Method, apparatus, and system for determining the amount of thermal energy delivered to a room in a building over a predetermined period of time

Abstract

The invention relates to a method (100) for determining an amount of thermal energy to be supplied by a collective thermal management device (30) to a premises (20) in a building (2) over a given period of time, said determination being performed by one or more processors and comprising using a plurality of ambient temperature values ​​of the building, external temperature values ​​of the building (2), a volume of the premises (20) in the building and a heat loss coefficient, said method being characterized in that the heat loss coefficient is calculated (130) from a thermal simulation model (41) of the building, taking into account the shape of the premises and the configuration of the walls. The invention also relates to an allocator (10) for determining the amount of thermal energy to be supplied by the collective thermal management device (30) and to a system (1) for allocating heating costs, comprising an allocator (10) according to the invention.

Description

[Technical Field]

[0001] The present invention relates to the field of buildings, and in particular to the management of thermal energy in buildings. In particular, the present invention relates to a method for determining the amount of thermal energy to be supplied to premises in a building over a predetermined period of time. Furthermore, the present invention relates to an apparatus and a system for determining the amount of thermal energy to be supplied to premises in a building, in particular for the allocation of energy costs. [Background technology]

[0002] The known prior art on which the present invention was developed is described below.

[0003] Building on the goals set out in the Paris Agreement, the European Union has set ambitious targets for the energy transition by 2030 and 2050, with a focus on promoting renewable energy, energy efficiency and reducing greenhouse gas emissions.

[0004] In 2016, buildings accounted for almost 40% of final energy consumption in the European Union. Therefore, although there are large differences in building performance between countries, this sector has considerable room for progress in terms of energy efficiency. In particular, heating and cooling in the residential sector, services and industry account for around 50% of the EU's primary energy supply (European Commission - Information Sheet - 16 February 2016). In particular, the origin of primary energy for residential heating is mainly dependent on fossil fuels (82% in 2016), while 68% of total gas imports to Europe come from the heating and cooling sector.

[0005] Heating buildings is therefore a key target for achieving the energy transition targets for 2030 and 2050.

[0006] Therefore, buildings have been the subject of numerous studies and models aimed at characterizing their energy performance. For example, dynamic thermal simulations (DTS) of buildings have been proposed. These studies are primarily aimed at building owners and allow them to simulate the energy performance of various envisaged technological solutions, such as CMV, heat generators, and even artificial lighting management solutions. These studies based on dynamic thermal simulations of buildings have also been implemented in construction project programs to analyze summer thermal comfort without air conditioning and to quantify heating needs.

[0007] Such simulations have also been proposed to estimate predicted energy consumption. However, buildings are complex, non-stationary energy systems. Therefore, significant variations between simulated and measured consumption can be observed due to errors in input data, occupancy scenarios, or environmental conditions. Furthermore, these simulations are typically based on heavy software clients and not on embedded systems configured to perform analysis in real time. Recently, modeling buildings and their systems to optimize energy management has been proposed (Hugo Viot. Modelisation et instrumentation d'un batiment et de ses systems pour optimiser sa gestion energetique. University of Bordeaux, 2016. In French. NNT:2016BORD0349; tel. +33 (0)1503037). In particular, DTS models are considered too heavy, so it has been proposed to build models with small dimensions so that they can be embedded in controllers to improve energy management. Nevertheless, such simplified models have performance that could be improved. Furthermore, given that one building does not behave in the same way as another, its performance depends heavily on the learning base used, and the model may need to be trained for each new building.

[0008] Therefore, where this consumption cannot be efficiently predicted, monitoring is carried out, which, especially in the context of RT2012, requires a minimum monthly measurement of energy consumption by location and by energy type. However, this is only monitoring and not a measure to promote the energy transition.

[0009] In this regard, in apartments with collective heating, the division of heating costs is usually proportionate to the percentage of ownership or based on the surface area of ​​the apartment. Such division does not encourage thrift, since heating costs are calculated according to the surface area of ​​the apartment or the percentage of ownership, even though heating consumption may vary from apartment to apartment.

[0010] Therefore, individualized heating bills are being implemented in France, not only to assess improvements in the energy performance of buildings but also to hold residents accountable. Individualized heating bills (or allocations) allow each apartment to pay according to the amount it actually consumes. Residents are therefore encouraged to control their consumption and avoid wasting energy. Individualized heating bills have achieved energy savings of around 15% on average and have allowed for bill reductions for residents who wish to adjust their consumption (ADEME, September 2019, ISBN 979-10-297-1399-6).

[0011] To be able to know what each occupant has consumed, remotely readable measuring devices must be installed. Currently, there are two main devices on the market: individual thermal energy meters (TEMs) and heat cost allocators (HCAs).

[0012] TEMs are installed at the entrance of each dwelling and display the actual heating consumption, allowing residents to monitor their own consumption. HCAs are installed when installing TEMs is technically impossible or not economical. HCAs use measurements of the surface temperature of the heating element. They are placed on each radiator in the dwelling. "Traditional" HCAs have three main operating modes. These are defined by standard NFEN834: i) single-probe measurement (a probe that measures the surface temperature of the heating element or the temperature of the heat transfer fluid); ii) two-probe measurement (one probe measures the surface temperature of the heating element or the temperature of the heat transfer fluid and the other probe measures the ambient temperature); iii) multi-probe measurement (several probes to measure the average temperature of the heat transfer fluid and one probe for the ambient temperature).

[0013] The installation results of these solutions have fallen far short of expectations. More specifically, many buildings do not allow for individualized heating costs, mainly due to the complexity of installing these systems.

[0014] Furthermore, these systems for allocating heating costs are unable to provide a reliable calculation of the energy consumed in relation to the location of the dwelling. More specifically, it is proposed to calculate heating costs based on a fixed portion of 30%, with the possibility of incorporating correction factors to take into account unfavourable thermal conditions (houses located on the north side have less solar inflow, houses on the top floor have greater heat losses, etc.). However, these measures are unable to accurately take into account the consumption of the dwelling. Therefore, strong inequities remain for residents depending on their location.

[0015] Furthermore, these systems remain vulnerable to "heat theft." More specifically, dwellings with lower heating capacity located in the center of a building will benefit from heat transfer from adjacent, hotter dwellings. Therefore, the inequities associated with the use of individual heat energy meters and the allocation of actual heating costs remain.

[0016] Therefore, there is a need for a solution that can effectively (e.g., in real time and accurately) determine residential thermal energy consumption, and in particular the amount of thermal energy provided to the building premises by collective thermal management devices. Furthermore, such a solution should also allow for more accurate calculation of heating costs, thereby enhancing fairness in heating payment in collective buildings. Summary of the Invention [Problem to be solved by the invention]

[0017] The present invention aims to remedy these drawbacks of the prior art. In particular, the present invention aims to propose a method for determining the amount of thermal energy to be supplied to a building premises over a given period of time. This method is capable of determining such an amount precisely and accurately, and does so without the need for the installation of expensive dedicated equipment. Furthermore, taking into account the thermal simulation model of the building and the ability to perform allocation taking into account the ambient temperature, a compensation system can be implemented, which improves fairness between users and ultimately takes into account the level of thermal comfort rather than the level of energy consumption.

[0018] Furthermore, the invention aims to propose an allocator for determining the amount of thermal energy to be supplied to the premises of a building by a collective thermal management device, said allocator being able to operate on the basis of simple measurements of the ambient temperature of the building and the outside air temperature in order to determine the amount of thermal energy to be supplied to the premises of the building by the collective thermal management device. [Means for solving the problem]

[0019] The present invention aims to overcome these drawbacks.

[0020] The present invention particularly aims to provide a method for allocating energy costs in a collective building comprising a plurality of 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 steps of: determining an amount of thermal energy supplied to at least one individual premises of the collective building by at least one collective thermal management device over a predetermined period of time; calculating at least one heat loss coefficient for each individual site based on a thermal simulation model of the collective building; The determining may be performed by one or more processors and may include: a thermal simulation model of an apartment building, which virtually represents the apartment building and takes into account, for each individual site, the shape of the individual sites, the wall configuration of the individual sites, and the heat exchange between the various sites of the apartment building; a plurality of ambient temperature values ​​for a collective building, including a plurality of temperatures for individual premises and a plurality of temperatures for premises or common spaces of the collective building adjacent to the individual premises; One or more values ​​of the temperature outside the collective building; individual lot volumes; and At least one heat loss coefficient for each individual premises.

[0021] The applicant has developed a method that allows for the rapid calculation of the amount of energy supplied to an individual premises, essentially based on internal and external ambient temperature data. In particular, in the context of the present invention, it is possible to use as many heat loss coefficients as there are premises. In particular, in a particularly advantageous embodiment, several heat loss coefficients can be associated with the same individual premises as a function of the period under consideration. This method can be used, in particular, to calculate heating costs and to incorporate the concept of fairness between users of the same complex.

[0022] According to other optional features of the method, said method may optionally include one or more of the following features, either alone or in combination:

[0023] If individual sites are additionally defined by the fixture and fixture configuration, the thermal simulation model of the complex building is a model that further takes the fixture and fixture configuration into account. In this way, by directly incorporating the fixture and fixture configuration when building the thermal simulation model of the complex building, it is possible to improve performance without imposing a burden on the calculations.

[0024] The thermal simulation model of an apartment building further takes into account data representing local solar radiation over a given period of time. In this way, by directly incorporating data representing solar radiation when building the thermal simulation model of an apartment building, performance can be improved without increasing the computational burden.

[0025] The thermal simulation model of the multi-unit building is a dynamic thermal simulation model of the multi-unit building. The use of a dynamic thermal simulation model makes it possible to obtain an improved accuracy of the determined heat supply using the method according to the invention.

[0026] The heat loss coefficient for an individual premises is selected from at least two predetermined heat loss coefficients, each of which corresponds to a period of time, so that it is possible to select a heat loss coefficient that allows more accurately determining the amount of heat energy to be supplied, for example as a function of the season or month.

[0027] Determining the amount of thermal energy to be supplied by at least one collective thermal management device to at least one individual site of the collective building includes the use of one or more adjustment factors, each of which is applied to an adjustment variable measured or calculated for a predetermined period of time. Preferably, the adjustment factors are calculated based on a dynamic thermal simulation model of the collective building. Thus, it is possible to adjust the amount of energy to be supplied, which is determined as a function of additional parameters that best represent the use of the individual site. This allows for a more accurate determination of the amount of thermal energy to be supplied.

[0028] The adjustment variables are selected from the following: variables representing heat loss associated with window openings, variables representing the occupancy level of the individual premises, variables representing weather conditions (e.g., extreme weather conditions), and / or variables representing the use of auxiliary heating. This allows incorporating actual usage, either measured directly or modeled for each individual premises, without burdening the model, resulting in greater accuracy.

[0029] The adjustment variables are either measured directly at individual sites, modeled probabilistically based on statistics, or reconstructed based on measurements from machine learning algorithms.

[0030] For each adjustment factor, the method includes calculating a plurality of adjustment factor values, each as a function of a period of the year, and the adjustment factor value used to determine the amount of thermal energy to be supplied is a function of the predetermined period. For example, there are adjustment factor values ​​calculated for each month as a function of time. The value corresponding to the month under consideration is used.

[0031] The method includes a step of calibrating the thermal simulation model of the multi-unit building, which is preferably performed based on a first measurement period and a Bayesian calibration algorithm, so that the hourly consumption and internal temperature predictions of the thermal simulation model of the multi-unit building reproduce the measured values ​​as accurately as possible.

[0032] The method includes a step of calibrating the thermal simulation model of the collective building, which step is preferably carried out based on a first measurement period of ambient and external temperature values ​​and a Bayesian calibration algorithm, so that the temperatures calculated by the thermal simulation model of the collective building reproduce the measured temperature values ​​as accurately as possible.

[0033] The ambient temperature of an individual site corresponds to the temperature measured at multiple locations within the site. The various locations can correspond to different rooms.

[0034] The method includes calculating an individual heating cost for an individual premises as a function of the determined amount of thermal energy supplied to said individual premises by the collective thermal management device.

[0035] The step of calculating the individual heating costs takes into account the orientation and location characteristics of the individual premises within the complex, making it possible to correct disparities between premises and common spaces in the complex. This introduces the principle of fairness into the allocation of heating costs, making it possible to correct imbalances between premises and common spaces in the complex (e.g. different orientations, different floors, etc.). Furthermore, it allows tenants to pay for the part of the heating consumption for which they are actually responsible, i.e. the heating temperature.

[0036] The method includes calculating a confidence interval for the determined amount of thermal energy. Preferably, said calculation of the confidence interval includes use of at least one uncertainty value for one or more adjustment variables used during the determination of the amount of thermal energy to be delivered.

[0037] Calculating at least one heat loss coefficient for an individual site based on the thermal simulation model of the collective building may include calculating, for the individual site, the at least one heat loss coefficient based on multiple simulations in which the geometric values, wall configurations, and optionally the fixture configurations of the individual site are varied in a manner that incorporates uncertainties associated with the collective building.

[0038] Calculating at least one heat loss coefficient for the individual site based on the thermal simulation model of the multi-unit building may include calculating, for the individual site, the at least one heat loss coefficient based on multiple simulations in which values ​​for the use and / or weather conditions are further varied in a manner that incorporates uncertainties associated with the use and weather conditions.

[0039] According to a second object, the present invention relates to an allocator of energy costs in a collective building comprising a plurality of 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 determining an amount of thermal energy to be supplied to at least one individual premises of the collective building by at least one collective thermal management device, said allocator comprising one or more processors configured to determine the amount of thermal energy to be supplied using: a thermal simulation model of an apartment building, which virtually represents the apartment building and takes into account, for each individual site, the shape of the individual sites, the wall configuration of the individual sites, and the heat exchange between the various sites of the apartment building; a plurality of ambient temperature values ​​for a collective building, including a plurality of temperatures for individual premises and a plurality of temperatures for premises or common spaces of the collective building adjacent to the individual premises; One or more values ​​of the temperature outside the collective building; individual lot volumes; and At least one heat loss coefficient for the individual premises, representing the proportion of heat energy supplied to the individual premises that is transferred to premises adjacent to the individual premises or to common spaces; The allocator is configured to calculate at least one heat loss coefficient for each individual site based on a thermal simulation model of the complex building.

[0040] In certain implementations, if the individual sites are further defined by fixture configurations, the allocator is characterized in that the thermal simulation model of the collective building further takes the fixture configurations into account.

[0041] Preferably, the allocator is such that the thermal simulation model of the collective building is a model that further takes into account data representing local solar radiation over a given period of time.

[0042] As explained below, an allocator has multiple heat loss coefficients, each dedicated to a particular site. Additionally, a site may be associated with multiple heat loss coefficients, each dedicated to a given time period.

[0043] According to a third object, the invention relates to a system for allocating heating costs, including an allocator according to the invention. In particular, such an allocation system allows tenants to pay for the portion of the heating consumption for which they are actually responsible, i.e. the heating temperature.

[0044] Other characteristics and advantages of the present invention will be better understood from the following description, taken in conjunction with the accompanying drawings, which are given for purposes of illustration and are in no way limiting. [Brief explanation of the drawings]

[0045] [Figure 1] FIG. 1 illustrates a method according to one embodiment of the present invention. [Figure 2] 1 illustrates a system for determining the amount of thermal energy provided by a collective thermal management device, according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0046] For purposes of illustration, the figures are not necessarily drawn to scale, particularly in terms of thickness.

[0047] Aspects of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the invention.

[0048] In the figures, flow diagrams 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 a flow diagram or block diagram may represent a system, device, module, or code, which is comprised of one or more executable instructions for implementing one or more specified logical functions. In certain 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 concurrently or in the reverse order, depending on the functionality involved. Each block in the block diagrams and / or flow diagrams, and combinations of blocks in the block diagrams and / or flow diagrams, may be implemented by a specialized hardware system that performs the specified functions or actions or executes a combination of specialized hardware and computer instructions.

[0049] Below, we provide an overview of the invention and related terms, then present the shortcomings of the prior art, and finally show in more detail how the present invention overcomes them.

[0050] The expression "heat loss coefficient", also known as the building's total volume heat loss coefficient, corresponds to the overall performance of the insulation in terms of heat loss per degree difference between the internal and external environment. The building's total volume heat loss coefficient expresses the heat loss per unit heated volume. It is generally expressed in watts per cubic meter (Watts / m) for a change of one degree Celsius between the outdoor and indoor temperatures. 3 The coefficient G was introduced in the 1974 Thermal Regulations: "Order of April 10, 1974 on Thermal Insulation and Automatic Control of Heating Installations in Residential Buildings." Generally, it is between 0.5 and 3 watts / m 3 and °C.

[0051] In the remainder of the description, the expression "area within a building" corresponds to a part of a building consisting of one or more rooms. The area corresponds, for example, to an office or a residence. The expression "volume of the area" therefore corresponds to the volume of one or more rooms within a building.

[0052] The expression "individual heating costs" generally corresponds to heating costs calculated based on the actual consumption of the premises within the building, which consumption is established based on a device that determines the amount of heating or cooling consumed within each premises.

[0053] In the context of the present invention, the expression "thermal simulation model" may correspond to a model configured to virtually represent a building and describe its behavior when faced with stresses related to the climate or the behavior of users (these stresses are modeled probabilistically). In particular, the expression "dynamic thermal simulation of a building" in the context of the present invention may correspond to a model configured to calculate the evolution of the thermal state of a building over time. This notably makes it possible to determine, at every selected time of the simulation, the temperatures of a certain number of points of the elements that constitute the elements and that evolve according to the different laws governing heat exchange (convection, conduction, radiation, change of state). The thermal simulation model is thus able to estimate the thermal demand of the building, taking into account the building shell and its inertia, the different heat inputs, the behavior of the occupants, and the local climate.

[0054] The expression "local solar radiation" in the context of the present invention can correspond to the amount of solar energy received per unit surface area, for example in watts per square meter (Watts / m 2 ) can be expressed as

[0055] The terms "correlation model" or "algorithm" are to be understood within the meaning of the present invention as a finite sequence of operations or instructions that allow a value to be calculated based on one or more input values. The implementation of this finite sequence of operations makes it possible to attribute a value Y, such as a label Y, to an observation described by a set of features or parameters X used, thus implementing, for example, a function f that reproduces Y given the observed X. Y=f(X)+e During the ceremony: e: noise and measurement error.

[0056] The term "supervised machine learning model" should be understood within the meaning of the present invention to mean a correlation model that is automatically generated based on data called observations (which are labeled).

[0057] The term "unsupervised machine learning model" should be understood within the meaning of the present invention to mean a correlation model that is automatically generated based on data called observations (unlabeled).

[0058] Within the meaning of the present invention, the terms "processing," "calculating," "determining," "displaying," "converting," "extracting," "comparing," or more broadly, "executable operations" are to be understood as actions performed by a device or processor, unless the context dictates otherwise. In this regard, operations relate to the operation and / or processes of a data processing system, e.g., a computer system or electronic computing device (which processes and transforms data represented as physical (electronic) quantities in the memory of a computer system or other storage device, and transmits or displays information). These operations can be based on applications or software.

[0059] The terms or phrases "application," "software," "program code," and "executable code" mean a representation, code, or notation of a sequence of instructions intended to cause data processing to perform a particular function directly or indirectly (e.g., after translation into another code). Examples of program code may include, but are not limited to, subprograms, functions, executable applications, source code, object code, libraries, and / or other sequences of instructions designed to be executed on a computer system.

[0060] The term "processor" in the sense of the present invention is intended to mean at least one hardware circuit configured to perform operations according to instructions contained in a code. The hardware circuit may be an integrated circuit. Examples of processors include, but are not limited to, central processing units, graphic processors, application specific integrated circuits (ASICs), and programmable logic circuits.

[0061] The term "electronic device" is intended to mean any device comprising a processing unit or processor, for example in the form of a microcontroller cooperating with a data memory and optionally with a program memory (which may be separable), the processing unit cooperating with said memory via an internal communication bus.

[0062] The term "coupled" in the sense of the present invention shall mean directly or indirectly connected with one or more intermediate elements. Two elements may be mechanically or electrically coupled, or may be linked by a communication channel.

[0063] The terms "accurate" or "precision", "reliable" or "reliability", "highly accurate" or "precision" mean a reproducible and precise measurement. Furthermore, this means that the measurement is free of errors, or at least that the error rate of the measurement is, for example, less than 5%, preferably less than 2%, most preferably less than 1%. Furthermore, the expression "improved precision" may correspond within the meaning of the present invention to a very precise consumption value, which value has an accuracy of, for example, an error rate of less than 5%, preferably less than 2%, most preferably less than 1%.

[0064] Individual thermal energy meters (TEMs) exist, but these systems are expensive and complex to install (one meter per hot water loop is required and plumbing is required for installation). Heat cost allocation tools (HCAs) also exist that are based on measuring radiator surface temperatures. However, these systems are inaccurate and complex to install (especially since they must be installed on each radiator). Neither TEMs nor HCAs establish fairness for building occupants.

[0065] The inventors have developed a solution that makes it possible to determine the amount of thermal energy supplied to a building premises over a given period of time without installing heavy electronic equipment.

[0066] In particular, the inventors have developed a determination solution that takes into account the heat loss coefficient of a site calculated based on a thermal simulation model of a building, taking into account the site's shape and wall configuration. Furthermore, if the site includes fixtures, the building's thermal simulation model can also take into account the fixture configuration. Furthermore, preferably, data representing local solar radiation over a predetermined period of time can also be taken into account. Such a solution can combine the accuracy of solutions using thermal simulations using large-scale computing systems with the responsiveness and lightness of solutions based on heat loss coefficients. More specifically, while attempts have been made to directly incorporate thermal simulation models into methods for determining the amount of thermal energy to be supplied to a site, these solutions have not been sufficiently accurate and have generally been too heavy to implement.

[0067] Thus, according to a first aspect, the invention relates to a method 100 for determining the amount of thermal energy to be supplied to a premises 20 in a building 2, as shown according to the example of FIG.

[0068] The amount of thermal energy supplied to the premises 20 corresponds, for example, to the amount of energy supplied by the collective heat management device 30. The collective heat management device 30 generally corresponds to a collective gas boiler. Nevertheless, it can also correspond to another heating system (possibly electric), such as a heat pump or a heat network.

[0069] In particular, the determined amount of thermal energy to be supplied can be relative, although the determined amount of thermal energy to be supplied is preferably expressed in international measurement units of thermal energy, such as watt-hours. The amount of thermal energy to be supplied can correspond, for example, to an amount per month.

[0070] Preferably, the site 20 for which the amount of thermal energy to be supplied is determined corresponds to an individual site, more preferably to a dwelling. The site may be defined by a geometric shape. The geometric shape of the site may correspond to the dimensions of the walls forming said site. These dimensions may also refer to the dimensions of the fittings and may include the orientation of the walls forming the site. Preferably, the geometric shape of the site 20 includes the surface area of ​​the walls and, optionally, the surface area of ​​the fittings, as well as the location and orientation of the fittings within the building 2. Furthermore, the site is defined in particular by the configuration of the walls and, if necessary, by the configuration of the fittings. More specifically, the behavior of the site, and therefore the amount of thermal energy to be supplied, may vary as a function of the dimensions and properties of the materials used.

[0071] The determination method 100 according to the present invention advantageously allows for calculating the amount of thermal energy supplied over a predetermined period of time.

[0072] The predetermined period may correspond, for example, to a week, a month, a quarter, half a year, or even a year. As will be explained in more detail below, the invention can take into account differential variations related to climatic conditions (e.g., solar radiation), and the predetermined period may preferably correspond to a period during which a single, identical heat loss coefficient is used. Advantageously, the predetermined period is a calendar period, preferably related to a season.

[0073] In the context of the present invention, determining the amount of thermal energy to be supplied to the site 20 of building 2 involves using: a plurality of ambient temperature values ​​of the building, a temperature value outside building 2, the volume of the building site 20, and the heat loss coefficient of the site.

[0074] The building ambient temperature values ​​can be provided by a temperature sensor dedicated to the determination method or by a temperature sensor integrated into a system already installed in the building. The ambient temperature sensor is preferably a temperature sensor connected to a communication network (e.g. internet, wifi, sigfox, LoRa, LoRaWAN, Zigbee, Z-Wave, Enocean, 3G / 4G / 5G). The building ambient temperature values ​​preferably include a plurality of temperatures of the premises under investigation (i.e. those for which the amount of thermal energy to be supplied needs to be determined), of premises adjacent to the premises under investigation or of common spaces adjacent to the premises under investigation.

[0075] The ambient temperature of the premises 20 may correspond to temperatures measured at least hourly in multiple locations (i.e., rooms) of the premises 20. The ambient temperature value used for the premises 20 may also correspond to an average or median value.

[0076] In particular, the determination may involve the use of at least two ambient temperature measurement locations on the premises 20 (preferably a day zone and a night zone).

[0077] The determination method may use one or more values ​​of the temperature outside the building 2. The one or more temperatures outside the building may be provided by a temperature sensor dedicated to the determination method or by a temperature sensor integrated into a system already installed around or on the building. The external temperature sensor is preferably a temperature sensor connected to a communication network (e.g., internet, wifi, sigfox, LoRa, LoRaWAN, Zigbee, Z-Wave, Enocean, 3G / 4G / 5G). Alternatively, the temperature value outside the building may correspond to a local external temperature value measured or calculated for the geographical area in which the building 2 is located. The temperature value outside the building may also correspond to a local external temperature value estimated for the geographical area in which the building 2 is located.

[0078] The volume of the building site 20 generally corresponds to a known value or a value calculated taking into account, among other things, the building and site geometry. In certain embodiments, the site volume can correspond to multiple volumes, each associated with one or more spaces on the site. For example, determining the amount of thermal energy to be supplied to the site 20 can take into account a specific volume for each ambient temperature value of the site to be used. Hotter zones associated with a temperature value can be associated with a sub-volume of the site 20, while cooler zones would be associated with other temperature values ​​and different sub-volumes of the site 20.

[0079] In the context of the present invention, the determination of the amount of thermal energy to be supplied to the premises will be based on the heat loss coefficient of the premises calculated on the basis of the thermal simulation model 41 of the building 2 .

[0080] By calculating the site heat loss coefficient based on the thermal simulation model 41 of the building 2, a more precise and accurate site heat loss coefficient can be obtained than heat loss coefficients calculated or estimated by other biases.

[0081] Preferably, the building thermal simulation model 41 used to calculate the heat loss coefficient of the site is a dynamic building thermal simulation model. More specifically, the dynamic thermal simulation model is based on an accurate description of the wall geometry and configuration. Furthermore, if the site includes building fixtures, the building thermal simulation model can also take into account the configuration of these fixtures. This allows the relevant physical phenomena (heat exchange by convection, conduction, and radiation) to be taken into account, allowing for an accurate calculation of the amount of thermal energy supplied to the site.

[0082] There are other methods besides the 3CL method for calculating the consumption of conventional residential buildings, which are used to generate EPCs. However, these methods are based on a more coarse description of the heat loss surface area and a more coarse modeling of heat exchange. For example, the 3CL method does not incorporate consideration of dynamic aspects, unlike the present invention, which is preferably based on a dynamic thermal simulation model of the building. It can only calculate annual heating / cooling / DHW consumption, but cannot calculate, for example, temperature changes over time. Furthermore, it only applies to frame analysis and does not take into account the use. In fact, it is less accurate than the method of the present invention, which preferably performs calculations in hourly time steps for both consumption and internal temperature, taking into account the use.

[0083] Thus, a thermal simulation model of a building advantageously takes into account the geometry of the site, the wall configuration (and optionally the composition of the fittings) and the stresses to which the building is subjected (preferably climate, use, etc.).

[0084] In the context of the present invention, a Dynamic Thermal Simulation Model (DTS) makes it possible to calculate the evolution of the thermal state of a building over time, using a numerical model approximated to it, which makes it possible to obtain, at every selected time instant of the simulation, the temperatures of a certain number of points in the building, which vary according to the different laws governing heat exchange (convection, conduction, radiation, change of state).

[0085] Thus, a dynamic thermal simulation model in the context of the present invention is able to estimate the thermal demand (heating and cooling demand) and temperature of each zone of a building during operation. The model takes into account the building shell and its inertia (based on a description of the building's interior and exterior walls), the heat exchange flows between thermal zones, the various heat inputs, the behavior of the occupants and the local climate. Furthermore, advantageously, when using the model to estimate actual energy consumption, the calculations can also take into account the energy system (production system, type of emitter, etc.).

[0086] The thermal simulation model is preferably a multi-zone model, more preferably has a finite volume, and even more preferably is reduced by modal analysis. Such a configuration can reduce computation time by a factor of three. Furthermore, the model is configured to determine the heating and cooling needs and / or temperatures of each zone of the building at each time step. Advantageously, the model is configured to incorporate heat exchange between zones, e.g., between the grounds 20. Furthermore, the model is advantageously configured to consider the thermal inertia of each wall.

[0087] In addition, in order 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 further take into account data representative of local solar radiation.

[0088] In particular, by considering data on local solar radiation per period (e.g., a given period), energy input from solar radiation, whether direct or indirect, can be taken into account.

[0089] In particular, the local solar radiation data includes global horizontal solar radiation and diffuse horizontal solar radiation. These values ​​for a particular building 2 vary significantly as a function of the period considered. Also, in a particular period, the impact of solar radiation on two sites with different geographical situations (location, orientation, etc.) can be significantly different. Therefore, in the context of the present invention, it is highly advantageous to use a thermal simulation model 41 for the building 2 that takes into account local solar radiation data for the period (i.e., time period) under study or data representative of local solar radiation for the period under study.

[0090] The local solar radiation data or data representative of local solar radiation may be constructed based on, and preferably is modeled based on, local historical data. More specifically, if measurements of specific radiation values ​​need to be taken for each building under investigation, the measurements can be particularly expensive to obtain.

[0091] Alternatively, the data representing local solar radiation is obtained from at least one equipment device or from a computer server containing data relating to local solar radiation.

[0092] Various embodiments of the method according to the invention for determining the amount of thermal energy to be supplied to the premises 20 of a building 2 over a predetermined period of time will now be described.

[0093] As shown in FIG. 1, the determination method according to the present invention may include a step 110 of generating a thermal simulation model of the building.

[0094] Many tools exist for generating 110 a thermal simulation model of a building. The thermal simulation model of a building 2 is preferably built on the basis of a computer solution that combines one or more libraries dedicated to the elements necessary for the description of the building with a modeling module that allows to graphically describe in 3D the building 2 under investigation. It is thus possible to create a digital model of the building 2 that includes all information regarding materials, fittings, equipment and energy performance.

[0095] Beyond these aspects, it is possible to obtain better results by generating and using dynamic thermal simulation models of buildings, and more specifically, by using modeling solutions that can further incorporate factors related to environmental impacts, especially light radiation.

[0096] The thermal simulation model 41 of the building 2 used in connection with calculating the heat loss coefficient of the site is preferably a dynamic thermal simulation model that can include the characteristics of the surrounding solar mask.

[0097] In particular, the thermal simulation model 41 of the building 2 used in connection with the calculation of the heat loss coefficient of the premises includes characteristics of the wall composition and optionally the composition of the fittings. Advantageously, it may further include characteristics of the air renewal flow rate and / or the distribution losses of the heating network.

[0098] Additionally, the thermal simulation model 41 of the building 2 is a dynamic thermal simulation model that includes characteristics related to the occupancy of the building, such as the building's occupancy times and / or occupancy levels, and may further include characteristics such as specific electrical applications, heating or air conditioning temperature settings, and / or heating or air conditioning schedules.

[0099] Advantageously, the thermal simulation model 41 of the building 2 is also a dynamic thermal simulation model configured to take into account the heat exchange between the sites 20, 20b of a given building 2.

[0100] 1, the determination method according to the invention can advantageously include a step 120 of calibrating the thermal simulation model of the building. Such a step can improve the accuracy of the method and therefore of the value of the determined amount of thermal energy supplied.

[0101] The calibration step 120 can be performed based on a first measurement period and a calibration algorithm, preferably a Bayesian calibration algorithm, which allows the predictions of consumption and internal temperature at each hourly time step of the building thermal simulation model to more accurately reproduce the measurements.

[0102] The calibration step 120 makes it possible to increase the reliability of the thermal simulation model in such a way as to ensure that it reproduces as accurately as possible the actual thermal behavior of the building 2. The purpose of this step is to obtain a reliable and accurate representation of the actual behavior of the building by calibrating the on-site measurements (based on ambient and external temperature data) and the output of the thermal simulation model (temperatures and consumptions simulated by the model).

[0103] This step therefore makes it possible, on the one hand, to incorporate measurements from the digital model generated by the thermal simulation model, and, on the other hand, to calibrate the model taking into account sources of uncertainty and error that can explain the differences between the simulated data and the measurements. Advantageously, the error estimation is based on the use of a Bayesian calibration algorithm.

[0104] This calibration step 120 may therefore result in modifications to the parameters of the thermal simulation model, which modifications will be reflected in the site heat loss coefficient values ​​generated over a given period of time.

[0105] In the context of the present invention, the thermal simulation model 41 of the building 2 is 2 , e.g. at least 100Wh / m 2 It can be calibrated as a function of the period under consideration under varying sunlight conditions, with the outside temperature varying by at least 10° C., preferably by at least 30° C. Furthermore, it can be calibrated as a function of the period under consideration under conditions of outside relative humidity varying by at least 15%. More specifically, the inventors have determined that these particular variables with these amplitude levels are most suitable in the context of the present invention in order to provide the amount of thermal energy determined as accurately as possible.

[0106] Furthermore, the method for determining the amount of thermal energy supplied to the premises 20 of the building 2 over a particular period of time may include a step of calculating an uncertainty score for the value of the determined amount of thermal energy supplied, particularly if a prior calibration step is included.

[0107] In particular, this uncertainty score can be calculated based on variables generated during the calibration procedure and representing the deviation between the consumption predictions and the internal temperature at hourly time steps of the building's thermal simulation model and measurements.

[0108] As shown in FIG. 1, the determination method according to the invention advantageously comprises a step 130 of calculating at least one heat loss coefficient of the building based on a thermal simulation model of the building.

[0109] The heat loss coefficient for each site 20 can therefore be calculated based on an uncalibrated, or preferably calibrated, thermal simulation model of the building. The heat loss coefficient thus estimated can advantageously correspond to the expected heat losses within the site 20 under the fluctuating operating conditions of the building 2. This calculation minimizes errors in the heat loss estimation, which can be quite large if a purely statistical calculation is performed that does not take into account the inertia of the building relative to the dynamic aspects of the stresses to which it is subjected (weather, use, etc.).

[0110] As mentioned above, ideally, the calculation of the heat loss coefficient should take into account many factors, such as the type (glazed or not), surface area, and orientation of the various walls of the house, as well as adjacent contacts, air renewal, various thermal bridges, and even free inputs linked to solar input and use (metabolic heat, specific power). In this context, the use of calibrated thermal simulation models to estimate this heat loss coefficient is of great interest. The goal of calculating the heat loss coefficient of a site is to be able to estimate the value of the coefficient G for the site 20, taking into account the various sources of uncertainty related to the building 2 and its environment (uncertainties related to the frame, systems, use, and weather conditions).

[0111] An uncertainty analysis can be carried out with the aim of quantifying the variability of the outputs (particularly consumption) of the calibrated thermal simulation model of the building caused by uncertainties in the model's input coefficients. 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 a variability model of the use and weather conditions is used on the other hand. For example, based on these data, more than 500 simulations can be carried out, obtaining the variability of the consumption performance of each site 20, and based on these simulations, one or more heat loss coefficients G of the site can be recalculated.

[0112] Therefore, calculating 130 at least one heat loss coefficient for an individual site based on a thermal simulation model of the collective building may preferably include calculating at least one heat loss coefficient for an individual site 20 based on multiple simulations in which the geometric values, wall configuration, and optionally the fitting configuration of the individual site 20 are varied in a manner that incorporates uncertainties associated with the collective building 2.

[0113] More preferably, calculating 130 at least one heat loss coefficient for an individual site based on the building thermal simulation model may include calculating the at least one heat loss coefficient for the site based on multiple simulations in which values ​​of the use and / or weather conditions (temperature and / or solar radiation) are further varied in a manner that incorporates uncertainties associated with the use and weather conditions. In these embodiments, the values ​​may be modified according to a probability density function and / or a variation model.

[0114] The use of uncertainty analysis to calculate the coefficient G allows the present invention to provide greater accuracy compared to conventional methods without burdening the operation of the present invention.

[0115] Furthermore, the determination method according to the invention can include the use of multiple heat loss coefficients for the given premises 20, each calculated based on a thermal simulation model of the building. Preferably, the determination method according to the invention includes the use of at least four heat loss coefficients. Advantageously, these heat loss coefficients are specific to a given period, for example a calendar period. More preferably, in the context of the method, the heat loss coefficient for the premises 20 is selected from at least two, preferably at least three, more preferably at least four heat loss coefficients, each heat loss coefficient corresponding to a period of one year. Even more preferably, the heat loss coefficient is selected from at least 12 heat loss coefficients (monthly coefficients) for the given premises 20.

[0116] The determination method according to the invention may include a step 130 of calculating multiple heat loss coefficients based on a single and same thermal simulation model of the building or based on different thermal simulation models of the building.

[0117] The method according to the present invention comprises determining the amount of thermal energy to be supplied to the premises 20 in the building 2 over a predetermined period of time.

[0118] As previously explained, the determination takes into account a number of ambient temperature values ​​for the building, one or more temperature values ​​outside the building 2, the volume of the building site 20, and the heat loss coefficient of the site.

[0119] This determination can be compared to a calculation performed by a heating cost allocator using measurements of ambient temperature at the premises. In particular, a determination according to the present invention can correspond to an estimation of the amount of energy that will be personally consumed at the premises before being subject to allocation, based on a plurality of ambient temperature values ​​and one or more temperature values ​​outside.

[0120] For example, the calculation might take the form:

number

[0121] Example of an expression using tuning variables:

number

[0122] As discussed, the present invention can be implemented without tuning variables, with tuning variables, or with multiple tuning variables.

[0123] Advantageously, the determination may incorporate the use of one or more adjustment factors calculated based on a thermal simulation model of the building, which, when used in combination with adjustment variables representing environmental factors, can further improve the accuracy of the model.

[0124] Therefore, the method according to the invention advantageously comprises a step 140 of adjusting the amount of thermal energy supplied. Said adjustment step 140 comprises the use of one or more adjustment factors, each of which is applied to an adjustment variable.

[0125] The one or more adjustment variables may correspond to human or environmental factors that affect the thermal energy supplied to the premises. Thus, the adjustment variables may correspond to meteorological data such as atmospheric pressure or wind speed.

[0126] The adjustment variables may be measured directly within the site 20 of the building 2. Alternatively, the adjustment variables may be measured in the geographic area in which the building 2 is located. Another means of obtaining the adjustment variables is to model them probabilistically, for example based on statistics, or to generate them based on machine learning algorithms.

[0127] The adjustment variable is preferably calculated based on weekly, daily, or hourly data. These values ​​can be combined in a manner to produce a monthly value. In particular, if the heat loss coefficient is monthly, the adjustment variable will also be monthly.

[0128] For example, the determination of the amount of thermal energy supplied to site 20 may incorporate the use of adjustment variables selected from: variables representing heat losses associated with window openings and / or natural ventilation; and / or variables representing the use of auxiliary heating, thereby allowing the incorporation of actual usage, either measured directly or modeled for each site.

[0129] Preferably, the determination of the amount of thermal energy to be supplied to the premises 20 incorporates the use of a conditioning variable representing the premises' electricity consumption, including auxiliary heating.

[0130] This determination may also incorporate the use of an adjustment variable representing heat loss due to opening at least one window 24 in the building.

[0131] In this case, and in a preferred method, an adjustment variable representing the heat loss associated with the window opening 24 may be calculated as a function of the changes in the ambient temperature of the premises and the humidity within the premises. In particular, time series of indoor and outdoor temperatures and humidity may be used. This therefore allows for an accurate estimation of the duration for which the windows are open, without the need to place sensors on each window.

[0132] The adjustment factor corresponds to a value that is used, for example, together with the adjustment variable value in determining the amount of thermal energy to be supplied to the premises 20. In particular, the adjustment factor is used in a formula that determines the amount of thermal energy to be supplied to the premises 20. The value of the adjustment factor is preferably calculated based on a thermal simulation model of the building 2.

[0133] The adjustment factor values ​​may be updated, for example, every three months or less, preferably every two months or less, more preferably every month or less. Thus, it is possible to best account for the value of the adjustment variable as a function of season, or more generally, the usual climate for a particular period of time. Preferably, the method uses multiple predetermined adjustment factor values ​​for the same adjustment factor as a function of predetermined periods of time.

[0134] In connection with the method of the present invention for determining the amount of thermal energy to be supplied to the premises 20, a number of adjustment variable / adjustment factor pairs are advantageously used, which allows for a more accurate calculation of the amount of thermal energy to be supplied compared to other systems.

[0135] In particular, the method according to the invention may involve the use of multiple adjustment factors, the predetermined values ​​of which are selected as a function of a predetermined time period. For example, an adjustment factor associated with an adjustment variable representing heat loss through the opening of at least one window 24 on a premises will not have the same value in July as in November. Preferably, the method according to the invention involves the use of at least two adjustment factors, more preferably at least three adjustment factors, and even more preferably at least four adjustment factors for the same premises 20.

[0136] The use of adjustment variables and adjustment factors also allows for more accurate calculations of thermal energy supply and reduces the complexity of the calculations, which in turn allows a single use of the thermal simulation model to generate a large number of adjustment factor and heat loss coefficient values ​​for the site in a manner that is flexible and adaptable to the annual study period.

[0137] As mentioned above, one of the advantages of the method according to the invention is that it allows to calculate a confidence interval for the determined amount of thermal energy supplied.

[0138] The calculation of this confidence interval may depend on the use of uncertainty values ​​of the adjustment variables used during the determination of the amount of thermal energy to be delivered.

[0139] The uncertainty value of the adjustment variable can be a predetermined value or a value calculated as a function of multiple adjustment values ​​of the variable over the same period (e.g., standard deviation). Alternatively, or in addition, the calculation of this confidence interval can depend on the calculated amount of thermal energy delivered and the calculation of the uncertainty value of the adjustment variable used.

[0140] The performance of the present invention is particularly illustrated by Table 1 below.

[0141] [Table 1]

[0142] Table 1 shows how the accuracy of the prediction is improved compared to traditional methods for determining the amount of thermal energy supplied to the building 2 site 20 over a given period. Specifically, without using heat loss coefficients calculated based on a dynamic thermal simulation model, the results are far from reality and do not properly take into account external factors such as radiation, window openings, and auxiliary heating.

[0143] Furthermore, Table 1 shows that taking radiation into account for the site heat loss coefficient calculated based on the dynamic thermal simulation improves accuracy, and even more so if the building is well insulated. Furthermore, Table 1 shows that taking window openings ("Openings") and the use of auxiliary heating ("Auxiliary Heat") into account as adjustment variables (together with the adjustment factors) improves accuracy even further.

[0144] As mentioned above, the calculation of the heat loss coefficient for site G is preferably carried out via uncertainty propagation varying a number of parameters, which may include radiation and internal inputs (see application), which makes it possible to take into account the average impact (expectations) of these variables (radiation, internal inputs, etc.).

[0145] To further adjust the calculation of the consumption and bring it closer to the actual energy consumption, the solution according to the invention proposes to incorporate adjustment variables, which allow the calculation of the consumption to be adjusted taking into account the actual values ​​of the variables. For example, when adjusting for radiation, the heat loss coefficient of site G can be calculated to determine the average impact of radiation (for example, 1200 kWh / m2 per year). 2 , or 120 kWh / m per month 2 ) can be calculated. Adding an adjustment variable for radiation allows us to modify the calculation of consumption. For example, if this is actually 1300 kWh / m2 measured over a year, 2 (or 100kWh / m for the entire period 2 ) The calculation of the value of the heat loss coefficient G for the site is modified using the radiation adjustment factor and adjustment variable (e.g., adjusted consumption = average consumption + Coeff × (1300 / 1200-1)).

[0146] As shown in FIG. 1, the determination method according to the invention advantageously includes a step 150 of analysing the power consumption.

[0147] In particular, the use of auxiliary heating can be detected based on power consumption analyses 150. These power consumption analyses can correspond to estimations by statistical models, calculations based on analysis of electricity prices, or even measurements by electronic power meters.

[0148] Therefore, as shown in FIG. 1, the determination method according to the invention can advantageously include a step 160 of quantifying the use of additional heating devices.

[0149] This quantification step 160 of the use of additional heating devices is preferably based on an analysis of documents, including, inter alia, data from electronic electricity meters or consumption reports such as electricity bills.

[0150] As shown in FIG. 1, the determination method according to the invention can advantageously include a step 170 of calculating an individualized heating cost as a function of the determined amount of thermal energy supplied.

[0151] In particular, the step 170 of calculating the individual heating cost may be a step 170 of calculating the individual heating cost for the premises 20 of the building 2 as a function of the determined amount of thermal energy supplied to said premises 20 by the collective thermal management device 30.

[0152] Preferably, the method may include calculating a predicted comfort cost for the premises as a function of a predetermined expected internal temperature, local variables, and an adjustment factor.

[0153] As shown in FIG. 1, the determination method according to the invention may further comprise a step of calculating expected savings in the amount of thermal energy supplied to the premises as a function of the modification of the value of the adjustment variable.

[0154] Thus, when implementing the use of adjustment variables, the method according to the invention can include a calculation step of what the amount of heat energy supplied to the premises will be as a function of the value of the other adjustment variables, for example, it can be calculated how much savings will be made in terms of the amount of heat energy supplied to the premises if the number of window openings is reduced by two.

[0155] These steps may generate recommendations for energy improvement measures that may reduce the user's heating costs.

[0156] As shown in FIG. 1, the determination method according to the present invention may further include a step 180 of transmitting data to the electronic display device 50 .

[0157] The transmitted data may include, for example, a determined amount of heat energy to be delivered.

[0158] According to another aspect, the present invention relates to an allocator 10 for determining an amount of thermal energy to be supplied to a premises 20 in a building 2 by a collective thermal management device 30 for a predetermined period of time.

[0159] The allocator 10 may be an electronic device and may include one or more processors. Preferably, the allocator 10 according to the present invention includes one or more processors configured to execute the method according to the present invention, preferably the method 100 for determining the amount of thermal energy to be supplied, whether advantageous or not, and various preferred embodiments thereof.

[0160] The allocator 10 may be installed inside the building 2. However, as shown in FIG. 2, the allocator 10 may also be located outside the building 2 housing the site 20 under investigation. For example, the allocator 10 may be 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 multiple allocators 10, 10b, each dedicated to a specific building 2. As shown in FIG. 2, the computer server 40 may also host thermal simulation models 41, 41b used to calculate the heat loss coefficients used by the allocators 10, 10b. Alternatively, and preferably, the allocator does not need to store the thermal simulation model used to generate the heat loss coefficients. More specifically, once multiple thermal coefficients have been generated, at least one for each site 20, the model is no longer required for determining the amount of thermal energy to be supplied. This allows the applicant to propose a lightweight and responsive solution for real-time calculations without sacrificing the accuracy of the results.

[0161] To determine the amount of thermal energy to be supplied by the collective thermal management device 30 to the site 20 of the building 2 over a given period of time, the allocator 10, and in particular one or more processors of the allocator 10, are preferably configured to use a plurality of ambient temperature values ​​of the building 2, a temperature value outside the building 2, the volume of the site 20, and the heat loss coefficient of the site.

[0162] Advantageously, the allocator 10 uses a heat loss coefficient for the site calculated based on a thermal simulation model 41 of the building 2, which takes into account the geometry of the site 20, the wall configuration and optionally the fixture configuration of said site 20.

[0163] Additionally, the thermal simulation model 41 of the building 2 may take into account data representing local solar radiation over a given period of time.

[0164] As mentioned above, local solar radiation data is not measured but is preferably taken into account as it is generated based on a model using local historical data.

[0165] The allocator 10 may include one or more processors configured to calculate allocation data, in particular to allocate financial costs associated with collective heating as a function of all the amounts of supplied energy determined for each site 20.

[0166] According to another aspect, the present invention relates to a system 1 for allocating heating costs. The system 1 for allocating heating costs may include an electronic device with one or more processors. Preferably, the system 1 for allocating heating costs according to the invention includes one or more processors configured to execute the method according to the invention, preferably the method 100 for determining the amount of thermal energy to be supplied, whether advantageous or not, and various preferred embodiments thereof.

[0167] In particular, a system 1 for allocating heating costs may include an allocator 10 according to the present invention. As mentioned above, this allocator 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 other electronic device capable of receiving data relating to heating costs or the amount of thermal energy supplied to a premises.

[0168] Furthermore, the system 1 for allocating heating costs according to the present invention may include a plurality of electronic temperature measuring devices 22. These devices may be electronic temperature measuring devices 22 dedicated to the system 1 for allocating heating costs according to the present invention, or may be devices already present in the home that are configured to communicate data to the system 1 for allocating heating costs according to the present invention. For example, these devices may be integrated into smoke detectors or connected thermostats.

[0169] 2, electronic temperature measurement devices 22 may be located in various zones or rooms 21 of premises 20. For example, multiple electronic temperature measurement devices 22 may be located in rooms 21 within the same premises 20 that may or may not include auxiliary heating means 23.

[0170] In particular, these electronic temperature measuring devices 22 can include sensors in addition to temperature sensors. In particular, the electronic temperature measuring devices 22 can include humidity sensors. They can also include CO2 sensors. Using such sensors, placing the electronic temperature measuring device 22 in a room containing a window 24 can generate enough data to estimate the amount of time the window 24 has been open.

[0171] The electronic temperature measuring device 22 is preferably configured to transmit measurement data directly or indirectly to the allocator 10. The temperature may be measured at least once a day, preferably at least twice a day, more preferably at least hourly, more preferably at least every 30 minutes. Conversely, these data may be stored in the electronic temperature measuring device 22 and transmitted directly or indirectly to the allocator 10 at least once a month, preferably at least weekly, more preferably at least daily, more preferably at least six times a day.

[0172] The electronic temperature measurement device 22 is preferably configured to transmit measurement data directly to the allocator 10 via wireless or wired communication, most preferably via wireless communication. Alternatively, the data may be transmitted to the data collector 60 via a wireless or wired connection.

[0173] The data collector 60 may in particular be located in a common area of ​​the building 2 or outside the building 2. If the system 1 includes a data collector 60, the system 1 may be configured to implement, on the one hand, a first wireless communication carried out inside the building 2 between the data collector 60 and the electronic temperature measurement devices 22 associated with each premises, and, on the other hand, a second wireless communication from the data collector 60 of the building 2 to the allocator 10 (usually centralized and accessible via a communication network such as the Internet).

[0174] The present invention can include many alternatives and applications other than those described above. In particular, unless otherwise specified, the various structural and functional features of each of the above implementations should not be considered as being coupled and / or adjacent and / or closely related to one another, but rather as being simply juxtaposed. Furthermore, the structural and / or functional features of the various embodiments described above can be the subject of any different juxtaposition or any different combination, in whole or in part.

Claims

1. 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 thermal management device (30), the method (100) being applicable to all individual premises (20) of the collective building (2) and comprising determining an amount of thermal energy provided to at least one individual premises (20) of the collective building (2) over a predetermined period of time by at least one collective thermal management device (30), the determination being performed by one or more processors and comprising using: Simulation models; a plurality of ambient temperature values ​​for the collective building (2), the plurality of ambient temperature values ​​for the collective building (2), including a plurality of temperatures for the individual premises (20); one or more values ​​of the temperature outside the collective building (2); The volume of the individual lot (20); and At least one heat loss coefficient of the individual premises (20); The method (100) includes calculating (130) at least one heat loss coefficient for the individual site (20) based on the simulation model; the simulation model is a thermal simulation model (41) that virtually represents the complex building (2) and takes into account, for each individual site (20), the shape of the individual site (20), the wall configuration of the individual site (20), and the heat exchange between the various sites of the complex building (2); The method is characterized in that it uses a plurality of temperatures of a collective building (2) site or common space adjacent to the individual site (20).

2. 2. The method (100) according to claim 1, characterized in that if the individual site (20) is further defined by a fixture configuration, the method further comprises the thermal simulation model (41) of the complex building (2) taking into account the fixture configuration.

3. 3. The method (100) according to claim 1 or 2, characterized in that the thermal simulation model (41) of the apartment building (2) further takes into account data representative of local solar radiation over a given period of time.

4. 2. The method (100) according to claim 1, characterized in that the thermal simulation model (41) of the complex building (2) is a dynamic thermal simulation model (41) of the complex building (2).

5. 2. The method (100) of claim 1, wherein at least one heat loss coefficient for an individual premises (20) is selected from at least two predetermined heat loss coefficients, each of said predetermined heat loss coefficients corresponding to a time period.

6. The method (100) according to claim 1, characterized in that the determination of the amount of thermal energy supplied by at least one collective thermal management device (30) to at least one individual site (20) of the collective building (2) includes the use of one or more adjustment factors, each adjustment factor being applied to an adjustment variable measured or calculated over a predetermined period of time.

7. The method (100) according to claim 6, characterized in that the adjustment variables are selected from the following: variables representative of heat losses associated with window openings, variables representative of the occupancy level of the individual premises (20), variables representative of weather conditions and / or variables representative of the use of auxiliary heating.

8. 8. The method (100) according to claim 6 or 7, characterized in that the adjustment variables are measured directly on the individual sites (20) of the collective building (2), modeled probabilistically based on statistics, or reconstructed based on measurements of machine learning algorithms.

9. 2. The method (100) according to claim 1, characterized in that it comprises a step (120) of calibrating the thermal simulation model of the collective building (2), said step (120) being preferably carried out on the basis of a first measurement period of ambient and external temperature values ​​and a Bayesian calibration algorithm, so that the temperatures calculated by the thermal simulation model (41) of the collective building (2) reproduce the measured temperature values ​​as accurately as possible.

10. The method (100) of claim 1, characterized in that the ambient temperature of the individual premises (20) corresponds to temperatures measured at a plurality of locations within the individual premises (20).

11. 2. The method (100) of claim 1, characterized in that it comprises a step (170) of calculating the individual heating costs of the individual premises (20) of the collective building (2) as a function of the determined amount of thermal energy supplied to said individual premises (20) by the collective thermal management device (30).

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

13. 2. The method (100) of claim 1, wherein the calculation (130) of at least one heat loss coefficient for an individual site (20) based on a thermal simulation model of the building may include calculating, for the individual site (20), at least one heat loss coefficient based on a plurality of simulations in which values ​​of the shape and wall configuration of the individual site (20) are varied in such a way as to incorporate uncertainties related to the collective building (2).

14. 3. The method (100) of claim 2, characterized in that the calculation (130) of at least one heat loss coefficient for an individual site (20) based on a thermal simulation model (41) of the collective building (2) can include calculating, for the individual site (20), at least one heat loss coefficient based on a plurality of simulations in which values ​​of the building construction are changed in such a way as to incorporate uncertainties associated with the collective building (2).

15. 2. The method (100) of claim 1, wherein calculating (130) at least one heat loss coefficient for an individual site (20) based on the thermal simulation model (41) of the building can include calculating, for the individual site (20), at least one heat loss coefficient based on multiple simulations in which values ​​of the use and / or weather conditions are further varied in such a way as to incorporate uncertainties related to the use and weather conditions.

16. An energy cost allocator (10) in a collective building (2) including a plurality of individual sites (20) and at least one collective thermal management device (30), said allocator being applied to all individual sites (20) of the collective building (2) and comprising determining an amount of thermal energy to be supplied to at least one individual site (20) of the collective building (2) over a predetermined period of time by at least one collective thermal management device (30), said allocator (10) comprising one or more processors configured to determine the amount of thermal energy to be supplied using: Simulation models; a plurality of ambient temperature values ​​for the collective building (2), the plurality of ambient temperature values ​​for the collective building (2), including a plurality of temperatures for the individual premises (20); one or more values ​​of the temperature outside the collective building (2); The volume of the individual lot (20); and At least one heat loss coefficient of the individual premises (20); the allocator (10) is configured to calculate (130) at least one heat loss coefficient for each individual site (20) based on the simulation model; the simulation model is a thermal simulation model (41) that virtually represents the complex building (2) and takes into account, for each individual site (20), the shape of the individual site (20), the wall configuration of the individual site (20), and the heat exchange between the various sites of the complex building (2); The allocator (10) is characterized in that it includes one or more processors configured to further use a plurality of temperatures of the complex building (2) site or common space adjacent to the individual site (20) to determine the amount of thermal energy to be supplied.

17. The allocator (10) of claim 16, characterized in that if the individual sites (20) are further defined by a building construction, the system further takes into account the building construction in the thermal simulation model (41) of the complex building (2).

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

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