Method and arrangement for adjusting a room climate with air conditioning preferences of users

Abstract

Air-conditioning preferences (T1, T2) of room users are read in order to compare a room temperature with air-conditioning preferences (T1, T2) of the room users. Additionally, factors (EF, WD) influencing the room temperature are detected and fed to a simulator (SIM) in order to simulate the room temperature. A respective energy requirement (E1,..., EN) for adapting the room temperature to the air-conditioning preferences (T1, T2) is simulated by means of the simulator (SIM) on the basis of the detected influencing factors (EF, WD) for different distributions (D1,..., DN) of room users in the room (R). An energy-saving distribution (D2) of the room users is then ascertained on the basis of the simulated energy requirement (E1,..., EN). Position allotment information (POS) for room users is also output according to the energy-saving distribution (D2).

Description

[0001] The settings for heating, air conditioning, ventilation, or other systems for regulating temperature, humidity, or other parameters of a room climate are essential for personal well-being at work or in a home. Especially in open-plan offices with many people, it is often difficult to find an optimal setting for the climate control systems that meets the needs of everyone in the room. This problem occurs especially when the room climate is centrally controlled. However, even with individual settings for local radiators, cooling systems, or ventilation, the individual needs of room users often cannot be fully met, as individual settings usually affect the overall room climate. Furthermore, climate control systems often react slowly, making the effects of settings difficult to estimate.

[0002] Recently, mobile phone applications have been developed that facilitate consensus-building between different room users and also allow for active control of the room climate during periods of absence. However, these settings often lead to average results that not all room users are satisfied with. Furthermore, changes in occupancy often result in inadequate responses. Patent document DE202020105811U shows an exemplary computer-implemented method for adjusting the room climate of a room.

[0003] It is an object of the present invention to provide a method and an arrangement which allow a more efficient adjustment of a room climate with the climate preferences of room users.

[0004] This object is achieved by a method having the features of patent claim 1, by an arrangement having the features of patent claim 12, by a computer program product having the features of patent claim 13 and by a computer-readable storage medium having the features of patent claim 14.

[0005] To compare a room climate with the climate preferences of room users, the climate preferences of room users are read in. These climate preferences can relate, in particular, to temperature, humidity, ventilation, brightness, shading, and / or sunlight in a room. Furthermore, physical factors influencing the room climate are recorded and fed into a simulator to simulate the room climate. The simulator simulates the energy expenditure required to adapt the room climate to the climate preferences for different distributions of room users within the room, depending on the recorded influencing factors. Based on the simulated energy expenditure, an energy-saving distribution of room users is then determined. Furthermore, location allocation information for room users is output based on the energy-saving distribution.

[0006] To carry out the method according to the invention, an arrangement for comparing a room climate with climate preferences of room users, a computer program product and a computer-readable, preferably non-volatile storage medium are provided.

[0007] The method according to the invention, the arrangement according to the invention and the computer program product according to the invention can be carried out in particular by means of one or more computers, one or more processors, application-specific integrated circuits (ASICs), digital signal processors (DSPs), a cloud infrastructure and / or so-called "field programmable gate arrays" (FPGAs).

[0008] By distributing room users based on their climate preferences, the room climate can be aligned with the room users' climate preferences in an efficient and energy-saving manner. In many cases, this can significantly improve user comfort and thus user satisfaction.

[0009] Advantageous embodiments and further developments of the invention are specified in the dependent claims.

[0010] According to an advantageous embodiment of the invention, the room climate can be adjusted to the climate preferences of room users distributed according to the energy-saving distribution. This can be achieved, in particular, by actively controlling a heating, air conditioning, ventilation, and / or shading system. Due to the inherent inertia of the aforementioned climate control systems, these can preferably be controlled before the room users are actually positioned or are positioned according to the energy-saving distribution.

[0011] According to further advantageous embodiments of the invention, influencing factors can include temperature, air humidity, ventilation, brightness, shading, or other room climate data; current, historical, or forecast weather data; room usage behavior; and / or a window position, a door position, or a position of a shading system, preferably recorded by sensors and / or in a location-specific manner. Alternatively or additionally, historical room climate data and / or other historical influencing factors can also be recorded and used. Taking the aforementioned influencing factors into account generally allows for a relatively accurate simulation of a room climate.

[0012] According to a particularly advantageous embodiment of the invention, a digital building model can be imported for the room. Energy consumption can then be simulated based on the digital building model. Since a room's geometry and the properties of building elements generally have a significant influence on the room's climate, the simulation can often be significantly simplified or improved by using a digital building model.

[0013] In particular, a semantic building model can be imported as a digital building model. A building element type of the semantic building model can be assigned to a building element type-specific simulator component, which can be initialized by specifying the semantic building model via a building element of this building element type. This allows the simulator to be modularized efficiently in many cases, which generally simplifies configuration or initialization of the simulator.

[0014] According to a further advantageous embodiment of the invention, the room or a construction plan of the room can be scanned and the digital building model can be generated depending on this.

[0015] Furthermore, a thermal image of the room can be captured, which is used to calibrate the simulator. Such calibration based on real thermal data can generally improve the accuracy of the simulation, particularly a temperature or flow simulation. Alternatively or additionally, the current temperature distribution in the room can be determined or estimated for calibration of the simulator using temperature sensors, another simulation, weather data, data from a digital building model, and / or data from a building management system.

[0016] According to a further advantageous embodiment of the invention, a deviation between a simulated room climate and the climate preferences of room users distributed according to a respective distribution can be determined to simulate a respective energy consumption. This allows the energy consumption required for an adjustment of the room climate that reduces or minimizes the deviation to be determined. In particular, a minimum energy consumption can be determined, if necessary, at which the resulting deviation does not exceed a predetermined tolerance value.

[0017] According to an advantageous development of the invention, the energy expenditures for variations in climate preferences and / or influencing factors can be simulated. This allows a sensitivity value to be determined for each room user distribution, which quantifies a variation in energy expenditure when the climate preferences and / or influencing factors vary. The energy-saving distribution can then be determined based on the determined sensitivity values. A smaller sensitivity value generally indicates a lower dependence of energy expenditure on the climate preferences and / or influencing factors. If influencing factors or climate preferences change, less sensitive distributions generally require fewer adjustments and are therefore often preferable to more sensitive distributions.

[0018] Furthermore, a fluctuation value can be input regarding the expected fluctuation in room occupancy by room users. The energy-saving distribution can then be determined based on the fluctuation value. Based on such fluctuation value, the simulation can be improved in many cases. The fluctuation value can, in particular, include historical data on room occupancy over the course of a day, week, or year.

[0019] In addition, the current occupancy of the room by room users can be recorded. The energy-saving distribution can then be determined based on the current occupancy. Since the distribution of climate preferences usually also depends on the current room occupancy, this information can usually be used to improve the simulation.

[0020] An embodiment of the invention is explained in more detail below with reference to the drawings, each of which shows a schematic representation: Figure 1 shows an arrangement according to the invention for adjusting the room climate of a room with climate preferences of room users, Figure 2 shows various distributions of room users with different climate preferences, Figure 3 shows a first diagram to illustrate a relationship between fulfillment of climate preferences and energy expenditure, and Figure 4 shows a second diagram to illustrate a less sensitive relationship between fulfillment of climate preferences and energy expenditure.

[0021] Figure 1shows a schematic representation of an arrangement A according to the invention for adjusting the room climate of a room R with the climate preferences of room users. The arrangement A is computer-controlled and has one or more processors PROC for carrying out the method steps according to the invention and one or more memories MEM for storing data to be processed by the arrangement A. The room R can be part of a building or structure, such as an open-plan office, a factory hall, a living space or another room whose room climate is to be adjusted with the climate preferences of room users. The room climate can in particular include or relate to a temperature, air humidity, ventilation, brightness, shading and / or solar radiation of the room R. The room climate is preferably observed or recorded in a location-dependent manner.

[0022] Room R has a control system H for preferably location-dependent control of the room climate. Control system H may include, for example, a heating system, an air conditioning system, a ventilation system, and / or a shading device.

[0023] Furthermore, the room R and / or its surroundings have a sensor system S that preferably measures or otherwise records physical influencing factors EF on the room climate in a location-specific manner. Furthermore, the sensor system S preferably also records the current occupancy of the room R by room users. Influencing factors EF can include, in particular, temperature, air humidity, ventilation, brightness, shading, solar radiation, window position, door position, position of a shading system, room usage behavior, or other room climate data of the room, preferably recorded in a location-specific manner.

[0024] As further physical influencing factors EF, predicted, current or historical weather data WD can be retrieved, for example, from the Internet IN.

[0025] While current room climate data or environmental data, such as an outside temperature, are preferably recorded using the sensor S, historical room climate data or other factors influencing the room climate can be read in, for example, from a database DB.

[0026] In the present exemplary embodiment, arrangement A reads in from database DB, in particular, a digital, semantic building model BIM, which structurally specifies room R. The semantic building model BIM is preferably a so-called BIM model (BIM: Building Information Model) or another CAD model. The semantic building model BIM describes a geometry of room R as well as a large number of its building elements, such as walls, ceilings, floors, windows or doors, in machine-readable form using a large number of building element details. Insofar as the geometry and the specific building elements of a room have a significant influence on its indoor climate, the semantic building model BIM or the details contained therein can also be understood as physical influencing factors.

[0027] According to the invention, the arrangement A is intended to compare the indoor climate of room R with the climate preferences of the room occupants. For this purpose, the arrangement A queries the climate preferences of the room occupants via their mobile phones MT and / or reads in stored or historical climate preferences. The climate preferences can, in particular, relate to temperature, air humidity, ventilation, brightness, shading, and / or solar radiation of room R.

[0028] In the present embodiment, for reasons of clarity, only two temperature preferences of the room occupants are considered as climate preferences. T1 could represent a "rather cool" temperature preference and T2 a "rather warm" temperature preference. Climate preferences T1 and T2 can be specified, for example, by temperature intervals.

[0029] To simulate the indoor climate of room R, arrangement A is equipped with a simulator SIM. For this simulation, the semantic building model BIM, the physical influencing factors EF, the weather data WD, and the climate preferences T1 and T2 are fed into the simulator SIM.

[0030] The SIM simulator can include specific simulator components, e.g., for temperature simulation and / or flow simulation. If necessary, a temperature simulation of the SIM simulator can be calibrated using thermal images of the room R.

[0031] In addition, the SIM simulator can include a building element type-specific simulator component for each of the different building element types, such as windows, doors, or walls of the semantic building model (BIM). The latter can then be initialized using information from the semantic building model (BIM) about specific building elements of the respective building element type. For example, a particular wall of room R can be coupled with a simulator component that specifically simulates heat conduction through the wall and is initialized using information about the thermal conductivity of the wall from the semantic building model (BIM). In this way, the configuration or initialization of simulation models or other simulator components of the SIM simulator can be automated or simplified in many cases.

[0032] The arrangement A further comprises a generator GEN coupled to the simulator SIM for generating distributions D1,...,DN of room users in room R. A respective distribution D1,... or DN can preferably be represented by a data structure that specifies the positions of room users in room R.

[0033] The climate preferences, in this case T1 and T2, are fed into the generator GEN. Based on the climate preferences T1 and T2, the generator GEN generates preferential distributions D1,...,DN in which room users with the same or similar climate preferences are positioned adjacent to each other. The generated distributions D1,...,DN are transmitted from the generator GEN to the simulator SIM.

[0034] Depending on the influencing factors EF, the SIM simulator simulates an energy expenditure E1,... or EN for the transmitted distributions D1,...,DN for adapting the room climate to the climate preferences distributed according to D1,... or DN, here T1 and T2. To determine the respective energy expenditure E1,... or EN, deviations between various simulated room climates and the climate preferences of the room users distributed according to D1,... or DN are determined. Based on the deviations, an energy expenditure E1,... or EN is determined for a respective distribution D1,... or DN, by which a deviation is reduced or minimized. Preferably, a tolerance value for the deviations can be specified. This allows a minimum energy expenditure E1,... or EN to be determined, if necessary, at which the resulting deviation does not exceed the specified tolerance value.

[0035] In the present exemplary embodiment, the above energy expenditures E1,...,EN are additionally simulated for a multitude of variations in the climate preferences, here T1 and T2, and / or the influencing factors EF. For each distribution D1,..., or DN, it is determined how strongly a respective energy expenditure E1,... or EN varies with variations in the climate preferences T1, T2 and / or the influencing factors EF. The resulting variation in the respective energy expenditure E1,... or EN is quantified by a distribution-specific sensitivity value S1,... or SN. A smaller sensitivity value S1,... or SN indicates a lower dependence of the energy expenditure E1,... or EN on the climate preferences T1, T2 and / or the influencing factors EF. Distributions with smaller sensitivity values are therefore more robust against fluctuations in climate preferences and / or influencing factors.If influencing factors or climate preferences change, robust distributions usually require smaller adjustments and are therefore often preferable to less robust distributions.

[0036] The distributions D1,...,DN, the determined energy expenditures E1,...,EN as well as the determined sensitivity values S1,...,SN are transmitted from the simulator SIM to a selection module SEL coupled to the simulator SIM.

[0037] In addition, the room occupancy currently measured by the sensor S and / or a fluctuation indication regarding an expected fluctuation in room occupancy are transmitted to the selection module SEL. The fluctuation indication can be read from the database DB and, in particular, include historical data on room occupancy over the course of a day, week, or year.

[0038] The selection module SEL is used to determine and select an energy-saving distribution of room users based on the energy expenditures E1,...,EN and the sensitivity values S1,...,SN. A distribution with a relatively low energy demand and a relatively low sensitivity value is selected. If necessary, a weighted sum of the respective energy expenditure E1,... or EN and the associated sensitivity value S1,... or SN can be calculated. In this case, a distribution with the smallest weighted sum can be selected as the energy-saving distribution.

[0039] In addition to the energy expenditures E1,...,EN and the sensitivity values S1,...,SN, the room occupancy and / or the fluctuation data can also be taken into account when selecting the energy-saving distribution. In particular, the fluctuation data can be compared with the sensitivity values S1,...,SN. Depending on this, distributions that are too sensitive to the expected fluctuations according to their sensitivity value can be rejected for selection.

[0040] For the present embodiment, it is assumed that the distribution D2 best meets the above criteria for a low-sensitivity energy-saving distribution and is therefore selected.

[0041] The selected energy-saving distribution D2 is transmitted by the selection module SEL to a location allocation device POE linked to it. The location allocation device POE determines the individual position in room R specified for each room user in the distribution D2 and inserts this into a room-user-specific location allocation information POS. The respective location allocation information POS is then transmitted individually by the location allocation device POE for each room user to their mobile phone MT. The respective location allocation information POS assigns the respective room user an individually optimized position, for example, in an open-plan office.

[0042] Furthermore, the selected energy-saving distribution D2 and the associated energy consumption E2 are transmitted from the selection module SEL to a control device CTL coupled to it. The control device CTL serves to control and adjust the control system H depending on the selected energy-saving distribution D2 and the determined energy consumption E2. For this purpose, the control device CTL transmits corresponding control data CD to the control system H. Since such climate control systems often react sluggishly, the control system H can preferably be controlled before the room occupants are or will be distributed according to the selected distribution D2.

[0043] By distributing room users within the room based on their climate preferences and actively controlling the H control system, a room climate can be efficiently and energy-efficiently aligned with the room users' climate preferences. In many cases, this can significantly improve user comfort and thus user satisfaction.

[0044] Figure 2 illustrates different distributions D1,...,D6 of room users in room R, grouped according to their different climate preferences, here T1 and T2. The distributions D1,...,D6 are an exemplary selection from the distributions D1,...,DN described above. Possible locations of the room users within room R are shown in Figure 2 illustrated by small rectangles.

[0045] By grouping the room users according to their climate preferences T1 and T2, the room R is divided into different room climate zones TZ1 and TZ2 for a respective distribution D1,...,D6. The room climate zone TZ1 is the area of the room R in which the room users with the climate preference T1 are located. Accordingly, the room climate zone TZ2 is the area of the room R in which the room users with the climate preference T2 are located. The room climate zones TZ1 and TZ2 are divided into Figure 2 Each is marked by a dotted line. In this example, the room climate zones TZ1 and TZ2 are temperature zones.

[0046] As already explained above, the simulator SIM simulates for each distribution D1,...,D6 the energy expenditure E1,...,E6 that is required to create the corresponding indoor climate in the respective indoor climate zones TZ1 and TZ2.

[0047] The uniform distributions D4 and D5 are evidently less robust in the above sense. Distributions D4 and D5 are only comfortable for all room users if they have the same climate preference. Experience has shown, however, that this is only the case for a few room user distributions.

[0048] The Figures 3 and 4 Each illustrates, by way of example, a relationship between an energy expenditure E and the resulting fulfillment of the climate preferences of room users. The energy expenditure E can, in particular, be a heating output. In the schematic diagrams shown, a deviation DEL between a simulated room climate and the climate preferences of the room users is plotted against the energy expenditure E. Since the comfort of the room users decreases with increasing deviation DEL, the smallest possible deviation DEL should be aimed for to optimize comfort.

[0049] In the Figure 3The first diagram shows the deviation DEL for room user distributions that have a higher sensitivity value, i.e., are less robust. Distributions D4, D5, and D6 are highlighted. The lower robustness of the distributions shown is shown in Figure 3 This is particularly evident in the fact that the minimum deviation DEL is relatively narrow. This means that even relatively minor variations in the comfort-optimizing distribution D6 significantly reduce comfort.

[0050] In contrast, in the Figure 4 The second diagram shows the deviation DEL for room user distributions that have a lower sensitivity value, i.e., are more robust. Distributions D1, D2, and D3 are highlighted. The greater robustness of the distributions shown is shown in Figure 4This is particularly evident in the fact that the minimum of the deviation DEL is relatively broad. This means that variations in the comfort-optimizing distribution D2 reduce comfort relatively little.

[0051] To ensure that comfort does not decrease significantly or require excessive energy consumption due to changes in influencing factors or the addition of new room users with different climate preferences, the robust yet energy-saving distribution D2 is selected in the present example. The room users are then distributed in room R according to the selected distribution D2, as described above, using individual location allocation information POS.

Claims

1. Computer-implemented method for comparing a room climate of a room (R) with climate preferences (T1, T2) of room users, wherein a) climate preferences (T1, T2) of room users are input, b) physical influencing factors (EF, WD) on the room climate are detected, characterized in that c) the detected influencing factors (EF, WD) are fed into a simulator (SIM) for simulating the room climate, d) depending on the detected influencing factors (EF, WD), in each case one energy expenditure (E1,...,EN) for an adaptation of the room climate to the climate preferences (T1, T2) is simulated for different distributions (D1,...DN) of room users in the room (R) by means of the simulator (SIM), e) depending on the simulated energy expenditures (E1,..., EN), an energy-saving distribution (D2) of the room users is determined, and f) in accordance with the energy-saving distribution (D2), position assignment indications (POS) for room users are output.

2. Method according to Claim 1, characterized in that the room climate is brought close to the climate preferences (T1, T2) of room users distributed in accordance with the energy-saving distribution (D2).

3. Method according to either of the preceding claims, characterized in that the following are detected, preferably using sensors, as influencing factors (EF, WD): - a temperature, an air humidity, a ventilation, a brightness, a shading or other room climate data of the room, - present, historical or predicted weather data (WD), - a room utilization behaviour and / or - a window position, a door position or a position of a shading installation.

4. Method according to one of the preceding claims, characterized in that a digital building model (BIM) for the room (R) is input, and in that the energy expenditures (E1,...,EN) are simulated on the basis of the digital building model (BIM).

5. Method according to Claim 4, characterized in that a semantic building model is input as digital building model (BIM), in that a building element type of the semantic building model (BIM) is assigned to a building element type-specific simulator component, and in that the building element type-specific simulator component is initialized by an indication of the semantic building model (BIM) regarding a building element of this building element type.

6. Method according to Claim 4 or 5, characterized in that the room (R) or a building plan of the room is scanned, and in that, in dependence thereon, the digital building model (BIM) is generated.

7. Method according to one of the preceding claims, characterized in that a thermal image of the room (R) is captured, and in that the simulator (SIM) is calibrated by means of the thermal image.

8. Method according to one of the preceding claims, characterized in that, for the simulation of a respective energy expenditure (E1,...,EN), - a discrepancy between a simulated room climate and the climate preferences (T1, T2) of room users distributed in accordance with a respective distribution (D1,...,DN) is determined, and - an energy expenditure (E1,...,EN) for an adaptation of the room climate which reduces or minimizes the discrepancy is determined.

9. Method according to one of the preceding claims, characterized in that the energy expenditures for variations in the climate preferences and / or the influencing factors are simulated, in that in each case one sensitivity value (S1,...,SN) which quantifies a variation in the energy expenditures in the case of a variation in the climate preferences and / or the influencing factors is determined for the distributions (D1,...,DN) of the room users, and in that the energy-saving distribution (D2) is determined depending on the determined sensitivity values (S1,...,SN) .

10. Method according to one of the preceding claims, characterized in that a fluctuation indication regarding a fluctuation to be expected in an occupancy of the room (R) by room users is input, and in that the energy-saving distribution (D2) is determined depending on the fluctuation indication.

11. Method according to one of the preceding claims, characterized in that a present occupancy of the room (R) by room users is detected, in that the energy-saving distribution (D2) is determined depending on the present occupancy.

12. Arrangement (A) for comparing a room climate of a room (R) with climate preferences of room users, comprising means for carrying out the steps of a method according to one of the preceding claims.

13. Computer program product comprising instructions, which, when the program is executed by a computer, cause the latter to carry out a method according to one of Claims 1 to 11.

14. Computer-readable storage medium having a stored computer program product according to Claim 13.

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