Method, apparatus, and system for determining the amount of thermal energy supplied to the interior of a building during a given period

Abstract

The present invention relates to a method (100) for determining the amount of thermal energy supplied to an premises (20) within a building (2) during a given period by a collective thermal management device (30), wherein the determination is performed by one or more processors including the use of a plurality of ambient temperature values ​​for the building, an external temperature value of the building (2), the volume of the premises (20) within the building, and a heat loss coefficient, wherein the method is characterized in that the heat loss coefficient is calculated (130) from a thermal simulation model (41) of the building, taking into account the geometric structure of the premises and the configuration of the walls. The present invention also relates to an allocator (10) for determining the amount of thermal energy supplied by a collective heat management device (30) and a heating cost allocation system (1) comprising the allocator (10) according to the present invention.

Description

Technology Field

[0001] The present invention relates to the field of buildings, and more particularly to the management of thermal energy within buildings. In particular, it relates to a method for determining the amount of thermal energy supplied to the premises of a building during a given period. Furthermore, the present invention relates particularly to an apparatus and system for determining the amount of thermal energy supplied to the premises of a building for the purpose of allocating energy costs. Background Technology

[0002] The known prior art that forms the basis for the development of the present invention is described below.

[0003] In the context of the goals set in the Paris Agreement, the European Union has set ambitious targets for energy transition by 2030 and 2050, focusing on increasing the adoption 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. Therefore, despite significant differences in building performance across countries, this sector has the potential to make substantial progress in terms of energy efficiency. In particular, heating and cooling in the residential, service, and industrial sectors account for approximately 50% of the EU's primary energy supply (European Commission - Information Sheet - February 16, 2016). Notably, primary energy sources for residential heating rely primarily on fossil fuels (82% in 2016), and 68% of total gas imports into Europe are attributed to the heating and cooling sector.

[0005] This made building heating a major target for achieving energy transition goals by 2030 and 2050.

[0006] Therefore, buildings have been the subject of numerous studies and models to characterize energy performance. For example, Dynamic Thermal Simulation (DTS) of buildings has been proposed. These studies, primarily aimed at building owners, allow for the simulation of the energy performance of various anticipated technical solutions, such as comparisons of CMVs, heat generators, or even artificial lighting management solutions. Research based on Dynamic Thermal Simulation of buildings is also implemented in construction project programs to analyze thermal comfort during the summer without air conditioning or to quantify heating requirements.

[0007] Such simulations have also been proposed to estimate predicted consumption. However, buildings are complex and non-static energy systems. Consequently, significant variations are observed between the consumption predicted in simulations and the measured consumption due to errors in input data, occupancy scenarios, or environmental conditions. Furthermore, these simulations are generally based on resource-intensive software clients and are not used in embedded systems configured to perform real-time analysis. Recently, modeling buildings and systems has been proposed to optimize energy management (Hugo Viot. Modelisation and Instrumentation d'un batiment et de ses systemes pour optimiser sa gestion energetique . University of Bordeaux, 2016. French. NNT :2016BORD0349; Tel +33 (0)1503037). In particular, the DTS model is too heavy, so a small-sized model configuration has been proposed to be embedded in controllers to improve energy management. Nevertheless, this simplified model has performance that can be improved. Furthermore, these performances depend mainly on the learning base used, and considering that specific buildings do not operate in the same way as other buildings, the model may require training for each new building.

[0008] Therefore, monitoring was conducted in a situation where such consumption could not be efficiently predicted, and particularly in the case of RT2012, minimum monthly measurements of energy consumption by location and energy type are required. However, this is merely monitoring and is not a measure to promote energy transition.

[0009] In this regard, in apartment buildings with communal heating, the distribution of heating costs is generally proportional to ownership shares or the surface area of ​​the apartment. This distribution does not encourage conservation because heating costs are calculated based on the surface area or ownership share, even though heating consumption varies from residence to residence.

[0010] Therefore, in France, beyond evaluating building energy performance improvements, the individualization of heating costs is being implemented to hold residents accountable. Individualizing (or allocating) heating costs allows each apartment to pay based on the actual amount consumed. Consequently, residents are motivated to control their consumption and avoid energy waste. Through the individualization of heating costs, an average energy savings of approximately 15% can be achieved, and residents who adjust their consumption can reduce their bills (ADEME. September 2019. ISBN 979-10-297-1399-6).

[0011] To calculate the amount consumed by each occupant, it is necessary to install remotely readable measuring devices. The two main devices currently dominating the market are individual thermal energy meters (TEMs) and heat cost allocators (HCAs).

[0012] TEMs are placed at the entrance of each house to display actual heating consumption, allowing residents to monitor their usage. HCAs are installed when installing TEMs is technically impossible or unprofitable. HCAs utilize the measurement of the surface temperature of the heat emitter. They are placed on each radiator in the residence. "Conventional" HCAs have three main operating modes, defined by the standard NF EN 834: i) a single-probe measurement method (a probe measuring the surface temperature of the heat source or the temperature of the heat transfer fluid); ii) a two-probe measurement method (one probe measuring the surface temperature of the heat source or the temperature of the heat transfer fluid, and another probe measuring the ambient temperature); and iii) a multi-probe measurement method (multiple probes to measure the average temperature of the heat transfer fluid and one probe for the ambient temperature).

[0013] The installation objectives of these solutions are much lower than expected. More specifically, due to the complexity of installing these systems, many buildings are still unable to individualize heating costs.

[0014] Moreover, such heating cost allocation systems cannot provide reliable calculations of energy consumption in relation to the location of the residence. More specifically, it has been proposed to calculate heating costs based on a 30% fixed portion, combined with the possibility of incorporating correction factors to account for adverse thermal conditions (such as the fact that residences located to the north have low solar heat input and residences on the top floor suffer from greater heat loss). Nevertheless, these measures cannot accurately account for the consumption of the residence. Consequently, strong inequality persists among residents based on the location of the residence.

[0015] In addition, these systems are still susceptible to "heat theft." More specifically, a residence located in the center of a building with reduced heating will benefit from heat transferred from an adjacent, hot residence. Consequently, inequalities related to the use of individual heat energy meters and actual heating cost allocators also persist. Examples of heat meters for buildings are described in literature FR 2 672 991 A1 and FR 3 062 723 A1.

[0016] Therefore, there is a need for a solution capable of effectively (e.g., accurately in real-time) determining the thermal energy consumption of residential areas, particularly the amount of thermal energy supplied to the building premises by collective thermal management systems. Furthermore, such a solution will enable more accurate calculation of heating costs, thereby enhancing the fairness of heating bill payments for collective buildings.

[0017] The present invention aims to overcome these drawbacks of the prior art. In particular, the present invention aims to propose a method for determining the amount of thermal energy supplied to a building premises during a given period. This method can determine such an amount with precision and accuracy, allowing it to be performed without requiring the installation of costly dedicated equipment. Furthermore, the fact that a thermal simulation model of the building can be considered and ambient temperature taken into account for allocation allows for the establishment of a compensation system, thereby enhancing fairness among users and ultimately enabling consideration of thermal comfort levels rather than energy consumption levels.

[0018] In addition, the present invention aims to propose an allocator for determining the amount of thermal energy supplied to the premises of a building by a collective thermal management device, said allocator may operate based on simple measurements of ambient temperature and external building temperature to determine the amount of thermal energy supplied to the premises of a building by a collective thermal management device. The problem to be solved

[0019] The present invention aims to overcome the above disadvantages. means of solving the problem

[0020] The present invention aims to provide a method for allocating energy costs in a collective building comprising a plurality of individual building premises and at least one collective thermal management device, wherein the method is applicable to all individual building premises of the collective building and comprises the step of determining the amount of thermal energy supplied to at least one individual building premises of the collective building during a given period by at least one collective thermal management device, wherein the determination step is performed by one or more processors comprising the use of the following:

[0021] - A thermal simulation model of a multi-unit building that virtually represents the multi-unit building and, for each individual building premises, considers the geometric structure and wall configuration of the individual building premises, as well as heat exchange between the various building premises of the multi-unit building.

[0022] - A plurality of ambient temperature values ​​of a collective building, wherein the plurality of ambient temperature values ​​of the collective building include a plurality of temperatures within the premises of an individual building and a plurality of temperatures within the premises or common space of a collective building adjacent to the premises of the individual building, ambient temperature values

[0023] - One or more temperature values ​​outside the above collective building,

[0024] - Volume within individual building premises, and

[0025] - At least one heat loss coefficient of the individual building premises mentioned above, and

[0026] Calculation of at least one heat loss coefficient of the individual building premises based on the thermal simulation model of the collective building.

[0027] The applicant has developed a method capable of rapidly calculating the amount of energy supplied to individual building premises based essentially on indoor and outdoor ambient temperature data. In particular, in the context of the present invention, this enables the use of as many heat loss factors as there are building premises, and in a specific advantageous embodiment, multiple heat loss factors may be associated with the same individual building premises as a function of the period being considered. In particular, this method can be used to calculate heating costs and to introduce the concept of fairness among users of the same collective building.

[0028] According to another optional feature of the above method, the method may optionally include one or more of the following features alone or in combination:

[0029] When the aforementioned individual building premises are additionally defined by the joinery configuration, the thermal simulation model of the collective building is a model that additionally considers the joinery configuration. Therefore, by directly including the joinery configuration when constructing the thermal simulation model of the collective building, performance can be improved without placing a computational burden.

[0030] - The thermal simulation model for multi-unit buildings additionally considers data representing local solar radiation over a given period. Therefore, when constructing a thermal simulation model for multi-unit buildings, performance can be improved without placing a computational burden by directly including data representing solar irradiance.

[0031] - The thermal simulation model of a collective building is a dynamic thermal simulation model of a collective building. By using the dynamic thermal simulation model, a determined amount of supplied heat can be obtained with enhanced accuracy using the method according to the present invention.

[0032] - The heat loss factor for an individual building premises is selected from at least two predetermined heat loss factors, each of which corresponds to a period. Thus, the amount of heat energy supplied can be determined more accurately by selecting the heat loss factor as a function of, for example, the season or the month of the year.

[0033] - The step of determining the amount of thermal energy supplied to at least one individual building premises of a collective building by at least one collective thermal management device includes the use of one or more adjustment coefficients, each adjustment coefficient being applied to an adjustment variable measured or calculated over a given period; preferably, said adjustment coefficient is calculated based on a dynamic thermal simulation model of the collective building. Thus, it is possible to adjust the amount of supplied energy, which is determined as a function of additional parameters that best describe the use of the individual building premises. This allows for a more accurate determination of the amount of supplied thermal energy.

[0034] - Adjustment variables are selected from the following: variables representing heat loss related to window openings, variables representing occupancy levels of individual building premises, variables representing weather conditions (e.g., exceptional weather conditions), and / or variables representing the use of auxiliary heating. This allows for the reflection of actual usage, directly measured or modeled for each individual building premises, without burdening the model, thereby leading to higher accuracy.

[0035] - Adjustment variables are measured directly within individual building premises, modeled using statistically based probabilistic methods, or reconstructed based on measurements from machine learning algorithms.

[0036] - For each adjustment factor, the above method includes the step of calculating multiple adjustment factor values ​​as a function of the period of the corresponding year, and the adjustment factor values ​​used to determine the amount of supplied thermal energy are functions of the given period. For example, there are adjustment factor values ​​calculated as a function of the time of each month; the value corresponding to the month being considered is used.

[0037] - This includes a calibration step for the thermal simulation model of a collective building, wherein the calibration step is preferably performed based on a first measurement period and a Bayesian calibration algorithm to reproduce the predicted values ​​of the consumption and internal temperature of the thermal simulation model of the collective building so as to be as close as possible to the measured values ​​in the hourly step.

[0038] - This includes a calibration step for the thermal simulation model of a collective building, wherein the calibration step is preferably performed based on a first measurement period of ambient and external temperature values ​​and a Bayesian calibration algorithm to reproduce the temperature calculated by the thermal simulation model of the collective building so as to be as close as possible to the measured temperature value.

[0039] - The ambient temperature within an individual building premises corresponds to the temperatures measured at various locations within the premises. These various locations may correspond to different rooms.

[0040] - Includes the step of calculating individual heating costs for individual building premises based on the determined amount of thermal energy supplied to the individual building premises by the collective thermal management device.

[0041] - In the stage of calculating individual heating costs, the orientation and location features of individual premises within a multi-unit building are taken into account. This allows for the correction of discrepancies between individual premises and common spaces within the building. This induces the principle of fairness in the distribution of heating costs and enables the correction of differences (e.g., different orientations, different floors, etc.) between individual premises and common spaces. Furthermore, this allows tenants to pay for the portion of heating consumption for which they are actually responsible—that is, the cost of the heated temperature.

[0042] - This includes the step of calculating a confidence interval for the determined amount of thermal energy, and preferably, the calculation of the confidence interval includes the use of at least one uncertainty value for one or more adjustment variables used while determining the amount of supplied thermal energy.

[0043] - Calculating at least one heat loss coefficient for an individual building premises based on a thermal simulation model of a collective building may include, for an individual building premises, calculating at least one heat loss coefficient based on a plurality of simulations modified in such a way that the geometric values ​​of the individual building premises, the configuration of the walls, and the configuration of optional furnishings incorporate the uncertainty associated with the collective building.

[0044] - Calculating at least one heat loss coefficient for an individual building premises based on a thermal simulation model of a collective building may include calculating at least one heat loss coefficient for the individual building premises based on multiple simulations modified in a manner that incorporates uncertainties related to use and weather conditions, in addition to use values ​​and / or weather conditions.

[0045] According to the second objective, the present invention relates to an energy cost allocator for a collective building comprising a plurality of individual building premises and at least one collective thermal management device, wherein the method is applicable to all individual building premises of the collective building and comprises the step of determining the amount of thermal energy supplied to at least one individual building premises of the collective building by at least one collective thermal management device, and wherein the allocator comprises one or more processors configured to determine the amount of thermal energy supplied by using the following:

[0046] - A thermal simulation model of a multi-unit building that virtually represents the multi-unit building and, for each individual building premises, considers the geometric structure of the individual building premises and the configuration of the walls of the individual building premises, as well as heat exchange between the various premises of the multi-unit building.

[0047] - A plurality of ambient temperature values ​​of a collective building, wherein the plurality of ambient temperature values ​​of the collective building include a plurality of temperatures within the premises of an individual building and a plurality of temperatures within the premises or common space of a collective building adjacent to the premises of the individual building, ambient temperature values

[0048] - One or more temperature values ​​outside the above collective building,

[0049] - Volume within individual building premises, and

[0050] - At least one heat loss coefficient of an individual building premises that describes the ratio of heat energy supplied to the individual building premises to a premises or common space adjacent to the individual building premises, and

[0051] The above allocator is characterized by being configured to calculate at least one heat loss coefficient for an individual building premises based on a thermal simulation model of a collective building.

[0052] In a specific embodiment, where individual building premises are additionally defined by furniture configurations, the allocator is characterized in that the thermal simulation model of the collective building additionally considers the furniture configurations.

[0053] Preferably, the allocator is designed to make the thermal simulation model of the collective building a model that additionally considers data representing local solar radiation during a given period.

[0054] As described below, the allocator has a plurality of heat loss factors dedicated to each individual building premises. Additionally, each individual building premises may be associated with a plurality of heat loss factors allocated for a given period.

[0055] According to the third objective, the present invention relates to a heating cost allocation system comprising an allocator according to the present invention. Through such an allocation system, in particular, a tenant can pay for a portion of the heating consumption for which he is actually responsible, namely, the cost for the heated temperature. Brief explanation of the drawing

[0056] Other features and advantages of the present invention are given for illustrative purposes only and are not intended to be limited; they will be better understood by reading the following description with reference to the accompanying drawings. FIG. 1 illustrates a method according to one embodiment of the present invention. FIG. 2 shows a system for determining the amount of thermal energy supplied by a collective heat management device according to one embodiment of the present invention. For the purpose of illustration, the above drawing does not necessarily reflect the scale, particularly in terms of thickness. Aspects of the present invention are described with reference to flowcharts and / or block diagrams of a method according to an embodiment of the present invention, an apparatus (system), and a computer program product. In the drawings above, flowcharts and block diagrams illustrate the structure, function, and operation of possible embodiments of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block of a flowchart or block diagram may represent a system, device, module, or code comprising one or more executable instructions for implementing one or more specific logical functions. In certain embodiments, functions associated with a block may appear in an order different from that shown in the drawings. For example, two blocks shown consecutively may actually be executed substantially simultaneously, or blocks may occasionally be executed in reverse order as a function of associated functions. Each block of a block diagram and / or flowchart, and combinations of blocks of a block diagram and / or flowchart, may be implemented by a special hardware system that executes a specific function or task or executes a combination of special hardware and computer instructions. Specific details for implementing the invention

[0057] Below, the disadvantages of the prior art are presented, and finally, the present invention explains the summary and related terms before showing in more detail how the present invention overcomes these disadvantages.

[0058] The term "heat loss coefficient," also known as the total volume heat loss factor of a building, corresponds to the overall performance of insulation in terms of heat loss based on the difference between the internal and external environments. The total volume heat loss coefficient represents the heat loss per unit of heated volume. Generally, it is expressed in watts / cubic meter (watts / m²) for every 1-degree Celsius difference between the external and internal temperatures. 3 and °C). The coefficient G was introduced during the 1974 heat-related legislation: "Order of April 10, 1974 concerning insulation and automatic control of heating systems in residential buildings"; generally 0.5 to 3 Watts / m² 3 and may vary within the ℃ range.

[0059] In the remainder of the description, the expression “premises in a building” refers to a part of a building that may consist of one or more rooms. For example, the said premises may be an office or a residence. Thus, the expression “volume of the premises” refers to the volume of one or more rooms in the building.

[0060] The term "individualized heating costs" generally refers to heating costs calculated based on the actual consumption within a building premises, and said consumption is set based on a device that determines the amount of heat or cold consumed within each building premises.

[0061] In the context of the present invention, the expression "thermal simulation model" may refer to a model configured to virtually represent a building and explain the building's response to stress related to climate change or user behavior (such stress may be probabilistically modeled). In particular, the expression "dynamic thermal simulation of the building" may refer to a model configured to calculate changes in the building's thermal state over time in the context of the present invention. In particular, it is possible to determine the temperature at specific points of an element that unfolds according to various laws governing heat exchange (convection, conduction, radiation, and state change) at every selected point in the simulation. Thus, the thermal simulation model can estimate the building's thermal requirements by considering the building's shell and its inertia, various thermal inputs, occupant behavior, and local climate.

[0062] In the context of the present invention, the expression "local solar radiation" may correspond to the amount of solar energy received per unit surface area. For example, watts per square meter (watts / m²) 2 It can be displayed as ).

[0063] The terms "correlation model" or "algorithm" shall be understood within the meaning of the present invention as a finite sequence of operations or instructions that enable a value to be calculated based on one or more input values. By implementing this finite series of operations, for example, the characteristics or parameters used X Labels for observations described by the set of Y such Y It becomes possible to assign a value, for example, the observed X having Y A function that can reproduce f It can implement.

[0064] Y = f(X) + e

[0065] From the above:

[0066] e : Noise or measurement error.

[0067] Within the meaning of the present invention, the term "supervised machine learning model" should be understood to mean a correlation model that is automatically generated based on data called labeled observations.

[0068] Within the meaning of the present invention, the term "non-supervised machine learning model" should be understood to mean a correlation model that is automatically generated based on data referred to as unlabeled observations.

[0069] Within the meaning of the present invention, the terms “process,” “calculate,” “determine,” “display,” “transform,” “extract,” “compare,” or more broadly “executable operation” shall be understood as operations performed by a device or processor unless otherwise specified in the context. In this regard, said operations relate to the operations and / or processes of a data processing system, such as, for example, a computer system or an electronic computing device, which process and transform data expressed as physical (electronic) quantities within the memory or other storage devices of a computer system, and transmit or display information. Such operations may be based on applications or software.

[0070] The terms or expressions “application,” “software,” “program code,” and “executable code” refer to a representation, code, or notation of a set of instructions intended to trigger data processing to perform a specific function directly or indirectly (e.g., after conversion to other 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 by a computer system.

[0071] Within the meaning of the present invention, the term "processor" means at least one hardware circuit configured to execute operations according to instructions contained in code. The hardware circuit may be an integrated circuit. Examples of processors include, but are not limited to, central processing units, graphics processors, application-specific integrated circuits (ASICs), and programmable logic circuits.

[0072] The term "electronic device" refers to any device comprising, for example, a data memory, a processing unit in the form of a microcontroller that cooperates with a program memory, or a processor, said memory may be separate. The processing unit cooperates with said memory via an internal communication bus.

[0073] Within the meaning of the present invention, the term "coupled" means being directly or indirectly connected to one or more intermediate elements. The two elements may be mechanically or electrically coupled or connected through a communication channel.

[0074] The terms "precise" or "precision," "reliable" or "reliability," "accurate" or "accuracy" refer to repeatable and accurate measurements. Furthermore, this implies that the measurement has no error or at least an error rate of, for example, less than 5%, preferably 2%, and most preferably less than 1%. Additionally, the expression "improved accuracy" may correspond to very accurate consumption within the meaning of the present invention; the value indicates a precision having an error rate of, for example, less than 5%, preferably 2%, and most preferably less than 1%.

[0075] Individual thermal energy meters (TEMs) exist, but these systems are expensive and complex to install (1 meter is required per hot water loop, and plumbing work is necessary for installation). Heat cost allocators (HCAs) based on radiator surface temperature measurements also exist. Nevertheless, these systems are not precise and are complex to install (especially, they must be installed for each radiator). Neither TEMs nor HCAs can establish fairness for building occupants.

[0076] The inventors have developed a solution that makes it possible to determine the amount of thermal energy supplied to a building premises for a given period without installing heavy electronic devices.

[0077] In particular, the inventors have developed a decision solution that considers the heat loss coefficient of a building premises calculated based on a thermal simulation model of the building that takes into account the geometric structure and wall configuration of the building premises. Furthermore, if the building premises contain furniture, the thermal simulation model can also consider the configuration of the furniture. Additionally, preferably, data representing local solar radiation over a given period can also be considered. Through this solution, it is possible to combine the precision of a solution utilizing thermal simulation with heavy computational systems with the responsiveness and lightness of a solution based on heat loss coefficients. More specifically, although there have been attempts to directly integrate thermal simulation models into methods for determining the amount of thermal energy supplied to a building premises, such solutions were not sufficiently precise and were generally too heavy to implement.

[0078] Accordingly, according to the first aspect, the present invention relates to a method (100) for determining the amount of thermal energy supplied to the premises (20) of a building (2) as illustrated in the example of FIG. 1.

[0079] The amount of thermal energy supplied to the building premises (20) corresponds, for example, to the amount of energy supplied by a collective heat management device (30). The collective heat management device (30) generally corresponds to a common gas boiler. However, it may also correspond to an electric heating system such as a heat pump or a heat network.

[0080] In particular, the determined amount of supplied thermal energy may be relative. Nevertheless, it is desirable to express the determined amount of supplied thermal energy in international thermal energy measurement units such as watt-hours. The amount of supplied thermal energy may correspond, for example, to a monthly amount.

[0081] Preferably, the building premises (20) where the amount of supplied thermal energy is determined corresponds to individual building premises, and more preferably corresponds to a residential area. The building premises may be defined by a geometric structure. The geometric structure of the premises may correspond to the dimensions of the walls forming the premises. These dimensions may represent the dimensions of furniture and may include the orientation of the walls forming the building premises. Preferably, the geometric structure of the building premises (20) will include the surface area of ​​the walls and optionally the surface area of ​​the furniture, their location in the building (2), and their orientation. Additionally, the building premises are defined particularly by the configuration of the walls and optionally the configuration of the furniture. More specifically, the size and characteristics of the materials used, the behavior of the building premises, and the amount of thermal energy supplied accordingly may vary.

[0082] The determination method (100) according to the present invention can advantageously calculate the amount of thermal energy supplied during a given period.

[0083] The given period corresponds, for example, to a week, a month, a quarter, a half-year, or even a year. As described in detail below, the present invention may account for differential variations related to climatic conditions (e.g., solar radiation), and the given period may preferably correspond to a period in which a single identical heat loss coefficient is used. Advantageously, the given period is a lunar period and is preferably associated with a season.

[0084] In the context of the present invention, determining the amount of thermal energy supplied to the premises (20) of a building (2) includes the following uses: a plurality of ambient temperature values ​​of the building, a temperature value outside the building (2), the volume of the building premises (20), and a heat loss coefficient of the building premises.

[0085] The ambient temperature value of 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 in the building. The ambient temperature sensor is preferably a temperature sensor connected to a communication network (e.g., Internet, Wi-Fi®, Sigfox®, LoRa®, LoRaWAN®, Zigbee®, Z-Wave®, Enocean®, 3G / 4G / 5G). The ambient temperature value of the building may preferably include a plurality of temperatures within the premises under study and temperatures within premises adjacent to the premises under study (i.e., through which the amount of thermal energy supplied should be determined) or temperatures in common spaces adjacent to the premises under study.

[0086] The ambient temperature of the building premises (20) may correspond to the temperature measured at least every hour at multiple locations (i.e., rooms) of the building premises (20). The ambient temperature value used for the building premises (20) may also correspond to an average value or a median value.

[0087] In particular, the determination may involve the use of at least two ambient temperature measurement locations within the building premises (20) (preferably a day zone and a night zone).

[0088] The determination method may use one or more values ​​of the external temperature of the building (2). One or more external temperatures may be supplied by a temperature sensor dedicated to the determination method or by a temperature sensor integrated into a system already installed around or on top of the building. The external temperature sensor is preferably a temperature sensor connected to a communication network (e.g., Internet, Wi-Fi®, Sigfox®, LoRa®, LoRaWAN®, Zigbee®, Z-Wave®, Enocean®, 3G / 4G / 5G). Alternatively, the external temperature value may correspond to a local external temperature value measured or calculated for the geographical area where the building (2) is located. The external temperature value may also correspond to a local external temperature value estimated for the geographical area where the building (2) is located.

[0089] The volume of the building premises (20) will generally correspond to a known or calculated value, taking into account the geometric structure of the building and, in particular, the building premises. In a specific embodiment, the volume of the building premises may correspond to multiple volumes, each associated with one or more spaces within the building premises. For example, when determining the amount of thermal energy supplied to the building premises (20), a specific volume for each ambient temperature value of the building premises used may be considered. A somewhat hot zone associated with a temperature value may be associated with a sub-volume of the building premises (20), while a somewhat cool zone may be associated with a different temperature value and another sub-volume of the building premises (20).

[0090] In the context of the present invention, the determination of the amount of thermal energy supplied to the building premises will be based on the heat loss coefficient of the building premises calculated based on the thermal simulation model (41) of the building (2).

[0091] When calculating the heat loss coefficient of a building premises based on the thermal simulation model (41) of a building (2), a much more precise and accurate heat loss coefficient of a building premises can be obtained than a heat loss coefficient calculated or estimated by other bias values.

[0092] Preferably, the thermal simulation model (41) of a building used to calculate the heat loss coefficient of the building premises is a dynamic thermal simulation model of the building. More specifically, the dynamic thermal simulation model will be based on an accurate description of the geometric structure and composition of the walls. Additionally, if the building premises are composed of furniture, the thermal simulation model of the building may also take into account the composition of said furniture. This allows for the consideration of related physical phenomena (heat exchange by convection, conduction, and radiation), thereby enabling the accurate calculation of the amount of thermal energy supplied to the building premises.

[0093] There are methods other than the 3CL method used to generate Energy Performance Certification (EPC) to calculate traditional consumption of residential areas. However, these methods are based on approximate descriptions of heat loss surface areas and approximate modeling of heat exchange. For example, the aforementioned 3CL method does not include consideration of dynamic aspects, unlike the present invention, which is preferably based on a dynamic thermal simulation model of a building. It can only calculate annual heating / cooling / DHW consumption and cannot calculate, for example, temperature changes over time. Furthermore, it is applied only to frame analysis without considering usage. In fact, the method according to the present invention can consider usage and preferably perform calculations for both consumption and internal temperature in hourly time steps, but it will be less accurate than the method according to the present invention.

[0094] Therefore, the thermal simulation model of a building favorably considers the stresses the building receives, such as the geometric structure of the building premises, the configuration of walls, optionally the configuration of furniture, and preferably climate and use.

[0095] In the context of the present invention, a dynamic thermal simulation model (DTS) will enable the calculation of changes over time in the thermal state of a building using an approximated numerical model. Using this model, temperatures at a specific number of points in the building can be obtained at all selected simulation times, which vary according to the various laws governing heat exchange (convection, conduction, radiation, and state change).

[0096] Accordingly, in the context of the present invention, a dynamic thermal simulation model can estimate thermal requirements (heating and cooling requirements) and temperatures in each zone of an operating building. This model considers the building envelope and inertia (based on descriptions of the building's inner and outer walls), heat exchange flow between thermal zones, various heat inputs, occupant behavior, and local climate. Furthermore, when using the model to estimate actual energy consumption, there is an advantage in that energy systems (production systems, types of emitters, etc.) can also be taken into account in the calculations.

[0097] The above thermal simulation model is preferably a multi-zone model, more preferably a model having a finite volume, and even more preferably a model reduced by modal analysis. Using this configuration can reduce computation time by a factor of three. Additionally, at each time step, the model is configured to determine the heating and cooling requirements and / or temperatures of each zone of the building. It is advantageous for the model to be configured to incorporate heat exchange between zones, for example, between building premises (20). Additionally, it is advantageous for the model to be configured to take into account the thermal inertia of each wall.

[0098] In addition, to further improve the precision and accuracy of the determination according to the present invention, the thermal simulation model (41) of the building (2) used has the advantage of being able to additionally consider data representing local solar radiation.

[0099] In particular, considering data on local solar radiation per period (e.g., a given period) allows for the consideration of energy input from solar radiation in any form that can be taken directly or indirectly.

[0100] In particular, local solar radiation data includes total horizontal radiation and diffuse horizontal radiation. These values ​​for a given building (2) will vary significantly as a function of the period being considered. Additionally, the impact of solar radiation on two building premises may differ significantly over a specific period due to different geographical conditions (e.g., location and / or orientation). Therefore, in the context of the present invention, it is highly advantageous to use a thermal simulation model (41) of the building (2) by considering local solar radiation data for the period being studied (i.e., the period of time) or data representing local solar radiation for the period being studied.

[0101] Local solar radiation data, or data representing local solar radiation, can be constructed based on local historical data and, preferably, modeled based on local historical data. More specifically, it is particularly costly if specific radiation values ​​must be measured for each building under study.

[0102] Alternatively, data representing local solar radiation was obtained from at least one instrument device or from a computer server containing data related to local solar radiation.

[0103] Various embodiments of a method for determining the amount of thermal energy supplied to the premises (20) of a building (2) during a given period according to the present invention will now be described.

[0104] As illustrated in FIG. 1, the determination method according to the present invention may include the step (110) of generating a thermal simulation model of a building.

[0105] There are many tools available for creating a thermal simulation model (110) of a building. The thermal simulation model of a building (2) is preferably constructed based on a computer solution that combines one or more libraries dedicated to elements necessary for describing the building and a modeling module capable of describing the building (2) under study in 3D graphics. Thus, it is possible to create a digital model of the building (2) that includes all information regarding materials, furniture, equipment, and energy performance.

[0106] In addition to these aspects, creating and using dynamic thermal simulation models of buildings can yield much better results. More specifically, it is possible to use modeling solutions that can additionally incorporate environmental influences, particularly factors related to light radiation.

[0107] The thermal simulation model (41) of the building (2) used in relation to the calculation of the heat loss coefficient within the building premises is preferably a dynamic thermal simulation model that can include the characteristics of the surrounding solar shading.

[0108] In particular, the thermal simulation model (41) of the building (2) used in relation to the calculation of the heat loss coefficient within the building premises includes the compositional characteristics of the walls and optionally the compositional characteristics of the furniture. This has the advantage of being able to additionally include the characteristics of the air regeneration flow rate and / or the distribution loss of the heating network.

[0109] Additionally, the thermal simulation model (41) of the building (2) is a dynamic thermal simulation model that includes characteristics regarding the occupancy of the building, such as occupancy time and / or occupancy level. Additionally, it may include characteristics such as specific electrical uses, temperature settings for heating or cooling, and / or heating or cooling schedules.

[0110] Advantageously, the thermal simulation model (41) of the building (2) is also a dynamic thermal simulation model configured to consider heat exchange between the premises (20, 20b) of the given building (2).

[0111] As illustrated in FIG. 1, the determination method according to the present invention may advantageously include a step (120) of correcting a thermal simulation model of a building. This step can increase the precision of the method and, accordingly, increase the value of the determined amount of thermal energy supplied.

[0112] The above correction step (120) can be performed based on a first measurement period and a correction algorithm, preferably a Bayesian correction algorithm, so that the prediction of consumption and internal temperature at each time step of the building's thermal simulation model is reproduced more closely to the measured value.

[0113] The above correction step (120) can make the thermal simulation model more reliable by reproducing the actual thermal behavior of the building (2) as accurately as possible. The purpose of this step is to reliably and accurately represent the actual behavior of the building by adjusting the field measurements (based on ambient and external temperature data) and the output of the thermal simulation model (temperature and consumption simulated by the model).

[0114] Therefore, this step can, on the one hand, integrate measurements derived from the digital model generated by the thermal simulation model, and on the other hand, calibrate the model by considering the sources of uncertainty and error that explain the difference between the simulated data and the measurements. Advantageously, the estimation of errors is based on the use of Bayesian correction algorithms.

[0115] Accordingly, this correction step (120) may cause a modification of the parameters of the thermal simulation model, and this modification will be reflected in the heat loss coefficient values ​​of the building premises generated during a given period.

[0116] In the context of the present invention, the thermal simulation model (41) of the building (2) is at least 50 Wh / m² 2 , for example, at least 100 Wh / m 2 It varies by; and can be corrected as a function of the period considered under sunlight conditions where the external temperature varies by at least 10°C, preferably at least 30°C. Additionally, if the external relative humidity condition varies by at least 15%, it can be corrected as a function of the period considered. More specifically, the inventors determined that these specific variables having these amplitude levels are most suitable in the context of the invention to supply an amount of thermal energy determined as accurately as possible.

[0117] Additionally, a method for determining the amount of thermal energy supplied to the premises (20) of a building (2) during a given period may include, in particular, a pre-correction step, a step of calculating an uncertainty score of the value of the determined amount of supplied thermal energy.

[0118] In particular, this uncertainty score is generated during the calibration procedure and can be calculated based on the building's thermal simulation model and a variable representing the deviation between consumption predictions and internal temperature at the hourly time step of the measurement.

[0119] As illustrated in FIG. 1, the determination method according to the present invention may advantageously include a step (130) of calculating at least one heat loss coefficient of a building premises based on a thermal simulation model of the building.

[0120] Accordingly, the heat loss coefficient for each building premises (20) can be calculated based on an uncorrected, or preferably corrected, thermal simulation model of the building. The heat loss coefficient estimated in this way can favorably correspond to the expected heat loss of the building premises (20) under various operating conditions of the building (2). This calculation minimizes errors that occur in the heat loss estimation. These errors can be significantly large when performing purely statistical calculations that do not account for the inertia of the building regarding the dynamic aspects of stress (weather, use, etc.) experienced by the building.

[0121] As described above, ideally, to calculate the heat loss coefficient, numerous factors must be considered, such as the type, surface area, and orientation of various walls (whether glass is present) of the dwelling, as well as adjacent contact surfaces, air regeneration, various thermal bridges, and additionally, free inputs connected to solar input and use (metabolic heat, specific electricity). In this context, using a calibrated thermal simulation model to estimate the heat loss coefficient is of great interest. The purpose of calculating the heat loss coefficient of a building premises is to enable the estimation of the coefficient G value of the building premises (20) while considering various sources of uncertainty related to the building (2) and its environment (uncertainties associated with frames, systems, uses, and weather conditions).

[0122] Uncertainty analysis may be performed with the aim of quantifying the variability of the output (particularly consumption) of a corrected thermal simulation model of a building caused by the uncertainty of the input factors of the model. According to one embodiment, to perform this uncertainty analysis, each static parameter is defined by a probability density function on one hand, and a variability model of use and weather conditions on the other. For example, more than 500 simulations can be performed based on these data in a manner that obtains the variance of the consumption results for each building premises (20), and one or more heat loss coefficients G of the building premises can be recalculated based on these simulations.

[0123] Accordingly, the calculation (130) of at least one heat loss coefficient of an individual building premises based on a thermal simulation model of a collective building may preferably include the calculation of at least one heat loss coefficient based on a plurality of simulations modified in such a way that the geometric values ​​of the individual premises (20), the configuration of the walls, and optionally the configuration of the furniture are incorporated into the uncertainty associated with the collective building (2).

[0124] More preferably, the calculation (130) of at least one heat loss coefficient of a building premises based on a thermal simulation model of the building may include the calculation of at least one heat loss coefficient based on a plurality of simulations for the building premises, in which values ​​for use and / or weather conditions (temperature and sunlight) are modified in a manner that incorporates uncertainty related to use and weather conditions. In these embodiments, the values ​​may be modified according to a probability density function and / or variability model.

[0125] Using uncertainty analysis to calculate the coefficient G can provide greater accuracy to the present invention compared to existing methods without burdening the work.

[0126] Additionally, the determination method according to the present invention may include the use of a plurality of heat loss coefficients for a given building premises (20), each calculated based on a thermal simulation model of the building. Preferably, the determination method according to the present invention will include the use of at least four heat loss coefficients. Advantageously, these heat loss coefficients will be specified according to a given period, e.g., a lunar period. More preferably, in the context of the method, the heat loss coefficients of the building premises (20) are selected from at least two, preferably at least three, and more preferably at least four heat loss coefficients, each corresponding to a period of the year. Even more preferably, the heat loss coefficients are selected from at least twelve heat loss coefficients (monthly coefficients) for a given building premises (20).

[0127] The determination method according to the present invention may include a calculation step (130) of a plurality of heat loss coefficients based on the same single heat simulation model of a building or based on various heat simulation models of a building.

[0128] The determination method according to the present invention will include determining the amount of thermal energy supplied to the premises (20) of a building (2) during a given period.

[0129] As previously described, the above determination takes into account a plurality of ambient temperature values ​​of the building, one or more temperature values ​​outside the building (2), the volume of the building premises (20), and the heat loss coefficient of the building premises.

[0130] This determination can be compared with a calculation performed by a heat cost allocator using ambient temperature measurements within the building premises. In particular, the determination according to the present invention may correspond to an estimate based on a plurality of ambient temperature values ​​and one or more external temperature values ​​for the amount of energy privately consumed within the building premises prior to being allocated.

[0131] For example, the calculation can have the following expression.

[0132] [Mathematical Formula 1]

[0133]

[0134] From the above:

[0135] E : Amount of energy privately consumed in the study area (20)

[0136] Ta : Ambient temperature of the study subject premises (20)

[0137] Te : External temperature of the building (2)

[0138] V : Volume of the study subject area (20)

[0139] G : Heat loss coefficient of the study subject premises (20)

[0140] t : Time interval

[0141] An example of a formula with adjustment variables is as follows:

[0142] [Mathematical Formula 2]

[0143]

[0144] From the above:

[0145] E : Amount of energy privately consumed within the study area (20)

[0146] Ta : Ambient temperature of the study subject premises (20)

[0147] Te : External temperature of the building (2)

[0148] V : Volume of the study subject area (20)

[0149] G : Heat loss coefficient of the study subject premises (20)

[0150] C adjustment Adjustment coefficients linked to adjustment variables

[0151] V adjustment : Adjustment variable

[0152] t : Time interval

[0153] As described, the present invention may be implemented without a control variable, or with a control variable or a plurality of control variables.

[0154] Advantageously, the above decision may include the use of one or more adjustment factors calculated based on a thermal simulation model of the building. Using these adjustment factors along with adjustment variables representing environmental factors can further improve the accuracy of the model.

[0155] Accordingly, the method according to the present invention advantageously includes a step (140) for adjusting the amount of heat energy supplied, said adjustment step (140) includes the use of one or more adjustment coefficients, each adjustment coefficient being applied to an adjustment variable.

[0156] One or more adjustment variables may correspond to human or environmental factors affecting the thermal energy supplied to the building premises. Therefore, adjustment variables may correspond to meteorological data such as atmospheric pressure or wind speed.

[0157] The adjustment variable can be measured directly within the premises (20) of the building (2). Alternatively, the adjustment variable can be measured in the geographical area where the building (2) is located. Another means of obtaining the adjustment variable may be, for example, to probabilistically model it based on statistics or to create a model based on a machine learning algorithm.

[0158] Adjustment variables are preferably calculated based on weekly, daily, or hourly data. These values ​​can be combined to generate monthly values. In particular, if the heat loss factor is monthly, the adjustment variable is also monthly.

[0159] For example, determining the amount of thermal energy supplied to a building premises (20) may involve the use of adjustment variables selected from the following: a variable representing heat loss related to window opening and / or natural ventilation; and / or a variable representing the use of auxiliary heating. This may include actual usage directly measured or modeled for each building premises.

[0160] Preferably, determining the amount of thermal energy supplied to the building premises (20) involves the use of adjustment variables representing the electrical consumption of the building premises, including auxiliary heating.

[0161] The above decision may also include the use of an adjustment variable representing heat loss resulting from opening at least one window (24) in the building premises.

[0162] In this case and in a preferred manner, the adjustment variable representing the heat loss connected to the window opening (24) can be calculated as a function of the ambient temperature and humidity change within the building premises. In particular, time series of internal and external temperatures and humidity can be used. Thus, the time when the window opens can be accurately estimated without the need to place a sensor at each window.

[0163] The adjustment coefficient corresponds to a value used in conjunction with the adjustment variable value while determining, for example, the amount of thermal energy supplied to the building premises (20). In particular, this adjustment coefficient is used in the equation determining the amount of thermal energy supplied to the building premises (20). This adjustment coefficient value is preferably calculated based on a thermal simulation model of the building (2).

[0164] For example, the adjustment factor value can be updated at a frequency of 3 months or less, preferably 2 months or less, and more preferably 1 month or less. Thus, it is possible to best consider the value of the adjustment variable as a function of the season, or more generally, the general climate of a given period. Therefore, preferably, the method uses a plurality of predetermined adjustment factor values ​​as a function of a given period for the same adjustment factor.

[0165] In relation to a method for determining the amount of thermal energy supplied to a building premises (20) according to the present invention, a plurality of pairs of adjustment variables / adjustment coefficients will be advantageously used. Thus, the amount of thermal energy supplied can be calculated more accurately compared to other systems.

[0166] In particular, the method according to the present invention may include the use of a plurality of adjustment factors, the predetermined values ​​of which are selected as a function of a given period. For example, an adjustment factor associated with an adjustment variable representing heat loss due to the opening of at least one window (24) in a building premises will not have the same value in July as in November. Preferably, the method according to the present invention includes 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 building premises (20).

[0167] Furthermore, the use of adjustment variables and adjustment coefficients reduces the complexity of calculating the amount of supplied thermal energy by ensuring higher accuracy. Additionally, this allows the thermal simulation model to be used only once to generate numerous adjustment coefficient and heat loss coefficient values ​​for the building premises in a manner that flexibly adapts to the period of the year being considered.

[0168] As described above, one of the advantages of the method according to the present invention is that it is possible to calculate a confidence interval for a determined amount of supplied thermal energy.

[0169] The calculation of these confidence intervals may rely on the use of uncertainty values ​​for the adjustment variables used while determining the amount of supplied thermal energy.

[0170] The uncertainty value for the adjustment variable may be a predetermined value or a value calculated as a function of the adjustment values ​​of multiple variables over the same period (e.g., standard deviation). Alternatively, or additionally, the calculation of this confidence interval may rely on the calculated amount of supplied thermal energy and the calculation of the uncertainty value for the adjustment variable used.

[0171] The performance of the present invention is particularly exemplified in Table 1 below.

[0172] Prediction accuracy Inadequate insulation EPC < C Well insulated EPC ≥ C In the case of no DTS 50 50 + Solar radiation 55 55 + Auxiliary heating 53 53 + Open 51 51 When using coefficients calculated from DTS without considering solar radiation 70 76 When considering solar radiation and using coefficients calculated from DTS 80 86 + Auxiliary heating 89 92 + Open 82 88 + Opening + Auxiliary Heating 100 100

[0173] Table 1 shows how the accuracy of the prediction is improved compared to the existing method for determining the amount of thermal energy supplied to the premises (20) of a building (2) during a given period. More specifically, if the heat loss coefficient calculated based on a dynamic thermal simulation model is not used, the results are out of touch with reality and external factors such as solar radiation, window opening, or auxiliary heating cannot be accurately taken into account.

[0174] In addition, Table 1 shows that accuracy can be improved by considering radiation in relation to the heat loss coefficients of a building premises calculated based on dynamic thermal simulation, and even more so when the building is well insulated. Table 1 also shows that accuracy can be further improved by using auxiliary heating ("auxiliary heating") while considering adjustment variables (including adjustment coefficients) and window openings ("open").

[0175] As described, the calculation of the heat loss coefficient G within a building premises is preferably performed through the propagation of uncertainty by varying various parameters that may include radiation and internal input (see reference). This allows for the consideration of the average (expected) influence of these variables (radiation, internal input, etc.).

[0176] To further adjust the consumption calculation and bring it closer to actual energy consumption, the solution according to the present invention proposes incorporating adjustment variables. This allows the consumption calculation to be adjusted by taking into account the actual values ​​of the variables. For example, when calculating the heat loss coefficient G within a building premises during adjustments related to radiation, the average effect of radiation (e.g., 1200 kWh / m² per year) 2 or 120 kWh / m per month 2 Average consumption can be calculated by considering ). The consumption calculation can be modified by adding adjustment variables to the radiation. For example, if 1300 kWh / m² was actually measured annually (or 100 kWh / m² during that period) 2 ), the calculation of the heat loss factor G value within the building premises is modified using adjustment factors and adjustment variables for radiation (e.g., Adjusted consumption = Average consumption + coefficient × (1300 / 1200 - 1)).

[0177] As illustrated in FIG. 1, the determination method according to the present invention may advantageously include a step (150) of analyzing the amount of electricity consumed.

[0178] In particular, the use of auxiliary heating can be detected based on an analysis of electricity consumption (150). This analysis of electricity consumption may correspond to an estimation by a statistical model, an analysis of electricity rates, or a calculation based on measurements through an electronic electricity meter.

[0179] Accordingly, as illustrated in FIG. 1, the determination method according to the present invention may advantageously include a quantification step (160) for the use of an additional heating device.

[0180] This quantification step (160) regarding the use of additional heating devices may preferably be based on the analysis of documents, particularly data from electronic electric meters or consumption reports such as electricity charges.

[0181] As illustrated in FIG. 1, the determination method according to the present invention may advantageously include a step (170) of calculating individualized heating costs as a function of a determined amount of supplied thermal energy.

[0182] In particular, the calculation step (170) of individual heating costs may be a calculation step of individual heating costs (170) for the premises (20) of the building (2) as a function of the determined amount of thermal energy supplied to the premises (20) of the building by the collective heat management device (30).

[0183] Preferably, the method may include a step of calculating the predicted comfort cost for a building premises as a function of a predetermined expected internal temperature, local variables, and adjustment coefficients.

[0184] As illustrated in FIG. 1, the determination method according to the present invention may further include the step of calculating an expected savings amount regarding the amount of thermal energy supplied to a building premises as a modification function of the adjustment variable value.

[0185] Accordingly, when implementing the use of adjustment variables, the method according to the present invention may include the step of calculating how much thermal energy is supplied to the building premises as a function of other adjustment variable values. For example, if the number of open windows is reduced by two, the amount saved in terms of the amount of thermal energy supplied to the building premises can be calculated.

[0186] These steps can generate recommendations for energy improvement measures and enable users to reduce heating costs.

[0187] As illustrated in FIG. 1, the determination method according to the present invention may further include the step (180) of transmitting data to an electronic display device (50).

[0188] The transmitted data may include, for example, a determined amount of supplied thermal energy.

[0189] According to another aspect, the present invention relates to an allocator (10) for determining the amount of thermal energy supplied by a collective thermal management device (30) to the premises (20) of a building (2) during a given period.

[0190] 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 supplied, and various preferred embodiments thereof, regardless of advantage or disadvantage.

[0191] The allocator (10) may be installed in the building (2). However, as illustrated in FIG. 2, the allocator (10) may be located outside the building (2) that houses the premises (20) under study. 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 the building (2). As illustrated in FIG. 2, the computer server (40) may also host a thermal simulation model (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 heat coefficients are generated, at least one model per building premises (20) is no longer essential for determining the amount of thermal energy supplied. This allows the applicant to propose a lighter and more responsive solution for real-time calculations without sacrificing the accuracy of the results.

[0192] The allocator (10), 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 building premises (20), and a heat loss coefficient of the building premises, in order to determine the amount of thermal energy supplied to the premises (20) within the building (2) by the collective thermal management device (30) for a certain period.

[0193] Advantageously, the allocator (10) uses a heat loss coefficient of the building premises calculated based on a thermal simulation model (41) of the building (2), and takes into account the geometric structure of the building premises (20), the configuration of the walls, and optionally the configuration of the furniture of the building premises (20).

[0194] Additionally, the thermal simulation model (41) of the building (2) can take into account data representing local solar radiation during a given period.

[0195] As explained, preferably, local solar radiation data is not measured but is considered, because it is generated based on a model using local historical data.

[0196] In particular, the allocator (10) may include one or more processors configured to calculate allocation data to allocate financial costs associated with collective heating as a function of all supply energy amounts determined for each building premises (20).

[0197] According to another aspect, the present invention relates to a system (1) for allocating heating costs. The heating cost allocation system (1) may include an electronic device equipped with one or more processors. Preferably, the system (1) for allocating heating costs according to the present invention includes one or more processors configured to execute, regardless of advantage or disadvantage, the method according to the present invention, preferably the method (100) for determining the amount of heat energy supplied, and various preferred embodiments thereof.

[0198] In particular, the system (1) for allocating heating costs may include an allocator (10) according to the present invention. As described above, this allocator may be supported by a computer server (40). Additionally, the computer server (40) may be configured to communicate with an electronic display device (50) or any other electronic device capable of receiving data regarding heating costs or the amount of thermal energy supplied to the building premises.

[0199] Additionally, the heating cost allocation system (1) 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 heating cost allocation system (1) according to the present invention, or they may be devices already existing in a residence and configured to transmit data to the system (1) to allocate heating costs according to the present invention. For example, these devices may be integrated into a smoke detector or a connected thermostat.

[0200] As illustrated in FIG. 2, the electronic temperature measuring device (22) may be located in various zones or rooms (21) of the building premises (20). For example, a plurality of electronic temperature measuring devices (22) may be located in the same building premises (20) in rooms (21) that may or may not include auxiliary heating means (23).

[0201] In particular, this electronic temperature measuring device (22) may include other sensors in addition to a temperature sensor. In particular, the electronic temperature measuring device (22) may include a humidity sensor. A CO2 sensor may also be included. By using such sensors, positioning the electronic temperature measuring device (22) in a room containing a window (24) can enable the generation of data sufficient to estimate the opening time of the window (24).

[0202] The electronic temperature measuring device (22) will preferably be configured to transmit the measured data directly or indirectly to the allocator (10). The temperature will 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. In contrast, 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 every week, more preferably at least every day, and more preferably at least six times a day.

[0203] The electronic temperature measuring device (22) is preferably configured to transmit the measured data directly to the allocator (10) via wireless communication or a wired connection, most preferably via wireless communication. Alternatively, the data may be transmitted to a data collector (60) via a wireless connection or a wired connection.

[0204] The data collector (60) may be located in a common area of ​​the building (2) or outside the building (2). If the system (1) includes the 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 measuring device (22) associated with each building, on the one hand, and to implement a second wireless communication from the data collector (60) of the building (2) to the allocator (10), which is generally centralized and accessible via a network such as the Internet.

[0205] The present invention may include various alternatives and applications other than those described above. In particular, unless otherwise indicated, the various structural and functional features of each embodiment described above should not be considered as being coupled and / or closely and / or inseparably linked to one another, but rather as being considered as simple juxtapositions. Furthermore, the structural and / or functional features of the various embodiments described above may be subject to any other juxtaposition or any other combination, wholly or partially.

Claims

Claim 1 A method (100) for allocating energy costs in a collective building (2) comprising a plurality of individual building premises (20) and at least one collective heat management device (30), wherein the method (100) is applicable to all individual building premises (20) of the collective building (2) and includes the step of determining the amount of heat energy supplied to at least one individual building premises (20) of the collective building (2) during a given period by at least one collective heat management device (30), wherein the determination is performed by one or more processors, - a simulation model, - a plurality of ambient temperature values ​​of the collective building (2), wherein the plurality of ambient temperature values ​​of the collective building (2) include a plurality of temperatures of the individual building premises (20), - one or more temperature values ​​outside the collective building (2), - the volume of the individual building premises (20), and - at least one heat loss coefficient of the individual building premises (20), and - a calculation (130) of the at least one heat loss coefficient of the individual building premises (20) based on the simulation model. The method (100) includes the use of a simulation model as a thermal simulation model (41) of a collective building (2), and is characterized by virtually representing the collective building (2) and considering the geometric structure of the individual building premises (20) and the wall configuration of the individual building premises (20) for each individual building premises (20), as well as heat exchange between various premises of the collective building (2); furthermore, the method is characterized by using a plurality of temperatures of the premises or common spaces of the collective building (2) adjacent to the individual building premises (20). Claim 2 In claim 1, when the individual building premises (20) are further defined by a furniture configuration, the method (100) is characterized in that the thermal simulation model (41) of the collective building (2) further takes into account the furniture configuration. Claim 3 In claim 1, the thermal simulation model (41) of the collective building (2) is characterized by additionally considering data representing local solar radiation during a given period, in a method (100). Claim 4 A method (100) characterized in that, in claim 1, the thermal simulation model (41) of the collective building (2) is a dynamic thermal simulation model (41) of the collective building (2). Claim 5 In claim 1, at least one heat loss coefficient of the individual building premises (20) is selected from at least two predetermined heat loss coefficients, and each of the predetermined heat loss coefficients corresponds to a period, method (100). Claim 6 In claim 1, the step of determining the amount of thermal energy supplied to at least one individual building premises (20) of the collective building (2) by at least one collective thermal management device (30) includes using one or more adjustment coefficients, each adjustment coefficient being applied to an adjustment variable measured or calculated over a given period, method (100). Claim 7 In claim 6, the above adjustment variable is selected from a group including a variable representing heat loss related to window opening, a variable representing the occupancy level of an individual building premises (20), a variable representing weather conditions, and a variable representing the use of auxiliary heating, the method (100). Claim 8 In claim 6, the above adjustment variable is directly measured in the individual building premises (20) of the collective building (2) and is modeled in a statistically based probabilistic manner or reconstructed based on measurements of a machine learning algorithm, characterized in a method (100). Claim 9 A method (100) according to claim 1, comprising a correction step (120) of a thermal simulation model of a collective building (2), wherein the correction step (120) is performed based on a first measurement period of ambient and external temperature values ​​and a Bayesian correction algorithm so that the temperature calculated by the thermal simulation model (41) of the collective building (2) is reproduced as closely as possible to the measured temperature value. Claim 10 A method (100) characterized in that, in claim 1, the ambient temperature of the individual building premises (20) corresponds to the temperature measured at multiple locations within the individual building premises (20). Claim 11 A method (100) characterized in that, in claim 1, it includes the step (170) of calculating the individualized heating cost for an individual building premises (20) of a collective building (2) as a function of the determined amount of heat energy supplied to the individual building premises (20) by a collective heat management device (30). Claim 12 In claim 11, the step (170) of calculating individual heating costs is characterized by considering the orientation and location characteristics of individual building premises (20) within the collective building (2) in a manner that compensates for the difference between the premises or common spaces of the collective building (2). Claim 13 A method (100) characterized in that, in claim 1, the calculation (130) of at least one heat loss coefficient of the individual building premises (20) based on a thermal simulation model of the building may include the calculation of at least one heat loss coefficient based on a plurality of simulations in which values ​​for the individual building premises (20) regarding the geometric structure and wall configuration of the individual building premises (20) are modified in a manner that incorporates uncertainty related to the collective building (2). Claim 14 In paragraph 2, the calculation (130) of at least one heat loss coefficient of the individual building premises (20) based on the thermal simulation model (41) of the collective building (2) may include the calculation of at least one heat loss coefficient based on a plurality of simulations in which the furniture configuration values ​​for the individual building premises (20) are modified in a manner that incorporates uncertainty related to the collective building (2). Claim 15 A method (100) characterized in that, in claim 1, the calculation (130) of at least one heat loss coefficient of the individual building premises (20) based on a thermal simulation model (41) of the building may include the calculation of at least one heat loss coefficient based on a plurality of simulations for the individual building premises (20) in which the usage value or weather condition is modified in a manner that incorporates uncertainty related to usage and weather conditions. Claim 16 An energy cost allocator (10) for a collective building (2) comprising a plurality of individual building premises (20) and at least one collective heat management device (30), wherein the allocator is applicable to all individual building premises (20) of the collective building (2) and includes the step of determining the amount of heat energy supplied to at least one individual building premises (20) of the collective building (2) during a given period by at least one collective heat management device (30), wherein the allocator (10) comprises one or more processors configured to determine the amount of heat energy supplied, and the amount of heat energy is determined using the following items: - a simulation model; - a plurality of ambient temperature values ​​of the collective building (2), wherein the plurality of ambient temperature values ​​of the collective building (2) include a plurality of temperatures of the individual building premises (20); - one or more temperature values ​​outside the collective building (2); - the volume of the individual building premises (20); and - at least one heat loss coefficient of the individual building premises (20). The allocator (10) determines the amount of heat energy using the The allocator (10) is characterized by being configured to calculate (130) at least one heat loss coefficient of the individual building premises (20) based on a simulation model, wherein the simulation model is a thermal simulation model (41) of a collective building (2), which virtually represents the collective building (2) and, for each individual building premises (20), considers the geometric structure of the individual building premises (20) and the wall configuration of the individual building premises (20), as well as heat exchange between various premises of the collective building (2); furthermore, the allocator (10) is characterized by including one or more processors configured to determine the amount of supplied heat energy by additionally using a plurality of temperatures of the premises or common space of the collective building (2) adjacent to the individual building premises (20). Claim 17 In paragraph 16, when the individual building premises (20) are further defined by a furniture configuration, the system is characterized by an allocator (10) in which the thermal simulation model (41) of the collective building (2) further takes into account the furniture configuration. Claim 18 A heating cost allocation system (1) including an allocator (10) according to either Article 16 or Article 17.

Images