Method and apparatus for comparing indoor climate to climate preferences of room users

Abstract

To compare the indoor climate with the climate preferences (T1, T2) of the room users, the climate preferences (T1, T2) of the room users are read in. Furthermore, physical factors influencing the indoor climate (EF, WD) are detected and fed into a simulator (SIM) for simulating the indoor climate. The simulator (SIM) simulates energy consumption (E1, ..., EN) for different distributions (D1, ..., DN) of the room users in the room (R) based on the detected influencing factors (EF, WD) in order to adapt the indoor climate to the climate preferences (T1, T2). An energy saving profile (D2) for the room users is then determined based on the simulated energy consumption (E1, ..., EN). Furthermore, a position assignment (POS) for the room users is output based on the energy saving profile (D2).

Description

[0001] The settings for heating, air conditioning, ventilation, or other systems used to regulate the temperature, humidity, or other parameters of the indoor climate play a significant role in the well-being of individuals in the workplace or living there. Especially in large offices with many people, it is often difficult to find optimal settings for the climate control system that meet the needs of all those in the room. This problem arises particularly when the indoor climate is centrally regulated. However, even with individual settings for local heaters, cooling systems, or ventilation, the individual needs of the room's users are often not fully met, as these settings often affect the overall indoor climate. Furthermore, climate control systems are often slow to respond, making the impact of settings difficult to assess.

[0002] Recently, mobile phone applications have become available that facilitate consensus among different room users and allow for proactive monitoring of the indoor climate during absences. However, the settings found in this way often result in average results that are not satisfactory for all room users. Furthermore, in many cases, responses to changes in the number of people present are only inadequate.

[0003] The object of the present invention is to specify a method and a device which allow a more efficient comparison of the indoor climate with the climate preferences of the room users.

[0004] This object is achieved by a method having the features of patent claim 1 , by an apparatus 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 the indoor climate with the climate preferences of the room users, the room users' climate preferences are read in. In this case, the climate preferences may specifically relate to the room's temperature, humidity, ventilation, brightness, shading, and / or solar radiation. Furthermore, physical factors influencing the indoor climate are detected and fed into a simulator for simulating the indoor climate. The simulator simulates energy consumption for different distributions of room users in the room based on the detected influencing factors to adapt the indoor climate to the climate preferences. The energy-saving profiles of the room users are then determined based on the simulated energy consumption. Furthermore, location assignments for the room users are output based on the energy-saving profiles.

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

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

[0008] By distributing the room users' climate preferences in the rooms, the indoor climate can be compared with the room users' climate preferences in an efficient and energy-saving manner. As a result, user comfort and thus user satisfaction can be significantly improved in many cases.

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

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

[0011] According to another advantageous embodiment of the present invention, influencing factors may include, preferably using sensors and / or in a location-specific manner, room temperature, humidity, ventilation, brightness, shading, or other indoor climate data; current, historical, or forecasted weather data; room occupancy behavior; and / or the positions of windows, doors, or shading devices. Alternatively or additionally, historical indoor climate data and / or other historical influencing factors may also be detected and used. Taking these influencing factors into account generally allows for a more accurate simulation of the indoor climate.

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

[0013] In particular, a semantic building model can be imported as a digital building model. Building element types of the semantic building model can be assigned to building element type-specific simulation components, which are initialized using the description of the semantic building model for building elements of this building element type. This allows the simulator to be modularized in many cases in an efficient manner, which generally simplifies the configuration or initialization of the simulator.

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

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

[0016] According to another advantageous embodiment of the present invention, in order to simulate the corresponding energy consumption, the deviation between the simulated indoor climate and the climate preferences of the room users according to the corresponding distribution can be determined. Thus, the energy consumption can be determined for adapting the indoor climate in a manner that reduces or minimizes the deviation. In particular, the minimum energy consumption can be determined, where the resulting deviation does not exceed a predefined tolerance value.

[0017] According to an advantageous refinement of the present invention, energy consumption can be simulated for changes in climate preferences and / or influencing factors. Thus, a sensitivity value can be determined for each room user profile, which quantifies the change in energy consumption when climate preferences and / or influencing factors change. An energy saving profile can then be determined based on the determined sensitivity values. Lower sensitivity values ​​generally indicate a lower correlation between energy consumption and climate preferences and / or influencing factors. If influencing factors or climate preferences change, less sensitive profiles generally require less adaptation and, for this reason, should often be preferred over more sensitive profiles.

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

[0019] Furthermore, the current occupancy of a room by its users can be detected. The energy saving distribution can then be determined based on the current occupancy. As the distribution of climate preferences generally also depends on the current room occupancy, this information can generally be used to improve the simulation.

[0020] The embodiments of the present invention are described in more detail below with reference to the accompanying drawings.

[0021] Figure 1The invention shows a device for comparing the room climate of a room with the climate preferences of a room user.

[0022] Figure 2 shows the different distributions of room users with different climate preferences,

[0023] Figure 3 shows a first graph illustrating the relationship between satisfaction of climate preferences and energy consumption, and

[0024] Figure 4 A second diagram is shown to illustrate the less sensitive relationship between satisfaction of climate preferences and energy consumption.

[0025] Figure 1 A schematic diagram shows an apparatus A according to the present invention for comparing the indoor climate of a room R with the climate preferences of a room user. Apparatus A is computer-controlled and has one or more processors PROC for executing the method steps according to the present invention, as well as one or more memories MEM for storing data to be processed by apparatus A. Room R can be part of a building or construction project, such as a large office, a workshop hall, a living room, or any other room whose indoor climate can be compared with the climate preferences of the room user. The indoor climate can include or relate to, in particular, the temperature, humidity, ventilation, brightness, shading, and / or solar radiation of room R. The indoor climate is preferably taken into account or detected in a location-dependent manner.

[0026] The room R has a conditioning system H for regulating the indoor climate, preferably in a location-dependent manner. The conditioning system H can, for example, include a heating system, an air-conditioning system, a ventilation device and / or a shading device.

[0027] Furthermore, the room R and / or its surroundings have a sensor system S, which preferably measures or otherwise detects physical factors influencing the room climate in a location-specific manner. Furthermore, the sensor system S preferably also detects the current occupancy of the room R by a room user. In particular, temperature, humidity, ventilation, brightness, shading, solar radiation, window position, door position, position of shading devices, room usage behavior, or other room climate data can preferably be detected in a location-specific manner as influencing factors EF.

[0028] For example, predicted current or historical weather data WD can be called from the Internet IN as other physical influencing factors EF.

[0029] Current room climate data or environmental data, such as the outside temperature, are preferably detected by means of a sensor system S, while historical room climate data or other influencing factors on the room climate can be read in, for example, from a database DB.

[0030] In this exemplary embodiment, a digital semantic building model BIM is read in from a database DB by device A, which describes the room R in structural detail. 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 the geometry of the room R and a large number of its building elements, such as walls, ceilings, floors, windows, or doors, in machine-readable form with the aid of a large number of building element descriptions. If the room geometry and specific building elements have a significant influence on its indoor climate, the semantic building model BIM or the descriptions contained therein can also be interpreted as physical influencing factors.

[0031] According to the present invention, the indoor climate of a room R is to be compared with the climate preferences of the room user by means of device A. For this purpose, the room user's climate preferences are queried via their mobile phone MT by means of device A and / or stored or historical climate preferences are read in. The climate preferences may relate, in particular, to the temperature, humidity, ventilation, brightness, shading, and / or solar radiation of the room R.

[0032] In this embodiment, for the sake of clarity, only two temperature preferences, T1 and T2, of the room user are considered as climate preferences. Here, T1 may represent a temperature preference of "fairly cool," and T2 may represent a temperature preference of "fairly warm." Climate preferences T1 and T2 may be specified, for example, by temperature intervals.

[0033] Installation A has a simulator SIM for simulating the indoor climate of a room R. For the purpose of this simulation, the semantic building model BIM, physical influencing factors EF, weather data WD and climate preferences T1 and T2 are fed into the simulator SIM.

[0034] The simulator SIM may comprise, for example, specific simulation components for temperature simulation and / or flow simulation. If necessary, the temperature simulation of the simulator SIM may be calibrated based on the recorded thermal images of the room R.

[0035] Furthermore, the simulator SIM can include building element type-specific simulation components for different building element types, such as windows, doors, or walls in the semantic building model BIM. These simulation components can then be initialized via the semantic building model BIM using a specific building element of the corresponding building element type. Thus, the corresponding wall of room R can be coupled to a simulation component that specifically simulates heat conduction through the wall and is initialized based on the description of the wall's thermal conductivity from the semantic building model BIM. In this way, the configuration or initialization of simulation models or other simulation components of the simulator SIM can be automated or simplified in many cases.

[0036] The device A also has a generator GEN coupled to the simulator SIM for generating a distribution D1, ..., DN of room users in the room R. The respective distribution D1, ... or DN can preferably be represented by a data structure which specifies the positions of the room users in the room R.

[0037] The climate preferences (here, T1 and T2) are fed into a generator GEN. Based on the climate preferences T1 and T2, the generator GEN preferably generates distributions D1, ..., DN, in which room users with the same or similar climate preferences are positioned adjacent to one another. The generated distributions D1, ..., DN are transmitted from the generator GEN to the simulator SIM.

[0038] The simulator SIM simulates the energy consumption E1, ..., or EN, respectively, based on the influencing factors EF for the transmitted profiles D1, ..., DN, for adapting the indoor climate to the climate preferences, here T1 and T2, distributed according to D1, ..., or DN. To determine the corresponding energy consumption E1, ..., or EN, the deviations between the various simulated indoor climates and the climate preferences of the room occupants, distributed according to D1, ..., or DN, are determined. Based on the deviations, the energy consumption E1, ..., or EN is determined for the corresponding profiles D1, ..., or DN, which energy consumption is used to reduce or minimize the deviations. Preferably, a tolerance value for the deviations can be predefined. This allows the determination of a minimum energy consumption E1, ..., or EN, if necessary, where the resulting deviation does not exceed the predefined tolerance value.

[0039] In this embodiment, the energy consumption E1, ..., EN is additionally simulated for a large number of changes in the climate preferences (here, T1, T2) and / or the influencing factors EF. For each distribution D1, ..., or DN, it is determined how strongly the corresponding energy consumption E1, ..., or EN fluctuates when the climate preferences T1, T2, and / or the influencing factors EF change. The resulting changes in the corresponding energy consumption E1, ..., or EN are quantified using distribution-specific sensitivity values ​​S1, ..., or SN. Here, smaller sensitivity values ​​S1, ..., or SN indicate a smaller correlation between the energy consumption E1, ..., or EN and the climate preferences T1, T2, and / or the influencing factors EF. Therefore, distributions with smaller sensitivity values ​​are more robust to fluctuations in the climate preferences and / or influencing factors. If the influencing factors or climate preferences change, a robust distribution generally requires less adaptation and, for this reason, can often be preferred over a less robust distribution.

[0040] , DN, the determined energy consumptions E1, . . . , EN and the determined sensitivity values ​​S1 , . . . , SN are transmitted from the simulator SIM to a selection module SEL coupled to the simulator SIM.

[0041] Furthermore, the room occupancy currently measured by the sensor system S and / or a fluctuation profile regarding expected fluctuations in the room occupancy are optionally transmitted to the selection module SEL. The fluctuation profile can be read in from a database DB and can in particular include historical data regarding the room occupancy over the course of a day, week, or year.

[0042] The selection module SEL is used to determine and select an energy-saving profile for a room user based on the energy consumptions E1, ..., EN and the sensitivity values ​​S1, ..., SN. In this case, a profile with a relatively low energy demand and a relatively low sensitivity value is selected. If necessary, a weighted sum of the corresponding energy consumptions E1, ... or EN and the assigned sensitivity values ​​S1, ... or SN can be formed. In this case, the profile with the smallest weighted sum can be selected as the energy-saving profile.

[0043] When selecting an energy saving profile, in addition to the energy consumption E1, ..., EN and the sensitivity values ​​S1, ..., SN, the room occupancy and / or the fluctuation information can also be taken into account. In particular, the fluctuation information can be compared with the sensitivity values ​​S1, ..., SN. Based on this, profiles that react too sensitively to the expected fluctuations, based on their sensitivity values, can be discarded from the selection.

[0044] For the present embodiment it shall be assumed that profile D2 best meets the above criteria for a low-sensitivity energy-saving profile and is therefore selected.

[0045] The selected energy-saving profile D2 is transmitted from the selection module SEL to the location allocation device POE coupled to it. The location allocation device POE determines the individual location in the room R specified for the respective room user specified in the profile D2 and inserts it into the room user's individual location allocation information POS. The location allocation information POS is then transmitted individually for each room user from the location allocation device POE to their mobile phone MT. Using the respective location allocation information POS, for example, in a large office, the respective room user is assigned an individually optimized location.

[0046] Furthermore, the selected energy-saving profile D2 and the associated energy consumption E2 are transmitted from the selection module SEL to a control device CTL coupled to the selection module. The control device CTL is used to control and adjust the climate control system H according to the selected energy-saving profile D2 and the determined energy consumption E2. For this purpose, the control device CTL transmits corresponding control data CD to the climate control system H. If such climate control systems are often slow to react, the climate control system H can preferably already be controlled before the room users are distributed or assigned according to the selected profile D2.

[0047] Due to the distribution of the room users' climate preferences in the room and due to the active control of the conditioning system H, the indoor climate can be compared with the room users' climate preferences in an efficient and energy-saving manner. In many cases, user comfort and thus user satisfaction can be significantly improved as a result.

[0048] Figure 2 Different distributions D1, ..., D6 of room users in a room R are illustrated, said room users being grouped according to their different climate preferences, here T1 and T2. In this case, the distributions D1, ..., D6 are exemplary selections from the distributions D1, ..., DN described above. The possible locations of the room users in the room R are Figure 2 The diagram is illustrated by a small rectangle.

[0049] By grouping the room users according to their climate preferences T1 and T2, the room R is divided into different indoor climate zones TZ1 and TZ2 for the corresponding distributions D1, ..., D6. Indoor climate zone TZ1 is the area of ​​the room R where the room user with climate preference T1 is located. Correspondingly, indoor climate zone TZ2 is the area of ​​the room R where the room user with climate preference T2 is located. Indoor climate zones TZ1 and TZ2 are respectively Figure 2 In the present embodiment, the indoor climate zones TZ1 and TZ2 are temperature zones.

[0050] As already discussed above, the simulator SIM simulates for each profile D1 , . . . , D6 respectively that energy consumption E1 , . . . , E6 which is required for creating the corresponding indoor climate in the respective indoor climate zone TZ1 and TZ2 .

[0051] In this sense, uniform distributions D4 and D5 are clearly less robust. Distributions D4 and D5 are only comfortable for all room users if they have the same climate preferences. However, empirically, this is only the case for a small number of room user distributions.

[0052] Figure 3 and 4 The relationship between energy consumption E and the resulting satisfaction of the room user's climate preferences is explained in each example. Energy consumption E can, in particular, be heating power. In the diagram shown, the deviation DEL between the simulated room climate and the room user's climate preferences is plotted against energy consumption E. If the room user's comfort decreases with increasing deviation DEL, the smallest possible deviation DEL should be sought to optimize comfort.

[0053] exist Figure 3 The first diagram shown in FIG shows the course of the deviation DEL for a room user distribution with a higher sensitivity value, i.e. a less robust one. Here, the distributions D4, D5 and D6 are highlighted. In particular, the following can be done: Figure 3 The low robustness of the distribution shown can be seen in FIG, ie the minimum of the deviation DEL is relatively narrow. This means that even relatively slight changes in the distribution D6 for optimizing comfort significantly reduce the comfort.

[0054] In contrast, in Figure 4 The second diagram shown in shows the course of the deviation DEL for a room user distribution with a lower sensitivity value, ie a more robust one. Here, the distributions D1, D2 and D3 are highlighted. Figure 4 The great robustness of the distribution shown can be seen in particular by the fact that the minimum of the deviation DEL is relatively broad. This means that changes in the comfort-optimizing distribution D2 reduce the comfort relatively little.

[0055] In order to prevent a significant decrease in comfort or excessively high energy consumption in the event of changes in influencing factors or the addition of new room users with different climate preferences, in this embodiment, a robust and energy-efficient distribution D2 is selected. Based on the selected distribution D2, the room users are then distributed in the room R using the individual position assignment instructions POS, as described above.

Claims

1. A computer-implemented method for comparing the indoor climate of a room (R) with the climate preferences (T1, T2) of a user of the room, wherein a) Read in the room user's climate preferences (T1, T2), b) detecting physical factors influencing the indoor climate (EF, WD), c) feeding the detected influencing factors (EF, WD) into a simulator (SIM) for simulating the indoor climate, d) simulating the energy consumption (E1, ..., EN) for different distributions (D1, ..., DN) of room users in the room (R) using the simulator (SIM) in order to adapt the room climate to the climate preferences (T1, T2), e) determining the energy saving distribution (D2) of the users of the room according to the simulated energy consumption (E1, ..., EN), and f) Outputting a position assignment (POS) for a room user based on the energy saving profile (D2).

2. The method according to claim 1, characterized in that The indoor climate is made close to the climate preferences (T1, T2) of room users distributed according to the energy saving distribution (D2).

3. The method according to any one of the preceding claims, characterized in that Influencing factors (EF, WD) are preferably detected by sensors - the temperature, air humidity, ventilation, brightness, shading or other indoor climate data of the room, - Current, historical or forecast weather data (WD), -Room usage behavior, and / or - Position of windows, doors or screening devices.

4. The method according to any one of the preceding claims, characterized in that reading in a digital building model (BIM) for said room (R), and The energy consumption (E1, . . . , EN) is simulated according to the digital building model (BIM).

5. The method according to claim 4, characterized in that Import semantic building models as digital building models (BIM), Assigning a building element type of the semantic building model (BIM) to a simulation component specific to the building element type, and The simulation component specific to a building element type is initialized by a description of a semantic building model (BIM) for building elements of the building element type.

6. The method according to claim 4 or 5, characterized in that scanning the room (R) or a floor plan of the room, and The digital building model (BIM) is generated accordingly.

7. The method according to any one of the preceding claims, characterized in that recording a thermal image of the room (R), and The simulator (SIM) is calibrated with the aid of the thermal images.

8. The method according to any one of the preceding claims, characterized in that In order to simulate the corresponding energy consumption (E1, ..., EN), - determining the deviation between the simulated indoor climate and the climate preferences (T1, T2) of the room users distributed according to the corresponding distribution (D1, ..., DN), and - determining the energy consumption (E1, . . . , EN) for adapting the indoor climate in such a way that the deviation is reduced or minimized.

9. The method according to any one of the preceding claims, characterized in that simulating said energy consumption for changes in said climate preference and / or said influencing factors, determining a sensitivity value (S1, ..., SN) for each of the distributions of room users (D1, ..., DN), said sensitivity value quantifying the change in the energy consumption in the event of a change in the climate preference and / or the influencing factors, and The energy saving distribution (D2) is determined according to the determined sensitivity values ​​(S1, . . . , SN).

10. The method according to any one of the preceding claims, characterized in that Reading in a fluctuation specification about expected fluctuations in the occupancy of the room (R) by the room users, and The energy saving distribution (D2) is determined based on the fluctuation description.

11. The method according to any one of the preceding claims, characterized in that detecting the current occupancy of said room (R) by room users, and The energy saving distribution (D2) is determined based on the current occupancy. 12 . A device (A) for comparing the indoor climate of a room (R) with the climate preferences of a room user, the device being configured to carry out the method according to claim 1 . 13 . A computer program product configured to carry out the method according to claim 1 .

14. A computer-readable storage medium having a storable computer program product according to claim 13.

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