METHOD, DEVICE AND SYSTEM FOR DETERMINING THE THERMAL ENERGY SUPPLIED TO A ROOM IN A BUILDING OVER A DEFINITE PERIOD OF TIME

Description

Technical field

[0001] The invention relates to the field of construction, in particular the management of thermal energy in a building. In particular, it relates to a method for determining a quantity of thermal energy supplied to a room in a building over a given period. The invention further relates to a device and a system for determining a quantity of thermal energy supplied to a room in a building, in particular for the distribution of energy costs. Prior art

[0002] Below we describe the known prior art from which the invention was developed.

[0003] As part of the objectives set out in the Paris Agreement, the European Union has set ambitious targets for the energy transition by 2030 and 2050, focusing on increasing the penetration of renewable energy, energy efficiency and reducing greenhouse gas emissions.

[0004] In 2016, buildings accounted for nearly 40% of the European Union's final energy consumption. Thus, although there are large disparities in building performance across countries, this sector has significant room for improvement in terms of energy efficiency. In particular, heating and cooling in the residential, services, and industrial sectors account for approximately 50% of the EU's primary energy supply (European Commission - Factsheet - February 16, 2016). In particular, the origin of primary energy for residential heating is mainly based on fossil fuels (82% in 2016), and 68% of total gas imports into Europe are attributable to the heating and cooling sector.

[0005] This makes building heating a prime target for meeting the energy transition objectives for 2030 and 2050.

[0006] Buildings have thus been the subject of numerous studies and modeling aimed at characterizing their energy performance. For example, dynamic thermal simulations (DTS) of buildings have been proposed. These studies, mainly intended for project owners, allow the energy performance of various technical solutions to be simulated, with, for example, comparisons of VMC, heat generators or even artificial lighting management solutions. These studies based on dynamic thermal simulations of buildings are also implemented in construction project programs to analyze summer thermal comfort in the absence of air conditioning or to quantify heating requirements.

[0007] Such simulations have also been proposed to estimate forecast consumption. However, buildings are complex and non-stationary energy systems. Thus, significant differences are observed between the consumption predicted in simulation and measured due to errors in input data, occupancy scenarios or environmental conditions. In addition, these simulations are generally based on heavy software clients, and are not used in embedded systems configured to perform real-time analysis. It has recently been proposed to model a building and its systems to optimize its energy management (Hugo Viot. Modeling and instrumentation of a building and its systems to optimize its energy management. University of Bordeaux, 2016. French. NNT:2016BORD0349; tel-01503037).In particular, given that STD models are considered too heavy, it has been proposed to build small models in order to be able to embed them in a controller to improve energy management. Nevertheless, such simplified models present performances that can be improved. In addition, their performance will largely depend on the learning base used and a model could be trained for each new building since a given building will not behave in the same way as another.

[0008] Thus, in the absence of being able to effectively predict these consumptions, monitoring has been put in place and, in particular within the framework of RT2012, a minimum monthly count of energy consumption is imposed by item and by type of energy. However, this only constitutes monitoring and not a measure to promote the energy transition.

[0009] In this respect, in a collectively heated building, the sharing of heating costs is usually done according to percentages or in proportion to the surface area of ​​the apartment. Such sharing does not encourage frugality, because even if heating consumption differs from one dwelling to another, heating costs will still be calculated according to the surface area or percentages of the apartment.

[0010] Thus, beyond the assessment of the improvement of the energy performance of buildings, in order to empower occupants, individualization of heating costs has been implemented in France. The individualization (or distribution) of heating costs allows for payment based on what each apartment has actually consumed. Occupants are thus encouraged to control their consumption and avoid energy waste. Individualization of heating costs allows for energy savings of around 15% on average and lower bills for residents wishing to adapt their consumption (ADEME. September 2019. ISBN 979-10-297-1399-6).

[0011] In order to be able to account for each occupant's consumption, it is necessary to install remotely readable measuring devices. Two main devices currently share the market: individual thermal energy meters (CET) on the one hand and heating cost allocators (RFC) on the other.

[0012] CETs are placed at the entrance to each home and display actual heating consumption, allowing occupants to track their consumption. RFCs are installed when it is technically impossible or uneconomical to install CETs. RFCs use the surface temperature measurement of heat emitters. They are placed on each radiator in the home. There are three main operating modes for "classic" RFCs.They are defined by the NF EN 834 standard: i) the single probe measurement method (probe measuring the temperature of the surface of the heating body or heat transfer fluid); ii) the measurement method using two probes (one probe measuring the temperature of the surface of the heating body or heat transfer fluid, another probe measuring the ambient temperature); and iii) the multiple probe measurement method (several probes to measure the average temperature of the heat transfer fluid, and one probe for the ambient temperature).

[0013] The installation targets for these solutions are falling far short of expectations. In fact, mainly due to the complexity of installing these systems, many buildings still do not have individualized heating costs.

[0014] Furthermore, these heating cost allocation systems are unable to provide a reliable calculation of energy consumption in relation to the location of the dwellings. Indeed, it has been proposed to calculate heating costs based on a fixed share of 30% coupled with the possibility of integrating correction coefficients to take into account thermally unfavorable situations (north-facing dwellings benefiting from less solar gain, top-floor dwellings suffering from greater heat loss, etc.). However, these artifices do not allow for an accurate account of a dwelling's consumption. Thus, there remains a significant inequity for occupants depending on the location of the dwellings.

[0015] Furthermore, these systems remain susceptible to "heat theft." A dwelling located in the center of a building with reduced heating will benefit from the heat transferred from adjacent warm dwellings. Thus, there also remains an inequity associated with the use of current individual thermal energy meters and heat cost allocators.

[0016] Examples of heat meters for buildings are described in documents FR 2 672 991 A1 and FR 3 062 723 A1.

[0017] There is therefore a need for a solution that can efficiently (e.g., in real time and accurately) determine the thermal energy consumption of a dwelling, and in particular, the amount of thermal energy supplied by a collective thermal management device to a room in a building. In addition, such a solution should also make it possible to more accurately calculate heating costs and thus improve fairness in the coverage of heating costs in a collective building.

[0018] The invention aims to overcome the drawbacks of the prior art. In particular, the invention aims to propose a method for determining a quantity of thermal energy supplied to a room of a building over a given period, said method being capable of determining such a quantity precisely and accurately and without requiring the costly installation of dedicated equipment. In addition, taking into account a thermal simulation model of the building and the fact of being able to take into consideration an ambient temperature to carry out the distribution, makes it possible to set up compensation systems making it possible to improve fairness between users and ultimately to take into account a level of thermal comfort rather than a level of energy consumption.

[0019] The invention further aims to propose a distributor for determining a quantity of thermal energy supplied by a collective thermal management device to a room in a building, said distributor being able to operate from simple measurements of ambient and exterior temperatures of the building to determine a quantity of thermal energy supplied by a collective thermal management device to a room in a building. Summary of the invention

[0020] The invention aims to overcome these drawbacks.

[0021] The invention relates in particular to a method for distributing energy costs in a collective building comprising several individual premises and at least one collective thermal management device, the method being applicable to all the individual premises of the collective building and comprising the determination of a quantity of thermal energy supplied by the at least one collective thermal management device, to at least one individual premises of the collective building, over a given period, said determination, carried out by one or more processors, comprising the use, of a thermal simulation model of the collective building which virtually represents the collective building and which takes into account, for each individual room, a geometry of the individual room and a composition of the walls of the individual room, as well as the thermal exchanges between different rooms of the collective building, of a plurality of ambient temperature values ​​of the collective building, the plurality of ambient temperature values ​​of the collective building comprising several temperatures of the individual room and several temperatures of rooms or common areas of the collective building which are adjacent to the individual room, of one or more temperature values ​​outside the collective building, of a volume of the individual room, and of at least one heat loss coefficient of the individual room, and the calculation of at least one heat loss coefficient of the individual premises from the thermal simulation model of the collective building.

[0022] The applicant has developed a method capable of rapidly calculating a quantity of energy supplied to an individual room essentially from indoor and outdoor ambient temperature data. In particular, within the framework of the present invention, there may be use of as many heat loss coefficients as there are rooms and in certain advantageous embodiments, several heat loss coefficients may be associated with the same individual room depending on the period considered. The method may in particular be used to calculate heating costs and integrate a notion of fairness between users of the same collective building.

[0023] According to other optional features of the method, the latter may optionally include one or more of the following features, alone or in combination: when the individual premises are also defined by a composition of the joinery, the thermal simulation model of the collective building is a model which also takes into account the composition of the joinery. Thus, it is possible by directly integrating the composition of the joinery during the construction of the thermal simulation model of the collective building to improve its performance without increasing the calculation. the thermal simulation model of the collective building is a model which also takes into account representative data of local solar radiation over the given period. Thus, it is possible by directly integrating representative data of solar radiation during the construction of the thermal simulation model of the collective building to improve its performance without increasing the calculation. the thermal simulation model of the collective building is a dynamic thermal simulation model of the collective building.The use of a dynamic thermal simulation model makes it possible to obtain, with the method according to the invention, determined quantities of supplied heat having improved accuracy. The heat loss coefficient of the individual room is selected from at least two predetermined heat loss coefficients, said predetermined heat loss coefficients each corresponding to a time period. Thus, it is possible to choose, for example depending on the season or the month of the year, the heat loss coefficient which will make it possible to determine a quantity of supplied thermal energy with greater accuracy.the determination of the quantity of thermal energy supplied by the at least one collective thermal management device, to the at least one individual room of the collective building, involves the use of one or more adjustment coefficients, each adjustment coefficient being applied to an adjustment variable measured or calculated for the given period; preferably the adjustment coefficient has been calculated from a dynamic thermal simulation model of the collective building. Thus, it is possible to adjust the quantity of energy supplied determined according to additional parameters best describing the use of the individual room. This will make it possible to determine a quantity of thermal energy supplied with greater accuracy.The adjustment variable is selected from: a variable representing heat losses linked to window openings, a variable representing the occupancy rate of the individual room, a variable representing weather conditions, for example exceptional weather conditions, and / or a variable representing the use of additional heating. This makes it possible to integrate real uses that are measured directly or modeled for each individual room without making the model more cumbersome and leads to higher accuracy. The adjustment variable is measured directly at the level of the individual room, modeled probabilistically from statistics, or reconstructed from measurements of machine learning algorithms.for each adjustment coefficient, the method included a step of calculating several adjustment coefficient values, each a function of a period in the year, and the value of the adjustment coefficient used to determine the quantity of thermal energy supplied is a function of the given period. For example, there is an adjustment coefficient value calculated for each month and depending on the time, it is the value corresponding to the month in question which is used. it includes a step of calibrating the thermal simulation model of the collective building, said calibration step is preferably carried out from a first measurement period and a Bayesian calibration algorithm so that the predictions of consumption and interior temperatures at the hourly time step of the thermal simulation model of the collective building reproduce the measurements as closely as possible.It comprises a step of calibrating the thermal simulation model of the collective building, said calibration step is preferably carried out from a first period of measurement of ambient and external temperature values ​​and a Bayesian calibration algorithm so that temperatures calculated by the thermal simulation model of the collective building reproduce as closely as possible the measured temperature values. the ambient temperature of the individual room corresponds to temperatures measured in several locations of the room. The different locations can correspond to different rooms. it comprises a step of calculating individual heating costs, for an individual room, according to a determined quantity of thermal energy supplied, by the collective thermal management device, to said individual room.The individual heating cost calculation step takes into account the orientation and positioning characteristics of the individual premises in the collective building. This makes it possible to correct disparities between the premises or common areas of the collective building. This induces a principle of fairness in the distribution of heating costs and makes it possible to correct disparities between the premises or common areas of the collective building (for example, different orientation, different floor, etc.). In addition, this makes it possible to charge tenants for the part of the heating consumption for which they are truly responsible, namely the temperature at which they heat their homes.it comprises a step of calculating a confidence interval on the quantity of thermal energy determined, preferably said calculation of a confidence interval comprising the use of at least one uncertainty value on the adjustment variable(s) used when determining the quantity of thermal energy supplied. the calculation of at least one coefficient of heat loss of the individual premises from a thermal simulation model of the collective building may comprise, for an individual premises, the calculation of at least one coefficient of heat loss from a plurality of simulations in which the values ​​of geometry of the individual premises and composition of the walls and possibly the composition of the joinery have been modified so as to integrate uncertainties associated with the collective building.the calculation of at least one coefficient of heat loss of the individual premises from a thermal simulation model of the collective building may include, for an individual premises, the calculation of at least one coefficient of heat loss from a plurality of simulations in which, in addition, usage values ​​and / or meteorological conditions have been modified so as to integrate uncertainties associated with the uses and meteorological conditions.

[0024] According to a second object, the invention relates to an energy cost distributor in a collective building comprising several individual premises and at least one collective thermal management device, the method being applicable to all the individual premises of the collective building and comprising the determination of a quantity of thermal energy supplied by the at least one collective thermal management device, to at least one individual premises of the collective building, said distributor comprising one or more processors configured to determine the quantity of thermal energy supplied using, a thermal simulation model of the collective building which virtually represents the collective building and which takes into account, for each individual room, a geometry of the individual room and a composition of the walls of the individual room, as well as the thermal exchanges between different rooms of the collective building, a plurality of ambient temperature values ​​of the collective building, the plurality of ambient temperature values ​​of the collective building comprising several temperatures of the individual room and several temperatures of rooms or common areas of the collective building which are adjacent to the individual room, one or more temperature values ​​outside the collective building, a volume of the individual room, and at least one heat loss coefficient of the individual room which describes the proportion of the thermal energy supplied to the individual room which is transferred to the rooms or common areas adjacent to the individual room, said distributor being characterized in that said distributor is configured to calculate at least one heat loss coefficient of the individual premises from the thermal simulation model of the collective building.

[0025] In a particular implementation, when the individual room is further defined by a composition of the joinery, the distributor is characterized in that the thermal simulation model of the collective building also takes into account the composition of the joinery.

[0026] Preferably, the distributor is such that the thermal simulation model of the collective building is a model also taking into account data representative of local solar radiation over the given period.

[0027] As will be described later, the distributor has a plurality of heat loss coefficients, each dedicated to an individual room. In addition, a room can be associated with several heat loss coefficients, each dedicated to a given period.

[0028] According to a third object, the invention relates to a heating cost distribution system comprising a distributor according to the invention. Such a distribution system makes it possible in particular to make tenants pay for the part of the heating consumption for which they are truly responsible, namely the temperature at which they heat their homes. Brief description of the drawings

[0029] Other characteristics and advantages of the invention will be better understood upon reading the description which follows and with reference to the appended drawings, given for illustrative purposes and in no way limiting. [ Fig. 1 ] There figure 1represents a method according to one embodiment of the invention. [ Fig. 2 ] There figure 2 represents a system for determining a quantity of thermal energy supplied by a collective thermal management device according to an embodiment of the invention.

[0030] The figures do not necessarily respect the scales, particularly in thickness, and this is for illustration purposes.

[0031] Aspects of the present invention are described with reference to flowcharts and / or functional diagrams of methods, apparatuses (systems) and computer program products according to embodiments of the invention.

[0032] In the figures, flowcharts and block diagrams illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowcharts or block diagrams may represent a system, device, module, or code, which includes one or more executable instructions for implementing the specified logical function(s). In some implementations, the functions associated with the blocks may appear in a different order than shown in the figures. For example, two blocks shown in succession may, in fact, be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order, depending on the functionality involved.Each block of the block diagrams and / or flowchart, and combinations of blocks in the block diagrams and / or flowchart, may be implemented by special hardware systems that perform the specified functions or acts or carry out combinations of special hardware and computer instructions. Description of the embodiments

[0033] Below, we describe a summary of the invention and the associated vocabulary, before presenting the disadvantages of the prior art, and finally showing in more detail how the invention overcomes them.

[0034] The expression "heat loss coefficient" also called the overall volumetric loss coefficient of the building corresponds to an overall performance of the insulation in heat losses per degree of difference between the interior and exterior environments. The overall volumetric loss coefficient of the building expresses the losses per unit of heated volume. It is generally expressed in Watts per cubic meter for a variation of 1 degree Celsius between the exterior and interior temperature (Watt / m 3 < and °C). The G coefficient was introduced during the thermal regulations of 1974: "Order of April 10, 1974 relating to thermal insulation and automatic adjustment of heating installations in residential buildings", generally it can vary from 0.5 to 3 Watts / m 3 < and °C.

[0035] In the remainder of the description, the expression "building premises" corresponds to a part of a building which may consist of one or more rooms. The premises could, for example, correspond to offices or a home. Thus, the expression "volume of the premises" corresponds to the volume of one or more rooms in a building.

[0036] The expression "individualized heating costs" corresponds to heating costs calculated, generally for a room in a building, from the actual consumption of the room, this consumption being established from devices which determine the quantity of heat or cold consumed within each room.

[0037] The expression "thermal simulation model" may correspond, within the scope of the invention, to a model configured to virtually represent the building and describe its behavior in the face of stresses related to the climate or the behavior of users (these stresses may be modeled stochastically). In particular, the expression "dynamic thermal simulation of the building" may correspond, within the scope of the invention, to a model configured to calculate the temporal evolution of the thermal state of a building. In particular, it makes it possible to determine, at all chosen times of a simulation, the temperature at a certain number of points of the elements composing it and which evolve according to the different laws governing thermal exchanges (convection, conduction, radiation, change of state).Thus, a thermal simulation model makes it possible to estimate the thermal needs of a building by taking into account the building envelope and its inertia, the various thermal inputs, the behavior of the occupants and the local climate.

[0038] The expression "local solar radiation" may correspond in the context of the present invention to the quantity of solar energy received per unit of surface. It may for example be expressed in Watts per square meter (Watt / m 2 < ).

[0039] For the purposes of the invention, the term "correlation model" or "algorithm" means a finite sequence of operations or instructions for calculating a value from one or more input values. The implementation of this finite sequence of operations makes it possible, for example, to assign a value Y, such as a label Y, to an observation described by a set of characteristics or parameters. Xthanks for example to the implementation of a function f , likely to reproduce Y having observed X. Y = f X + e with : e : noise or measurement error.

[0040] For the purposes of the invention, the term "supervised machine learning model" means a correlation model generated automatically from data, called observations, which have been labeled.

[0041] For the purposes of the invention, the term "unsupervised machine learning model" means a correlation model generated automatically from data, called observations, which have not been labeled.

[0042] “Process”, “calculate”, “determine”, “display”, “transform”, “extract”, “compare” or more broadly “executable operation”, within the meaning of the invention, means an action performed by a device or processor unless the context indicates otherwise. In this regard, operations refer to actions and / or processes of a data processing system, for example a computer system or an electronic computing device, which manipulate and transform data represented as physical (electronic) quantities in the memories of the computer system or other devices for storing, transmitting or displaying information. These operations may be based on applications or software.

[0043] The terms or phrases "application", "software", "program code", and "executable code" mean any expression, code or notation, of a set of instructions intended to cause data processing to perform a particular function directly or indirectly (e.g., after a conversion operation to other code). Examples of program code may include, but are not limited to, a subroutine, a function, an executable application, source code, object code, a library, and / or any other sequence of instructions designed for execution on a computer system.

[0044] For the purposes of the invention, the term "processor" means at least one hardware circuit configured to execute operations according to instructions contained in a code. The hardware circuit may be an integrated circuit. Examples of a processor include, but are not limited to, a central processing unit, a graphics processor, an application-specific integrated circuit (ASIC), and a programmable logic circuit.

[0045] The term "electronic device" means any device comprising a processing unit or a processor, for example in the form of a microcontroller cooperating with a data memory, possibly a program memory, said memories being able to be dissociated. The processing unit cooperates with said memories by means of an internal communication bus.

[0046] For the purposes of the invention, the term "coupled" means connected, directly or indirectly, with one or more intermediate elements. Two elements can be coupled mechanically, electrically or linked by a communication channel.

[0047] “Precise” or “precision”, “reliable” or “reliability”, “accurate” or “accuracy” means repeatable and exact measurements. Furthermore, this means that the measurements are error-free or at least have an error rate of less than, for example, 5%, preferably 2% and more preferably less than 1%. Furthermore, the expression “improved accuracy” may correspond, within the meaning of the invention, to very accurate consumption; the value of which has a precision with an error rate of less than, for example, 5%, preferably 2% and more preferably less than 1%.

[0048] There are individual thermal energy meters (CET), but these systems are expensive and complex to install (one meter is required per hot water loop and their installation requires plumbing work). There are also heat cost allocators (RFC) based on measuring the surface temperature of radiators. However, these systems are not accurate and are complex to install (they must be installed on each radiator). Neither CET nor RFC provide fairness for the occupants of a building.

[0049] The inventors have developed a solution that allows, without installing heavy electronic devices, to determine the amount of thermal energy supplied to a room in a building over a given period.

[0050] In particular, they developed a determination solution taking into account a heat loss coefficient of the premises having been calculated from a thermal simulation model of the building taking into account the geometry of the premises and the composition of the walls. Furthermore, if the premises include joinery, the thermal simulation model of the building also takes into account the composition of these joinery. In addition, preferably, it can also take into account data representative of local solar radiation over a given period. Such a solution makes it possible to combine the precision of a solution using thermal simulation implementing heavy calculation systems with the reactivity and lightness of a solution based on a heat loss coefficient.Indeed, attempts have been made to integrate thermal simulation models directly into processes for determining the quantity of thermal energy supplied to a room, but these solutions were not sufficiently precise and generally too cumbersome to implement.

[0051] Thus, according to a first aspect, the invention relates to a method 100 for determining a quantity of thermal energy supplied to a room 20 of a building 2 as illustrated according to an example in figure 1 .

[0052] The quantity of thermal energy supplied to the room 20 corresponds, for example, to the quantity of energy supplied by a collective thermal management device 30. The collective thermal management device 30 generally corresponds to a collective gas boiler. However, it may also correspond to another heating system, possibly electric, of the heat pump type, or even a heat network.

[0053] In particular, the quantity of thermal energy supplied determined may be relative. However, preferably, the quantity of thermal energy supplied determined will be expressed in an international unit of measurement for thermal energy such as watt hours. The quantity of thermal energy supplied may, for example, correspond to a monthly quantity.

[0054] Preferably, the room 20 for which a quantity of thermal energy supplied will be determined corresponds to an individual room, and even more preferably a dwelling. The room may be defined in particular by a geometry. The geometry of the room may correspond to the dimensions of the walls forming said room. These dimensions may also refer to the dimensions of the joinery and may include the orientation of the walls forming the room. Preferably, the geometry of the room 20 will include the surface area of ​​the walls and possibly the surface area of ​​the joinery, their positions in the building 2 and their orientations. In addition, the room may be defined in particular by the composition of the walls and possibly by the composition of the joinery. Indeed, depending on the dimensions and characteristics of the materials used, the behavior of the room and therefore the quantity of thermal energy to be supplied may vary.

[0055] The determination method 100 according to the invention advantageously makes it possible to calculate a quantity of thermal energy supplied over a given period.

[0056] The given period will correspond, for example, to a week, a month, a quarter, a half-year or even a year. As will be detailed later, the present invention makes it possible to take into account fluctuations associated with climatic conditions (e.g. solar radiation) and the given period may preferably correspond to a period of time over which a single heat loss coefficient is used. Advantageously, the given period is a calendar period and is preferably associated with the seasons.

[0057] In the context of the present invention, the determination of a quantity of thermal energy supplied to a room 20 of a building 2 comprises the use of: a plurality of ambient temperature values ​​of the building, a temperature value outside the building 2, a volume of the room 20 of the building and a heat loss coefficient of the room.

[0058] The ambient temperature values ​​of the building may be provided by temperature sensors dedicated to the determination process or temperature sensors integrated into systems already installed in the building. The ambient temperature sensors will preferably be temperature sensors connected to a communication network (e.g., internet, wifi ®< , sigfox ®< , LoRa ®< , LoRaWAN ®< , Zigbee ®< , Z-Wave ®< , Enocean ®< , 3G / 4G / 5G). The ambient temperature values ​​of the building may preferably include several temperatures of a room to be studied and temperatures of rooms adjacent to the room to be studied (i.e. for which a quantity of supplied thermal energy must be determined) or of common areas adjacent to the room to be studied.

[0059] An ambient temperature of room 20 may correspond to temperatures measured at least every hour in several locations (i.e. rooms) of room 20. The ambient temperature values ​​used for room 20 may also correspond to averages or medians.

[0060] In particular, the determination may involve the use of at least two locations for measuring the ambient temperature of the room 20 (preferably a day zone and a night zone).

[0061] The determination method may use one or more temperature values ​​outside the building 2. The building's outside temperature value(s) may be provided by temperature sensors dedicated to the determination method or temperature sensors integrated into systems already installed around or on the building. The outside temperature sensors will preferably be temperature sensors connected to a communication network (e.g., internet, wifi ®< , sigfox ®< , LoRa ®< , LoRaWAN ®< , Zigbee ®< , Z-Wave ®< , Enocean ®< , 3G / 4G / 5G). Alternatively, the building's outside temperature values ​​may correspond to local outside temperature values ​​measured or calculated for a geographical area in which the building 2 is located.The building's outdoor temperature values ​​may also correspond to estimated local outdoor temperature values ​​for a geographic area in which building 2 is located.

[0062] The volume of the room 20 of the building will generally correspond to a known or calculated value considering the geometry of the building and the room in particular. In certain embodiments, the volume of the room may correspond to several volumes each associated with one or more spaces of the room. For example, the determination of the quantity of thermal energy supplied to a room 20 may take into consideration a particular volume for each ambient temperature value of the room used. A rather hot zone associated with temperature values ​​may be associated with a sub-volume of the room 20 while a rather cool zone will be associated with other temperature values ​​and with another sub-volume of the room 20.

[0063] In the context of the present invention, the determination of the quantity of thermal energy supplied to a room will be based on a heat loss coefficient of the room calculated from a thermal simulation model 41 of the building 2.

[0064] Calculating the heat loss coefficient of the premises from a thermal simulation model 41 of building 2 makes it possible to obtain a heat loss coefficient of the premises that is much more precise and accurate than heat loss coefficients calculated or estimated by other means.

[0065] Preferably, the thermal simulation model 41 of the building used to calculate the heat loss coefficient of the premises is a dynamic thermal simulation model of the building. Indeed, a dynamic thermal simulation model will be based on a precise description of the geometry and composition of the walls. Furthermore, if the premises include joinery, the thermal simulation model of the building also takes into account the composition of these joinery. This will allow consideration of the physical phenomena involved (heat exchanges by convection, conduction, radiation) and thus a precise calculation of the quantity of thermal energy supplied to the premises.

[0066] There are other methods such as the 3CL method for calculating conventional housing consumption used for producing energy performance certificates. However, these methods are based on a coarser description of heat loss surfaces and a coarser modeling of heat exchanges. For example, the 3CL method does not take into account dynamic aspects, unlike the present invention, which is preferably based on a dynamic thermal simulation model of the building. It only allows for the calculation of annual heating / cooling / DHW consumption, but not, for example, temperature changes over time. In addition, it is only an analysis of the building without taking into account uses.In fact, it will be less precise than the method according to the present invention, which preferably allows calculations to be carried out at hourly time steps, both on consumption and interior temperatures while being able to take into account usage.

[0067] Thus, the thermal simulation model of the building advantageously takes into account the geometry of the premises, the composition of the walls, possibly the composition of the joinery and the stresses to which the building is subjected, such as preferably the climate and uses.

[0068] In the context of the invention, a dynamic thermal simulation (DTS) model will make it possible to calculate the temporal evolution of the thermal state of a building using an approximate numerical model of it. This model then makes it possible to obtain at all chosen times of the simulation, the temperature at a certain number of points of the building and which evolve according to the different laws governing thermal exchanges (convection, conduction, radiation, change of state).

[0069] Thus, a dynamic thermal simulation model within the framework of the invention makes it possible to estimate the thermal requirements (heating and cooling requirements) and the temperatures in each zone of the building in operation. This model takes into account the building envelope and its inertia (from a description of the internal and external walls of the building), the exchange flows between thermal zones, the various thermal inputs, the behavior of the occupants and the local climate. Furthermore, advantageously, when the model is used to estimate actual energy consumption, the calculation can also take into account the energy systems (production system, types of emitters, etc.).

[0070] Preferably, the thermal simulation model is a multi-zone model, more preferably with finite volumes and even more preferably reduced by modal analysis. Such a configuration makes it possible to reduce the calculation time by a factor of three. In addition, at each time step, the model is configured to determine the heating, cooling and / or temperature requirements in each zone of the building. Advantageously, the model is configured to integrate exchanges between zones, for example between rooms 20. In addition, the model is advantageously configured to take into account the thermal inertia at each wall.

[0071] Furthermore, to further improve the precision and accuracy of the determination according to the invention, the thermal simulation model 41 of the building 2 used can advantageously also take into account data representative of local solar radiation.

[0072] Taking into account local solar radiation data, particularly by period of time (e.g. given period), makes it possible to take into account the energy contribution of solar radiation, whatever form it may take, direct or indirect.

[0073] In particular, the local solar radiation data include horizontal global radiation and horizontal diffuse radiation. These values, for the same building 2, may vary greatly depending on the period considered. Furthermore, for the same period, the influence of solar radiation on two premises with different geographical locations (e.g. location and / or orientation) may be significantly different. Thus, it is very advantageous to use, within the framework of the invention, a thermal simulation model 41 of the building 2 taking into account local solar radiation data for the period studied (i.e.: time period) or data representative of local solar radiation for the period studied.

[0074] Local solar radiation data or representative local solar radiation data may have been constructed from local historical data and preferably are modeled from local historical data. Indeed, measurements of some radiation values ​​are particularly expensive to obtain if they have to be obtained for each of the buildings studied.

[0075] Alternatively, representative local solar radiation data were obtained from at least one instrumental device or computer server containing local solar radiation data.

[0076] We will now describe in more detail different embodiments of a method for determining a quantity of thermal energy supplied to a room 20 of a building 2 over a given period according to the invention.

[0077] As illustrated in the figure 1, a determination method according to the invention may comprise a step 110 of generating a thermal simulation model of the building.

[0078] There are many tools for generating a thermal simulation model of the building. 110. Preferably, the thermal simulation model of the building 2 is constructed from a computer solution combining one or more libraries dedicated to the elements necessary for describing a building and a modeling module for graphically describing the building 2 studied in 3D. Thus, it is possible to create digital models of the building 2 containing all the information on the materials, joinery, equipment and energy performance.

[0079] Beyond these aspects, the generation and use of a dynamic thermal simulation model of the building will allow for much better results. Indeed, it is possible to use modeling solutions capable of also integrating environmental impacts and in particular factors associated with light radiation.

[0080] Preferably, the thermal simulation model 41 of the building 2 used in the context of calculating the heat loss coefficient of the premises is a dynamic thermal simulation model which may include characteristics of surrounding solar masks.

[0081] In particular, the thermal simulation model 41 of building 2 used in the calculation of the heat loss coefficient of the premises includes characteristics of the composition of the walls and possibly the composition of the joinery. It may advantageously also include characteristics of air renewal flow rate and / or distribution losses of the heating network.

[0082] In addition, the thermal simulation model 41 of the building 2 is a dynamic thermal simulation model comprising characteristics relating to the occupancy of the building such as occupancy schedules and / or an occupancy rate. It may further comprise characteristics such as specific electrical uses, temperature setpoints for heating or air conditioning, and / or heating or air conditioning schedules.

[0083] Also, the thermal simulation model 41 of the building 2 is advantageously a dynamic thermal simulation model configured to take into account the heat exchanges between the rooms 20, 20b of the same building 2.

[0084] As illustrated in the figure 1 , a determination method according to the invention may advantageously include a calibration step 120 of the thermal simulation model of the building. Such a step will make it possible to increase the precision of the method and therefore the value of the quantity of thermal energy supplied determined.

[0085] The calibration step 120 can be carried out from a first measurement period and a calibration algorithm, preferably a Bayesian calibration algorithm, so that the predictions of consumption and interior temperatures, at the hourly time step, of the thermal simulation model of the building reproduce the measurements as closely as possible.

[0086] The calibration step 120 can make it possible to make the thermal simulation model more reliable so as to ensure that it reproduces as accurately as possible the thermal behavior of the real building 2. The objective of this step is to obtain a reliable and precise representation of the real behavior of the building by reconciling in situ measurements (from ambient and outside temperature data) and outputs from the thermal simulation model (temperatures and consumptions simulated by the model).

[0087] Thus, this step allows on the one hand to integrate the measurements coming from a digital model generated by the thermal simulation model and on the other hand to calibrate the model by taking into account sources of uncertainty and errors that can explain the differences between the simulated data and the measurements. Advantageously, the estimation of the error is based on the use of a Bayesian calibration algorithm.

[0088] Thus, this calibration step 120 may result in a modification of the parameters of the thermal simulation model and this modification will be reflected in the value of the thermal loss coefficient of the premises generated for a given period.

[0089] In the context of the present invention, the thermal simulation model 41 of the building 2 can be calibrated, as a function of the periods considered, under sunshine conditions varying by at least 50 Wh / m 2 <, for example at least 100 Wh / m 2 <; of outside temperature varying by at least 10 °C, preferably at least 30 °C. In addition, it can be calibrated, as a function of the periods considered, under outside relative humidity conditions varying by at least 15%. Indeed, the inventors have determined that these particular variables with these amplitude levels were the most suitable in the context of the present invention for providing a determined quantity of thermal energy that is the most accurate.

[0090] Furthermore, a method for determining a quantity of thermal energy supplied to a room 20 of a building 2 over a given period, in particular if it includes a prior calibration step, may include a step of calculating an uncertainty score of the value of the determined quantity of thermal energy supplied.

[0091] In particular, this uncertainty score can be calculated from a variable generated during the calibration process and representing the difference between the predictions of consumption and interior temperatures at the hourly time step of the thermal simulation model of the building and the measurements.

[0092] As illustrated in the figure 1 , a determination method according to the invention may advantageously include a step 130 of calculating at least one coefficient of heat loss of the premises from a thermal simulation model of the building.

[0093] The heat loss coefficient, for each room 20, can therefore be calculated from a non-calibrated or preferably calibrated thermal simulation model of the building. The heat loss coefficient thus estimated can advantageously correspond to the expected losses in a room 20 under variable operating conditions of the building 2. This calculation minimizes the error made in estimating losses. This error can be quite significant when a purely static calculation is carried out which does not take into account the inertia of the building or the dynamic aspect of the stresses it undergoes (weather, uses, etc.).

[0094] As mentioned, ideally, the calculation of the heat loss coefficient requires taking into account many elements such as the type, surface area and orientation of the various walls (glazed or not) of the dwelling, but also the common contacts, the air renewal, the various thermal bridges as well as the free contributions linked to solar gains and uses (metabolic heat, specific electricity). In this context, the use of a calibrated thermal simulation model to estimate this heat loss coefficient takes on its full interest. The objective of calculating the heat loss coefficient of the room is to be able to estimate the value of the coefficient G of room 20 while taking into account the different sources of uncertainty linked to building 2 and its environment (uncertainties linked to the building, systems, uses and weather conditions).

[0095] An uncertainty analysis can be carried out to quantify the variability of the outputs of the calibrated thermal simulation model of the building (and in particular the consumptions) induced by the uncertainty on the input factors of the model. According to one embodiment, to carry out this uncertainty analysis, each static parameter is defined by a probability density function on the one hand and variability models of uses and meteorological conditions are used, on the other hand. For example, more than 500 simulations can be carried out on the basis of these data so as to obtain a dispersion of the consumption results for each room 20, and one or more heat loss coefficients G of the room can then be recalculated on the basis of these simulations.

[0096] Thus, preferably, the calculation 130 of at least one coefficient of heat loss of the premises from a thermal simulation model of the building may comprise, for a premises 20, the calculation of at least one coefficient of heat loss from a plurality of simulations in which the values ​​of geometry of the premises 20 and composition of the walls and possibly the composition of the joinery, have been modified so as to integrate uncertainties associated with the building 2.

[0097] More preferably, the calculation 130 of at least one heat loss coefficient of the premises from a thermal simulation model of the building may comprise, for a premises 20, the calculation of at least one heat loss coefficient from a plurality of simulations in which, in addition, the usage values ​​and / or meteorological conditions (temperatures and sunshine) have been modified so as to integrate uncertainties associated with the uses and the meteorological conditions. In these embodiments, the values ​​may be modified according to probability density functions and / or variability models.

[0098] The use of uncertainty analysis to calculate the G coefficients makes it possible to provide the invention with greater accuracy compared to conventional methods without complicating its operation.

[0099] Furthermore, the determination method according to the invention may include the use of several heat loss coefficients, for the same room 20, each calculated from the thermal simulation model of the building. More preferably, the determination method according to the invention will include the use of at least four heat loss coefficients. Advantageously, these heat loss coefficients will be specific to a given period and for example to a calendar period. More preferably, within the framework of the method, the heat loss coefficient of a room 20 is selected from at least two, preferably at least three and more preferably at least four heat loss coefficients, each of the heat loss coefficients corresponding to a period of the year.Even more preferably, the heat loss coefficient is selected from at least 12 heat loss coefficients (monthly coefficient) for the same room 20.

[0100] A determination method according to the invention may include a step 130 of calculating several heat loss coefficients from a single thermal simulation model of the building or from different thermal simulation models of the building.

[0101] A determination method according to the invention will comprise a determination of a quantity of thermal energy supplied to a room 20 of a building 2 over a given period.

[0102] As already explained, the determination takes into account a plurality of ambient temperature values ​​of the building, one or more values ​​of temperatures outside the building 2, a volume of the room 20 of the building and a heat loss coefficient of the room.

[0103] This determination may be likened to the calculation carried out by a heating cost allocator using a measurement of the ambient temperature of the premises. In particular, the determination according to the invention may correspond to an estimate, based on several ambient temperature values ​​and one or more outside temperature values, of the quantity of energy consumed privately in the premises to be allocated.

[0104] The calculation could, for example, take the following form: E = ∫ t = 0 t final G . V . Ta t − Te t dt with : E : the quantity of energy consumed privately in the room 20 studied Your : the ambient temperature of room 20 studied You : the outside temperature of building 2 V : the volume of room 20 studied G : the heat loss coefficient of room 20 studied t : the time step

[0105] Example of a formula with an adjustment variable: E = ∫ t = 0 t final G . V . Ta t − Te t + C ajustement ⋅ V ajustement t dt with : E : the quantity of energy consumed privately in the room 20 studied Your : the ambient temperature of room 20 studied You : the outside temperature of building 2 V : the volume of room 20 studied G : the heat loss coefficient of room 20 studied C adjustment : the adjustment coefficient linked to the adjustment variable V adjustment : the adjustment variable t : the time step

[0106] As shown, the present invention may be implemented without an adjustment variable, with one adjustment variable, or with multiple adjustment variables.

[0107] Advantageously, the determination may integrate the use of one or more adjustment coefficients calculated from the thermal simulation model of the building. The use of these adjustment coefficients in combination with adjustment variables representative of environmental factors makes it possible to further improve the accuracy of the model.

[0108] Thus, the method according to the invention advantageously comprises a step 140 of adjusting the quantity of thermal energy supplied, said adjustment step 140 comprising the use of one or more adjustment coefficients, each adjustment coefficient being applied to an adjustment variable.

[0109] The adjustment variable(s) may correspond to human or environmental factors that impact the thermal energy to be supplied to the premises. Thus, an adjustment variable may correspond to meteorological data such as atmospheric pressure or wind speed.

[0110] An adjustment variable could be measured directly at the level of room 20 of building 2. Alternatively, an adjustment variable could be measured in a geographical area in which building 2 is located. Another way of obtaining an adjustment variable could be to model it, for example probabilistically from statistics, or to generate it from machine learning algorithms.

[0111] An adjustment variable is preferably calculated based on weekly, daily, or hourly data. These values ​​can be integrated to generate a monthly value. In particular, if the loss coefficient is monthly, the adjustment variable is also monthly.

[0112] For example, the determination of the quantity of thermal energy supplied to a room 20 may integrate the use of an adjustment variable selected from: a variable representative of heat losses linked to window openings and / or natural ventilation; and / or a variable representative of the use of additional heating. This makes it possible to integrate actual uses which are measured directly or modeled for each room.

[0113] Preferably, the determination of the quantity of thermal energy supplied to a room 20 incorporates the use of an adjustment variable representative of the electricity consumption of the room, including additional heating.

[0114] The determination may also include the use of an adjustment variable representative of heat losses due to the opening of at least one window 24 in the room.

[0115] In this case and preferably, the adjustment variable representative of the heat losses linked to the window openings 24 can be calculated as a function of the changes in ambient temperatures of the room and the humidity in the room. In particular, a time series of the indoor and outdoor temperatures and humidities can be used. This thus makes it possible to obtain a precise estimate of the durations of window opening without having to position a sensor on each of the windows.

[0116] An adjustment coefficient will correspond, for example, to a value used with an adjustment variable value when determining the quantity of thermal energy supplied to a room 20. In particular, this adjustment coefficient is used in an equation for determining the quantity of thermal energy supplied to a room 20. This adjustment coefficient value is preferably calculated from a thermal simulation model of the building 2.

[0117] An adjustment coefficient value may, for example, be updated at a frequency less than or equal to 3 months, preferably less than or equal to 2 months and more preferably less than or equal to 1 month. Thus, it is possible to take into account as best as possible the values ​​of the adjustment variables according to the season or more generally the usual climate at a given period. Preferably, the method therefore uses, for the same adjustment coefficient, several predetermined adjustment coefficient values ​​according to the given period.

[0118] In the context of the method for determining the quantity of thermal energy supplied to a room 20 according to the invention, several adjustment variable / adjustment coefficient pairs will advantageously be used. Thus, it will be possible to calculate a more accurate quantity of thermal energy supplied in comparison with other systems.

[0119] In particular, the method according to the invention may include the use of several adjustment coefficients whose predetermined value is selected according to the given period. For example, an adjustment coefficient associated with an adjustment variable representative of the heat losses due to the openings of at least one window 24 of the premises will not have the same value in July compared to November. Preferably, the method according to the invention includes the use of at least two adjustment coefficients, more preferably at least three adjustment coefficients and even more preferably at least four adjustment coefficients, for the same premises 20.

[0120] Furthermore, the use of adjustment variables and adjustment coefficients reduces the complexity of calculating the amount of thermal energy supplied while ensuring high accuracy. In addition, this allows the thermal simulation model to be used only once to produce numerous adjustment coefficient values ​​and room heat loss coefficient values ​​so as to adapt flexibly to the time of year considered.

[0121] As mentioned, one of the advantages of the method according to the invention is to be able to calculate a confidence interval on the determined quantity of thermal energy supplied.

[0122] The calculation of this confidence interval can be based on the use of uncertainty values ​​on the adjustment variables used when determining the quantity of thermal energy supplied.

[0123] The uncertainty values ​​on the adjustment variables can be predetermined values ​​or values ​​calculated based on several adjustment variable values ​​for the same period (e.g. standard deviation). Alternatively, or in addition, the calculation of this confidence interval can be based on the calculation of an uncertainty value on the quantity of thermal energy supplied calculated in terms of the adjustment variables used.

[0124] The performance of the present invention is particularly illustrated by Table 1 below. [Table 1] Accuracy of prediction Poorly insulated DPE < C Well insulated DPE >= C Without STD 50 50 + radiation 55 55 + additional heating 53 53 + opening 51 51 With Coeff calculated from a STD without radiation 70 76 With Coeff calculated from an STD with radiation 80 86 + additional heating 89 92 + opening 82 88 + opening + additional heating 100 100

[0125] Table 1 illustrates how the accuracy of the prediction is improved compared to conventional methods for determining the amount of thermal energy supplied to a room 20 of a building 2 over a given period. Indeed, without the use of a heat loss coefficient calculated from a dynamic thermal simulation model, the results are far from reality and are not able to correctly take into account external factors such as radiation, window openings or additional heating.

[0126] Furthermore, Table 1 illustrates that taking into account radiation in the context of a room heat loss coefficient calculated from a dynamic thermal simulation improves accuracy, even more so when the building is well insulated. Furthermore, Table 1 illustrates that taking into account the adjustment variables (with their adjustment coefficient) window openings ("opening") and use of supplementary heating ("supplementary heating") further improves accuracy.

[0127] As has been presented, the calculation of the heat loss coefficient of the room G is preferably carried out via a propagation of uncertainties varying numerous parameters which may include radiation and internal contributions (see uses). This makes it possible to take into account the average impact (the expectation) of these variables (radiation, internal contributions, etc.).

[0128] In order to further adjust the consumption calculation and get even closer to real energy consumption, the solution according to the invention proposes to integrate adjustment variables. This makes it possible to adjust the consumption calculation by considering the real value of the variables. For example, during an adjustment associated with radiation, the calculation of the heat loss coefficient G of the room makes it possible to calculate an average consumption by considering an average effect of radiation (e.g. 1200 kWh / m 2< over the year, or 120 kWh / m 2< over the month). Adding an adjustment variable on the radiation will make it possible to modify the consumption calculation. For example, if in reality it is 1300 kWh / m 2< which is measured over the year (or 100 kWh / m 2< over the period). The calculation of the value of the heat loss coefficient G of the room will be modified thanks to the adjustment coefficient of the radiation and the adjustment variable (e.g. adjusted consumption = average cons+ Coeff × 1300 1200 − 1 ).

[0129] As illustrated in the figure 1 , a determination method according to the invention may advantageously include a step 150 of analyzing electrical consumption.

[0130] In particular, the use of supplementary heating can be detected from an analysis of electricity consumption. The analysis of this electricity consumption may correspond to an estimate using statistical models, a calculation based on an analysis of electricity bills or even measurements via electronic electricity meters.

[0131] So, as illustrated in the figure 1 , a determination method according to the invention may advantageously include a step 160 of quantifying the use of the additional heating device.

[0132] This step of quantifying 160 the use of the additional heating device may preferably be based in particular on data from electronic electricity meters or on the analysis of documents containing consumption reports such as electricity bills.

[0133] As illustrated in the figure 1 , a determination method according to the invention may advantageously include a step 170 of calculating individualized heating costs based on a determined quantity of thermal energy supplied.

[0134] In particular, the step 170 of calculating individual heating costs may be a step of calculating individual heating costs 170, for a room 20 of the building 2, as a function of a determined quantity of thermal energy supplied, by the collective thermal management device 30, to said room 20.

[0135] Preferably, the method may include a step of calculating a predicted comfort cost, for a room, as a function of a predetermined expected interior temperature, local variables and adjustment coefficients.

[0136] As illustrated in the figure 1 , a determination method according to the invention may further comprise a step of calculating expected gains in terms of quantity of thermal energy supplied to a room as a function of modification of adjustment variable values.

[0137] Thus, when it implements the use of an adjustment variable, the method according to the invention may include a step of calculating what the quantity of thermal energy supplied to a room could be as a function of other values ​​of adjustment variables. For example, it could be calculated what the gain in terms of quantity of thermal energy supplied to a room would be if the number of window openings were reduced by two,

[0138] Such a step can then lead to the generation of recommendations for energy improvement actions and could enable users to reduce heating costs.

[0139] As illustrated in the figure 1 , a determination method according to the invention may further comprise a step 180 of transmitting data to an electronic display device 50.

[0140] The transmitted data may, for example, include the determined quantity of thermal energy supplied.

[0141] According to another aspect, the invention relates to a distributor 10 for determining a quantity of thermal energy supplied, by a collective thermal management device 30, to a room 20 of a building 2 for a given period.

[0142] The distributor 10 may correspond to an electronic device and it may comprise one or more processors. Preferably, a distributor 10 according to the invention comprises one or more processors configured to execute a method according to the invention, preferably the method 100 for determining a quantity of thermal energy supplied, and its various embodiments, preferred, advantageous or not.

[0143] Distributor 10 may be installed in building 2. However, as illustrated in figure 2, the distributor 10 may be located outside the building 2 housing the premises 20 studied. For example, the distributor 10 may correspond to a functional module of an electronic device, such as a functional module of a computer server. Under these conditions, the same computer server 40 may host several distributors 10, 10b each dedicated to a building 2. As illustrated in figure 2, the computer server 40 may also host the thermal simulation models 41, 41b having been used to calculate the heat loss coefficients used by the distributors 10, 10b. Alternatively and preferably, it is not necessary for the distributor to store the thermal simulation models used for the generation of the heat loss coefficients. Indeed, once a plurality of thermal coefficients have been generated, at least one per room 20, the model is no longer essential for determining a quantity of thermal energy supplied. This allows the applicant to propose a lightweight and responsive solution for real-time calculations without sacrificing the accuracy of the result.

[0144] The distributor 10, in particular one or more processors of the distributor 10 are preferably configured to use a plurality of ambient temperature values ​​of the building 2, a temperature value outside the building 2, a volume of the room 20 and a heat loss coefficient of the room, to determine a quantity of thermal energy supplied, by a collective thermal management device 30, to a room 20 of a building 2 for a given period.

[0145] Advantageously, the distributor 10 uses a heat loss coefficient of the room calculated from a thermal simulation model 41 of the building 2 taking into account the geometry of the room 20 and the composition of the walls and possibly the composition of the joinery of said room 20.

[0146] In addition, the thermal simulation model 41 of building 2 can take into account representative data of local solar radiation over the given period.

[0147] As has been preferably described, local solar radiation data are not measured, but taken into account, as they are generated on the basis of modeling using local historical data.

[0148] The distributor 10 may in particular comprise one or more processors configured to calculate distribution data for distributing the financial costs associated with collective heating based on all of the quantities of energy supplied determined for each of the premises 20.

[0149] According to another aspect, the invention relates to a system 1 for distributing heating costs. The system 1 for distributing heating costs may comprise an electronic device equipped with one or more processors. Preferably, a system 1 for distributing heating costs according to the invention comprises one or more processors configured to execute a method according to the invention, preferably the method 100 for determining a quantity of thermal energy supplied, and its various preferred, advantageous or not embodiments.

[0150] In particular, a system 1 for distributing heating costs may comprise a distributor 10 according to the invention. As mentioned, this distributor may be supported by a computer server 40. Furthermore, the computer server 40 may be configured to communicate with an electronic display device 50 or any other electronic device capable of receiving data relating to heating costs or the quantity of thermal energy supplied to a room.

[0151] Furthermore, a heating cost allocation system 1 according to the invention may comprise several electronic temperature measuring devices 22. These devices may be electronic temperature measuring devices 22 dedicated to the heating cost allocation system 1 according to the invention or devices already present in a dwelling and configured to communicate data to the heating cost allocation system 1 according to the invention. For example, these devices may be integrated into smoke detectors or connected thermostats.

[0152] As illustrated in the figure 2 , the electronic temperature measuring devices 22 may be positioned in different zones or rooms 21 of a room 20. For example, several electronic temperature measuring devices 22 may be positioned, in the same room 20, in rooms 21 comprising or not additional heating means 23.

[0153] In particular, these electronic temperature measuring devices 22 may comprise sensors in addition to a temperature sensor. In particular, an electronic temperature measuring device 22 may comprise a humidity sensor. It may also comprise a CO2 sensor. With such sensors, the positioning of this electronic temperature measuring device 22 in a room comprising a window 24 may allow the generation of sufficient data to estimate the opening time of the window 24.

[0154] The electronic temperature measuring devices 22 will preferably be configured to send measured data to the distributor 10, directly or indirectly. The temperatures may be measured at least once a day, preferably at least twice a day, more preferably at least every hour and more preferably at least every 30 minutes. However, these data may be stored on the electronic temperature measuring device 22 and may be sent directly or indirectly to the distributor 10 at least once a month, preferably at least every week, more preferably at least every day and more preferably at least six times a day.

[0155] The electronic temperature measuring devices 22 will preferably be configured to send measured data directly to the distributor 10 via wireless communication or a wired connection, more preferably via wireless communication. Alternatively, this data may be sent via a wireless connection or a wired connection to a data collector 60.

[0156] The data collector 60 may in particular be located in a common part of the building 2 or outside the building 2. When the system 1 comprises a data collector 60, it may be configured to implement a first wireless communication, implemented in the building 2, between the data collector 60 and the electronic temperature measurement devices 22 associated with each room on the one hand and a second wireless communication, from the data collector 60 of the building 2 to a distributor 10, generally centralized and accessible via a communication network such as the Internet.

[0157] The invention may be the subject of numerous variants and applications other than those described above. In particular, unless otherwise indicated, the different structural and functional characteristics of each of the implementations described above should not be considered as combined and / or closely and / or inextricably linked to each other, but on the contrary as simple juxtapositions. Furthermore, the structural and / or functional characteristics of the different embodiments described above may be the subject in whole or in part of any different juxtaposition or any different combination.

Claims

1. A method (100) for allocating energy costs in a collective building (2) including a plurality of individual premises (20) and at least one collective heat management device (30), the method (100) being applicable to all the individual premises (20) of the collective building (2) and comprising determining an amount of thermal energy supplied by the at least one collective heat management device (30), to at least one individual premises (20) of the collective building (2), over a given period, said determination, carried out by one or more processors, including the use of, - a simulation model, - a plurality of ambient temperature values of the collective building (2), the plurality of ambient temperature values of the collective building (2) including a plurality of temperatures of the individual premises(20), - one or more values of temperatures outside the collective building (2), - a volume of the individual premises (20), and - at least one heat loss coefficient of the individual premises (20), and - the calculation (130) of the at least one heat loss coefficient of the individual premises (20) on the basis of the simulation model characterized in that the simulation model is a thermal simulation model (41) of the collective building (2) which virtually represents the collective building (2) and which takes into account, for each individual premises (20), a geometry of the individual premises (20) and a composition of the walls of the individual premises (20), as well as the heat exchange between various premises of the collective building (2); and in that the method uses a plurality of temperatures of premises or common spaces of the collective building (2) which are adjacent to the individual premises (20).

2. The method (100) according to claim 1, wherein when said individual premises (20) is additionally defined by a joinery composition, the method being characterised in that the thermal simulation model (41) of the collective building (2) additionally takes into account the joinery composition.

3. The method (100) according to any one of claims 1 to 2, characterised in that the thermal simulation model (41) of the collective building (2) additionally takes into account data representative of local solar radiation over the given period.

4. The method (100) according to any one of claims 1 or 3, characterised in that the thermal simulation model (41) of the collective building (2) is a dynamic thermal simulation model (41) of the collective building (2).

5. The method (100) according to any one of claims 1 to 4, characterised in that the at least one heat loss coefficient of the individual premises (20) is selected from at least two predetermined heat loss coefficients, said predetermined heat loss coefficients each corresponding to a time period.

6. The method (100) according to any one of claims 1 to 5, characterised in that the determination of the amount of thermal energy supplied by the at least one collective heat management device (30) to the at least one individual premises (20) of the collective building (2), includes the use of one or more adjustment coefficients, each adjustment coefficient being applied to an adjustment variable measured or calculated for the given period.

7. The method (100) according to claim 6, characterised in that the adjustment variable is selected from: a variable representative of heat losses linked to the window openings, a variable representative of the level of occupation of the individual premises (20), a variable representative of the meteorological conditions and / or a variable representative of the use of auxiliary heating.

8. The method (100) according to any one of claims 6 to 7, characterised in that the adjustment variable is measured directly in the individual premises (20) of the collective building (2), modelled in a probabilistic manner on the basis of statistics, or reconstructed on the basis of measurements of machine learning algorithms.

9. The method (100) according to any one of claims 1 to 8, characterised in that it includes a calibration step (120) of the thermal simulation model of the collective building (2), said calibration step (120) is preferably carried out on the basis of a first measurement period of ambient and outside temperature values and of a Bayesian calibration algorithm such that temperatures calculated by the thermal simulation model (41) of the collective building (2) reproduces as closely as possible the measured temperature values.

10. The method (100) according to any one of claims 1 to 9, characterised in that the ambient temperature of the individual premises (20) corresponds to the temperatures measured in a plurality of locations in the individual premises (20) .

11. The method (100) according to any one of claims 1 to 10, characterised in that it includes a step of calculating individualised heating costs (170) for an individual premises (20) of the collective building (2), as a function of a determined amount of thermal energy supplied by the collective heat management device (30) to said individual premises (20).

12. The method (100) according to claim 11, characterised in that the step of calculating individualised heating costs (170) takes into account characteristics of orientation and positioning of the individual premises (20) in the collective building (2), in such a way as to correct the disparities between the premises or common spaces of the collective building (2).

13. The method (100) according to any one of claims 1 to 12, characterised in that the calculation (130) of at least one heat loss coefficient of the individual premises (20) on the basis of a thermal simulation model of the building can include, for an individual premises (20), the calculation of at least one heat loss coefficient on the basis of a plurality of simulations in which the values for the geometry of the individual premises (20) and composition of the walls have been modified in such a way as to incorporate the uncertainties associated with the collective building (2).

14. The method (100) according to any one of preceding claims when they are dependent on claim 2, characterised in that the calculation (130) of at least one heat loss coefficient of the individual premises (20) on the basis of a thermal simulation model (41) of the collective building (2) can include, for an individual premises (20), the calculation of at least one heat loss coefficient on the basis of a plurality of simulations in which the joinery composition values have been modified in such a way as to incorporate the uncertainties associated with the collective building (2).

15. The method (100) according to any one of claims 1 to 14, characterised in that the calculation (130) of at least one heat loss coefficient of the individual premises (20) on the basis of a thermal simulation model (41) of the building can include, for an individual premises (20), the calculation of at least one heat loss coefficient on the basis of a plurality of simulations in which, in addition, values for use and / or meteorological conditions have been modified in such a way as to incorporate the uncertainties associated with the uses and with the meteorological conditions.

16. An allocator(10) of energy costs in a collective building (2) including a plurality of individual premises (20) and at least one collective heat management device (30), the allocator being applicable to all the individual premises (20) of the collective building (2) and comprising the determination of an amount of thermal energy supplied by the at least one collective heat management device (30) to at least one individual premises (20) of the collective building (2) over a given period, said allocator (10) comprising one or more processors configured to determine the amount of thermal energy supplied by using, - a simulation model, - a plurality of ambient temperature values of the collective building (2), the plurality of ambient temperature values of the collective building (2) including a plurality of temperatures of the individual premises (20), - one or more values of temperatures outside the collective building (2), - a volume of the individual premises (20), and - at least one heat loss coefficient of the individual premises (20), said allocator (10) being characterised in that said allocator (10) is configured to calculate (130) the at least one heat loss coefficient of the individual premises (20) on the basis of the simulation model, in that the simulation model is a thermal simulation model (41) of the collective building (2) which virtually represents the collective building (2) and which takes into account, for each individual premises (20), a geometry of the individual premises (20) and a composition of the walls of the individual premises (20), as well as the heat exchange between various premises of the collective building (2); and in that said allocator (10) comprising one or more processors configured to determine the amount of thermal energy supplied by further using a plurality of temperatures of premises or common spaces of the collective building (2) which are adjacent to the individual premises (20).

17. The allocator (10) according to claim 16, wherein when said individual premises (20) is additionally defined by a joinery composition, the system being characterised in that the thermal simulation model (41) of the collective building (2) additionally takes into account the joinery composition.

18. The system (1) for allocating heating costs including an allocator (10) according to any one of claims 16 to 17.