned methods are generally based on the principle of decomposition of the load curve, that is to say chronograms of consumption of a given installation.

0012The methods of breaking down the load curve can be distinguished according to their intrusive nature when the targeted uses are instrumented with sensors, or non-intrusive.

0013The main existing non-intrusive methods are based on the following models:<ol id="ol0001" compact="compact" ol-style=""><li>(1) the model <b>NILM</b> (Non-intrusive Load Monitor), initially developed by researchers from MIT (Massachusetts Institute of Technology) and EPRI (Electric Power Research Institute). This model is implemented in a system named after the model, then another system called<b>NIALM</b> (Non-Intrusive Appliance Load Monitor) in English.</li><li>(2) the model <b>HELP</b><i>(Heuristic End-Use Load Profiler)</i> developed by the group <i>Quantum Consulting</i> in California. This model is implemented in a system named after the model.</li><li>(3) <b>NALRA</b><i>(Non-intrusive Appliance Load Recognition Algorithm),</i> developed at Concordia University in Canada;</li><li>(4) <b>RPRA</b> (<i>Rule based Pattern Recognition Algorithm</i>) developed at Concordia University in Canada;</li><li>(5) A method of recognition of uses based on a genetic algorithm, developed at the University of Paderborn in Germany (2003-2004).</li></ol>

0014The first two models mentioned above resulted in commercial systems (ENETICS).

0015For the following two models, no information has currently been highlighted on the possible existence of a system used in the sale of services. The latest model is the result of a fairly recent study in Germany.

0016From the point of view of the techniques used, the approaches to decomposition of the load curve can be divided into three classes:

•

Engineering approaches

0017It is an approach that combines knowledge <i>a priori</i> or hypotheses, relating to the frequency of use of electrical devices and the temperature set points for certain devices, with a thermodynamic model of the building or the room studied. The major drawback of this approach is the need to perform a building audit, which is both costly and time consuming.

0018The main systems using this approach are:<ul id="ul0003" list-style="none" compact="compact"><li>✔ <b>HOT2000:</b> It is a system developed at CTEC-CANMET (CANMET Energy Technology Center) and which is mainly used in the residential sector for the estimation of the consumption of heating and water heaters.</li><li>✔ <b>DOE2.1:</b> developed at the University of California (<i>Lawrence Berkley Laboratory</i>) in 1991. It is a simulator very suitable for simulating the overall consumption of a building or an aggregation of several dwellings or industrial sites.</li></ul>

•

Statistical approaches

0019These approaches are based on the description of each use by a model configured by economic and climatic data, physical characteristics of the use, information on the occupants and the type of housing. They also require a learning phase to constitute a history of consumption.

0020The following three methods are distinguished:<ul id="ul0004" list-style="none" compact="compact"><li>■ Conditional demand analysis <i>(Condional demand analysis: CDA</i>) in English This method consists in building an econometric model of the individual's housing (demographic and economic data). It is mainly used for aggregating consumption or profiling consumers, and not for estimating consumption by use. This model was used for the decomposition of the load curve in the residential sector, mainly in Canada.</li><li>■ Multivariate regression <i>(Multivariate Regression Analysis)</i> in English A regression model as a function of temperature, sunshine and other climatic factors is defined. The parameters of the model are estimated by minimizing a quadratic criterion. This method is simple, but the results of estimation of consumption by use are often poor. This approach is implemented in the PRISM software developed at Princeton University.</li><li>■ Classification <i>(Objective classification analysis)</i> in English This method combines principal component analysis (PCA) and a grouping method <i>(clustering</i> to identify the days when consumption is correlated with the climate, and then classify them by standard day depending on the climate. This method was initially developed for predicting consumption, in particular for HVAC systems<i>(Heating Ventilation Air-Condtionning)</i> in English (for industrial and commercial subscribers).</li></ul>

•

Hybrid approaches

0021These are methods which combine techniques used in the two previous approaches in order to improve the estimation of uses. The main models used for the decomposition of the load curve are:<ul id="ul0005" list-style="none" compact="compact"><li>✔ the model <b>SAE</b> (<i>Statistically Adjusted Engineering Model</i>), 1992;</li><li>✔ the model <b>RECAP</b> (<i>Residential Energy Consumption Analysis Program</i>);</li><li>✔ the model <b>REEPS</b> (<i>Residential End-Use Planning Systems</i>), EPRI, 1995;</li><li>✔ the model <b>EDA</b> (<i>End-Use Disaggregation Algorithm: EDA</i>).</li></ul>

0022Several non-intrusive methods of analysis and decomposition of the electric charge curve have been proposed during the last two decades. A major part of the work was carried out at MIT, more precisely at<b>LEES</b> (Laboratory for Electromagnetic and Electronic Systems).

0023The work on the load curve in the United States was initiated by Georges Hart in 1984 and resulted in a method of detecting and estimating electrical uses in the residential sector (Confer in particular <nplcit id="ncit0001" npl-type="s"><text>GW Hart “Residential Energy Monitoring and Computerized Surveillance via Utility Power Plan IEEE Technology and Society Magazine, vol. 6, pp 12-16, 1989</text></nplcit>). This method is based on the variations of active power and reactive power, sampled at 1 Hz, each time an electrical appliance is switched on or off. In fact, an event (switching on / off of an electrical device) is defined as the transition from a permanent state (constant consumption level) to another permanent state.

0024Non-intrusive methods of studying the load curve, conducted at <b>LEES</b>, can be classified into two families:<ul id="ul0006" list-style="none" compact="compact"><li>■ identification of uses based on the consumption signature (active power and reactive power) of devices in steady state;</li><li>■ identification of uses based on the consumption signature of devices in transient mode.</li></ul>

0025The approach relating to the identification of electrical uses in steady state is based on the detection of switching on and off of the various devices, in view of the variations in active power and reactive power. More precisely, the currents and voltages of the three phases are measured. Events are detected both on the variations of the active power and those of the reactive power. The architecture of the software part of the system<i>NILM</i> (Non-Intrusive Load Monitoring) is illustrated on the <figref idref="f0001">figure 1a</figref>. The estimation of uses is carried out in 5 steps:<ul id="ul0007" list-style="none"><li>■ Event detection: this involves detecting the variations in the active power ΔP and the reactive power ΔQ calculated using a pre-processor from current and voltage measurements. The events are then represented in space (ΔP, ΔQ); we obtain groupings, designated clusters in English, illustrated by the<figref idref="f0001">figure 1b</figref>. In the above-mentioned figure, the abscissa axis ΔP is graduated in Watt W, and the ordinate axis ΔQ in reactive Volts-Amperes, VAr. The entity ΔQ3 designates the variation of reactive power at harmonic 3.</li><li>■ Groups of the same amplitude and opposite signs are grouped, and correspond to the signature of a device operating in “all or nothing” (mode <i>onloff).</i></li><li>■ Events not belonging to any group are either associated with one of the existing groups according to a criterion of distance or maximum likelihood, or considered in a family of unknown signatures.</li><li>■ Each group is associated with an electrical use, knowing its nominal power, and possibly other information. This information on the amplitudes of uses is collected during a learning phase. The time series for each use is then reconstructed. Note, however, that the method only takes into account two-state electrical devices.</li><li>■ The last step consists in estimating the energy consumed by each use, knowing its amplitude and the associated series of events.</li></ul>

0026This method has been integrated into certain products (NIALMs, NALM and SPEED) sold by the company ENETICS, Inc. VICTOR, NY USA. However, this method has drawbacks which limit its use on a large scale or as part of a package of services such as that desired by large electric power distributors and which is intended for residential customers:<ul id="ul0008" list-style="none"><li>■ it requires a learning phase on the site in question;</li><li>■ machines made up of several loads (household appliances) are not taken into account, only devices with two states are treated;</li><li>■ it is not suitable for the tertiary and industrial sectors, where we observe the existence of a greater diversity of devices, many more events and several cases of overlapping events.</li></ul>In order to extend the method to the tertiary and industrial sectors, a new approach was introduced. It consists in increasing the sampling frequency, and in using the signature of the devices in transient mode to identify them.

0027The identification of electrical uses in transient mode by exploiting their signatures was initially proposed in 1993 (Confer <nplcit id="ncit0002" npl-type="s"><text>GW Hart "Non-intrusive Appliance Load Monitoring" Proceedings of the IEEE, vol. 80, (n ° 12) pp 1870-1891, 1992</text></nplcit>). The contribution of this method consists in exploiting the link between the consumption signature of a device in transient state, its physical model and the function it performs. The system developed is called transient-NILM, and detects events in two stages, as shown in<figref idref="f0002">figure 1c</figref>.

0028The first module of the system determines the spectral envelopes, at the fundamental frequency and at a few harmonics, from a spectral decomposition of the current i (t) measured at the sampling frequency 8 kHz. The second module detects transient events (ruptures) on these envelopes. Detection is followed by a classification step, where a classifier identifies the forms detected by comparing them to forms in a previously constructed database. The author also took into account the case of several machines having the same physical characteristics (example: induction machines), but of different dimensions for example, and this by applying the procedure to several time scales.

0029It is noted that the aforementioned method is based on a reference database, and uses the consumption signature of the use in transient mode only through its form, without however modeling it.

0030Thus, the latest work carried out in the field of load detection and identification consists a priori of a hybrid approach of the two regimes. More specifically, the power of steady state use is modeled by a Gaussian process. A classification of uses is then made in the power variation / power variation space (ΔP, ΔQ). Another classifier, which uses the signature of uses in transient mode, overcomes the limitations of the first classifier.

0031All the methods developed and integrated into the different generations of the NILM system have the major drawback of requiring the use of a reference database for their learning phase. The simplicity of the algorithms developed has to be qualified by the need for a more complex hardware architecture (current and voltage measurements of the three phases, volume of data to be managed, etc.) and the need for professional intervention to install the system. In addition, the systems sold are quite expensive. These factors limit the marketing of these products on a large scale, especially in the residential sector. On the other hand, these methods have been widely studied in the tertiary sector and the industrial sector, with the objective of operating the NILM system with a view to detecting faults, diagnosing and identifying systems.

0032Other methods have been developed more recently.

0033Among these, the study carried out by <nplcit id="ncit0003" npl-type="s"><text>P. WAGNER "Estimation of consumption of uses from the load curve of an individual - state of the art", Electricité de France, EDF-R &amp; D Note, HP-1B / 05/011 / A, December 2005</text></nplcit>.

0034The research work at the aforementioned University of Paderborn aims to set up a system for recognizing electrical uses and estimating the energy consumed in a non-intrusive manner, by using the only measurement available at the meter output. of electricity and without prior information. Exploitation of this single measure allows a priori to reduce the cost of the final system.

0035The input data is the active power, sampled at a period between 1 s and 16 s.

0036The uses targeted by the above method are only “frequent” or recurrent uses, such as heating, food cold, hot water. The algorithm developed consists of 4 steps:<ul id="ul0009" list-style="none" compact="compact"><li>■ Detect events, those with a low amplitude are neglected.</li><li>■ Fuzzy grouping of events: events with a similar structure (amplitude, duration between successive events, ...) are grouped in the same grouping or "cluster". Combinations of different groupings are possible candidates for different devices.</li><li>■ Generate all potential candidates (state machines) from possible combinations of groupings. This step is performed by a genetic algorithm, to code and determine the different candidates.</li><li>■ The identification of the devices is obtained by optimizing the sequences of the candidate state machines. The method used for this optimization phase is dynamic programming.</li></ul>

0037The recognition of electrical uses is carried out based on the result of the previous algorithm and on a reference database, containing measurements of the active power of the targeted uses (for approximately one week).

0038This fairly recent method (2003-2004) attempts to reduce the cost and eliminate the installer intrusion from a current NILM system, by exploiting the available data, namely active power alone. This simplification of the hardware part unfortunately results in a great complexity of the algorithm for detection and estimation of uses. In addition, the method requires collecting data for the learning phase and is only valid for frequently used uses, known as recurring.

0039It is important to note that this study is aimed at obtaining a product that will be marketed and integrated into “smart homes” in English, by using the means of communication, in particular the Internet.

0040In the publications of <nplcit id="ncit0004" npl-type="s"><text>L. Farinaccio, "The Disaggregation of Whole-House Load into the Major End Uses Using a Rule-Based Pattern Recognition Algorithm", Doctoral thesis, Concordia University, Canada, 1999</text></nplcit> and of <nplcit id="ncit0005" npl-type="s"><text>ML Marceau and R. Zmeureanu "Non-intrusive load disaggregation computer program to estimate the energy consumption of major-end uses in residential buildings" Energy Conversion and Management, vol. 41, pp 1389-1403, 2000</text></nplcit>), the authors present two methods for estimating consumption of uses from active power, and possibly reactive power, based on shape recognition methods, based on a series of association rules. These rules make it possible to decide sequentially which usage is triggered. It should be noted that this method requires a learning phase of about a week on the site in question, in order to identify the consumption signatures of the targeted uses. In addition, there must be enough interlocking scenarios to estimate certain characteristics of the load curve.

0041Finally, the work carried out at CRIEPI (Central Research Institute of Electric Power Industry - Japan), is strongly oriented towards learning at the customer's site for a few days. The procedure is described on the<figref idref="f0002">figure 1d</figref>.

0042More recently, the patent application <patcit id="pcit0001" dnum="CA2552824"><text>CA 2,552,824 published January 19, 2007</text></patcit> has disclosed a process and a system for monitoring the charge curve of an installation consuming electrical energy making it possible to reveal fraudulent or illicit behavior by the user, during the prohibited cultivation of cannabis.

0043However, in such a situation, the analysis of the load curve of the installation must be carried out without the knowledge or beyond the reach of the user.

0044Consequently, the analysis equipment is necessarily placed on the connection of the installation to the LV network, out of the reach of the user.

0045At best, such an arrangement only makes it possible to discriminate between a global use and the overall behavior of the user, without however allowing a detailed analysis of the uses of the latter.

0046The object of the present invention is to remedy the drawbacks and limitations of the prior techniques, in particular to delete any database relating to consumption signatures of developments in the prior art, while allowing a detailed analysis of the uses of the devices making up the device. installation of any subscriber.

0047Another object of the present invention is the implementation of a method and a system for detecting and estimating the consumption of the electrical uses of the facilities of a subscriber authorizing, thanks to a programmable modular design, a setting implemented by any subscribing client.

0048The method for detecting and estimating the consumption of the electrical uses of the installations of a meter, object of the present invention, is remarkable in that it consists at least in establishing the general electricity consumption curve of the subscriber's installations, and, to be carried out, by segmentation of the general consumption curve of the subscriber's installations and by monitoring events of electricity consumption in transient mode, detection of the monitoring of electrical consumption events and their corresponding consumption.

0049According to another remarkable aspect of the method which is the subject of the invention, the step consisting in establishing the general consumption curve of the subscriber's installations includes at least sampling of the consumption of the effluents consumed by the subscriber's installations, this sampling taking place by downstream of the effluent consumption meter connected to the connection to the general network.

0050According to another remarkable aspect of implementing the process which is the subject of the invention, the effluents consumed by the subscriber's installations comprise at least one of the effluents such as gas, electric current, water.

0051The system for detecting and estimating the consumption of the electrical uses of the facilities of a subscriber, object of the present invention, is remarkable in that it includes at least one module for calculating and memorizing the general electricity consumption curve of the subscriber's installations and a module for calculating by segmenting the general consumption curve of the subscriber's installations and by tracking detection transient electrical consumption events, recognition of electrical uses and their corresponding consumption.

0052According to another remarkable aspect of the system which is the subject of the present invention, the module for calculating and memorizing the general electrical consumption curve of the subscriber's installations comprises at least one module for sampling the consumption of the effluents consumed by the subscriber's installations and a module for memorizing and transmitting the sampled values ​​to a remote processing platform for the recognition of electrical uses and their corresponding consumption.

0053According to another remarkable aspect of the system which is the subject of the present invention, the module for transmitting the sampled values ​​comprises a wireless transmission resource connecting each of the sampling modules to a network access gateway and a network link from the gateway to the remote processing platform.

0054The method and the system for detecting and estimating the consumption of the electrical uses of the facilities of a subscriber, objects of the present invention, find application in the facilities of subscribers of all types, in particular industrial installations, domestic installations, and can, in particular, be suitable for installations handling the most diverse effluents.

0055They will be better understood on reading the description and on observing the drawings below in which, in addition to the <figref idref="f0001 f0002">Figures 1a to 1d</figref> relating to solutions of the prior art,<ul id="ul0010" list-style="dash" compact="compact"><li>the <figref idref="f0003">figure 2a</figref> represents, by way of illustration, a general flowchart for implementing the method for detecting and estimating the consumption of the electrical uses of the facilities of a subscriber, in accordance with the object of the present invention;</li><li>the <figref idref="f0003">figure 2b</figref> represents a specific flowchart of the steps of sampling of effluents and of the outside temperature of the subscriber's installations, allowing the implementation of the detection and the estimation of the electrical uses, in accordance with the object of the method according to the invention , represented in <figref idref="f0003">figure 2a</figref> ;</li><li>the <figref idref="f0004">figure 3</figref> represents, by way of illustration, a specific flowchart of the steps for implementing the segmentation of the general consumption curve of the subscriber's facilities;</li><li>the <figref idref="f0004">figure 4a</figref> represents, by way of illustration, a specific flowchart of the steps for implementing the detection and monitoring of consumption events;</li><li>the <figref idref="f0005">figure 4b</figref> represents, by way of illustration, a specific flowchart of the steps for implementing discrimination of the type of use;</li><li>the <figref idref="f0006">figure 5a</figref> represents, by way of illustration, a functional diagram of the architecture of a system for detecting and estimating the consumption of electrical uses of the facilities of a subscriber, in accordance with the object of the present invention;</li><li>the <figref idref="f0006">figure 5b</figref> represents, by way of illustration, a functional diagram of the remote platform allowing the execution of the detection of the uses and the estimation of the consumption of the uses, as well as the execution of administrative operations for example.</li></ul>

0056The method for detecting and estimating the consumption of the electrical uses of a subscriber, in accordance with the object of the present invention, will now be described in conjunction with the <figref idref="f0003">figure 2a</figref> and the following figures.

0057With reference to the above-mentioned figure, the process which is the subject of the invention consists at least, in a step A, of establishing the general electricity consumption curve of the subscriber's installations. This curve is noted ECCG. on the<figref idref="f0003">figure 2a</figref> in step A.

0058Step A is followed by step B and consisting in performing, by segmentation of the general consumption curve of the subscriber's facilities, ECCG curve, and by monitoring events of electricity consumption in transient regime, the recognition of electrical uses and to estimate their corresponding consumption.

0059On the <figref idref="f0003">figure 2a</figref>, the segmentation operation is executed during a substep B₀. and monitoring of events and executed during a sub-step B₁.

0060The operation to recognize electrical uses and estimate their corresponding consumption is noted:<ul id="ul0011" list-style="none" compact="compact"><li><b>Recognition → EU and CEU</b> where EU indicates the electrical uses and CEU the consumption of these.</li></ul>

0061A more detailed description of stage A and in particular of the sub-stages of the implementation of the latter will now be given in conjunction with the <figref idref="f0003">figure 2b</figref>.

0062In general, step A consisting in establishing the general consumption curve for the subscriber's facilities includes at least: sampling the consumption of the effluents consumed by the subscriber's facilities. This sampling occurs downstream of the effluent consumption meter connected to the connection to the general network.

0063As shown without limitation in <figref idref="f0003">figure 2b</figref>, the sampling of the consumption of the effluents can comprise in a step A1 the sampling of an effluent such as gas, in a step A2 the sampling of the sanitary or industrial water consumed by the subscriber, in a step A3 , the sampling of the electric current for example. On the<figref idref="f0003">figure 2b</figref>, the corresponding consumptions are noted CG, CE, and CWE. It is clearly understood that the modes and the sampling periods of each of the abovementioned effluents may be different in order to ensure a faithful recording of the consumption of the abovementioned effluents.

0064In addition, as shown on the same <figref idref="f0003">figure 2b</figref>, the process which is the subject of the invention may consist in sampling the temperature outside the installations. This makes it possible to correlate the events of electrical consumption with the evolution of the temperature outside the aforementioned installations. On the<figref idref="f0003">figure 2b</figref> outdoor temperature values ​​T_EXT sampled are noted CT in step A4 of the aforementioned figure.

0065A more detailed description of the implementation of step B of the <figref idref="f0003">figure 2a</figref> will now be given in conjunction with the <figref idref="f0004">figure 3</figref> and the following figures.

0066With reference to the <figref idref="f0004">figure 3</figref>, the step of segmenting the general consumption curve of the installations for the recognition of electrical uses and for estimating their consumption includes in a step B01 the measurement by sampling at a frequency of the order of Herz of the general electrical power recorded by the electric meter.

0067In step B01 of the <figref idref="f0004">figure 3</figref> the measurement operation by sampling is noted:

Measurement by sampling from EP to fe1 ∈ [1Hz; 5Hz].

0068Step B01 is followed by a step B02 of delayed discrimination of electrical uses by identification of the electrical signature from, in particular, the sampled consumption of water, gas and the sampled outside temperature.

0069A more detailed description of the sub-step B1 for monitoring events as represented in <figref idref="f0003">figure 2a</figref> will now be given in conjunction with the <figref idref="f0004">figure 4a</figref> and the <figref idref="f0005">figure 4b</figref>.

0070As shown in substep B11 of the <figref idref="f0004">figure 4a</figref>, the step of detecting the monitoring of the events of electrical consumption in transient mode is based on a sampling of the voltage V and of the electrical current I consumed by the installation of the subscriber at a sampling frequency fe2 of between 800 Hz and 10 kHz .

0071In step B1 of the <figref idref="f0004">figure 4a</figref> above, the corresponding operation is noted:<ul id="ul0012" list-style="none" compact="compact"><li><b>Sampling V, I to fe2 ∈ [800 Hz; 10 kHz].</b></li></ul>

0072At the same stage B11 of the <figref idref="f0004">figure 4a</figref> V and I denote the supply voltage respectively the electric current consumed by the subscriber installation and V * and I * denote the corresponding sampled values.

0073Step B11 is followed by a test step denoted B12 consisting in comparing the sampled values ​​V * and I * with the voltage signatures SV respectively of the intensity of the current SI.

0074In substep B12 of the <figref idref="f0004">figure 4a</figref>, the corresponding comparison operations are noted: <maths id="math0001" num=""><math display="block"><mi mathvariant="normal">V</mi><mo>*</mo><mo>=</mo><mi>SV</mi><mo>?</mo></math><img file="EP2000780A1_D0001.tif" /></maths><maths id="math0002" num=""><math display="block"><mi mathvariant="normal">I</mi><mo>*</mo><mo>=</mo><mi>IF</mi><mo>?</mo></math><img file="EP2000780A1_D0002.tif" /></maths>

0075When the sampled values ​​of voltage and or of currents do not correspond to the respective signature of voltage and of currents, a step of returning to step B11 is executed to continue the process.

0076When, in addition, the discrimination by successful comparison of the sampled values ​​of voltage and currents with respect to the corresponding signatures is achieved, that is to say on a positive response to the aforementioned comparisons, then a recognition of the electrical uses in the content can be carried out in step B13, followed by the calculation of the consumption of the corresponding electrical uses recognized CEU.

0077It is thus understood that the step of recognizing the uses of electrical consumption is carried out by a comparison of the electrical signals and their signatures in transient mode, due to the sampling carried out at the aforementioned sampling frequency fe2, which can be chosen. high enough to allow substantially real-time execution of the assembly.

0078In addition, as shown in <figref idref="f0005">figure 4b</figref>, delayed discrimination of electrical uses includes discrimination of the type of appliance use by correlating consumption with the mode of consumption of effluents.

0079So, with reference to the <figref idref="f0005">figure 4b</figref> mentioned above, by way of nonlimiting example, the discrimination of the type of uses can consist in particular in comparing the consumption of sampled water corresponding to a threshold value of water consumption. When this comparison is positive, we can conclude, for example, during a sudden increase in water consumption, sub-step B021b, the launch of a household appliance such as a washing machine for example, whereas, in negative response to the comparison test with the aforementioned threshold value, it is considered in a sub-step B021c, in the absence of significant variations in water consumption, that this device, for an electrical power called comparable, constitutes for example a dryer or the like.

0080Consequently in sub-steps B021b and B021c, the consumption of electrical uses corresponds to an electrical use accompanied by water noted CEUE respectively to an electrical use not accompanied by water noted <i><o ostyle="single">CEUE</o> .</i>

0081A more detailed description of a system for detecting and estimating the consumption of electrical uses of the facilities of a subscriber, in accordance with the object of the present invention, will now be given in conjunction with the <figref idref="f0006">figure 5a</figref> and the <figref idref="f0006">figure 5b</figref>.

0082With reference to the <figref idref="f0006">figure 5b</figref>, it is indicated that the system which is the subject of the invention includes at least resources for calculating and memorizing the general electrical consumption curve of the subscriber's installations. On the<figref idref="f0006">figure 5b</figref> the aforementioned resources are noted 1a, 1b, 1c. In addition, the aforementioned resources are noted GAS MODULE, WATER MODULE, ELECTRICITY MODULE or COMMERCIALIZER BOX. It is understood, in fact, that the aforementioned modules are installed at the subscriber at the level of the installations of the latter.

0083In addition, the system which is the subject of the invention comprises computing resources referenced 2, the aforementioned computing resources operating by segmenting the ECCG curve of general consumption by monitoring events of electrical consumption in transient mode, to execute the recognition of electrical uses and their corresponding consumption.

0084As was further represented in <figref idref="f0006">figure 5a</figref>, it is understood that the resources for calculating and memorizing the general electrical consumption curve of the subscriber's installations include circuits for sampling the consumption of the effluents consumed by the subscriber's installations, these circuits being contained in the modules 1a, 1b , 1 C. In addition, the system which is the subject of the invention comprises, in the computing resources, circuits for memorizing the sampled values ​​of the consumption of the effluents consumed and for transmitting the aforementioned sampled values ​​to a remote platform for processing the recognition of uses. and their corresponding consumption.

0085On the <figref idref="f0006">figure 5a</figref> the circuits for memorizing the aforementioned sampled values ​​are not shown, the resources for transmitting the aforementioned sampled values ​​to the remote platform 2 being represented by the arrows connecting each of the modules 1a, 1b, 1c to a gateway 1d, which is it - even connected to the remote platform 2 forming the computing resources by segmentation of the general consumption curve of the ECCG installations. The aforementioned remote platform also makes it possible to recognize electrical uses and their corresponding consumption.

0086Upon observation of the <figref idref="f0006">figure 5a</figref>, it is understood that the modules for sampling the consumption of the effluents consumed by the installation of the subscriber are placed in the vicinity of the corresponding effluent meters on the aforementioned installation side.

0087With reference to the same <figref idref="f0006">figure 5a</figref>, it is indicated that the system for transmitting the sampled values ​​may advantageously include a wireless transmission resource, connecting each of the modules 1a, 1b, 1c to the gateway 1d, as well as a network link 1e from the gateway 1d to the platform remote processing 2.

0088By way of nonlimiting example, it is indicated that the network link of the gateway to the remote processing platform can be constituted by a link of ADSL or GPRS type.

0089In a preferred, non-limiting mode of implementation, the remote processing platform 2 can be connected to a Web portal 3 allowing any controlled access by the subscriber to the result of consumption of the electrical uses of its installations.

0090The concept of controlled access covers in particular the usual criteria for identifying the subscriber from an identification code and any access security procedure which may include authentication and non-repudiation of the subscriber.

0091As was further represented in <figref idref="f0006">figure 5b</figref>, the remote platform 2 can advantageously include a module 2a for calculating signature parameters receiving the sampled values ​​of water consumption CONSO D'EAU, gas consumption CONSO GAS, electrical power sampled at 1 Hz, voltage V and electrical current consumed I. In addition, the module 2a can receive the sampled value of the temperature outside the installations.

0092The remote platform 2 may also include a module 2b for detecting uses of electrical consumption. This module is also associated with a module 2c for estimating the consumption of electrical uses.

0093Preferably, as also shown in <figref idref="f0006">figure 5b</figref>, the two aforementioned modules can advantageously be associated with a 2d module for detecting malfunctions.

0094Finally, as shown in the <figref idref="f0006">figure 5b</figref>, the module 2c can be associated with a database of consumption of electrical uses containing elements of billing data, which can be consulted by the only authorized subscriber via the private web portal 3.

0095The invention also covers a computer program product comprising a series of instructions executable by a computer or by a dedicated system for detecting and estimating the consumption of the electrical uses of the facilities of a subscriber.

0096This computer program product is remarkable in that it comprises at least one computer program module installed at the subscriber's facilities and making it possible to carry out the calculation, storage and transmission of sampled values ​​of consumption. effluents to a remote treatment platform for effluents, as previously described in the description, in particular electrical energy, gas, sanitary water for example.

0097On the <figref idref="f0006">figure 5a</figref>, it is thus understood that the product the computer program can for example advantageously consist of program modules denoted respectively [M1a], [M1b], [M1c] each of these modules being of course installed separately at the level of the physical modules module 1a, 1b, 1e.

0098It is thus understood that each program module is installed so as to sample the consumption of one of the effluents at a corresponding effluent counter.

0099Likewise, with reference to the <figref idref="f0006">figure 5a</figref>, and at the <figref idref="f0006">figure 5b</figref>, it is indicated that the program product, object of the invention, further comprises a computer program module denoted [M2], installed at the level of the remote processing platform 2.

0100The computer program module [M2] can be constituted either by a single program module, or on the contrary, as shown in <figref idref="f0006">figure 5b</figref>, by a plurality of program modules denoted respectively [M2a], [M2b], [M2c], [M2d]. The various aforementioned program modules allow during the execution of the instructions of the latter the execution of the recognition of the electrical uses and the calculation of their corresponding consumptions as described previously in the description.