FR2978569A1 - CONTROL METHOD FOR MANAGING THE COMFORT OF A ZONE OF A BUILDING ACCORDING TO A MULTICRITERIAL APPROACH AND INSTALLATION FOR IMPLEMENTING THE METHOD - Google Patents

Abstract

The control method for managing the comfort of a zone of a building makes it possible to determine, by means of a time horizon forecasting system (5), a prediction of the future evolutions of data relating to the temperature, the quality of the air and the brightness of the zone (1) according to measured data and operating states of equipment (2, 3, 4) of the zone able to modify the temperature, the air quality, the brightness of the zone . The method further enables an optimizer (9) to determine a set of commands to be applied to the equipment (2, 3, 4) according to the prediction to minimize a predetermined energy criterion and for the temperature values, quality of the the air and brightness of the area remain adjusted respectively according to at least one separate reference parameter for each value.

Description

1

Control method for managing the comfort of an area of a building according to a multi-criteria approach and installation for the implementation of the method Technical field of the invention

The invention relates to a control method for managing the comfort of an area of a building State of the art

Documents US2009 / 0112369 and US6,439,469 relate to a system 20 for controlling the temperature of a building using a prediction system. The prediction system only manages the temperature of the building by acting on the behavior of equipment of said building.

To date, this approach is limited because it does not allow optimal management of the building.

Document GR2004100045 describes a method using a logic controller to control the air quality and the optical and thermal comfort of a building. Object of the invention

The object of the invention is to achieve optimized management of an area of a building to improve the comfort perceived by the occupants.

It is intended that the method comprises the following steps: measuring data relating to temperature, air quality and brightness of the area, and transmitting these measured data to a time horizon forecasting system , transmit operating states of zone equipment capable of modifying the temperature, the air quality, the brightness of the zone to the time horizon prediction system, determine by the time horizon forecasting system a prediction of future evolutions of the data relating to the temperature, the air quality and the brightness of the zone according to the measured data and the operating states of the equipment, to determine by an optimizer a set of commands to be applied to the equipment in function prediction to minimize a predetermined energy criterion of the zone and for the values of temperature, air quality and the area of the zone remain adjusted respectively according to at least one separate reference parameter for each value.

Another subject of the invention relates to an installation for controlling the operation of an area of a building for the implementation of the method, the installation comprising: sensors capable of measuring the temperature, the quality of the air and brightness of the area, 2 3

equipment capable of modifying the temperature, the air quality and the brightness of the zone, a time horizon prediction system connected to the sensors and equipment for receiving associated data, an optimizer connected on the one hand to the system temporal horizon prediction to receive the prediction, and secondly to the equipment for controlling them according to the prediction, a predetermined energy criterion to be minimized and reference parameters respectively associated with the temperature, the luminosity and the air quality of the area.

BRIEF DESCRIPTION OF THE DRAWINGS Other advantages and features will emerge more clearly from the following description of particular embodiments of the invention given by way of nonlimiting example and represented in the accompanying drawings, in which: FIG. 1 shows schematically various elements of an installation, Figure 2 illustrates the various stages of implementation of the process, Figure 3 illustrates the evolution of the CO2 ratio in the air of the area for a day, Figure 4 illustrates the evolution ventilation system controls based on the ratio of CO2 in the air of Figure 3, Figure 5 illustrates a curve of temperature change in the area for a day, Figure 6 illustrates the evolution of the controls of the heating the area according to the temperature of Figure 5, 4

FIG. 7 illustrates the evolution of the brightness in the zone during a day, FIG. 8 illustrates the evolution of the flap controls of the zone as a function of the brightness of FIG. 7. Description of preferred embodiments

Unlike the prior art, the optimal management of an area of a building 1 o is carried out according to a multicriterion predictive approach taking into account at least the temperature, the air quality and the brightness of the zone to generate in said zone an environment with a satisfactory quality of life while not neglecting the energy consumption and / or the energy bill of the zone. The multi-criteria approach improves the overall comfort of the area for its occupants.

Thus, according to a particular embodiment illustrated in Figure 1, an area 1 of a building is representative of a living area where the man can live and / or work. The concept of zone 1 is to be taken in the broad sense, it can simultaneously aim at the building in its entirety, a particular piece of the building, a set of rooms of the building, or a space of a room.

The zone 1 may comprise equipment 2, 3, 4 able to modify its temperature, its air quality, its brightness. Equipment can impact a single parameter of the zone among the temperature, the air quality, the brightness, or impact several parameters at once. For example, a light bulb can impact two parameters such as the brightness and temperature of the area. Indeed, the higher the brightness of the bulb, the more calories are released, and this increase impacts the overall temperature of the area. In another example, equipment consisting of an oil-bath type electric heater will impact the temperature of the zone but not the air quality of the zone or the brightness of the zone.

The control method of the zone of a building to manage the comfort 5 uses a time horizon forecast system 5 of the evolution of the parameters of the zone 1. A time horizon forecasting system 5 makes it possible to from measurements of parameters, to predict the future evolutions of these parameters over time. Examples of parameters taken into account will be described below. In other words, it is based on measurements made physically to determine a chronology of behavioral evolution of said parameters.

As illustrated in FIG. 2, the method comprises a step E1 during which the data relating to the temperature, the air quality and the brightness of zone 1 are measured, and transmitted to the time horizon forecasting system. 5. In fact, the aforementioned parameters are assimilated to the measured data representative of the temperature (for example in degrees Celsius), of the air quality (for example as a measure of the proportion of CO2 in the air of the zone in ppm ), and the brightness of the area (for example by measuring lux in the area). These data can be measured by three sensors 6, 7 and 8 respectively associated with the data to be measured. Of course, the number of sensors is not limited to three, in fact, depending on the size and shape of the area, a plurality of sensors may be installed. Although a CO2-based example has been given as an air quality criterion, other criteria may be used, such as the concentration of various gaseous pollutants, the moisture content, and so on.

The time horizon forecasting system 5 also takes into account the operating states of the equipment 2, 3, 4 of the zone 1 capable of modifying the temperature, the quality of the air, the brightness of the zone 1. , in a step E2, these operating states can be transmitted to the 6

time transmission system 5. The transmission can be carried out at the request of the time horizon forecasting system 5, by the equipment 2, 3, 4 itself at regular intervals, or by the equipment 2, 3, 4 when it notes a change in its operating state, for example via a manual action of a user. A manual action can be likened to the modification of a thermostat in the zone, or to the manual opening of a shutter having an impact on one of the three parameters.

The steps E1 and E2 can be performed sequentially one after the other without importance of the order or concomitantly.

According to the data relating to the temperature, the air quality and the brightness of the zone, as well as the operating states of the equipment 2, 3, 4 able to modify these parameters (temperature, air, brightness), the horizon forecasting system 5 determines in a step E3 a prediction of future evolutions of the data relating to the temperature, the air quality and the brightness of the zone.

This prediction can be calculated on the basis of a temporal chronology, for example 12 hours or 24 hours, or other time steps.

The result of the prediction is transmitted to an optimizer 9. The role of the optimizer 9 is to generate (step E4) a set of commands to be applied to the equipment 2, 3, 4 according to the prediction. These commands generated by the optimizer 9 make it possible to minimize a predetermined energy criterion of the zone, and to ensure that the values of temperature, air quality and brightness of the zone remain adjusted respectively according to minus a separate reference parameter for each value. The reference parameters can be likened to instructions to ensure the comfort of people present in 7

The area. In fact, a reference parameter can be associated with two setpoint values, a so-called "low" and the other "high", generally it will be said that when the values of temperature, air quality and brightness of the zone are between these lower and upper limits the comfort of the occupants of the zone is respected. Therefore, if the setpoint is no longer, or will no longer be in the near future, the generated commands can be used to adjust the relevant value of the zone. By anticipating the problems, it is possible to solve them by limiting the costs and / or the electrical consumption necessary for their resolutions when these funds actually appear. In a step E5, following the step E4 commands to be applied are transmitted to the equipment 2, 3, 4. The transmitted commands can be representative of the set of commands generated over the time horizon (ie say the prediction), or part of the set of commands.

Preferably, these generated commands are also transmitted to the time horizon forecasting system 5. Thus, the time horizon forecasting system 5 can realize in the future a better prediction by knowing all or parts of the commands generated by the optimizer 9 .

In other words, at least a portion of the determined commands are transmitted to the time horizon prediction system 5 to assist in the generation of a future prediction.

Advantageously, the energy criterion is representative of the energy consumption or the energy bill.

Energy consumption refers to the consumption, preferably instantaneous in kilowatts, of the area. Indeed, depending on the hours of the day, the seasons, it may be better to limit the consumption in kilowatts because the general power grid has risks of overload, and overconsumption can then generate failures, this limitation allows 8

then to avoid the power grid failure. The present method also makes it possible to take into account global power limitations in a predictive manner on the consumption of all the equipment of a zone. According to one variant, it will be sought on the one hand to minimize consumption while having a high setpoint of consumption value not to be exceeded.

By energy bill, we mean the pecuniary vision of the operation of the area. For example, the rates of electricity consumed may vary over time (for example, according to the hours of the day). Therefore, it is possible to condition the operation of the area at a maximum cost, the optimizer for controlling the equipment so as to remain below this cost, while satisfying, when the cost limitation allows, the comfort of the area with respect to the reference parameters. According to one variant, it will therefore be sought on the one hand to minimize the cost of operation of the zone while having a high value set of cost not to be exceeded.

In the case where a maximum cost, or a maximum consumption, is to be respected, the optimizer can, if it is not possible to satisfy for this particular cost / consumption all the criteria of comfort (brightness, air quality and temperature), degrade certain reference parameters according to occupant preferences. By "degrade" is meant to work outside the instructions.

According to one variant, any quantitative criterion associated with the energy consumption of the zone can be minimized. Such an energy criterion may, for example, be representative of the greenhouse gas emissions per kW consumed.

As specified above, concomitantly with the energy criterion, the optimizer 9 conditions the commands that it will generate according to at least three reference parameters respectively associated with the quality of the air, the temperature, and the luminosity. These reference parameters can be seen as comfort levels. In addition, depending on the time of day, these settings may change. Thus, the optimizer 9 can take into consideration a pattern of evolution of the reference parameters of the zone in time. These patterns of evolution may for example be associated with the occupation or not of the zone according to the hours of the day.

In order to generate optimal commands, the optimizer 9 may include a memory containing the information of all the devices. The equipment can be classified into three groups respectively associated with temperature, air quality and brightness. The equipment can be classified in several groups at the same time according to the parameters which they are likely to impact. Therefore, the optimizer 9 composes with the different groups to satisfy the operating conditions of the zone. An installation making it possible to implement the method described above may comprise the sensors 6, 7, 8 able to measure the temperature, the air quality and the brightness of the zone, the equipment 2, 3, 4 capable of to modify the temperature, the air quality and the brightness of the zone, the time horizon prediction system 5 connected to the sensors 6, 7, 8 and the equipment 2, 3, 4 to receive associated data, and the optimizer 9 connected on the one hand to the time horizon prediction system 5 to receive the prediction, and on the other hand to the equipment 2, 3, 4 to drive them according to the prediction, a criterion predetermined energy to minimize and reference parameters respectively associated with the temperature, brightness and air quality of zone 1. The optimizer 9 10

and the forecasting system 5 can form a single control unit, for example a computer processing the data and equipped with a suitable program.

In order to achieve the most accurate prediction possible, the time horizon prediction system 5 may include a model of zone 1. Typically a model incorporates features of the area that can assist in the generation of the prediction, so it can be considered that the model is representative of the energy characteristics of the area also called energy balance of the area. Thus, such a model can integrate, in a non-limiting manner, the orientation of the zone, the volume of the zone, the type of insulation of the zone (to determine the heat exchange between the inside and the outside of the zone). zone), the number of windows in the area, and if applicable their orientations, the influence of shutters closed or open at the windows, etc.

From such a model, by knowing the inside temperature of the zone 1 and the temperature outside the zone 1, the temporal horizon prediction system 5 can determine the future evolutions of the temperature in the zone 1 from data relating to the quality of the thermal envelope of the building associated with the zone. In other words, by knowing the model of the area and the external disturbances (eg outside temperature, sunshine, etc.) to which the area is subject, the time horizon forecasting system 5 can make even more accurate forecasts. This prediction is enriched by the condition of the equipment influencing the temperature (eg heating, air conditioning, shutters and lighting).

Thus, thanks to the model, by knowing the position of the windows and the geographical location of the area, the time horizon forecasting system 5 is able to describe when the sun's brightness will penetrate to the 11

better the area, and can predict the evolution of brightness in the area during a day. This prediction is completed by the operating state of the equipment affecting the brightness (for example the lighting, the position of the shutters that will be controlled by the optimizer).

Knowing the difference in air quality between the inside and the outside of the zone, the occupation of the zone and the state of operation of the equipment affecting the quality of the air (for example ventilation), the prediction system can determine future changes in air quality.

Thus, a prediction can be refined by taking into account the external disturbances to which the zone is subjected. External disturbances can be associated with at least one criterion chosen from the outside temperature, the occupation of the zone, the humidity of the air outside the zone, the hours of sunrise and sunset, the sunshine, the share of CO2 in the outdoor air, the hours of occupation or not of the area etc. In fact, all meteorological information can be used to refine the prediction. Meteorology feeds the horizon forecasting system by taking into account the external conditions of the area. In addition, by combining the time horizon forecasting system with weather forecasts, it is notably easier to calculate the long-term changes (over several hours) of the zone's temperature and brightness values.

In order to determine the predictions, in particular according to the model of the zone and the values of the sensors, the prediction system may use mathematical calculation laws.

For example, the parameter associated with air quality may be a maximum value of CO2 in the air of the zone compared to a value of 12

CO2 measured in the area by the associated sensor. Thus, the optimizer 9 will do the necessary by controlling the equipment to maintain, from the prediction, the real CO2 value of the zone below the maximum value. In fact, in humans, it is known that CO2, at high concentrations in the breathing air, can be toxic. For example, from 0.1% of CO2 in the air, ie 1000ppm, CO2 becomes a risk factor triggering asthma attacks. A value of 0.5% CO2 in the air, ie 5000ppm, is considered as the maximum value of occupational exposure in most countries. Thus, the control of CO2 in the air of the zone is a significant parameter when one wishes to improve comfort. Figure 3 illustrates a representative curve of the change in the real value of CO2 in ppm for an area occupationally occupied by workers during a 24-hour day. The hatched portion represents an area in which the value of CO2 should preferably never be. In other words, if a measured value falls in the hatched portion for a given time, the time horizon forecasting system 5 and the optimizer 9 will take the necessary steps to find a correct value in the area. In fact, thanks to the time horizon forecasting system 5, the actual CO2 value of the zone will only exceptionally be in the hatched portion since, thanks to the prediction, the optimizer 9 anticipates the problem to solve it before its occurrence.

In the example of Figure 3, we see that between 6 am and 18 pm the value of CO2 in the area must remain below 1000ppm. In fact, this is the time range in which people occupy the area.

This Figure 3 can be associated with Figure 4 showing the evolution of ventilation controls during the day. In this FIG. 4, on the ordinate, it is considered that the ventilation is cut off to 0 and that the value 1 13

ventilation works at full speed. By superimposing Figures 3 and 4, we can see that when the CO2 level increases in the area, the ventilation starts.

To generalize, it can be considered that the optimizer 9 allows the value associated with the air quality in the zone to remain adjusted below its associated reference parameter when it is a simple high setpoint.

Thanks to the optimizer 9 associated with the prediction and the value of the setpoint reference parameter, it can be seen that the ventilation does not start as soon as the work zone is opened but a little before the maximum value of CO2 is reached. In fact, the time horizon forecasting system 5 has in this case anticipated the changes in the CO2 ratio in zone 1 and the optimizer 9 has decided to start the ventilation a little before the CO2 ratio reaches the maximum setpoint value of 1000ppm. From the diagram of the commands of figure 4, one sees that the system of prediction takes into account a factor of occupation of zone 1 because the ventilation is cut between 12 hours and 14 hours, and stops at 18 hours then at 12 and 18 hours, the CO2 level is still maximum. In fact, based on the occupancy factor, the forecasting system 5 determined that after 12 hours the workers would leave the area. However, fewer people in zone 1 involve less CO2 production due to people breathing. Therefore, the ventilation can be cut to limit costs or energy consumption. Thus, the prediction allows the optimizer 9, as shown in Figure 4, to cut off the ventilation from 12 o'clock and to put it back only later when the percentage of CO2 will be higher and cut at 18 hours. The predictions of the time horizon prediction system 5 can be improved in this case by taking into account the state of the installation and curves of values of the CO2 ratio of previous days. Indeed, in 14

depending on the past behavior of the CO2 ratio in the area for a similar state, the prediction can be refined.

The parameter associated with the temperature may be associated as mentioned above with a maximum temperature and / or a minimum temperature. In other words, the temperature can be a function of two setpoints, and the value of the temperature of the zone can be adjusted to oscillate between these two instructions. These two operating instructions are associated with the notion of comfort in temperature. In fact, if a measured temperature were to be outside the interval defined by the minimum value and the maximum value, the optimizer 9 would correct the shot on receipt of a new prediction, however thanks to the prediction system of temporal horizon 5 this case should occur only exceptionally because the interval output should be detected well in advance. Figure 5 illustrates an evolution curve of the temperature in an area during a day. It can be seen that between 18 o'clock in the evening and 6 o'clock in the morning, the reference parameter is associated with a single minimum temperature set at 5 ° C, which generally corresponds to a useful frost-free temperature in winter. Between 6 am and 6 pm, the minimum temperature is associated with 20 ° C and the maximum temperature is associated with a value oscillating between 22 and 25 ° C. The temperatures 20 ° C - 25 ° C correspond in fact to the comfort criterion generally recognized in terms of temperature in an area. Of course, these values can be adjusted on demand, for example via thermostats integrated in the zone and connected to the optimizer 9 so that their data is transmitted to the optimizer as soon as it changes.

This FIG. 5 can be associated with FIG. 6 representing the evolution of the heating controls in a day. In this figure 6, ordinate, it is considered that the heating is cut to 0 and that the value 1 heating is running at full speed. By superimposing figures 5 and 6, we can see 15

that the installation anticipates the occupation of the zone between 6 o'clock and 18 o'clock by turning on the heating shortly after 4 o'clock in the morning. This anticipation can be achieved thanks to the predictions of the temporal forecasting system 5 in combination with the reference parameters evolving in the course of the day. The optimizer 9 triggers the heating so that the energy criterion, to reach a temperature in the range defined above from 6 o'clock, is minimized.

In fact, it is possible to deduce from FIG. 5 that at 4 o'clock in the morning, the optimizer 9 will receive a prediction of the evolution of the temperature for example over the next five hours. At the precise moment of the realization of the prediction, the state of the heating equipment is at zero. The prediction then includes the evolution of the temperature during the next five hours for example based on the fact that the sun will rise and the temperature will increase slightly thanks to the heat exchanges between the inside and the outside of The area. From this prediction, the optimizer 9 knows that between 6 am and 6 pm the temperature of the building should be between 20 ° C and 25 ° C, so it calculates the best way to get the required temperature by 6 o'clock in the zone.

The reference parameter associated with the brightness can be a minimum and / or maximum setpoint in lux. Figure 7 illustrates a curve of change in brightness in the area during the day. In the example, between 6 am and 6 pm two instructions are respectively representative of a minimum value of brightness and a maximum value of brightness. These minimum and maximum values delimit an interval and the brightness value of the zone can be adjusted to oscillate between the minimum and maximum values.

Figure 8 is associated with the evolution of shutter controls. At 0 a flap is considered totally closed, and at 1 as totally open. The evolution of the shutter controls can be associated with the external brightness so that the reference parameter associated with the brightness is respected.

As indicated previously, some equipment may have influences on different values of zone 1 associated with the reference parameters. Therefore, the optimizer 9 will work with the different equipment to maintain the overall comfort of the area.

The examples in FIGS. 3 to 7 are based on a winter day with the criterion of minimizing the energy consumed. The person skilled in the art will understand, depending on the chosen criterion and the time of year, that the behaviors may be different.

In the case where the pricing is variable during the day, the off-peak / peak hours type, the optimizer may behave differently. In fact, the optimizer controls the equipment in this case so that when consumption in off-peak hours is maximized (while respecting the reference parameters), and so that in peak hours the values associated with the reference parameters in the zone derive while satisfying said reference parameters. For example, in the case of a late night time, or before the opening of the zone, the optimizer may overheat (in winter) or overclimate (in summer) before the occupancy hours of the zone and then let the temperature drift so that it satisfies its reference parameter during the occupation times.

According to another example in summer, the simultaneous consideration of the equipment used in an area allows to deduce a strategy of 17

optimal control depending on the situation. Generally, in summer the temperature of the outside air goes down during the night. As a result, the optimizer will be able to control the ventilation equipment to introduce fresh air. In this example, the ventilation system simultaneously influences the temperature and air quality of the area.

It follows from the above examples that it is advantageous for the optimizer to know the set evolution patterns for a day or occupation of the area during the day.

Preferably, the time horizon forecasting system 5 is self-adaptive. Thus, according to a particular embodiment that can be taken alone or in combination with the model of the zone, the time horizon forecasting system 5 can "learn" the different configurations in which the zone can be from measurements of previous days. . These configurations may be used for comparison with a current state of the installation to determine a best fit prediction. A configuration can be representative of the curves from Figures 3 to 7 for different periods of the year.

Advantageously, the installation implementing the method can self-calibrate to avoid a step of manual intelligence of the model of the zone. For example, to know the thermal losses between the outside and the inside of the zone, the installation may include an analysis unit connected to the indoor temperature sensor of the zone, and to an outside temperature sensor of the zone. . As a function of the evolution of the values of these sensors over time, the analysis unit can deduce the thermal properties of the zone and feed the model. According to another example, the analysis unit can be connected to a brightness sensor of the zone, and to a brightness sensor located on the roof of the building associated with the zone to establish a law of evolution over time. the brightness at 18

the interior of the zone according to the external luminosity without any light being preferentially lit in the zone. From this law, the model can be informed, and the prediction system can establish, depending on the time of day and the weather, the natural brightness of the area to which it will be possible to add the artificial brightness of associated equipment (s). In other words, with suitable sensors, the analysis unit is able to establish an energy diagnosis of the operation of the zone and to inform the model of the associated zone. In addition, the data used to feed the model of the area can be used to create a history of the data, this history can then be stored in a memory, and be used by the forecasting system to improve its predictions and compare them with data. real situations that have already existed. Thus, the installation may comprise automatic means for generating the energy balance of the zone to generate or modify the model. These means can be implemented in the method by a step of automatically determining the energy balance of the zone to generate or modify the model. Indeed, with such a diagnostic system the model can adapt over time. Such an adaptation can be advantageous in particular in case of degradation of the thermal insulation envelope of the building, or to manage the modification of the external environment of the building associated with the zone (for example new trees or a new building shady). Preferably, during the calibration, all the equipment of the zone are extinguished, in the case where the equipment is lit, they are controllable to allow to determine their influence on the parameters of comfort.

The present method, and its variants, differs from the algorithms of the prior art by the inclusion of all parameters (temperature, brightness, air quality) impacting significantly on the comfort of the area.

Moreover, the optimizer 9 is able to simultaneously generate commands for different equipment, or all the equipment, at the same time.

while maintaining the overall comfort of the area according to the reference parameters, and minimizing the energy criterion. The use of the same optimizer, able to control the different equipment, makes it possible to modify the operation of equipment of different natures and able to act on different values of the zone associated with the reference parameters (air quality, temperature , brightness).

According to an embodiment not shown, the zone is associated with means for generating energy based on local renewable sources (for example photovoltaic panels arranged on the roof of the building associated with the zone). In this particular case, the time horizon prediction system and the optimizer can interact synergistically with the power generation means to manage it. For example, if the energy criterion is the cost of consumption, and the cost associated with a third-party supplier is considered to be the lowest, it will be preferable to use the electricity of the third-party supplier while promoting the storage of solar energy. in batteries for the future increase of the cost of energy.

In order to optimize the management of the area, the prediction provided by the time horizon forecasting system 5 is over a predetermined time, typically a day. The set of commands calculated by the optimizer 9 is representative of the commands provided on the determined time. Only a portion of the commands are sent to the equipment and forecasting system 5 to determine a new prediction. This makes it possible to obtain a sliding prediction over a period less than the determined time. Thus, predictions and optimization are constantly re-evaluated. The use of slippery predictions makes it possible to re-evaluate a situation if a poor optimization of the orders has been made in the previous round. Thus, prediction errors can be absorbed by the perpetual re-evaluation mechanism of the best control strategy.

adopt. Preferably, the forecasts are calculated every minute or at an interval of several minutes. A short period, for example of ten minutes, makes it possible to wait for the stabilization of the comfort conditions in the zone following the application of new orders. The prediction horizon can be adjusted in line with the dynamic characteristics of the area in question, and can vary from one hour to several hours.

According to one particular embodiment, the equipment of the zone comprises one or more ventilation systems (acting on the quality 1 o of the air), one or more heating systems (acting on the temperature), one or more heating systems. climate control (acting on the temperature), one or more lighting systems (acting on brightness and temperature), and one or more shutter systems (acting on brightness and temperature). Each equipment has its own characteristics. The optimizer will also be able to preferentially deduce the use of one of the equipment when they have the same purpose depending on the situation in which the area is located.

The algorithms used to determine predictions and optimizations are preferably based on linear programming. Linear programming algorithms correspond to a form of mathematical optimization problems. In the case of linearity, the resolution of these problems is simple. Linear type algorithms have the advantage of being simpler to implement than other genetic, quadratic, or non-linear algorithms. Nevertheless, depending on the characteristics of the zone, these last algorithms can be integrated by the optimizer if the problem can not be expressed in the form of a linear programming problem.

According to one particular embodiment, the installation comprises several independent zones. Preferentially, different zones are associated with a 21

same forecasting system and the same optimizer that can then take into account the influence of one area on another. This allows in particular to have in a building areas whose comfort criteria are different.

Thus, by going through the forecasting system, the optimizer can formulate a mathematical optimization problem meeting the minimization objective described above and respecting the reference parameters. Advantageously, the forecasting system takes into account a model of the zone associated with external disturbances to deliver a most accurate forecast possible. The optimizer can take into account in its problem the data of the forecast, and advantageously the occupancy data of the zone to manage the zone so as to remain in adequacy with the reference parameters and the energy criterion to be minimized. When the problem is solved, the optimizer returns to the devices their operating states for them to apply. The occupancy data of the area can be used to determine and / or modify the reference parameters.

The zone optimizer may also take into account the nominal power limitation of each of the equipment taken independently, take into account the power limitation on the overall consumption of equipment in the zone, take into account the presence of occupants for adjust the reference parameters, take into account several equipments of the same nature.

Thus, a single optimizer can manage all the equipment in the same area optimally.

According to one variant, the optimizer can take into account tariff variations in a predictive manner. 22

Advantageously, the data taken into account by the optimizer mentioned above are simultaneously simultaneously respecting the criterion to be minimized and the reference parameters.

Alternatively, as the temperature and brightness, the air quality reference parameter can also be associated with two lower and upper setpoints.

Claims (10)

REVENDICATIONS1. Control method for managing the comfort of an area of a building characterized in that it comprises the following steps: measuring (El) data relating to the temperature, the air quality and the brightness of the area ( 1), and transmitting these measured data to a time horizon prediction system (5), transmitting (E2) operating states of equipment (2, 3, 4) of the zone (1) capable of changing the temperature , the air quality, the brightness of the zone (1) to the time horizon forecast system (5), determine (E3) by the time horizon prediction system (5) a prediction of future evolutions of the data relating to the temperature, the air quality and the brightness of the zone (1) according to the measured data and the operating states of the equipment (2, 3, 4), determined by an optimizer (9) a set of commands (E4) to be applied (E5) to the equipment (2, 3, 4) according to the prediction in ur minimizing a predetermined energy criterion of the zone and for the values of temperature, air quality and brightness of the area remain adjusted respectively according to at least one separate reference parameter for each value. 2. Method according to claim 1 characterized in that the energy criterion is representative of energy consumption or energy bill, or a quantitative criterion associated with energy consumption. 3. Method according to one of claims 1 to 2, characterized in that at least a portion of the determined commands is transmitted to the time horizon prediction system (5) to help generate a future prediction. 4. Method according to one of claims 1 to 3, characterized in that the optimizer (9) takes into consideration a pattern of evolution of the reference parameters of the zone in time. 5. Method according to one of claims 1 to 4, characterized in that the temperature and brightness values of the zone are each adjusted to 1 o function of two reference parameters respectively representative of minimum and maximum temperature values, and of minimum and maximum brightness values, said temperature and brightness values of the area being adjusted to oscillate between their associated minimum and maximum values. 15 6. Method according to one of claims 1 to 4, characterized in that the time horizon forecast system (5) comprises a model of the zone (1) representative of the energy characteristics of the zone (1), said model being used to generate the prediction. 7. Method according to claim 6, characterized in that it comprises a step of automatically determining the energy balance of the area to generate or modify the model. 25 8. Method according to one of claims 1 to 5, characterized in that the time horizon forecasting system (5) takes into account disturbances to which the zone is subjected, said disturbances comprising at least one criterion selected from the temperature outside, the 30 hours of sunrise and sunset, the amount of sunshine, the amount of CO2 in the outdoor air, the occupation times of the area. 20 25 9. Method according to any one of the preceding claims, characterized in that the prediction provided by the time horizon forecasting system (5) is over a predetermined time, the set of commands calculated by the optimizer ( 9) being representative of the commands provided on the determined time, only a portion of the commands being sent to the equipment (2, 3, 4) and the time horizon prediction system (5), to determine a new prediction, so as to obtain a slippery prediction over a period less than the determined time. 10. Installation for controlling the operation of an area of a building for the implementation of the method according to claims 1 to 9, characterized in that it comprises: sensors (6, 7, 8) able to measure the temperature, air quality and brightness of the area, equipment (2, 3, 4) able to modify the temperature, the air quality, the brightness of the area, a horizon forecast system time (5) connected to the sensors (6, 7, 8) and the equipment (2, 3, 4) for receiving associated data, an optimizer (9) connected on the one hand to the time horizon prediction system (5). ) to receive the prediction, and secondly to the equipment (2, 3, 4) to drive them according to the prediction, a predetermined energy criterion to be minimized and reference parameters respectively associated with the temperature, the brightness and air quality of the area (1).