Patent Description:
A heating, ventilation, and air-conditioning, HVAC, system may be employed to control an indoor environment (e.g., in a building) to provide a thermal comfort and/or a desired indoor air quality. A HVAC system may employ a user-in-the-loop approach, wherein a respective thermal comfort of users or occupants of the indoor environment is considered. Various aspects relate to a HVAC system and a method of controlling a HVAC system which employ a user-specific comfort model to predict a respective thermal comfort of the users or occupants and which are capable to control the HVAC system in accordance with the thermal comforts. The HVAC system and the method of controlling a HVAC system are further capable to consider energy requirements of the HVAC system and to control the HVAC system in accordance with the thermal comforts as well as the energy requirements. Various aspects relate to a method of training a comfort model to be used for controlling a HVAC system. The trained comfort model is capable to predict the thermal comfort of a user without requiring a real-time user feedback.

<CIT> describes a building control system which includes one or more controllers configured to: obtain comfort feedback provided by one or more occupants of a building in response to exposing the one or more occupants to a first set of environmental conditions of the building; generate, based on the comfort feedback, one or more thresholds defining a range of values for an environmental condition of the building within which the one or more occupants are predicted to be comfortable; and execute a predictive control process subject to one or more constraints based on the one or more thresholds to generate setpoints for building equipment, and operate the building equipment to drive the environmental condition of the building toward the setpoints. <CIT> describes a method of controlling a heating, ventilation and air conditioning (HVAC) system of a building by: (a) developing an initial thermal model of the building, and continuously updating the thermal model over time; (b) utilizing the thermal model to continuously develop a daily HVAC operating plan for the building; and (c) continuously examining a current HVAC operating plan and optimizing the alignment of the current HVAC operation with the current HVAC operating plan. <CIT>describes a method including identifying a hierarchical position of a particular distribution channel element; identifying potential set point configuration actions associated with the particular distribution channel element, wherein each potential set point configuration action is expected to cause transition of the particular distribution channel element from a current state to a future state; determining an overall cost measure for each potential set point configuration action based at least in part on the hierarchical position of the particular distribution channel element; and generating the supply stream temperature set-point based on each overall cost measure associated with a potential set point configuration action.

Various embodiments relate to a method of controlling a heating, ventilation, and air-conditioning, HVAC, system, the HVAC system comprising a plurality of HVAC devices, the method including: training a plurality of comfort models to be used for controlling the heating, ventilation, and air-conditioning, HVAC, system, wherein each comfort model of the plurality of comfort models is associated with a respective user of a plurality of users, wherein the training of a respective comfort model of the plurality of comfort models comprising: a user device associated with the respective comfort model providing a plurality of user feedbacks, each user feedback of the plurality of user feedbacks describing a thermal comfort of the respective user associated with the user device, wherein each user feedback of the plurality of user feedbacks is associated with a respective point in time, wherein the plurality of user feedbacks comprises two or more cold feedbacks indicating that the user of the user device associated with the comfort model feeling cold; determining a weight matrix, the weight matrix includes, for each trained comfort model of the plurality of trained comfort models, a respective weight value for each HVAC device of the plurality of HVAC devices; determining running parameters of each HVAC device of the plurality of HVAC devices such that a total predicted thermal comfort representing all predicted thermal comforts, which are generated by the plurality of comfort models responsive to inputting the running parameters of each of the plurality of HVAC devices into each comfort model, is increased, considering the determined weight value of each HVAC device of the plurality of HVAC devices associated with each comfort model of the plurality of comfort models; and controlling the HVAC system in accordance with the determined running parameters.

According to various embodiments, training of the respective comfort model of the plurality of comfort models may further include, at each point in time associated with the plurality of user feedbacks, each HVAC device of the plurality of HVAC devices associated with the HVAC system detecting running parameters of the respective HVAC device; for each user feedback of the plurality of user feedbacks: inputting the running parameters, which are detected at the point in time associated with the respective user feedback, of each of the plurality of HVAC devices into the respective comfort model to generate a predicted thermal comfort, determining a loss value by comparing the predicted thermal comfort with the thermal comfort described by the respective user feedback, and training the respective comfort model to be used for controlling the HVAC system to reduce the loss value.

According to various embodiments, the running parameters may include one or more of an environmental temperature (e.g., an air temperature), a fan speed, a valve opening, and/or a time of day.

According to various embodiments, at each point in time associated with the plurality of user feedbacks, each HVAC device of the plurality of HVAC devices associated with the HVAC system may detect running parameters of the respective HVAC device; for each user feedback of the plurality of user feedbacks, the running parameters, which are detected at the point in time associated with the respective user feedback, of each of the plurality of HVAC devices may be inputted into the comfort model to generate a predicted thermal comfort; and the plurality of user feedbacks may include two or more cold feedbacks indicating that the user of the user device associated with the comfort model feeling cold and the method may further include: for each point in time associated with the two or more cold feedbacks, determining a respective cooling load of each HVAC device of the plurality of HVAC devices using the detected running parameters of the respective HVAC device; for each HVAC device of the plurality of HVAC devices, determining a cooling load sum by summing up all cooling loads determined for the points in time associated with the two or more cold feedbacks; and for each HVAC device of the plurality of HVAC devices, determining a respective weight value as a fraction of the determined cooling load sum from a total cooling load, the total cooling load being a sum of all determined cooling load sums of the plurality of HVAC devices.

According to various embodiments, determining respective running parameters of each HVAC device of the plurality of HVAC devices such that a total predicted thermal comfort representing all predicted thermal comforts, which are generated by the comfort models responsive to inputting the running parameters of each of the plurality of HVAC devices into each comfort model, is increased may include: determining respective running parameters of each HVAC device of the plurality of HVAC devices such that a total predicted thermal comfort representing all predicted thermal comforts, which are generated by the comfort models responsive to inputting the running parameters of each of the plurality of HVAC devices into each comfort model, is increased considering: a predefined comfort constraint representing a minimum predicted thermal comfort, a predefined load constraint representing a maximum load of the HVAC system predicted for the running parameters, and/or predefined total comfort constraint representing a minimum total predicted thermal comfort.

According to various embodiments, the method may further include detecting a plurality of Media-Access-Control, MAC, addresses associated with a plurality of devices present in a local short range network associated with the HVAC system; for each detected MAC address, detecting vendor information representing a vendor of the device associated with the MAC address and classifying the MAC address either into a first class in the case that the vendor of the device is associated with a device capable to create a virtualization of a network card or into a second class otherwise; filtering the MAC addresses which are classified into the first class such that only one MAC address is selected for each device; determining a total number of occupants as a total number of MAC address, the total number of MAC addresses comprising the MAC addresses which are classified into the second class and the MAC addresses selected from the first class; adapting the determined running parameters using the determined total number of occupants; and wherein controlling the HVAC system in accordance with the determined running parameters comprises controlling the HVAC system in accordance with the adapted running parameters.

According to various embodiments, each trained comfort model may be associated with a respective feedback identification of a plurality of feedback identifications and the method may further include: providing a plurality of user feedbacks, wherein each user feedback of the plurality of user feedbacks is associated with a feedback identification of the plurality of feedback identifications and describes a thermal comfort of a user associated with the feedback identification, wherein each user feedback of the plurality of user feedbacks is provided at a respective point in time; at each point in time associated with a user feedback of the plurality of user feedbacks, detecting a plurality of Media-Access-Control, MAC, addresses, wherein each MAC address of the plurality of MAC addresses is associated with a device of a plurality of devices present in a local short range network associated with the HVAC system; and correlating each MAC address of the plurality of MAC addresses with a feedback identification of the plurality of feedback identifications using the points in time associated with the plurality of user feedbacks by employing a correlation metric.

According to various embodiments, determining respective running parameters of each HVAC device of the plurality of HVAC devices such that a total predicted thermal comfort representing all predicted thermal comforts, which are generated by the comfort models responsive to inputting the running parameters of each of the plurality of HVAC devices into each comfort model, is increased may include: for each trained comfort model, determining a region in which the device having the MAC address which is correlated with the feedback identification of the respective trained comfort model is located; for each trained comfort model, determining one or more HVAC devices of the plurality of HVAC devices which are associated with the determined region; and determining respective running parameters of each HVAC device of the plurality of HVAC devices such that a total predicted thermal comfort representing all predicted thermal comforts, which are generated by the trained comfort models responsive to inputting the running parameters of each HVAC device of the determined one or more HVAC devices into the respective trained comfort model, is increased.

According to various embodiments, the correlation metric may include a leverage metric, a co-occurrence metric, a lift metric, a confidence metric, and/or a conviction metric.

According to various embodiments, correlating each MAC address of the plurality of MAC addresses with a feedback identification of the plurality of feedback identifications using the points in time associated with the plurality of user feedbacks by employing a correlation metric may include: for each MAC address of the plurality of MAC addresses, detecting vendor information representing a vendor of the device associated with the respective MAC address and classifying the MAC address either into a first class in the case that the vendor of the device is associated with a device capable to create a virtualization of a network card or into a second class otherwise; filtering the MAC addresses which are classified into the first class such that at each point in time only one MAC address is selected for each device of the plurality of devices; and correlating each MAC address classified into the second class and each MAC address selected from the first class with a feedback identification of the plurality of feedback identifications using the points in time associated with the plurality of user feedbacks by employing the correlation metric.

According to various embodiments, a user device of the plurality of user devices is a button device having feedback buttons, a personal computer, a laptop, a tablet, a smartwatch, or a smartphone.

Various embodiments relate to a heating, ventilation, and air-conditioning, HVAC, system, the HVAC system including: a plurality of user devices, wherein each of the plurality of user devices is configured to provide user feedbacks; a plurality of HVAC devices, wherein each of the plurality of HVAC devices is associated with respective running parameters; and a control device configured to control the plurality of HVAC devices, to receive user feedbacks from the plurality of user devices, and to carry out the method of any of one of the above described embodiments.

Various embodiments relate to a method of controlling a heating, ventilation, and air-conditioning, HVAC, system, the method including: providing a plurality of user feedbacks, wherein each user feedback of the plurality of user feedbacks is associated with a feedback identification of a plurality of feedback identifications and describes a thermal comfort of a user associated with the feedback identification, wherein each user feedback of the plurality of user feedbacks is provided at a respective point in time; at each point in time associated with a user feedback of the plurality of user feedbacks, detecting a plurality of Media-Access-Control, MAC, addresses, wherein each MAC address of the plurality of MAC addresses is associated with a user device of a plurality of user devices present in a local short range network associated with the HVAC system; and correlating each MAC address of the plurality of MAC addresses with a feedback identification of the plurality of feedback identifications using the points in time associated with the plurality of user feedbacks by employing a correlation metric. According to various embodiments, the correlation metric comprises a co-occurrence metric, a lift metric, a confidence metric, and/or a conviction metric. According to various embodiments, correlating each MAC address of the plurality of MAC addresses with a feedback identification of the plurality of feedback identifications using the points in time associated with the plurality of user feedbacks by employing the correlation metric may include: for each MAC address of the plurality of MAC addresses, detecting vendor information representing a vendor of the user device associated with the respective MAC address and classifying the MAC address either into a first class in the case that the vendor of the user device is associated with a device capable to create a virtualization of a network card or into a second class otherwise; filtering the MAC addresses which are classified into the first class such that at each point in time only one MAC address is selected for each user device of the plurality of user devices; and correlating each MAC address classified into the second class and each MAC address selected from the first class with a feedback identification of the plurality of feedback identifications using the points in time associated with the plurality of user feedbacks by employing the correlation metric.

The invention will be better understood with reference to the detailed description when considered in conjunction with the non-limiting examples and the accompanying drawings, in which:.

The following detailed description refers to the accompanying drawings that show, by way of illustration, specific details and embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure.

Embodiments described in the context of one of the methods are analogously valid for the other methods. Similarly, embodiments described in the context of a HVAC system are analogously valid for a method, and vice-versa.

Features that are described in the context of an embodiment may correspondingly be applicable to the same or similar features in the other embodiments. Features that are described in the context of an embodiment may correspondingly be applicable to the other embodiments, even if not explicitly described in these other embodiments. Furthermore, additions and/or combinations and/or alternatives as described for a feature in the context of an embodiment may correspondingly be applicable to the same or similar feature in the other embodiments.

In the context of various embodiments, the articles "a", "an" and "the" as used with regard to a feature or element include a reference to one or more of the features or elements.

In an embodiment, a "computer" may be understood as any kind of a logic implementing entity, which may be hardware, software, firmware, or any combination thereof. Thus, in an embodiment, a "computer" may be a hard-wired logic circuit or a programmable logic circuit such as a programmable processor, e.g. a microprocessor (e.g. a Complex Instruction Set Computer (CISC) processor or a Reduced Instruction Set Computer (RISC) processor). A "computer" may also be software being implemented or executed by a processor, e.g. any kind of computer program, e.g. a computer program using a virtual machine code such as Java. A "computer "may be or may include one or more processors. Any other kind of implementation of the respective functions which will be described in more detail below may also be understood as a "computer" in accordance with an alternative embodiment.

A "memory" may be used in the processing carried out by a computer and/or may store data used by the computer. A "memory" used in the embodiments may be a volatile memory, for example a DRAM (Dynamic Random Access Memory) or a non-volatile memory, for example a PROM (Programmable Read Only Memory), an EPROM (Erasable PROM), EEPROM (Electrically Erasable PROM), or a flash memory, e.g., a floating gate memory, a charge trapping memory, an MRAM (Magnetoresistive Random Access Memory) or a PCRAM (Phase Change Random Access Memory).

A "comfort model" as used herein, may be any kind of model capable to predict a thermal comfort (in some aspects referred to as temperature comfort) responsive to inputting one or more parameters and/or information as described herein. Illustratively, a "comfort model" may map the inputted parameters and/or information in accordance with the ones described herein to a predicted thermal comfort. A "comfort model" may be associated with a specific user or occupant of an indoor environment controlled by a HVAC system. Illustratively, a "comfort model" may be a user-specific comfort model. A "model" may be, for example, based on machine learning (e.g., may employ a machine learning algorithm). Illustratively, a "model" may be adapted (e.g., trained) using machine learning. A "model" may be a decision tree model, a random forest model, a gradient boosting model, a linear regression model, a support vector machine, a k-nearest neighbor model, a neural network, etc. A "neural network" may be any kind of neural network, such as an autoencoder network, a convolutional neural network, a variational autoencoder network, a sparse autoencoder network, a recurrent neural network, a deconvolutional neural network, a generative adversarial network, a forward-thinking neural network, a sum-product neural network, etc. A "neural network" may include any number of layers. A neural network may be trained via any training principle, such as backpropagation.

A "temperature comfort" or "thermal comfort", as used herein, may be a condition of mind of an individual user that expresses a satisfaction of the user with the thermal environment. For example, a thermal comfort of a user or occupant may be described as a number in a range from -C to +C having arbitrary increments, wherein "C" may be any integer number equal to or greater than "<NUM>". The boundaries -C and +C of the range may refer to a category "hot" (i.e., the user or occupant feeling hot) and a category "cold" (i.e., the user or occupant feeling cold), respectively, or vice versa. For example, in the case of C=<NUM>, the user feedback may include one of the three options -<NUM> (e.g., indicating "cold"), <NUM> (e.g., indicating "comfortable"), or +<NUM> (e.g., indicating "hot").

Various aspects relate to a method which predicts a respective thermal comfort of each of a plurality of users or occupants and controls the HVAC system such that an overall thermal comfort of the plurality of users or occupants is increased. This allows to increase the overall thermal comfort without requiring real-time feedback from the plurality of users or occupants.

<FIG> shows an exemplary HVAC system <NUM> according to various embodiments. The HVAC system may be or may include a variable air flow, a variable refrigerant flow (VRF) system, and/or a (e.g., water-based) chiller system. The HVAC system <NUM> may include a plurality of HVAC devices <NUM>(n=<NUM> to N). N may be any integer number equal to or greater than one. A HVAC device as used herein may be any kind of device capable to change environmental conditions in a surrounding of the device. Each HVAC device <NUM>(n) of the plurality of HVAC devices <NUM>(n=<NUM> to N) may be configured to change one or more environmental parameters in accordance with set target parameters. Each HVAC device <NUM>(n) of the plurality of HVAC devices <NUM>(n=<NUM> to N) may be configured to control (e.g., to keep stable, e.g., to change) one or more environmental parameters in a surrounding of the respective HVAC device in accordance with set running parameters (e.g., a set HVAC temperature). Running parameters of a HVAC device, as used herein, may be any kind of parameters associated with changing environmental conditions, such as a fan speed, a valve opening (e.g., of a valve for controlling a flow of a cooling liquid, such as a throughput of cooling water), etc. The fan speed may be represented as an integer value (e.g., in the range from <NUM> to <NUM>) or as a non-numeric description (e.g., high, mid, low). The valve opening may be represented by a pulse value indicating the opening degree (e.g., a value in a range from about <NUM> to about <NUM>) or as a percentage indicating the opening degree (between closed and open). An environmental parameter of the one or more environmental parameters may be, for example, a temperature, a humidity, or a dew point. The HVAC system <NUM> may be associated with an indoor environment (e.g., an environment in a building). The indoor environment may include one or more zones (e.g., regions) <NUM>(m=<NUM> to M). M may be any integer number equal to or greater than one. A zone associated with the HVAC system <NUM> may be a room within the indoor environment. Each zone of the one or more of zones <NUM>(m=<NUM> to M) may be associated at least one HVAC device <NUM>(n) of the plurality of HVAC devices <NUM>(n=<NUM> to N). The at least one HVAC devices associated with a zone may be configured to control (e.g., to keep stable, e.g., to change) one or more environmental parameters within the zone. At a specific time, users (in some aspects referred to as occupants) may occupy a zone of the one or more of zones <NUM>(m=<NUM> to M). At a specific time, one or more users <NUM>(u=<NUM> to U) may occupy the one or more of zones <NUM>(m=<NUM> to M). U may be any integer number equal to or greater than one. It is understood that, at some time, the indoor environment may also be empty such that the one or more of zones <NUM>(m=<NUM> to M) may not be occupied by any user. Each user of the one or more users <NUM>(u=<NUM> to U) may be associated with at least one user device. The HVAC system <NUM> may include a control device <NUM>. The control device <NUM> may be coupled to a memory (e.g., the memory <NUM>). The memory may be an internal memory (i.e., the control device <NUM> may include the memory) and/or an external memory (e.g., a cloud). The control device <NUM> may be coupled to a computer (e.g., the computer <NUM>, e.g., the computer <NUM>). The computer may be an internal computer (i.e., the control device <NUM> may include the computer) and/or an external computer (e.g., the control device <NUM> may employ cloud computing). The memory associated with the control device <NUM> may be used in the processing carried out by the computer associated with the control device <NUM>. Each user device may be configured to provide (e.g., to transmit, e.g., to wirelessly transmit) user feedbacks which describe a thermal comfort of the user using the user device to the control device <NUM>.

<FIG> each show a processing system <NUM> for controlling a HVAC system according to various embodiments. The HVAC system <NUM> may be configured to carry out the processing system <NUM>. In the following, the processing system <NUM> is described with reference to the HVAC system <NUM> for illustration. It is noted that any other HVAC system may be capable to carry the processing system <NUM>. The processing system <NUM> may include a computer <NUM>. The processing system <NUM> may include a memory <NUM>. The computer <NUM> may be configured to acquire (e.g., from the memory <NUM>) respective current running parameters <NUM> of each of the plurality of HVAC devices <NUM>(n=<NUM> to N). The computer <NUM> may be configured to implement a plurality of trained comfort models <NUM>. The plurality of trained comfort models <NUM> may include a respective trained comfort model for each of the plurality of users <NUM>(U=<NUM> to U). Each trained comfort model of the plurality of trained comfort models <NUM> may be configured to generate a predicted thermal comfort of the user associated with the respective trained comfort model responsive to inputting running parameters of each of the plurality of HVAC devices <NUM>(n=<NUM> to N).

With reference to <FIG>, the computer <NUM> may be configured to determine a total predicted current thermal comfort <NUM> representing all predicted thermal comforts, which are generated by the plurality of trained comfort models <NUM> responsive to inputting the respective current running parameters <NUM> of each of the plurality of HVAC devices <NUM>(n=<NUM> to N). The total predicted current thermal comfort <NUM> may be a sum or an average of all predicted thermal comforts. The computer <NUM> may be configured to determine respective running parameters <NUM> of each of the plurality of HVAC devices <NUM>(n=<NUM> to N) such that a total predicted new thermal comfort <NUM> representing the predicted thermal comforts, which are generated by the plurality of trained comfort models <NUM> responsive to inputting the respective running parameters <NUM> of each of the plurality of HVAC devices <NUM>(n=<NUM> to N) is changed (e.g., increased) as compared to the total predicted current thermal comfort <NUM>. In the case that the total predicted new thermal comfort <NUM> is greater than the total predicted current thermal comfort <NUM> and/or that one or more predefined constraints (see, for example, <FIG>) are fulfilled (in <NUM>), the computer <NUM> may be configured to determine the respective running parameters <NUM> of each of the plurality of HVAC devices <NUM>(n=<NUM> to N) as new running parameters <NUM>.

With reference to <FIG>, the memory <NUM> may store a weight matrix <NUM>. The weight matrix <NUM> may include, for each trained comfort model of the plurality of trained comfort models <NUM>, a respective weight value for each of the plurality of HVAC devices <NUM>(n=<NUM> to N). The computer <NUM> may be configured to determine a weighted total predicted current thermal comfort <NUM> by (<NUM>) firstly multiplying each predicted current thermal comfort, which is predicted by the respective trained comfort model for the respective current running parameters <NUM> of each of the plurality of HVAC devices <NUM>(n=<NUM> to N), with the weight values of the weight matrix <NUM> associated with the respective trained comfort model to determine a respective weighted current thermal comfort, and (<NUM>) secondly determining the weighted total predicted current thermal comfort <NUM> using all (e.g., a sum of, e.g., an average of) weighted current thermal comforts. The computer <NUM> may be configured to determine a respective weighted predicted thermal comfort for each of the plurality of HVAC devices <NUM>(n=<NUM> to N). A respective weighted total predicted current thermal comfort (wpred) of a HVAC device <NUM>(n) may be determined as given by the equation (<NUM>): <MAT> wu,n, wherein wu,n is the weight value of the user u for the HVAC device n, and wherein pred(u) is the prediction of the user u as generated by the associated trained comfort model. The weighted total predicted current thermal comfort <NUM> may be determined by <MAT>. In this case, the computer <NUM> may be configured to determine the respective running parameters <NUM> of each of the plurality of HVAC devices <NUM>(n=<NUM> to N) such that a weighted total predicted new thermal comfort <NUM> determined similar to the weighted total predicted current thermal comfort <NUM>, but for the respective running parameters <NUM> is changed (e.g., increased) as compared to the weighted total predicted current thermal comfort <NUM>. In the case that the weighted total predicted new thermal comfort <NUM> is greater than the total predicted current thermal comfort <NUM> and/or that the one or more predefined constraints are fulfilled (in <NUM>), the computer <NUM> may be configured to determine the respective running parameters <NUM> of each of the plurality of HVAC devices <NUM>(n=<NUM> to N) as the new running parameters <NUM>.

The computer <NUM> may be configured to provide control instructions to control the HVAC system <NUM> in accordance with the determined new running parameters <NUM>. The new running parameters <NUM> may include respective new running parameters for each of the plurality of HVAC devices <NUM>(n=<NUM> to N), such as a new set HVAC temperature of the respective HVAC device <NUM>(n). The new set HVAC temperature may be described as a temperature change, ΔTn, compared to a current set HVAC temperature of the respective HVAC device. The temperature change, ΔTn, of a HVAC device <NUM>(n) may be determined by: ΔTn = -wpred * S, wherein S is a predefined constant. For example, the average users are predicted to feel hot (e.g., +C), the set HVAC temperature of the HVAC device <NUM>(n) may be reduced by S times the +C (in K). An increased thermal comfort may increase a productivity and/or may reduce a loss of working hours due to sickness of user or occupants.

With reference to <FIG>, the memory <NUM> may store occupancy information <NUM> which describe an occupancy of people within the indoor environment of the HVAC system <NUM>. The occupancy information <NUM> may be acquired via one or more (e.g., infrared-based, e.g., ultrasonic-based, e.g., radar-based, e.g., microwave-based, etc.) occupancy sensors. The computer <NUM> may be configured to determine the occupancy information <NUM> using devices connected to a local short range network (e.g., a Wireless Fidelity network), as described with reference to <FIG> and <FIG>. The computer <NUM> may be configured to adapt the determined new running parameters <NUM> using the occupancy information <NUM>. For example, a number of occupants (e.g., a total number of occupants, e.g., a number of occupants within the same zone) may influence a cooling/heating rate and the computer <NUM> may be configured to determine this influence to determine the adapted new running parameters <NUM>. The computer <NUM> may be configured to provide control instructions <NUM> to control the HVAC system <NUM> in accordance with the adapted new running parameters <NUM>.

Illustratively, the processing system <NUM> may be capable to handle thermal comfort needs of a plurality of users concurrently.

<FIG> shows a processing system <NUM> for training a comfort model <NUM> used to control a HVAC system (e.g., the HVAC system <NUM>) according to various embodiments. The processing system <NUM> shows the training of the comfort model <NUM> exemplarily for the user <NUM>(<NUM>) of the plurality of users <NUM>(u=<NUM> to U). Similarly, a comfort model may be trained for each user <NUM>(u) of the plurality of users <NUM>(u=<NUM> to U). The plurality of comfort models trained for each of the plurality of users <NUM>(u=<NUM> to U) may provide the plurality of comfort models <NUM>. The processing system <NUM> may include a computer <NUM> (e.g., configured similarly to the computer <NUM>). The user <NUM>(<NUM>) may be associated with at least one user device. The at least one user device may be configured to provide a plurality of user feedbacks <NUM>. Each of the plurality of user feedbacks <NUM> may describe a thermal comfort of the user <NUM>(<NUM>) at a respective point in time. At each point time associated with the plurality of user feedbacks <NUM>, the computer <NUM> may be configured to determine respective running parameters <NUM>(n) of each HVAC device <NUM>(n) of the plurality of HVAC devices <NUM>(n=<NUM> to N). For example, the plurality of HVAC devices <NUM>(n=<NUM> to N) and/or the computer <NUM> may be configured to continuously detect the respective running parameters of each of the plurality of HVAC devices <NUM>(n=<NUM> to N) and to store the detected running parameters in association with the point in time at which the running parameters are detected in the memory <NUM>. The computer <NUM> may be configured to acquire the respective running parameters <NUM>(n) of each HVAC device <NUM>(n) of the plurality of HVAC devices <NUM>(n=<NUM> to N) for each point time associated with the plurality of user feedbacks <NUM> from the memory <NUM>. The computer <NUM> may be configured to implement the comfort model <NUM>. The comfort model <NUM> may be configured to generate a predicted thermal comfort <NUM> responsive to inputting the running parameters <NUM>(n=<NUM> to N) of each of the plurality of HVAC devices <NUM>(n=<NUM> to N) associated with a respective user feedback of the plurality of user feedbacks <NUM> into the comfort model <NUM>. The computer <NUM> may be configured to use the comfort model <NUM> to generate a respective predicted thermal comfort <NUM> for each user feedback of the plurality of user feedbacks <NUM>. The computer <NUM> may be configured to, for each user feedback of the plurality of user feedbacks <NUM>, determine a loss value <NUM> by comparing the predicted thermal comfort <NUM> with the thermal comfort described by the user feedback. The computer <NUM> may be configured to, for each user feedback of the plurality of user feedbacks <NUM>, train the comfort model <NUM> to reduce the loss value <NUM>.

A respective comfort model <NUM> may be trained for each user <NUM>(u) of the plurality of users <NUM>(u=<NUM> to U) and the trained comfort models may be used as plurality of trained comfort models <NUM>. One or more of the users <NUM>(u) of the plurality of users <NUM>(u=<NUM> to U) may provide additional user feedbacks while the computer <NUM> carries out the processing system <NUM>. The computer <NUM> may be configured to further train each comfort model of the plurality of trained comfort models <NUM> in accordance with the processing system <NUM> using the additional feedbacks.

<FIG> shows a method <NUM> of controlling a HVAC system according to various embodiments. The method <NUM> may include providing a respective trained comfort model for each user (e.g., a user device associated with the user) of a plurality of users (e.g., of a plurality of user devices, wherein each user device is associated with a respective user) (in <NUM>). The method <NUM> may include determining respective running parameters of each HVAC device of a plurality of HVAC devices such that a total predicted thermal comfort representing all predicted thermal comforts, which are generated by the comfort models responsive to inputting the running parameters of each of the plurality of HVAC devices into each comfort model, is increased (in <NUM>). The method <NUM> may include controlling the HVAC system in accordance with the determined running parameters (in <NUM>). <FIG> shows a method to obtain a trained comfort model of the trained comfort models (via a training method). The method may include a user device associated with the comfort model providing a plurality of user feedbacks (in 402A). Each user feedback of the plurality of user feedbacks may describe a thermal comfort of a user associated with the user device. Each user feedback of the plurality of user feedbacks may be associated with a respective point in time (e.g., at which is user feedback is provided). The method may include, at each point in time associated with the plurality of user feedbacks, each HVAC device of the plurality of HVAC devices detecting running parameters of the respective HVAC device (in 402B). The method may include, for each user feedback of the plurality of user feedbacks (in 402C): inputting the running parameters, which are detected at the point in time associated with the respective user feedback, of each of the plurality of HVAC devices into the comfort model to generate a predicted thermal comfort (in 402C-<NUM>); determining a loss value by comparing the predicted thermal comfort with the thermal comfort described by the respective user feedback (in 402C-<NUM>); and training the comfort model to be used for controlling the HVAC system to reduce the loss value (in 402C-<NUM>).

<FIG> shows a processing system <NUM> for training a comfort model <NUM>. The processing system <NUM> may include a HVAC controller <NUM> configured to control the plurality of HVAC devices <NUM>(n=<NUM> to N) and to detect their respective running parameters <NUM>(n). The HVAC controller <NUM> may be a Bacnet mainframe in a Building Management System or may be a central controller of a VRF system and/or chiller system. The running parameters <NUM>(n) of a HVAC device <NUM>(n) may include an identification (Indoor Unit ID) of the HVAC device, an air temperature, a fan speed, a valve opening, and/or the associated point in time as time stamp. The processing system <NUM> may include a wireless device <NUM> (e.g., a router) configured to receive the plurality of user feedbacks <NUM>(u=<NUM> to U) of each user <NUM>(u). A user feedback may include an ID (e.g., a Voter ID) of the user <NUM>(u), the associated thermal comfort (e.g., as Voter Feedback), and the associated point in time as time stamp. <FIG> shows the processing system <NUM> used for controlling the HVAC system. The current running parameters <NUM>(n) of a HVAC device <NUM>(n) may include an identification (Indoor Unit ID) of the HVAC device, an air temperature, a fan speed, a valve opening, and/or the associated point in time as time stamp. One or more user devices associated with a respective user <NUM>(u) may provide additional user feedbacks <NUM>. An additional user feedback may include an ID of the user <NUM>(u), the associated thermal comfort, and the associated point in time as time stamp.

<FIG> shows an exemplary optimization routine <NUM> to determine the new running parameters <NUM>. The optimization routine <NUM> may include grouping the user feedbacks <NUM> and the trained comfort models <NUM> by zone <NUM>(m) (of the one or more of zones <NUM>(m=<NUM> to M)) (in <NUM>). The user feedbacks <NUM> may be grouped with the HVAC devices of the plurality of HVAC devices which are located in the same zone (for localization of user devices, which provide the respective user feedbacks <NUM>, see description of <FIG> and <FIG>). The optimization routine <NUM> may include generating an objective function (in <NUM>). A matrix, A, may include a predicted user comfort, am,n, as predicted by the associated comfort model: <MAT>, wherein m represents the respective user, u, and n represents the number of HVAC features associated with a user feedback (i.e., a number of increments in the range from -C to +C). The objective function may, for example, describe a total discomfort. The objective function may be determined by: <MAT>, wherein j represents the respective HVAC device, n. This optimization function may be minimized using an optimization algorithm.

The optimization routine <NUM> may include setting constraints for discomfort limits (in <NUM>). A discomfort limit constraint may be or may include a predefined comfort constraint representing a minimum predicted thermal comfort and/or a maximum change of a predicted current thermal comfort. A discomfort limit constraint may be or may include a predefined total comfort constraint representing a minimum total predicted thermal comfort and/or a maximum change of the total predicted current thermal comfort. The optimization routine <NUM> may include setting constraints for controllable parameters (in <NUM>). As an example, the limit constraints may be set as two inequality constraints described by the equation: <MAT>, wherein x is the vector of the HVAC features, wherein Vub is an upper bound, and wherein V1b is a lower bound. As an example, constraints for controllable parameters may be defined as equality constraints using a diagonal matrix, E:
Ex = b, wherein the vector b may include either <NUM> in the case that the respective control parameter is controllable or the current HVAC feature value in the case that the control parameter is not controllable (see, for example, <FIG>); <MAT> <MAT>.

The optimization routine <NUM> may include an optimization routine, such as a linear programming optimization routine (in <NUM>). The optimization routine may provide the new running parameters <NUM>. The HVAC system may be controlled to modify (e.g., to change, e.g., to keep) the set HVAC temperature of each of the plurality of HVAC devices to the new running parameters <NUM> (in <NUM>). <FIG> shows an exemplary method of determining the controllable parameters (used in <NUM>). For example, a predefined load constraint representing a maximum load of the HVAC system and/or a maximum change of a current load of the HVAC system may be set. The predefined load constraint may be associated with the new running parameters <NUM>. For example, a load prediction model may be used to predict the load of the HVAC system for the new running parameters <NUM>. The method may include providing a list of HVAC parameters (e.g., stored in the memory <NUM>) (in <NUM>). The method may include, for each HVAC parameter in the list of HVAC parameters, determining whether the HVAC parameter is controllable (in <NUM>). In the case that the respective HVAC parameter is controllable ("Yes" in <NUM>), the method may include determining whether a control of the HVAC parameter is desired (in <NUM>). In the case that the respective HVAC parameter is not controllable ("No" in <NUM>) or that a control of the HVAC parameter is not desired ("No" in <NUM>), it is determined that the HVAC parameter cannot be changed (in <NUM>). The method may include setting an equality constraint for this HVAC parameter (in <NUM>). In the case that a control of the HVAC parameter is desired ("Yes" in <NUM>), the method may include that the respective HVAC parameter is optimized (in <NUM>). No equality constraint may be set for this optimized parameter (in <NUM>). <FIG> shows an exemplary constraint setting. In this example, the thermal comfort of a user or occupant may be described as a number in a range from -<NUM> to +<NUM> (i.e., C=<NUM>). The constraint boundaries may be set around <NUM> to provide a balance between energy saving and a total thermal comfort of the users (e.g., a balance setting <NUM>). The constraint boundaries may be set positive to provide energy saving (e.g., an energy saving setting <NUM>) or negative to increase the total thermal comfort of the users (e.g., a comfort setting <NUM>), or vice versa. For example, a constraint defined by the boundaries [<NUM>, <NUM>] may allow to maintain the total thermal comfort and to allow for a slight energy saving (as represented by the <NUM> boundary). For example, a constraint defined by the boundaries [-<NUM>, <NUM>] may allow an increase of the total thermal comfort while spending additional energy (as indicated by the load). For example, a constraint defined by the boundaries [<NUM>, <NUM>] may allow a decrease of the total thermal comfort to save energy. Illustratively, thermal comforts of a plurality of users as well as energy saving requirements may be addressed. The optimization routine <NUM> may be carried in regular time intervals (e.g., every <NUM> minutes, e.g., every <NUM> hour, etc.).

<FIG> shows a processing system 700A for determining a weight matrix, such as the weight matrix <NUM>, according to various embodiments. A computer (e.g., the computer <NUM>, e.g., the computer <NUM>) may be configured to carry out the processing system 700A for each user <NUM>(u) of the plurality of users <NUM>(u=<NUM> to U) starting with the first user <NUM>(u=<NUM>). The computer may select all user feedbacks <NUM>(u) associated with the respective user <NUM>(u) (in <NUM>). Each user feedback of the plurality of user feedbacks <NUM>(u) associated with the respective user <NUM>(u) may be categorized into a first category "cold" indicating that the user <NUM>(u) is feeling cold (also referred to as cold feedback), a second category "comfortable" indicating that the user <NUM>(u) is feeling comfortable, and a third category "hot" indicating that the user <NUM>(u) is feeling hot. The computer may select the user feedbacks of the user <NUM>(u) which are categorized as "cold" (in <NUM>). For example, the plurality of user feedbacks <NUM>(u) associated with the respective user <NUM>(u) may include two or more cold feedbacks. The computer may be configured to, for each point in time associated with the cold feedbacks, determine a respective cooling load of each HVAC device <NUM>(n) of the plurality of HVAC devices <NUM>(n=<NUM> to N) using the running parameters of the respective HVAC device <NUM>(n). The cooling load of a HVAC device <NUM>(n) may be described by capacity value. The capacity value may be a product of a mass flow rate and an enthalpy change as a function of a temperature change. The temperature change of a fluid may be considered constant (e.g., equal to <NUM> in the case of a chiller system, e.g., equal to <NUM> in the case of a refrigerant-based system or a split system). The temperature change may also be detected using one or more sensors. The mass flow rate may be determined using (e.g., in a direct proportionality or multiplied by a factor indicating a size of a respective valve associated with the valve opening) the valve opening (which may be one of the running parameters of a HVAC device). An example of resulting cooling loads of each HVAC device <NUM>(n) of the plurality of HVAC devices <NUM>(n=<NUM> to <NUM>) for three cold feedbacks of the first user <NUM>(u=<NUM>) is shown in table <NUM>:.

The computer may be configured to determine, for each HVAC device <NUM>(n) of the plurality of HVAC devices <NUM>(n=<NUM> to N), a cooling load sum by summing up all cooling loads determined for the points in time associated with the respective user <NUM>(u) (in <NUM>). An example of resulting cooling load sums for each HVAC device is shown in table <NUM>:.

The computer may be configured to store the resulting cooling load sums in a memory (e.g., the memory <NUM>). The computer may be configured to determine whether all users of the plurality of users <NUM>(u=<NUM> to U) are processed (in <NUM>). In the case that not all users are processed ("No" in <NUM>), the processing, as described above, may be carried out for the next user, <NUM>(u=u+<NUM>). In the case that, for each of the plurality of users <NUM>(u=<NUM> to U), respective cooling load sums for each HVAC device are determined ("Yes" in <NUM>), the computer may determine the weight matrix <NUM> (in <NUM>). An example of resulting cooling load sums for U=<NUM> and N=<NUM> is shown in in table <NUM>:.

The computer may be configured to determine, for each of the plurality of users <NUM>(u=<NUM> to U), for each HVAC device <NUM>(n) of the plurality of HVAC devices <NUM>(n=<NUM> to N), a respective weight value, wu,n. Each weight value, wu,n, may be determined as a fraction of the determined cooling load sum, capu,n, associated with the user <NUM>(u) and the HVAC device <NUM>(n) from a total cooling load sum. The total cooling load sum may be a (e.g., cumulative) sum of all determined cooling load sums of the plurality of HVAC devices <NUM>(n=<NUM> to N). A respective weight value, wu,n, may be determined by the equation: <MAT>. The weight matrix <NUM> may include all weight value, wu,n, from u=<NUM> to u=U and from n=<NUM> to n=N. An exemplary weight matrix for U=<NUM> and N=<NUM> is shown in in table <NUM>:.

<FIG> shows an illustration of an exemplary weighted correlation of users <NUM>(u) and HVAC devices <NUM>(n). Illustratively, the weight matrix <NUM> allows to identify which HVAC device <NUM>(n) is affecting the thermal comfort of a user or occupant most improving a user-specific control of the HVAC system <NUM>. Optionally, the HVAC devices may be grouped to respective zones of the one or more zones <NUM>(m=<NUM> to M) and a respective weight matrix may be determined for each zone. <FIG> shows a flow diagram of a method 700C using the weight matrix <NUM> in accordance with the processing system <NUM>. The method may include, for the user feedbacks <NUM>(u) of a respective user <NUM>(u) (as acquired in <NUM>), acquire the current running parameters <NUM> of each of the plurality of HVAC devices <NUM>(n=<NUM> to N). The trained comfort model associated with the respective user <NUM>(u) may be used to determine a predicted current thermal comfort (in <NUM>). In the case that a respective predicted current thermal comfort is determined for each user of the plurality of users <NUM>(u=<NUM> to U) ("Yes" in <NUM>), the weighted total predicted current thermal comfort <NUM> is determined using equation (<NUM>). The method 700C may include determining the new running parameters <NUM> (e.g., using the optimization routine <NUM>). The method 700C may include providing control instructions to control the HVAC system in accordance with the new running parameters <NUM> (in <NUM>).

<FIG> shows a processing system 800A for determining a number of occupants to be consider in controlling the HVAC system <NUM> according to various embodiments. The computer <NUM> may be configured to carry out the processing system 800A to determine the occupancy information <NUM> (see <FIG>). Each user feedback of the plurality of user feedbacks <NUM> may be provided by a respective user <NUM>(u) via a user device <NUM>(k) of a plurality of user devices <NUM>(k=<NUM> to K). A user device <NUM>(k) may be, for example, a personal button device. The personal button device may include an interface (e.g., buttons) configured to allow a user to provide a user feedback. Each user feedback provided by a user device (such as the personal button) may further include a time stamp indicating a point in time at which the feedback is provided and/or location information indicating a location of the user device within the indoor environment (e.g., indicated a zone of the one or more zones <NUM>(m=<NUM> to M). The computer may be configured to determine the location information via static mapping and/or via real-time by sniffing the Media-Access-Control, MAC, address of the user device. A user device <NUM>(k) may provide the user feedbacks wirelessly (e.g., via Bluetooth, e.g., via infrared, e.g., via Wireless Fidelity). The number of MAC addresses may provide a number of occupants and may be used as occupancy information <NUM>. To ensure user data protection, a user feedback may not include any information regarding the user device (e.g., no localization information). To associate user feedbacks with specific zones of the one or more zones <NUM>(m=<NUM> to M), it may be necessary to associated user feedbacks <NUM>(u) with respective user devices <NUM>(k) since a user device <NUM>(k) may be localized. The computer <NUM> may be configured to correlate user feedbacks <NUM>(u) to user devices <NUM>(k) using the processing system 800A which may allow for a localization of the user feedbacks <NUM>(u) via the correlated user devices <NUM>(k). The plurality of user feedbacks <NUM> may be acquired (in <NUM>). At each point in time associated with a user feedback, a plurality of Media-Access-Control, MAC, addresses associated with the plurality of user devices <NUM>(k=<NUM> to K) (e.g., which are present in a local short range network) may be detected (in <NUM>). A table <NUM> may be created including, for each user feedback: a feedback identification, ID, which represents a user <NUM>(u) who provided the respective user feedback, the time stamp associated with the user feedback, and the detected MAC addresses of the user devices <NUM>(k) which are present in the local short range network at the respective time represented by the time stamp. The computer <NUM> may be configured to correlate each MAC address with a feedback ID employing a correlation metric (e.g., a leverage metric, co-occurrence-metric, a lift metric, a confidence metric, and/or a conviction metric). In the following, a leverage metric is exemplarily described. The computer <NUM> may count an occurrence of each pair of feedback ID and MAC address (in <NUM>). The computer <NUM> may determine a support metric for each (feedback ID, MAC address) pair (in <NUM>). The computer <NUM> may count an occurrence of each feedback ID and each MAC address separately (in <NUM>). The computer <NUM> may determine a support metric for each feedback ID and each MAC address separately (in <NUM>). The computer <NUM> may determine a leverage metric for each (feedback ID, MAC address) pair (in <NUM>). A leverage of a feedback ID to a user device may be described by the equation: <MAT>
, wherein Support is the support metric counting the respective occurrence.

The computer <NUM> may sort the (feedback ID, MAC address) pairs by highest leverage (in <NUM>). The computer <NUM> may consolidate (e.g., correlate) feedback ID and MAC address by leverage order as indicated by the sorted the (feedback ID, MAC address) pairs (in <NUM>). Some user devices, such as laptops, may be capable to create a virtualization of their network card. This may provide additional MAC addresses and, thus, a total number of occupants may be less than the number of MAC addresses and/or the correlation metric may be falsified. <FIG> shows a pre-processing 800B of user devices to filter the MAC addresses according to various embodiments. The computer <NUM> may be configured to carry out the pre-processing 800B. At each point in time associated with a respective user feedback, respective device information <NUM> may be detected for each user device of a plurality of user devices <NUM>. A user device may be button device having feedback buttons, a personal computer, a laptop, a tablet, a smartwatch, or a smartphone. The device information may include a time stamp, a MAC address, vendor information representing a vendor of the user device, and optionally an SSID. The computer <NUM> may classify (e.g., filter) the plurality of user devices <NUM> using the vendor information: The computer <NUM> may classify the MAC addresses either into a first class <NUM> (in some aspects referred to as first group) in the case that the vendor of the device is associated with devices which are capable to create a virtualization of their network card (e.g., a vendor associated with laptops) or into a second class <NUM> (in some aspects referred to as second group) otherwise. The computer <NUM> may filter the MAC addresses which are classified into the first class <NUM> such that at each point in time only one MAC address is selected for each user device (in <NUM>). The filtering (in <NUM>) may use the respective SSID of the device information <NUM> and only the user devices with the SSID in the state "DIRECT" may be selected. The filtering (in <NUM>) may compare the first byte and the last byte of the MAC addresses and MAC addresses associated with network virtualization may be filtered in the case that only the second digit of the first byte and/or of the second byte differentiates. This filtering (in <NUM>) may result in one or more MAC addresses representing a subgroup of the user devices <NUM> of the first class <NUM>. The computer <NUM> may determine the user devices categorized into the second class <NUM> and the user devices of the subgroup of user devices <NUM> as the plurality of user devices <NUM>(k=<NUM> to K) used in the processing system 800A.

Claim 1:
A method (<NUM>) of controlling a heating, ventilation, and air-conditioning, HVAC, system, the HVAC system comprising a plurality of HVAC devices, the method (<NUM>) comprising:
• training (<NUM>) a plurality of comfort models to be used for controlling the heating, ventilation, and air-conditioning, HVAC, system, wherein each comfort model of the plurality of comfort models is associated with a respective user of a plurality of users, wherein the training of a respective comfort model of the plurality of comfort models comprising:
a user device associated with the respective comfort model providing a plurality of user feedbacks, each user feedback of the plurality of user feedbacks describing a thermal comfort of the respective user associated with the user device, wherein each user feedback of the plurality of user feedbacks is associated with a respective point in time (402A), wherein the plurality of user feedbacks comprises two or more cold feedbacks indicating that the user of the user device associated with the comfort model feeling cold;
• determining a weight matrix (<NUM>), the weight matrix (<NUM>) includes, for each trained comfort model of the plurality of trained comfort models (<NUM>), a respective weight value for each HVAC device of the plurality of HVAC devices;
• determining respective running parameters of each HVAC device of the plurality of HVAC devices such that a total predicted thermal comfort representing all predicted thermal comforts, which are generated by the plurality of comfort models responsive to inputting the running parameters of each of the plurality of HVAC devices into each comfort model, is increased (<NUM>), considering the determined weight value of each HVAC device of the plurality of HVAC devices associated with each comfort model of the plurality of comfort models; and
• controlling the HVAC system in accordance with the determined running parameters (<NUM>).