EP4036488A1 - Method and arrangement for adjusting a room climate with air conditioning preferences of users - Google Patents

Abstract

In order to compare a room climate with climate preferences (T1, T2) of room users, climate preferences (T1, T2) are read in by room users. Furthermore, physical influencing factors (EF, WD) on the room climate are recorded and fed into a simulator (SIM) to simulate the room climate. Using the simulator (SIM), depending on the recorded influencing factors (EF, WD) for different distributions (D1,...,DN) of room users in the room (R), energy consumption (E1,...,EN) is Adaptation of the room climate to the climate preferences (T1, T2) simulated. Depending on the simulated energy expenditure (E1,...,EN), an energy-saving distribution (D2) of the room users is then determined. Furthermore, according to the energy-saving distribution (D2), location allocation information (POS) is issued for room users.

Description

The setting of heating, air conditioning, ventilation or other systems for controlling the temperature, humidity or other parameters of a room climate plays an important role 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 of the climate control systems that meets the needs of everyone in the room. This problem occurs in particular when the room climate is controlled centrally. But even with individual settings for local radiators, cooling systems or ventilation, the individual needs of the room users can often not be fully met, since individual settings usually affect the entire room climate. In addition, climate control systems often react sluggishly, so that the effects of settings are often difficult to assess.

Applications for mobile phones are known from more recently, which make it easier to reach a consensus between different room users and also allow active control of the room climate during periods of absence. However, settings found in this way often lead to average results with which not all room users are satisfied. In addition, in many cases it is only possible to react inadequately to changes in the number of people.

It is the object of the present invention to specify a method and an arrangement that allow a room climate to be compared more efficiently with the climate preferences of room users.

This problem is solved by a method with the features of patent claim 1, by an arrangement with 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.

In order to compare a room climate with the climate preferences of room users, climate preferences are read in by room users. In this case, the climate preferences can relate in particular to a temperature, air humidity, ventilation, brightness, shading and/or solar radiation of a room. Furthermore, physical factors influencing the room climate are recorded and fed into a simulator for simulating the room climate. Depending on the recorded influencing factors for different distributions of room users in the room, the simulator is used to simulate the energy expenditure for adapting the room climate to the climate preferences. Depending on the simulated energy expenditure, an energy-saving distribution of the room users is then determined. Furthermore, according to the energy-saving distribution, location allocation information for room users is issued.

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

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 (ASIC), digital signal processors (DSP), a cloud infrastructure and/or so-called "Field Programmable Gate Arrays " (FPGA) to be executed.

A room climate can be adjusted to efficient and be compared with the climate preferences of the room users in an energy-saving manner. In many cases, user convenience and thus user satisfaction can be significantly improved as a result.

Advantageous embodiments and developments of the invention are specified in the dependent claims.

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

According to further advantageous embodiments of the invention, temperature, humidity, ventilation, brightness, shading or other room climate data of the room can be used as influencing factors; current, historical or forecast weather data; a space usage behavior; and/or a window position, a door position or a position of a shading system are preferably detected by sensors and/or location-specifically. 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 a relatively precise simulation of a room climate.

According to a particularly advantageous embodiment of the invention, a digital building model for the room can be read. The energy consumption can then be simulated using the digital building model. Insofar as a room geometry and the properties of building elements of the room in usually have a significant influence on a room climate, the simulation can often be significantly simplified or improved by using a digital building model.

In particular, a semantic building model can be read in 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. As a result, the simulator can be efficiently modularized in many cases, which generally simplifies configuration or initialization of the simulator.

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 as a function of this.

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

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 in order to simulate a respective energy expenditure. In this way, an energy expenditure for an adaptation of the room climate that reduces or minimizes the deviation can be determined. Especially a possibly minimal energy consumption can be determined, in which case the resulting deviation does not exceed a specified tolerance value.

According to an advantageous development of the invention, the energy expenditure for variations in the climate preferences and/or the influencing factors can be simulated. In this way, a sensitivity value can be determined for the distribution of room users, which quantifies a variation in energy expenditure when climate preferences and/or influencing factors vary. The energy-saving distribution can then be determined depending on the determined sensitivity values. A smaller sensitivity value generally indicates that the energy consumption is less dependent on climate preferences and/or the influencing factors. If influencing factors or climate preferences change, less sensitive distributions usually require smaller adjustments and are therefore often preferable to more sensitive distributions.

Furthermore, a fluctuation information about an expected fluctuation in the occupancy of the room by room users can be read. The energy-saving distribution can then be determined as a function of the fluctuation information. In many cases, the simulation can be improved on the basis of such a fluctuation indication. The indication of fluctuation can in particular include historical data about room occupancy over the course of a day, week or year.

In addition, current occupancy of the room by room users can be recorded. The energy-saving distribution can then be determined depending on the current occupancy. Insofar as a distribution of climate preferences usually also depends on the current room occupancy, the simulation can usually be improved with this information.

Figur 1

eine erfindungsgemäße Anordnung zum Abgleichen eines Raumklimas eines Raumes mit Klimapräferenzen von Raumnutzern,

Figur 2

verschiedene Verteilungen von Raumnutzern mit unterschiedlichen Klimapräferenzen,

Figur 3

ein erstes Diagramm zur Veranschaulichung eines Zusammenhangs zwischen einer Erfüllung von Klimapräferenzen und einem Energieaufwand, und

Figur 4

ein zweites Diagramm zur Veranschaulichung eines weniger sensitiven Zusammenhangs zwischen einer Erfüllung von Klimapräferenzen und einem Energieaufwand.

An embodiment of the invention is explained in more detail below with reference to the drawing. In each case, in a schematic representation:

figure 1

an arrangement according to the invention for balancing a room climate of a room with climate preferences of room users,

figure 2

different distributions of room users with different climate preferences,

figure 3

a first diagram to illustrate a connection between the fulfillment of climate preferences and energy expenditure, and

figure 4

a second diagram to illustrate a less sensitive relationship between the fulfillment of climate preferences and energy expenditure.

figure 1 shows a schematic representation of an inventive arrangement A for balancing a room climate of a room R with climate preferences of room users. The arrangement A is computer-controlled and has one or more processors PROC for executing 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 a structure, such as an open-plan office, a factory building, a living room or another room whose indoor climate is to be matched to the climate preferences of room users. The room climate can in particular include or relate to a temperature, humidity, ventilation, brightness, shading and/or solar radiation of the room R. The room climate is preferably considered or recorded depending on the location.

The room R has a control system H for preferably location-dependent control of the room climate. The control system H can for example a heating system, an air conditioning system, a ventilation system and/or a shading device.

Furthermore, the room R and/or its surroundings have a sensor system S, which measures physical factors EF influencing the room climate, preferably in a location-specific manner, or records them in some other way. In addition, the sensor system S preferably also detects a current occupancy of the room R by room users. Temperature, 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 can be recorded as influencing factors EF, preferably in a location-specific manner.

Predicted, current or historical weather data WD can be retrieved from the Internet IN, for example, as further physical influencing factors EF.

While current room climate data or environmental data, such as an outside temperature, is preferably recorded by means of the sensor system S, historical room climate data or other factors influencing the room climate can be read in from a database DB, for example.

In the present exemplary embodiment, a digital, semantic building model BIM, by which the room R is structurally specified, is read in from the database DB by the arrangement A. 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 the room R and 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 specifications. Insofar as the geometry and the specific building elements of a room have a significant influence on its indoor climate, the semantic building model or BIM contained therein can Information can also be understood as physical influencing factors.

According to the invention, the indoor climate of the room R is to be matched with the climate preferences of the room users through the arrangement A. For this purpose, the climate preferences of the room users are queried by the arrangement A via their cell phones MT and/or stored or historical climate preferences are read. The climate preferences can relate in particular to a temperature, humidity, ventilation, brightness, shading and/or solar radiation of the room R.

In the present exemplary embodiment, only two temperature preferences T1 and T2 of the room users are considered as climate preferences for reasons of clarity. T1 could stand for a "rather cool" temperature preference and T2 for a "rather warm" temperature preference. The climate preferences T1 and T2 can be specified by temperature intervals, for example.

To simulate the indoor climate of the room R, the arrangement A has a simulator SIM. For the purpose of 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.

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

In addition, the SIM simulator can include a building element type-specific simulator component for different building element types, such as windows, doors or walls of the semantic building model BIM. The latter can then be specified by specifying the semantic building model BIM via concrete Building elements of the respective building element type are initialized. A respective wall of the 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 the above manner, a configuration or initialization of simulation models or other simulator components of the simulator SIM can be automated or simplified in many cases.

The arrangement A also has a generator GEN coupled to the simulator SIM for generating distributions D1, . . . DN of room users in the room R. A respective distribution D1, . . . or DN can preferably be represented by a data structure , which indicates the positions of room users in space R.

The climate preferences, here T1 and T2, are fed into the 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.

Depending on the influencing factors EF for the transmitted distributions D1,...,DN, the simulator SIM simulates an energy expenditure E1,... or EN for an adjustment of 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 different 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, through which a deviation is reduced or minimized. A tolerance value for the deviations can preferably be set here be specified. In this way, a possibly minimal energy consumption E1,... or EN can be determined, in which case the resulting deviation does not exceed the specified tolerance value.

In the present exemplary embodiment, the above energy expenditures E1,...,EN are additionally simulated for a large number of variations in the climate preferences, here T1 and T2, and/or the influencing factors EF. For a respective distribution D1,... or DN, it is determined in each case how strongly a respective energy expenditure E1,... or EN varies when the climate preferences T1, T2 and/or the influencing factors EF vary. The resulting variation of 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 dependency 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 to fluctuations in climate preferences and/or influencing factors. If influencing factors or climate preferences change, robust distributions usually require minor adjustments and are therefore often preferable to less robust distributions.

The distributions D1,...,DN, the determined energy expenditures 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.

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

The selection module SEL is used to determine and select an energy-saving distribution of room users depending on the energy expenditure E1,...,EN and the sensitivity values S1,...,SN. In this case, a distribution with a relatively low energy requirement and a relatively low sensitivity value is selected. If necessary, a weighted sum of a respective energy expenditure E1,... or EN and the respectively assigned sensitivity value S1,... or SN can be formed. In this case, a distribution with the smallest weighted sum can be selected as the energy-saving distribution.

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

For the present exemplary embodiment, it is assumed that the distribution D2 best meets the above criteria for a less sensitive, energy-saving distribution and is therefore selected.

The selected energy-saving distribution D2 is transmitted from the selection module SEL to a location allocation device POE coupled to it. The location allocation device POE determines the individual position specified there in the room R for a respective room user specified in the distribution D2 and inserts this into a room user-specific location allocation specification POS. The respective location allocation information POS is then individually transmitted by the location allocation device POE for each room user to their cell phone MT. Through the respective location allocation information POS, the respective room user is given an individually optimized Position assigned, for example in an open-plan office.

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 is used to control and set the control system H depending on the selected energy-saving distribution D2 and the determined energy consumption E2. Corresponding control data CD are transmitted to the control system H by the control device CTL for this purpose. Insofar as such climate control systems often react sluggishly, the control system H can preferably already be activated before the room users are or are being distributed according to the selected distribution D2.

Through the distribution of room users in the room based on climate preferences and through the active control of the control system H, a room climate can be adjusted to the climate preferences of the room users in an efficient and energy-saving manner. In many cases, user convenience and thus user satisfaction can be significantly improved as a result.

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 whereabouts of room users within room R are in figure 2 illustrated by small rectangles.

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 indoor climate zone TZ1 is that area of the room R in which the room users are with the climate preference T1. Correspondingly, the room climate zone TZ2 is that area of the room R in which room users with climate preference T2 are located. The room climate zones TZ1 and TZ2 are in figure 2 each marked by a dotted line. In the present exemplary embodiment, the room climate zones TZ1 and TZ2 are temperature zones.

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

The uniform distributions D4 and D5 are obviously 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 that this is only the case for a few room user distributions.

the Figures 3 and 4 each illustrate a connection between energy expenditure E and the resulting fulfillment of climate preferences by room users. The energy expenditure E can in particular be a heating output. In each of 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. Insofar as the comfort of the room users decreases as the deviation DEL increases, a deviation DEL that is as small as possible should be aimed for in order to optimize the comfort.

in the in figure 3 The first diagram shown shows a course of the deviation DEL for room user distributions that have a higher sensitivity value, ie are less robust. The distributions D4, D5 and D6 are highlighted. The lower robustness of the distributions shown is in figure 3 particularly evident from the fact that the minimum of the deviation DEL is relatively small. Ie even relatively small variations of the comfort-optimizing distribution D6 reduce comfort considerably.

In contrast, in the in figure 4 The second diagram shown shows a course of the deviation DEL for room user distributions that have a lower sensitivity value, ie are more robust. The distributions D1, D2 and D3 are highlighted. The greater robustness of the distributions shown is in figure 4 particularly evident from the fact that the minimum of the deviation DEL is relatively wide. Ie variations of the comfort-optimizing distribution D2 reduce the comfort relatively little.

So that the comfort does not drop significantly or require too much energy when there are changes in the influencing factors or when there are new room users with different climate preferences, the both robust and energy-saving distribution D2 is selected in the present exemplary embodiment. The room users are then distributed in the room R according to the selected distribution D2, as described above, by individual location allocation information POS.

Claims (14)

Computer-implemented method for adjusting an indoor climate of a room (R) with climate preferences (T1, T2) of room users, wherein a) climate preferences (T1, T2) are read in by room users, b) physical influencing factors (EF, WD) on the room climate are recorded, c) the recorded influencing factors (EF, WD) are fed into a simulator (SIM) to simulate the room climate, d) depending on the recorded influencing factors (EF, WD) by means of the simulator (SIM) for different distributions (D1,...DN) of room users in the room (R), one energy expenditure (E1,...,EN) for one Adaptation of the room climate to the climate preferences (T1, T2) is simulated, e) depending on the simulated energy expenditure (E1,...,EN), an energy-saving distribution (D2) of the room users is determined, and f) in accordance with the energy-saving distribution (D2), location allocation information (POS) is issued for room users. Method according to Claim 1, characterized in that the room climate is approximated to the climate preferences (T1, T2) of room users distributed according to the energy-saving distribution (D2). Method according to one of the preceding claims, characterized in that
that as influencing factors (EF, WD) - a temperature, humidity, ventilation, brightness, shading or other indoor climate data of the room, - current, historical or forecast weather data (WD), - a space usage behavior and/or - a window position, a door position or a position of a shading system preferably be detected by sensors. Method according to one of the preceding claims, characterized in that
that a digital building model (BIM) for the space (R) is read, and
that the energy costs (E1,...,EN) are simulated using the digital building model (BIM). Method according to claim 4, characterized in that
that a semantic building model is read in as a digital building model (BIM),
that a building element type of the semantic building model (BIM) is assigned to a building element type-specific simulator component, and
that the building element type-specific simulator component is initialized by an indication of the semantic building model (BIM) about a building element of this building element type. Method according to Claim 4 or 5, characterized in that the room (R) or a construction plan of the room is scanned, and
that the digital building model (BIM) is generated depending on this. Method according to one of the preceding claims, characterized in that
that a thermal image of the room (R) is recorded, and
that the simulator (SIM) is calibrated using the thermal image. Method according to one of the preceding claims, characterized in that
that for the simulation of a respective energy expenditure (E1,...,EN) - a deviation between a simulated indoor climate and the climate preferences (T1, T2) of according to a respective distribution (D1,...,DN) distributed room users is determined, and - an energy expenditure (E1,...,EN) for an adjustment of the indoor climate that reduces or minimizes the deviation is determined. Method according to one of the preceding claims, characterized in that
that the energy expenditure for variations in climate preferences and/or influencing factors are simulated,
that a sensitivity value (S1,...,SN) is determined for the distributions (D1,...,DN) of the room users, which quantifies a variation in the energy expenditure with variations in the climate preferences and/or the influencing factors, and
that the energy-saving distribution (D2) is determined depending on the determined sensitivity values (S1,...,SN). Method according to one of the preceding claims, characterized in that
that a fluctuation indication of an expected fluctuation in an occupancy of the room (R) by room users is read in, and
that the energy-saving distribution (D2) is determined depending on the fluctuation information. Method according to one of the preceding claims, characterized in that
that a current occupancy of the room (R) by room users is recorded,
that the energy-saving distribution (D2) is determined depending on the current occupancy. Arrangement (A) for adjusting a room climate of a room (R) with climate preferences of room users, set up to carry out a method according to one of the preceding claims. Computer program product set up to carry out a method according to one of Claims 1 to 11. A computer-readable storage medium storing a computer program product according to claim 13.

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