METHOD FOR SELECTING A CONTROL MODE FOR PASSIVE THERMAL CONTROL DEVICES IN A BUILDING

Abstract

A method for selecting a control mode for devices intended to regulate the indoor temperature of a building, from between an accumulative mode and a dissipative mode, the method comprising, for each of the control modes: - Obtaining estimates of the indoor temperature as a function of predefined parameters using a forecasting model, - Defining a forecasting error representative of the differences between measured indoor temperature values ​​and the estimates obtained, as a function of the predefined parameters, - Determining a set of optimal values ​​for the predefined parameters minimizing the forecasting error, - Determining (301, 311) a forecasting sequence comprising estimates of the indoor temperature calculated according to the forecasting model with the set of optimal values, - Determining (303,313) a criterion representative of a feeling of comfort and characterizing a crossing of a minimum or maximum comfort threshold by the predictive sequence. The process further includes: selecting (320) the piloting mode associated with the criterion representing the greatest feeling of comfort. Figure to be published with the abbreviation: Fig. 3,

Description

Title of the invention: METHOD FOR SELECTING A DRIVING MODE FOR PASSIVE DEVICES THERMAL REGULATION OF A BUILDING Technical field

[0001] The present invention relates to the field of thermal regulation of a building and relates in particular to a method of selecting, from two control modes, a control mode for thermal regulation devices. STATE OF PRIOR ART

[0002] The management of the thermal regulation of a building is generally based on two seasonal regulation modes, a first regulation mode adapted to thermally regulate the building in the summer season and a second regulation mode adapted to thermally regulate the building in the winter season.

[0003] Thus, the first mode of regulation generates the control of building openings in order to promote natural ventilation, in other words the heat transfers between the outside air and the inside air of the building, the control of solar protections in order to limit the heat inputs by solar radiation, and possibly the control of an air conditioning device. The second mode of regulation generates the control of a heating system and the control of solar protections in order to promote the heat inputs by solar radiation.

[0004] However, there are transitional periods between the winter season and the summer season for which meteorological fluctuations can be significant, such as significant variations in temperature or significant variations in sunshine duration. In such transitional periods, none of the seasonal control modes is suitable. Furthermore, alternating between the first control mode and the second control mode generates alternating heating and cooling while consuming energy.

[0005] It is then desirable to overcome these drawbacks of the state of the art.

[0006] It is particularly desirable to provide a solution which makes it possible to thermally regulate a building during periods of strong meteorological fluctuations while limiting the energy consumption used to ensure the cooling and heating of the building.

[0007] It is further desirable to provide a solution that is easy to implement and that uses readily available data.

[0008] Finally, it is desirable to provide a robust solution that requires low on-board power. Statement of the invention

[0009] An object of the present invention is to propose a method for selecting a control mode for thermal regulation devices of a building, from a first control mode, accumulative, and a second control mode, dissipative. Each thermal regulation device is intended to regulate the interior temperature of the building, called the interior temperature. The method is executed by a building control unit and comprises, for each of the control modes: - Obtain indoor temperature estimates based on predefined parameters using an indoor temperature forecast model, - Define a forecast error representative of deviations between measured indoor temperature values ​​and the indoor temperature estimates obtained, each deviation being defined at a time at which the measured indoor temperature is determined when said control mode is selected, the forecast error being a function of the predefined parameters, - Determine a set of optimal values ​​associated with said control mode, including a value associated with each predefined parameter and making it possible to minimize the forecast error, - Determine a forecast sequence associated with said control mode, the forecast sequence comprising estimates of the interior temperature calculated, from an initial time 11, according to the interior temperature forecast model by applying the determined set of optimal values ​​associated with the control mode, - Determine a comfort criterion associated with the control mode, representative of a feeling of comfort and characterizing a crossing of a minimum comfort temperature threshold and a maximum comfort temperature threshold by the estimates of the forecast sequence.

[0010] The method further comprises selecting the driving mode for which the associated determined comfort criterion is representative of the greatest feeling of comfort.

[0011] Thus, it is possible to propose a building thermal regulation solution that is easy to implement and that makes it possible to limit energy consumption during periods of significant weather fluctuations. It is also possible to carry out optimal thermal regulation of the building by choosing, from two control modes, the control mode that provides the greatest feeling of comfort.

[0012] According to a particular embodiment, determining the set of optimal values ​​of predefined parameters making it possible to minimize the forecast error comprises: consider that the forecast error is minimized when the forecast error is less than a predefined criterion.

[0013] According to a particular embodiment, determining the set of optimal values ​​making it possible to minimize the forecast error is carried out by implementing an optimization calculation based on a particle swarm optimization method or on the use of a genetic algorithm.

[0014] According to a particular embodiment, the forecasting model is a function of a first predefined parameter ¢, characteristic of a phase shift, of a second predefined parameter e corresponding to a damping factor, the estimation of the interior temperature Tintestitn at an instant ^«, situated between the initial instant h and an instant finaK / , being obtained from an interior temperature value Tinttnes known at the initial instant h, from an exterior temperature value Text known or estimated at the initial instant h and from a forecast exterior temperature value Text at the instant according to:

[0015] _ ■* int estim\ \^n) * int mesK^i) Tea(t)

[0016] Thus, the data necessary for the calculation of each forecast sequence are readily available since they only comprise measured temperature values ​​and temperature values ​​from an external weather forecast service. In addition, the predefined parameters characteristic of a phase shift and a damping factor respectively make it possible to characterize the thermal behavior, for example the thermal inertia, of the building and thus to obtain a more precise estimate of the interior temperature and its evolution. In addition, the small quantity of unknowns in the forecast model limits the risks of divergence during the implementation of the optimization calculation and thus provides a robust thermal regulation solution for the building requiring low on-board power.

[0017] According to a particular embodiment, the forecast error is an absolute mean error between the measured interior temperature values ​​y. and the estimates of the interior temperature y M obtained, and is written: * int estim\l7 estimated)- '■> where N represents the number of temperature values measured internal y M over the time interval [h; tf]. 2 int mes Vt

[0018] According to a particular embodiment, the comfort criterion is a duration between the initial instant 1i and an instant at which the forecast sequence crosses the minimum threshold or the maximum comfort temperature threshold for the first time.

[0019] According to a particular embodiment, the comfort criterion is inversely proportional to an area between the straight line corresponding to the minimum or maximum comfort temperature threshold which is crossed by the forecast sequence, and the curve representing the forecast sequence, between a start time tdeb of crossing said comfort temperature threshold, and an end time tfin of crossing said comfort temperature threshold.

[0020] According to a particular embodiment, each thermal regulation device is a passive device which does not consume energy to heat or cool the building.

[0021] Thus, the thermal regulation of the building can be easily carried out while limiting the energy consumption used to ensure the cooling and heating of the building. It is thus possible to carry out thermal regulation of the building at a lower cost while optimizing the feeling of comfort.

[0022] The invention also relates to a device intended to select a control mode for thermal regulation devices of a building, from a first control mode, accumulative, and a second control mode, dissipative, each thermal regulation device being intended to regulate the interior air temperature of the building, called interior temperature. The device comprises: - means for obtaining, for each of the control modes, estimates of the interior temperature based on predefined parameters using an interior temperature forecast model, - means for defining, for each of the control modes, a forecast error representative of deviations between measured interior temperature values ​​and the estimates of the interior temperature obtained, each deviation being defined at a time at which the measured interior temperature is determined when said control mode is selected, the forecast error being a function of the predefined parameters, - means for determining, for each of the control modes, a set of optimal values ​​associated with said control mode, comprising a value associated with each predefined parameter and making it possible to minimize the forecast error, - means for determining, for each of the control modes, a forecast sequence associated with said control mode, the forecast sequence comprising estimates of the interior temperature calculated, from an initial instant th according to the interior temperature forecast model by applying the determined set of optimal values ​​associated with the control mode, - means for determining, for each of the control modes, a comfort criterion associated with the control mode, representative of a feeling of comfort and characterizing a crossing of a minimum comfort temperature threshold and a maximum comfort temperature threshold by the estimates of the forecast sequence, - means for selecting the driving mode for which the associated determined comfort criterion is representative of the greatest feeling of comfort.

[0023] The invention also relates to a computer program, which can be stored on a medium and / or downloaded from a communication network, in order to be read by a processor. This computer program comprises instructions for implementing the above-mentioned method in any of their embodiments, when said program is executed by the processor. The invention also relates to an information storage medium storing such a computer program. Brief description of the drawings

[0024] The characteristics of the invention mentioned above, as well as others, will appear more clearly on reading the following description of at least one exemplary embodiment, said description being made in relation to the attached drawings, among which:

[0025] [Fig. 1] schematically illustrates a building in which a method for selecting a control mode is implemented;

[0026] [Fig.2] schematically illustrates a learning phase of the selection process of a piloting mode;

[0027] [Fig.3] schematically illustrates a selection phase of the selection process of a driving mode; and

[0028] [Fig.4] schematically illustrates an example of hardware architecture of a unit of control implementing the process of selecting a control mode.

[0029] DETAILED DESCRIPTION OF EMBODIMENTS

[0030] [Fig.l] thus schematically illustrates a building 10 in which a method for selecting a mode of control of thermal regulation devices of the building 10 is implemented.

[0031] The building 10 comprises openings 120 such as windows or bay windows. The building 10 further comprises passive devices 111, 112, 113 for thermal regulation. Each passive device 111, 112, 113 may be associated with an opening. A passive device is for example a motorized opening system 111 or motorized removable solar occultation devices such as a blind 113 or a windbreaker. sun 112. Building 10 may also include active thermal regulation devices.

[0032] The passive devices 111, 112, 113 and the active thermal regulation devices contribute to the thermal regulation of the building 10 by promoting the heating and / or cooling of the building 10. The active thermal regulation devices, such as heating or air conditioning, consume, during their operation, fossil, electrical or biomass energy in order to carry out actions making it possible to heat or cool the building 10.

[0033] The passive devices 111, 112, 113 make it possible to ensure heat transfers between the exterior and the interior of the building 10 and / or make it possible to limit heat transfers between the exterior air and the interior air of the building 10, thus making it possible to heat, cool or limit the heating or cooling of the building 10. In other words, the passive devices 111, 112, 113 comprise means configured to facilitate or reduce heat exchanges between the interior and the exterior of the building 10, for example by air renewal or management of solar gains. Thus, the passive devices 111, 112, 113 make it possible to modulate heat transfers between the exterior and the interior of the building 10.

[0034] The effects of heating, cooling, or limiting heating or cooling of the building 10 are obtained, for each passive device 111, 112, 113, according to a context of use of said passive device 111, 112, 113 and for a defined positioning or orientation of said passive device 111, 112, 113. Thus, for a defined positioning or a defined orientation, each passive device 111, 112, 113 makes it possible to ensure or limit heat transfers between the exterior and the interior of the building 10 without consuming fossil, electrical or biomass energy.

[0035] For example, the blind 113 can, when in the lowered position, prevent heat buildup in the building 10. On the other hand, when the blind 113 is raised, the blind 113 promotes heating of the building 10 by solar radiation through a glazed surface, when the context of use involves the presence of sun. Similarly, the sunshade 112 makes it possible to limit or promote heating of the building 10 depending on its position and on the orientation and inclination of the sun.

[0036] According to another example, the motorized opening system 111 can cause, when it opens and keeps the associated opening 120 open, either a heating of the building 10 if the outside air is warmer than the inside air of the building 10, or a cooling of the building 10 if the outside air is colder than the inside air of the building 10.

[0037] The building 10 further comprises a control unit 100 intended to control the passive devices 111, 112, 113.

[0038] The control unit 100 implements the method of selecting a control mode, from two control modes: an accumulative mode A and a dissipative mode D. The accumulative mode A is configured to maximize external heat inputs and minimize heat losses. The dissipative mode D is configured to minimize external heat inputs and maximize heat losses. The selection of one control mode rather than another can thus make it possible to maintain a feeling of comfort in the building 10.

[0039] According to a first embodiment, each of the accumulative A and dissipative D control modes generates the control of the passive devices 111, 112, 113 and the active thermal regulation devices of the building 10.

[0040] According to a second embodiment, each of the accumulative A and dissipative D control modes generates only the control of the passive devices 111, 112, 113 of the building 10. Thus, the accumulative A and dissipative D control modes make it possible to carry out thermal regulation at a lower cost by optimally exploiting the thermal inertia of the building, in other words the thermal storage capacity of the building 10. The accumulative mode A then aims to maximize the heat storage in the building 10 while the dissipative mode D aims to minimize the heat storage in the building 10, thus allowing it to remain cool. The selection of one control mode rather than another can then make it possible to maintain a feeling of comfort in the building 10 while avoiding the use of an active thermal regulation device to compensate for the heat inputs or losses resulting from external meteorological fluctuations.

[0041] When the accumulative mode A is selected, the control unit 100 determines for each passive device 111, 112, 113, taking into account the meteorological context and the orientation of said passive device, in which position said passive device 111, 112, 113 makes it possible to maximize the thermal inputs. For example, the control unit 100 controls the opening of the blind 113. In addition, the control unit 100 can control the motorized opening system 111 to keep the associated opening 120 in the open position if the outside air is warmer than the inside air of the building 10. On the other hand, if the outside air is colder than the inside air, the control unit 100 can control the motorized opening system 111 to keep the associated opening 120 in the closed position.Alternatively, the control unit 100 can indicate to a user which openings are to be opened and which openings are to be kept closed, as well as which removable sun protections are to be lowered or raised.

[0042] When the dissipative mode D is selected, the control unit 100 determines for each passive device 111, 112, 113, taking into account the meteorological context and of the orientation of said passive device, in which position said passive device 111, 112, 113 makes it possible to minimize the thermal inputs. For example, the control unit 100 controls the closing of the blind 113 at times when the solar radiation could potentially heat the interior air of the building 10. In addition, the control unit 100 can control the motorized opening system 111 to keep the associated opening 120 in the open position when the context causes a cooling of the interior air of the building 10. Alternatively, the control unit 100 can indicate to a user the openings which are to be opened and the openings which are to be kept closed, as well as the removable solar protections to be lowered or raised.

[0043] The building 10 thus comprises a bioclimatic control system comprising the control unit 100 and the passive devices 111, 112, 113. The bioclimatic control system may also include the active thermal regulation devices of the building 10.

[0044] In order to select the control mode, the control unit 100 needs to make a forecast of the interior temperature of the building 10, called the interior temperature. The control unit 100 uses for this purpose an interior temperature forecast model which makes it possible to obtain an estimate of the interior temperature as a function of predefined parameters 0). each predefined parameter being representative of a characteristic of the building. In addition, the interior temperature forecast model makes it possible to obtain an estimate of the interior temperature Tintestim at at least one instant in, located between an initial instant and a final instant from a known interior temperature value at the initial instant y LA, of a value of x intmesW known or estimated outside temperature Text at the initial time and at least one forecast value of outside temperature Text at each time ^n.

[0045] Thus, according to a first example, the indoor temperature forecast model is written:

[0046] _ / 1 \ — T i / \ e ' int estim 1 * — int mes t *i / '

[0047] The parameter <e>is a first predefined parameter, characteristic of a phase shift, and e is a second predefined parameter corresponding to a damping factor.

[0048] From a series of values ​​of the outside temperature Text defined on the time interval [h; tf] for each instant tn = n. A with A t a time step of predefined duration, and n an integer taking the values ​​from 0 to the temperature forecast model makes it possible to obtain an estimate of the temperature Tjnfestim[ at each instant tn.

[0049] According to a second example, the indoor temperature forecast model is a function of two other predefined parameters (P, tp) and also a function of data forecasts of solar radiation GH estimated at times tn. The temperature forecast model is then written:

[0050] ( t \ — T / f \ < P - int estiml \*n) ~ * int estiml iv ' max^ [0051 ] Where max[G^ represents the maximum value of solar radiation Gg on the time interval [f; tf].

[0052] Furthermore, is a third predefined parameter, characteristic of a phase shift, and 0 is a fourth predefined parameter corresponding to a damping factor.

[0053] [Fig.2] schematically illustrates a learning phase of the method for selecting a control mode. The purpose of the learning phase is to determine a set of optimal values ​​of the predefined parameters associated with each control mode. In other words, at the end of the learning phase, a first set of optimal values ​​is obtained in association with the accumulative mode A and a second set of optimal values ​​is obtained for the dissipative mode D.

[0054] In a first step 200, the learning phase starts.

[0055] In a following step 201, a first control mode is chosen, for example the accumulative mode A. Two steps 202 and 204 are then carried out in parallel.

[0056] In step 202, the control unit 100 obtains measured interior temperature values ​​Tint at times tn = tk at which the control mode chosen in the previous step 201 was selected. For example, the control unit 100 can select said control mode and carry out a series of indoor temperature measurements for a predefined duration. According to another example, the control unit 100 can retrieve from a database indoor temperature values ​​previously measured when said control mode was previously selected. Each measured indoor temperature value T thus obtained is determined at a time tn = tk located between an initial time 11 = ta and a final time tf = t h. According to one embodiment, the control unit 100 retrieves from the database all the indoor temperature values ​​previously measured when the control mode was selected and which are available.According to a second embodiment, the control unit 100 defines a time interval [ta; t J and recovers, by filtering, all the interior temperature values ​​previously measured when the control mode was selected and which belong to said time interval [ta; t J. According to a third embodiment, the control unit 100 recovers a predefined number of values, for example the last 1000 interior temperature values ​​previously measured when the control mode was selected, and then determines the time interval [ta; t J comprising said interior temperature values.

[0057] In step 204, the control unit 100 obtains indoor temperature estimates esnm based on the predefined parameters using the indoor temperature forecast model. The indoor temperature estimates estim are obtained for the same times tk as the measured indoor temperature values ​​obtained in step 202.

[0058] To obtain said indoor temperature estimates Tint estim, the control unit 100 retrieves a measured indoor temperature value Tintmes at the initial time 11 = ta, for example from a database accessible from the control unit 100. The control unit 100 further retrieves outdoor temperature values ​​Text defined at the initial time 11 = t fl and at times tk, for example from an external weather data service or a database. The outdoor temperature values ​​Text can alternatively be measured and recorded in a database accessible from the control unit 100.

[0059] According to one embodiment, the control unit 100 can retrieve external temperature values ​​Text at times tx defined over the time interval |t, ; tf]. The control unit 100 then determines the values ​​of the external temperatures Text at times tk by linear interpolation from the values ​​defined at times tx.

[0060] According to one embodiment, the control unit 100 further recovers solar radiation data GH estimated at the instants

[0061] In a step 206 following the parallel steps 202 and 204, the control unit 100 defines a forecast error representative of deviations between the measured interior temperature values ​​Tiatmes obtained in step 202 and the estimates of the interior temperature Tintestim obtained in step 204. The forecast error is expressed as a function of the predefined parameters according to the interior temperature forecast model used in step 204.

[0062] For example, the forecast error is an absolute mean error between the measured indoor temperature values ​​Tintmes and the obtained indoor temperature estimates Tintestim, and is written: , where K represents the K number of measured indoor temperature values ​​Tintmes obtained in step 202.

[0063] According to another example, the forecast error is the sum of the squares of the residuals of the measured indoor temperature values ​​Tpar ​​with respect to the obtained indoor temperature estimates Tfntestim.

[0064] According to a third example, the forecast error is the mean square error of the difference between the measured indoor temperature values ​​Tint ^g and the indoor temperature estimates Tintestim obtained. The forecast error is alternatively the square root of the mean square error of the difference between the measured indoor temperature values ​​Tintmes and the indoor temperature estimates Tintegtim obtained.

[0065] In a following step 208, the control unit 100 performs an evaluation of the prediction error for different values ​​of the predefined parameters, with the objective of identifying the values ​​of the predefined parameters making it possible to minimize the prediction error. An optimization method can be used to efficiently test different sets of predefined parameter values. For example, a first optimization method that can be used is a method based on the exploitation of a genetic algorithm. A second optimization method is an iterative optimization method such as the particle swarm optimization method or PSO method (for "Particle Swarm Optimization"), as defined in the book Introduction to Nature-Inspired Optimization (2017), chapter 3 "Particle Swarm Optimization Algorithm".The prediction error is then considered as a cost function to be minimized to implement an optimization calculation. The optimization calculation requires an initialization of the predefined parameters. When the optimization calculation is performed for the first time for the chosen control mode, in other words if the learning phase is performed for the first time for the chosen control mode, the predefined parameters are initialized to predefined initial values ​​such as: £ = 1; O = 0; P = 0 and . <p = 0. dans le cas contraire, si la phase d’apprentissage a déjà été effectuée préalablement pour mode de pilotage choisi, les paramètres prédéfinis sont initialisés à des valeurs optimales obtenues lors d’une précédente.

[0066] une étape 210 suivante, l’unité contrôle 100 détermine l’ensemble optimales, associé au qui permet minimiser l’erreur prévision, chaque valeur étant associée un paramètre prédéfini du modèle prévision température intérieure.

[0067] selon réalisation, est considérée minimale lorsque résultat inférieur critère d’erreur. le d’erreur prend prédéfinie dépend l’expression définie l’étape 206. par exemple, erreur moyenne absolue entre températures intérieures mesurées tjnt mes et estimations intérieure tintesttm obtenues, peut avoir 0,5 °c.

[0068] être utilisé dans cadre calcul d’optimisation mis en œuvre 208 afin déterminer l’arrêt ainsi choisi. contraire (cas non représenté sur figures), ne pas donner ou égal d’erreur, nouveau effectué l’aide autre méthode (en utilisant par exemple algorithme génétique lorsqu’une pso n’a abouti), 208, définissant d’estimations tintestim déterminées partir d’un

[0069] ainsi, détermination nécessite faible puissance embarquée.

[0070] 212 s’il y encore choisi 201 précédente cours, autrement dit depuis 200 s’il existe alors retourne choisit ledit pilotage. dissipatif d accumulatif a précédemment.

[0071] ensemble déterminé chacun modes d, 214 effectuée.

[0072] 214, attend l’écoulement durée prédéterminée p id puis réitérer d’apprentissage. la 3 heures 6 heures. correspondre réception deux séries prévisionnelles extérieures successives 100, provenance service externe données météorologiques. régulièrement réévalué.

[0073] alternativement, instruction démarrage avant retourner 200. l’instruction émise fois qu’une sélection procédé doit démarrée, nouvelle requise.

[0074] la [fig.3] illustre schématiquement but sélectionner, desdites intérieure, parmi limiter mieux sensation d’inconfort.

[0075] première 300, démarre. déclenché régulièrement, après prédéfinie. série prévisionnelle reçue météorologiques, toutes deux étapes 301 311 ensuite effectuées parallèle. envoie 300 confirmation d’aboutissement démarrer sélection.

[0076] 301, séquence a, dite a. comporte estimations, associées tin( définies instants t n="t" m situés instant initial 11="t" c final d. chaque estimation estim 4 calculée appliquant l’instant actuel passé, l’instant d futur.

[0077] 311, d. j) aux i="t" f="t"

[0078] pour effectuer prévisionnelle, récupère text tm reçues déduites, interpolation linéaire l’intervalle temporel [tc ;="t" td] d’autres que m. intervalle h égale 48 96 heures, tx="tc+X" t, avec premier at x entier. obtenir plus rapprochées, effectue tels - tc + t,="t" second at inférieure at, compris 5 minutes 30 minutes. l’unité tintesam , pilotage, c, distante temporellement l’estimation

[0079] outre t obtenue mesure, extérieure „ mesure issue t«xt 100.

[0080] nécessaires aisément disponibles puisqu’elles comportent uniquement issues prévisions

[0081] outre, rayonnement solaire gg estimées tm.

[0082] 302, suivant compare seuil minimal confort tconf min maximal max ■

[0083] de manière similaire, 312, tconj mi„ ma .

[0084] lorsque incluse comme respectant confort. en revanche, franchit l’un desdits seuils se retrouve au-dessous au-dessus tctmf>The indoor temperature is considered to generate a feeling of discomfort.

[0085] According to one embodiment, the minimum and maximum comfort temperature thresholds Tconf min and ^conf max -01^ each have a constant predefined value.

[0086] According to an alternative embodiment, the minimum and maximum comfort temperature thresholds Tconf min and TWHf are determined as a function of a sliding outside temperature Textgnss, and of a lower offset offset.nf, respectively of an upper offset f^^sup, for the minimum, respectively maximum, comfort temperature threshold.

[0087] For example, the NF EN 16798-1 standard defines the values ​​of the minimum and maximum thresholds of comfort temperature T^f tnin and Tconf max according to:

[0088] conf min ~ Q 33. Tg^gi^ + 18, S-offset^

[0089] T^f max ~ 0.33. Tgxtgifag +18, S + offs&tsup

[0090] The sliding outside temperature Text gnss of a day j is defined by the standard NF EN 16798-1 as a function of the average outside temperatures of the three previous days t with i = 1 to 3, and with a parameter a = 0.8, according to: r 1 extmoy 100911 (j) = ( + +

[0092] Furthermore, the lower offset offset.^ and upper offset offsetSUf( values ​​vary according to a building category 10, representative of the type of population using the building 10. For example, for a building 10 of category 1, offset.nf = 3 and offsetsulF = 2; for a building 10 of category 2, offset.nf - 4 and offsetsutf = 3, and for a building 10 of category 3, offset.nf = 5 and offsetgup = 4.

[0093] In a step 303 following step 302, the control unit 100 determines a comfort criterion associated with the accumulative mode A, called comfort criterion A, from the comparison of the forecast sequence A with the minimum and maximum comfort temperature thresholds Tconf min and T^f max •

[0094] In a step 313 following step 312, the control unit 100 determines a comfort criterion associated with the dissipative mode D, called comfort criterion D, from the comparison of the forecast sequence D with the minimum and maximum comfort temperature thresholds Tconj mia and Tconf .

[0095] The comfort criterion is representative of a feeling of comfort of a user of the building 10.

[0096] According to a first embodiment, each comfort criterion has a comfort duration determined between the initial instant 11 = tc of the associated forecast sequence and an instant at which said forecast sequence crosses the minimum comfort temperature threshold TCOnf mjR or the maximum comfort temperature threshold Tconf max for the first time. The higher the comfort duration, the higher the feeling of comfort.

[0097] According to a second embodiment, the comfort criterion is inversely proportional to an intensity of discomfort represented by a surface between the straight line corresponding to the minimum or maximum comfort temperature threshold which is crossed by the forecast sequence, and the curve representing the forecast sequence, between a start time t^eb of crossing said comfort temperature threshold, and an end time tfin of crossing said comfort temperature threshold.

[0098] The intensity of discomfort can be calculated, between the start time of crossing and the end time tfjn of crossing, by summing, for each time tm between the start time t^eb and the end time tfjn at which an estimate of the indoor temperature Tintestfm(tm\ is defined, the absolute value of the difference between the estimate of indoor temperature Tintestim and the minimum or maximum comfort temperature crossed multiplied by a time step At of predefined duration separating each consecutive estimate of indoor temperature es^m of the forecast sequence considered, i.e.:

[0099] , IA / estimvmj * conf min / max I

[0100] Alternatively, for an hourly average indoor temperature calculated from the indoor temperature estimates Tint estim of the forecast sequence, the discomfort intensity can be calculated according to:

[0101] (A T ^conf min / max |

[0102] The lower the intensity of discomfort, the higher the feeling of comfort and the higher the comfort criterion.

[0103] When the comfort criterion associated with each driving mode is determined, in other words each of steps 303 and 313 is completed, a step 320 is performed.

[0104] In step 320, the comfort criterion A is compared to the comfort criterion D.

[0105] If the comfort criterion A is representative of a greater feeling of comfort than the comfort criterion D, in other words if the comfort criterion A is higher than the comfort criterion D, a step 321 is carried out. Otherwise, a step 322 is carried out.

[0106] In step 321, the control unit 100 selects the accumulative mode A to regulate the interior temperature of the building 10.

[0107] In step 322, the control unit 100 selects the dissipative mode D to regulate the interior temperature of the building 10.

[0108] [Fig. 4] schematically illustrates an example of hardware architecture of the control unit 100 of the building 10. The control unit 10 then comprises, connected by a communication bus 410: a processor or CPU (“Central Processing Unit” in English) 401; a RAM (“Random Access Memory” in English) 402; a ROM (“Read Only Memory” in English) 403; a storage unit or a storage media reader, such as a hard disk HDD (“Hard Disk Drive” in English) 404; and a communication interface 405 allowing communication with the passive devices 111, 112, 113 of the building 10.

[0109] The processor 401 is capable of executing instructions loaded into the RAM 402 from the ROM 403, an external memory (not shown), a storage medium, or a communications network. When the control unit 100 is powered on, the processor 401 is capable of reading instructions from the RAM 402 and executing them. These instructions form a computer program causing the processor 401 to implement all or part of the algorithms and steps described herein in relation to the control unit 100.

[0110] Thus, all or part of the algorithms and steps described in relation to the control unit 100 can be implemented in software form by the execution of a set of instructions by a programmable machine, such as a DSP (“Digital Signal Processor” in English) or a microcontroller, or in hardware form by a machine or a dedicated component, such as an FPGA (“Field-Programmable Gate Array” in English) or an ASIC (“Application-Specific Integrated Circuit” in English). < / e>

Claims

1. Claims Method for selecting a control mode for thermal regulation devices (111, 112, 113) of a building, from a first control mode, accumulative, configured to maximize external thermal inputs and minimize thermal losses, and a second control mode, dissipative, configured to minimize external thermal inputs and maximize thermal losses, each thermal regulation device being intended to regulate the interior temperature of the building (10), called interior temperature, the method being executed by a control unit (100) of the building (10) and comprising, for each of the control modes: - Obtain (204) indoor temperature estimates based on predefined parameters using an indoor temperature forecast model, - Define (206) a forecast error representative of deviations between measured interior temperature values ​​(202) and the estimates of the interior temperature obtained, each deviation being defined at a time at which the measured interior temperature is determined when said control mode is selected, the forecast error being a function of the predefined parameters, - Determine (210) a set of optimal values ​​associated with said control mode, comprising a value associated with each predefined parameter, and making it possible to minimize the forecast error, - Determine (301, 311) a forecast sequence associated with said control mode, the forecast sequence comprising estimates of the interior temperature calculated, from an initial instant 11, according to the interior temperature forecast model by applying the determined set of optimal values ​​associated with the control mode, - Determine (303, 313) a comfort criterion associated with the control mode, representative of a feeling of comfort and characterizing a crossing of a minimum comfort temperature threshold and a maximum comfort temperature threshold by the interior temperature estimates of the forecast sequence. The method further comprising: - Select (321, 322) the control mode for which the associated determined comfort criterion is representative of the greatest feeling of comfort (320), characterized in that the forecast model is a function of a first predefined parameter ¢, characteristic of a phase shift, of a second predefined parameter e corresponding to a damping factor, the estimation of the interior temperature Tiatestim at an instant tn, located between the initial instant h and a final instant tf, being obtained from an interior temperature value mes known at the initial instant h, from an exterior temperature value Text known or estimated at the initial instant h and from a forecast exterior temperature value Text at the instant f«, according to: T • (t ) = T (t} + int estun'. n ) / T

2. The method of claim 1, wherein determining (210) the set of optimal values ​​of predefined parameters for minimizing the forecast error comprises: considering that the forecast error is minimized when the forecast error is less than a predefined criterion.

3. Method according to one of claims 1 and 2, in which determining (210) the set of optimal values ​​making it possible to minimize the forecast error is carried out by implementing an optimization calculation based on an iterative optimization method or on the use of a genetic algorithm.

4. Method according to one of claims 1 to 3 in which the forecast error is an absolute mean error between the measured interior temperature values ​​t-, ( / ) and the estimates of the interior temperature y ( / ) obtained, and is written: J int estùwf / ; where N represents the number of values ​​of 2- / , N interior temperatures measured t M over the time interval int mes y1' [h ; tf].

5. Method according to one of claims 1 to 4 in which the comfort criterion is a duration between the initial instant 1i and an instant at which the forecast sequence crosses the minimum threshold or the maximum comfort temperature threshold for the first time.

6. Method according to one of claims 1 to 4 in which the comfort criterion is inversely proportional to an area between the straight line corresponding to the minimum or maximum comfort temperature threshold which is crossed by the forecast sequence, and the curve representing the forecast sequence, between a start time tdeb of crossing said comfort temperature threshold, and an end time ^end of crossing said comfort temperature threshold.

7. Method according to one of claims 1 to 6 in which each thermal regulation device is a passive device (111, 112, 113) which does not consume energy to heat or cool the building.

8. Device intended to select a control mode for thermal regulation devices (111, 112, 113) of a building (10), from a first control mode, accumulative, configured to maximize external thermal inputs and minimize thermal losses, and a second control mode, dissipative, configured to minimize external thermal inputs and maximize thermal losses, each thermal regulation device (111, 112, 113) being intended to regulate the interior air temperature of the building (10), called the interior temperature, the device being characterized in that it comprises: - Means for obtaining (204), for each of the control modes, estimates of the interior temperature as a function of predefined parameters using an interior temperature forecast model, - Means for defining (206), for each of the control modes,a forecast error representative of deviations between measured interior temperature values ​​and the obtained interior temperature estimates, each deviation being defined at a time at which the measured interior temperature is determined when said control mode is selected, the forecast error being a function of the predefined parameters, - Means for determining (210), for each of the control modes, a set of optimal values ​​associated with said control mode, comprising a value associated with each predefined parameter and making it possible to minimize the forecast error, - Means for determining (301, 311), for each of the control modes, a forecast sequence associated with said mode of, control, the forecast sequence comprising estimates of the interior temperature calculated, from an initial instant th according to the interior temperature forecast model by applying the determined set of optimal values ​​associated with the control mode, - Means for determining (303, 313), for each of the control modes, a comfort criterion associated with the control mode, representative of a sensation of comfort and characterizing a crossing of a minimum comfort temperature threshold and a maximum comfort temperature threshold by the interior temperature estimates of the forecast sequence, - Means for selecting (321, 322) the control mode for which the associated determined comfort criterion is representative of the greatest sensation of comfort (320), characterized in that the forecast model is a function of a first predefined parameter ¢, characteristic of a phase shift,of a second predefined parameter e corresponding to a damping factor, the estimate of the interior temperature T at an instant *n, located between the initial instant h and a final instant tf, being obtained from an interior temperature value Tint mes known at the initial instant h, from an exterior temperature value Text known or estimated at the initial instant and from a forecast value of exterior temperature Text at the instant according to: ( f \ _ fif \ . t ' ^ea 1 int estimate1 n J — ' int mes 7 y,

9. Computer program that can be stored on a medium and / or downloaded from a communication network, in order to be read by a processor, and characterized in that it comprises instructions for implementing the method according to one of claims 1 to 7, when said program is executed by the processor.

10. An information storage medium storing a computer program according to the preceding claim.

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