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. However, to achieve desired indoor environmental conditions (e.g., a temperature, e.g., a humidity, etc.), the HVAC system may consume a specific load (e.g., a specific power consumption) which may depend on various parameters. It may be advantageous to predict a load of the HVAC system within a future time period (e.g., a future power consumption) to adapt a control of the HVAC system such that a load which is actually used within the time period is reduced as compared to the predicted load. Various aspects relate to a HVAC system and a method of controlling a HVAC system capable to predict a load for a future time period and to control the HVAC system to reduce an actual load within the future time period as compared to the predicted load. For example, a heating/cooling rate of the HVAC system may be adapted to the reduced the actually required load within the future time period. This may reduce an energy consumption of the HVAC system and, thus, also reducing costs as well as increasing an environmental sustainability. Illustratively, an energy-efficient HVAC system and a method of an energy-efficiently controlling a HVAC system are provided.

Document <CIT> discloses an air conditioning load forecasting model establishing method for a building, which involves establishing an air conditioning load prediction model based on a sample feature set and corresponding tag data using a preset machine learning algorithm.

The invention relates to a method of controlling a heating, ventilation, and air-conditioning, HVAC, system, as defined in claim <NUM>.

According to various embodiments, the HVAC system may include or may be a variable refrigerant flow system.

According to various embodiments, the predicted load may represent an amount of energy required by the HVAC system to achieve the requested indoor environmental conditions during the future time period.

According to various embodiments, the indoor environmental conditions further may include an indoor humidity.

According to various embodiments, the outdoor environmental conditions may include an outdoor temperature. According to various embodiments, the outdoor environmental conditions may include a solar surface radiation. According to various embodiments, the outdoor environmental conditions may include an outdoor humidity.

According to various embodiments, the method may include further include: detecting an occupancy rate of an indoor zone in which the indoor environmental conditions are controlled by the HVAC system; and determining a predicted occupancy rate within the future time period using calendar information and/or occupancy statistics representing an occupancy of the indoor zone; wherein inputting the detected indoor environmental conditions and the detected outdoor environmental conditions into the load prediction model to generate the predicted load may include inputting the detected indoor environmental conditions, the detected outdoor environmental conditions, and the detected occupancy rate into the load prediction model to generate the predicted load; and wherein inputting the requested indoor environmental conditions and the predicted outdoor environmental conditions into the trained load prediction model to determine the predicted load for the future time period may include inputting the requested indoor environmental conditions, the predicted outdoor environmental conditions, and the predicted occupancy rate into the trained load prediction model to determine the predicted load for the future time period.

According to various embodiments, inputting the detected indoor environmental conditions and the detected outdoor environmental conditions into the load prediction model to generate the predicted load may include inputting the detected indoor environmental conditions, the detected outdoor environmental conditions, and a time of day at which the indoor environmental conditions, the load of the HVAC system, and the outdoor environmental conditions are detected into the load prediction model to generate the predicted load; and inputting the requested indoor environmental conditions and the predicted outdoor environmental conditions into the trained load prediction model to determine the predicted load for the future time period may include inputting the requested indoor environmental conditions, the predicted outdoor environmental conditions, and a time of day associated with the future time period into the trained load prediction model to determine the predicted load for the future time period.

According to various embodiments, the method may further include: detecting a load of the HVAC system in the future time period; determining a further loss value by comparing the predicted load for the future time period with the load of the HVAC system detected in future time period; and further training the load prediction model to reduce the further loss value.

The invention further relate to a heating, ventilation, and air-conditioning, HVAC, system, as defined in claim <NUM>.

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 invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. Other embodiments may be utilized and structural, and logical changes may be made without departing from the scope of the invention. The various embodiments are not necessarily mutually exclusive, as some embodiments can be combined with one or more other embodiments to form new embodiments.

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 "load prediction model" as used herein, may be any kind of model capable to predict a load responsive to inputting one or more parameters and/or information as described herein. Illustratively, a "load prediction model" may map the inputted parameters and/or information in accordance with the ones described herein to a predicted load. 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 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 "load" of a HVAC system, as used herein, may represent an amount of energy required to achieve associated indoor environmental conditions (e.g., an indoor temperature, e.g., an indoor humidity). Illustratively, a "load" of a HVAC system may be a power consumption of the HVAC system. A "load" of a HVAC system may be an amount of energy required to keep a condition of an associated zone within required/requested conditions. A "load" of a HVAC system may be a cooling load and/or a heating load.

While the disclosure has been particularly shown and described with reference to specific embodiments, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.

Various aspects relate to a method which predicts a load of a HVAC system within a future time period and which controls the HVAC system (e.g., adapts currently set parameters) such that the HVAC system consumes less energy within the time period. For example, at a specific day a load of the HVAC system may be predicted for the next day and the control of the HVAC system is adapted such that the actually consumed load on the next day (i.e., then the present day) is reduced as compared to the predicted load. Illustratively, a future load is predicted and setting are changed such that the consumed load in the future is reduced.

<FIG> shows a processing system <NUM> for controlling a HVAC system according to various embodiments. The HVAC system may be or may include a Variable Refrigerant Flow (VRF) system. The HVAC system may be or may include a VRF system and/or a chiller system. The HVAC system may include one or more HVAC devices. The one or more HVAC devices may be configured to control (e.g., to keep stable, e.g., to change) one or more environmental parameters in a surrounding of the one or more HVAC devices 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. 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 may be associated with an indoor environment (e.g., an environment in a building) and an outdoor environment (e.g., an environment outside the building, e.g., an environment at an outer wall of the building). The indoor environment may include a plurality of zones (e.g., regions). For example, a zone of the plurality of zones associated with the HVAC system may be a room within the indoor environment. Each zone of the plurality of zones may be associated at least one HVAC device of the one or more devices. 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.

The processing system <NUM> may include a computer <NUM>. The computer <NUM> may be configured to control the HVAC system. For example, the computer <NUM> may be configured to control the one or more HVAC devices (e.g., via setting a respective HVAC temperature associated with each of the one or more HVAC devices). The computer <NUM> include one or more processors. The computer <NUM> may be any kind of logic implementing entity, as described above. The processing system <NUM> may include a memory <NUM>. The memory <NUM> may be used in the processing carried out by the computer <NUM>. The memory <NUM> may be part of the HVAC system. The memory <NUM> may be external to the HVAC system and may be, for example, a cloud memory. The memory <NUM> may include a plurality of memory devices and one or more of the plurality of memory devices may be part of the HVAC system and other ones of the plurality of memory devices may be part of a cloud memory. Data stored in the memory <NUM> may be stored in a local memory and/or in a cloud memory. The memory <NUM> may store requested indoor environmental conditions <NUM> (e.g., data representing requested indoor environmental conditions) associated with a future time period. The future time period may be any time period in the future of a present time. The future time period may start at the present time and may end at a future time (i.e., a point in time in the future of the present time). For example, the future time period may be a time period from the present time until one or more hours, h, (e.g., <NUM>, e.g., <NUM>, etc.), one or more days (e.g., <NUM> day, e.g., <NUM> days, etc.), etc. later than the present time. The future time period may start at a future time and may end at a point in time later than the future time. For example, the future time period may be a next day. For example, the future time period may start at a first point in time on the next day and may end at a second point in time, which is after the first point in time, on the next day. As used herein, "indoor environmental conditions" may describe one or more environmental parameters, such as an indoor temperature (e.g., a room temperature) and/or an indoor humidity. For example, the requested indoor environmental conditions may include an indoor temperature which is requested in the future time period. As used herein, "indoor environmental conditions" may refer to an indoor environment associated with the HVAC system. A predefined set point schedule may include predefined indoor environmental conditions (e.g., an indoor temperature and/or an indoor humidity) as a function of time of day and/or of day of the week. The predefined set point schedule may be stored in the memory <NUM>. The computer <NUM> may be configured to acquire the predefined set point schedule and to determine the predefined indoor environmental conditions within the future time period as the requested indoor environmental conditions <NUM>. The HVAC system may include a user interface. A user may be able to set indoor environmental conditions via the user interface. The set indoor environmental conditions may be stored in the memory <NUM>. The computer <NUM> may be configured to acquire the set indoor environmental conditions and to determine the set indoor environmental conditions within the future time period as the requested indoor environmental conditions <NUM>. The memory <NUM> may store predicted outdoor environmental conditions <NUM> within the future time period. As used herein, "outdoor environmental conditions" may describe one or more environmental parameters, such as an outdoor (e.g., an air) temperature, an outdoor humidity, and/or a solar surface radiation. As used herein, "outdoor environmental conditions" may refer to an outdoor environment associated with the HVAC system. The solar surface radiation may describe a cloud level. The computer <NUM> may be configured to determine a cloud level using the solar surface radiation. The predicted outdoor environmental conditions <NUM> may be determined using a weather forecast. For example, a weather forecast may provide future outdoor environmental conditions. The computer <NUM> may be configured to obtain (e.g., to download) the weather forecast for the future time period from a weather forecast service (e.g., from a cloud memory of the weather forecast service). The predicted outdoor environmental conditions <NUM> may impact the (cooling/heating) load required to achieve the requested indoor environmental conditions <NUM>. As an example, an outside temperature which is at least <NUM> higher than an indoor temperature and/or a comparatively high solar surface radiation indicating a cloudless state may increase a cooling load of the HVAC system required to reduce the indoor temperature.

The processing system <NUM> may be configured to implement a trained load prediction model <NUM>. The trained load prediction model <NUM> may be configured to generate (e.g., to output) a predicted load responsive to inputting indoor environmental conditions and outdoor environmental conditions. The trained load prediction model <NUM> may be obtained by training a load prediction model as described with reference to <FIG>. The trained load prediction model <NUM> may determine a predicted load <NUM> for the future time period responsive to inputting the requested indoor environmental conditions <NUM> associated with the future time period and the predicted outdoor environmental conditions <NUM>.

The computer <NUM> may be configured to provide control instructions <NUM> to control the HVAC system to reduce a load of the HVAC system within the future time period using the determined predicted load <NUM>. For example, in the case of a comparatively high predicted load <NUM>, the control instructions <NUM> may include instructions to start a cooling or heating earlier than a previously set schedule in order to reduce a slope of a cooling ramp or heating ramp. A lower ramp of heating or cooling may reduce an amount of energy required to achieve the requested indoor environmental conditions <NUM> within the future time period.

<FIG> shows a processing system <NUM> for training a load prediction model <NUM> used to control a HVAC system <NUM>. The HVAC system <NUM> may include an indoor environment <NUM> (e.g., a building, a room in a building, and/or a zone in a building). The HVAC system <NUM> may include one or more HVAC devices <NUM> within the indoor environment <NUM>. The one or more HVAC devices <NUM> may be configured to control the indoor environmental conditions within the indoor environment <NUM>. The HVAC system <NUM> may include one or more indoor environmental sensors <NUM> within the indoor environment <NUM>. The one or more indoor environmental sensors <NUM> may be configured to detect the indoor environmental conditions within the indoor environment <NUM>. The HVAC system <NUM> may include one or more outdoor environmental sensors <NUM> located outside the indoor environment <NUM>. The one or more outdoor environmental sensors <NUM> may be configured to detect the outdoor environmental conditions outside the indoor environment <NUM>. The processing system <NUM> may include a computer <NUM> (e.g., configured similar to the computer <NUM>). The computer <NUM> may be configured to control the HVAC system <NUM> (e.g., the one or more HVAC devices <NUM>) to control (e.g., to keep, e.g., to change) the indoor environmental conditions within the indoor environment <NUM>. The computer <NUM> may be configured to acquire (e.g., data representing) a load of the HVAC system <NUM> (e.g., a load of the one or more HVAC devices <NUM>). The computer <NUM> may be configured to acquire (e.g., data representing) indoor environmental conditions <NUM> detected by the one or more indoor environmental sensors <NUM>. The computer <NUM> may be configured to acquire (e.g., data representing) outdoor environmental conditions <NUM> detected by the one or more outdoor environmental sensors <NUM>. The computer <NUM> may be configured to implement the load prediction model <NUM>. The load prediction model <NUM> may be configured to generate a predicted load responsive to inputting indoor environmental conditions and outdoor environmental conditions. The load prediction model <NUM> may generate a predicted load <NUM> responsive to inputting the acquired indoor environmental conditions <NUM> and the acquired outdoor environmental conditions <NUM> into the load prediction model <NUM>. The computer <NUM> may be configured to determine a loss value <NUM> by comparing the predicted load <NUM> with the acquired load <NUM>. The computer <NUM> may be configured to train the load prediction model <NUM> to reduce the loss value <NUM>. The training of the load prediction model <NUM> as described herein may be one iteration of a training process and the training may be carried out as a plurality of iterations.

<FIG> shows a flow diagram of a method <NUM> of controlling a HVAC system according to various embodiments. The method <NUM> may include providing a trained load prediction model which is capable to generate a predicted load responsive to inputting indoor environmental conditions and outdoor environmental conditions into the load prediction model (in <NUM>). The method <NUM> may include determining requested indoor environmental conditions associated with a future time period (in <NUM>). The method <NUM> may include determining predicted outdoor environmental conditions within the future time period using a weather forecast (e.g., via a weather forecast service) (in <NUM>). The method <NUM> may include inputting the requested indoor environmental conditions and the predicted outdoor environmental conditions into the trained load prediction model to determine (e.g., to generate) a predicted load for the future time period (in <NUM>). The method <NUM> may include controlling the HVAC system to reduce a load of the HVAC system within the future time period using the determined predicted load. The trained load prediction model may be provided (in <NUM>) by training a load prediction model. <FIG> shows a flow diagram of a method of training a load prediction model used to control a HVAC system according to various embodiments. The training method may include controlling indoor environmental conditions using a HVAC system (in 302A). The training method may include detecting the indoor environmental conditions, a load of the HVAC system, and outdoor environmental conditions (in 302B). The training method may include inputting the detected indoor environmental conditions and the detected outdoor environmental conditions into the load prediction model to generate a predicted load (in 302C). The training method may include determining a loss value by comparing the predicted load with the detected load of the HVAC system (in 302D). The training method may include training the load prediction model to reduce the loss value (in 302E). The training method may include one or more iterations (e.g., plurality of iterations) and each iteration may include the above described training method.

<FIG> shows a processing system <NUM> for training the load prediction model <NUM> used to control the HVAC system <NUM>. In addition to the processing system <NUM>, the computer <NUM> may be configured to acquire occupancy information <NUM> which describe occupancy of people within the indoor environment <NUM>. For example, the HVAC system <NUM> may further include one or more occupancy sensors <NUM> (e.g., a wireless occupancy sensor, e.g., a passive infrared sensor, e.g., a motion sensor, etc.) configured to detect (e.g., infrared-based, e.g., ultrasonic-based, e.g., radar-based, e.g., microwave-based, etc.) an occupancy within the indoor environment <NUM>. The one or more occupancy sensors <NUM> may be configured to provide the detected occupancy as occupancy information <NUM> to the computer <NUM>. According to various aspects, a memory may store personal calendar information, meeting information, etc. The computer <NUM> may be configured to determine an occupancy of the indoor environment <NUM> using the personal calendar information, meeting information, etc. in order to acquire the occupancy information <NUM>. The computer <NUM> may be configured to implement an occupancy prediction model (e.g., using occupancy statistics) which describes, a clock in time of occupants of the indoor environment <NUM>, a clock out time of occupants of the indoor environment <NUM>, and/or daily habits of people within the indoor environment <NUM> to predict an occupancy within the indoor environment <NUM>. The computer <NUM> may be configured to use the occupancy prediction model to determine a predicted occupancy within the indoor environment <NUM> as occupancy information <NUM>. The computer <NUM> may be configured to acquire a time of day <NUM> (e.g., using an internal clock of the computer and/or an external server providing a clock time) at which the load <NUM> of the HVAC system <NUM>, the indoor environmental conditions <NUM>, the outdoor environmental conditions <NUM> are detected. Optionally, the computer <NUM> may be configured to acquire a user comfort <NUM> of each user within the indoor environment <NUM>. The user comfort may represent a thermal comfort of the user. The user comfort <NUM> may be a user feedback provided via a user device <NUM>. The computer <NUM> may be configured to implement a comfort model capable to predict a user comfort of each user using the indoor environmental conditions. The user comfort <NUM> may be a predicted mean vote representing a mean over all user comforts. The load prediction model <NUM> may be configured to generate the predicted load <NUM> responsive to inputting the acquired indoor environmental conditions <NUM>, the acquired outdoor environmental conditions <NUM>, the acquired occupancy information <NUM>, the acquired time of day <NUM>, and optionally the acquired user comfort <NUM> into the load prediction model <NUM>. As described with reference to the processing system <NUM>, the computer <NUM> may be configured to determine a loss value <NUM> by comparing the predicted load <NUM> with the acquired load <NUM> and may be configured to train the load prediction model <NUM> to reduce the loss value <NUM> (e.g., using a plurality of iterations).

A computer acquiring data (e.g., respective conditions), as described herein, may refer to a direct acquisition from a sensors or device or to an indirect acquisition of the data from a memory which stores the data. For example, one or more acquisition modules may be configured to acquire the data and to store the data in the memory. The data may be stored in a database within the memory. It is noted that a load prediction model, as described herein, may include a plurality of individual models and each individual may be configured to provide a respective predicted load responsive to inputting one or more of the data/information described herein. In this case, the predicted load described herein may be a sum of all respective predicted loads determined by the plurality of individual models.

<FIG> shows a processing system <NUM> for controlling a HVAC system according to various embodiments. The processing system <NUM> may be similar to the processing system <NUM>, wherein the computer <NUM> is configured to implement a trained load prediction model <NUM>. The trained load prediction model <NUM> may be configured to generate (e.g., to output) a predicted load responsive to inputting indoor environmental conditions, outdoor environmental conditions, a time of day <NUM>, and a predicted occupancy <NUM> (and optionally user comforts). The computer <NUM> may be configured to determine the predicted occupancy <NUM> using personal calendar information, meeting information, etc. stored in the memory, using occupancy statistics, and/or using an occupancy prediction model capable to predict an occupancy within the indoor environment. The trained load prediction model <NUM> may be obtained by training a load prediction model as described with reference to <FIG>. The time of day <NUM> may be determined using a time of the future time period.

In accordance with the processing system <NUM> and/or the processing system <NUM>, the computer <NUM> may be configured to detect a load of the HVAC system within the future time period. The computer <NUM> may be configured to determine a further loss value by comparing the predicted load for the future time period with the load of the HVAC system detected within the future time period. The computer <NUM> may be configured to adapt (e.g., to train) the load prediction model (e.g., the load prediction model <NUM>, e.g., the load prediction model <NUM>) to reduce the further loss value. The training may be carried out as described with the processing system <NUM> and the processing system <NUM>. Illustratively, the trained load prediction model may be further trained. For example, the trained load prediction model may be further trained at constant time intervals (e.g., of <NUM> day, e.g., of <NUM> week, e.g., of <NUM> month, etc.).

Claim 1:
A method (<NUM>) of controlling a heating, ventilation, and air-conditioning, HVAC, system, the method (<NUM>) comprising:
training a load prediction model (<NUM>), the training comprising:
• controlling indoor environmental conditions using the HVAC system (302A),
• detecting the indoor environmental conditions, a load of the HVAC system, and outdoor environmental conditions (302B), wherein the indoor environmental conditions comprise an indoor temperature,
• inputting the detected indoor environmental conditions and the detected outdoor environmental conditions into the load prediction model to generate a predicted load (302C),
• determining a loss value by comparing the predicted load with the detected load of the HVAC system (302D), and
• training the load prediction model to reduce the loss value (302E);
determining requested indoor environmental conditions associated with a future time period (<NUM>), the requested indoor environmental conditions comprising an indoor temperature requested in the future time period;
determining predicted outdoor environmental conditions within the future time period using a weather forecast (<NUM>);
inputting the requested indoor environmental conditions and the predicted outdoor environmental conditions into the trained load prediction model to determine a predicted load for the future time period (<NUM>); and
controlling the HVAC system to reduce a load of the HVAC system within the future time period using the determined predicted load (<NUM>).