EP4686885A1

EP4686885A1 - Controller for a heat pump-based water heating and/or cooling system

Info

Publication number: EP4686885A1
Application number: EP24192563.5A
Authority: EP
Inventors: Aurélien MASSEIN
Original assignee: BDR Thermea Group BV
Current assignee: BDR Thermea Group BV
Priority date: 2024-08-02
Filing date: 2024-08-02
Publication date: 2026-02-04
Also published as: WO2026027508A1

Abstract

An implementation of the disclosed technology provides a controller configured to control at least a hot water system, the hot water system includes a heat pump, an optional electrical backup heat source and/or an optional hydraulic backup heat source. The controller includes a thermal comfort determination module configured to predict a thermal comfort satisfaction level and an energy consumption determination module configured to predict an energy consumption level. The controller is configured to: receive a prediction of the thermal comfort satisfaction level from the thermal comfort determination module; receive a prediction of the energy consumption level from the energy consumption determination module; perform an optimization operation based, at least in part, on the predictions of the thermal comfort satisfaction level and the energy consumption level; and determine commands to operate the hot water system based, at least in part, on a result of the optimization operation.

Description

TECHNICAL FIELD

The disclosed technology relates to heat pump water heating and/or cooling systems for use in production of hot and/or cool water and/or heating systems and/or cooling systems, and in particular to intelligent control of such a heat pump water heating and/or cooling system.

BACKGROUND

Water heating and/or cooling systems are means of producing hot or cool water for heating and/or cooling applications, and may also be used in the production of domestic and/or sanitary hot water. It will be understood that while much of the discussion here will center on heat pump water heaters that are used for the production of domestic hot water, similar systems may be used for other heating and/or cooling applications, and the discussion of heat pump water heaters is intended merely to illustrate the technology. Similar systems and methods may be applied to a wide range of applications in which water or other liquids are heated and/or cooled for use in a wide range of heating and/or cooling applications.
A heat pump water heater is a water heater with at least one heat pump as a water heating element. A heat pump water heater may have other associated heating means such as an electric resistive element or heat exchangers associated with a hot water circuit. Backup heating, such as a gas-powered boiler, pellet boiler, thermal solar panel, and/or other heating systems may also be used.
Heat pump water heaters need an available energy/heat source to transfer heat to the water to be heated. Different sources can be used. The type of source can be used to differentiate and designate different types of heat pump water heaters.
Ambient air heat pump water heaters use ambient air as an energy source. Ambient air entering and leaving the heat pump is drawn in and returned to the volume of air available at the installation site. The place of installation is often an unheated, frost-free room such as a utility room in a building (e.g., a cellar, garage, attic, etc.), though controlled temperature rooms may also be used. Ambient air heat pumps provide a simplified product compared to other heat pump types. This is because, e.g., the ambient air used has a limited and positive temperature range. In addition, the pressure losses in the air flow are low as the air is pulled in and discharged in the same space as the product location.
Ducted air heat pump water heaters use outside air as an energy source - i.e., air that is drawn in and/or discharged from and/or to the outside. This type of heat pump water heater offers greater flexibility in installation modes and allows the user to choose a configuration that provides for comfort throughout the year. For example, it is possible to choose to discharge the air from the heat pump to the outside when the outside temperature is below the comfort temperature of the room. Alternatively, one can choose to recirculate the air from the heat pump at the installation site when this provides comfort. This type of heat pump water heater may be susceptible to significant pressure drops due to lengths, bends, and height differences in the ducts. A heat pump system could also be split, with an outside portion (e.g., an outdoor unit (ODU) with a mono-block or split-block heat pump) and an indoor portion (e.g., an indoor unit (IDU) with the tank or heating/cooling circuit).
Extracted air heat pump water heaters use air extracted from a ventilation network of the installation building as an energy source. This air has a relatively constant and high temperature, as it comes from living areas of a dwelling. In general, this air may be particularly humid and can contain significant levels of dust or other debris, because it may come from damp rooms, such as bathrooms or kitchens. Heat pumps using this type of air must be able to operate with a relatively low air flow rate (that of the building's ventilation system). A fan may be included in the heat pump water heater. Alternatively, the fan of the ventilation system of the installation building may be used, removing the need for a fan in the heat pump water heater.
Water or ground source heat pump water heaters use an open (e.g., for underground water pumping) or closed (e.g., for soil) water circuit as an energy source. The water circuit may be the return of a heating circuit, a geothermal circuit or any other closed or open water circuit.
For all above-mentioned heat pump water heater types, the heat pump includes at least one closed refrigerant circuit. The refrigerant circuit includes a first heat exchanger with the source medium (an evaporator), a compressor, a second heat exchanger with the destination medium (a condenser), in particular water or other liquids (e.g., for use in heating and/or cooling applications, and/or domestic hot water), and an expansion device.
Control of heat pump water heating and/or cooling systems can present a number of challenges. Because it can take some time to heat or cool water using a heat pump, it may be difficult to ensure that there is sufficient hot or cool water to meet demands. For example, when the demand for hot water becomes greater than can be readily supplied, systems may fall back on other heat sources, such as electric resistive heating or a backup gas boiler. While these sources may be faster at producing hot water than a heat pump, they may be less energy efficient and/or more expensive or environmentally harmful to operate.
Environmental conditions can also cause control challenges. For example, heat pump water heating systems may be more efficient when the source medium (e.g., outside air, ambient air) is warm than when it is cold. In systems that use outside air, the heat pump may not be able to operate when the outside air temperature is too cold. As with meeting demands for hot and/or cool water, the energy efficiency of heat pumps will vary with environmental conditions, and the use of other heat sources, such as electric resistive heating or a backup gas boiler may make sense, depending on environmental conditions.
Control systems for heat pump water heating and/or cooling systems, therefore, need to be able to take into account environmental conditions, anticipated hot or cool water demand, energy consumption, defrost cycles, and other factors to deliver a desired level of comfort and a reasonable level of energy usage. In addition to the challenges that are imposed by these conditions, users of such systems may have differing demands on how control is to be handled. For example, some users may place a greater emphasis on energy savings, while others may place a greater emphasis on comfort.

SUMMARY

Based on the above, it is an object to provide a controller for a hot water system, particularly a hot water system using a heat pump as a primary heat and/or cool source, that is able to balance thermal comfort and energy consumption.
It is a further object to provide a controller that uses predictions of a thermal comfort satisfaction level and an energy consumption level to balance thermal comfort with energy consumption. These predictions may be based on either conventional models of performance of a hot water system, or on artificial intelligence-based models, and/or machine learning models.
Because the proper balance between thermal comfort and energy consumption may be highly subjective, it is a further object to provide a controller that provides a balance between thermal comfort and energy consumption that is influenced by a user preference. It is a further object that the user preference be specified in a manner that is easy to use.
It is still a further object to provide an "intelligent" controller for a hot water system, which uses predictions of thermal comfort satisfaction, energy consumption, environmental conditions, and other information to attempt to optimize control of the hot water system to meet a balance of thermal comfort and energy consumption in accordance with a user preference.
It is another object to provide an "intelligent" controller for a hot water system that is able to refine the models that it uses for optimizing performance, and for predicting thermal comfort, energy consumption, and/or environmental conditions. Refining the models may improve the ability of the controller to provide an installation-specific and/or user-specific balance of thermal comfort and energy consumption.
It should be understood that implementations of the present technology each have at least one of the above-mentioned objects and/or aspects, but do not necessarily have all of them. Some aspects of the present technology that have resulted from attempting to attain the above-mentioned objects may not satisfy these objects and/or may satisfy other objects not specifically recited herein.
It should also be understood that, as used herein, the term "hot water system" may provide hot and/or cool water or other liquids for a variety of applications. For example, a hot water system may provide domestic hot water, hot water for heating applications, such as room heating, and/or cool water for cooling applications, such as room cooling.
In some implementations, the disclosed technology provides a controller configured to control at least a hot water system, the hot water system comprising a heat pump and an optional electrical backup heat source and/or an optional hydraulic backup heat source. The controller includes: a memory; a processor configured to access data and instructions from the memory; and a communication interface communicatively coupled to the processor and to the hot water system, the communication interface configured to communicate commands and data between the controller and the hot water system. The controller further includes a thermal comfort determination module configured to predict a thermal comfort satisfaction level delivered by the hot water system and an energy consumption determination module configured to predict an energy consumption level of the hot water system. The memory further includes instructions that when executed by the processor cause the processor to: receive a prediction of the thermal comfort satisfaction level from the thermal comfort determination module; receive a prediction of the energy consumption level from the energy consumption determination module; perform an optimization operation based, at least in part, on the prediction of the thermal comfort satisfaction level and the prediction of the energy consumption level; and determine commands to operate the hot water system based, at least in part, on a result of the optimization operation.
In some implementations, the controller further includes an environmental determination module configured to predict environmental conditions outside of the hot water system. In some implementations, the memory includes instructions that when executed by the processor cause the processor to receive predicted environmental conditions from the environmental determination module and to perform the optimization operation further based, at least in part, on the predicted environmental conditions.
In some implementations, the controller further includes an information module configured to provide operation history information and/or product information on the hot water system. In some implementations, the memory includes instructions that when executed by the processor cause the processor to receive operation history information and/or product information from the information module and perform the optimization operation further based, at least in part, on the operation history information and/or product information.
In some implementations, the optimization operation is influenced by a single parameter set by a user, the single parameter providing a user-selected balance between the thermal comfort satisfaction level and the energy consumption level in the optimization operation. This single parameter provides an easy to use means for the user to provide the controller with their preferences with respect to comfort and energy consumption.
In some implementations, the thermal comfort satisfaction level and the energy consumption level are both provided in terms of energy. While thermal comfort is usually expressed in terms of delivered temperature, ability to deliver a desired volume of water at a desired temperature at a desired time, or other definitions, by expressing thermal comfort in terms of energy, it may be easier to compare and balance thermal comfort with energy consumption.
In some implementations, the thermal comfort determination module, and/or the energy consumption determination module, and/or the environmental determination module uses an artificial intelligence model, more particularly a machine learning model, to generate its predictions. In some implementations, the machine learning model is trained using data on the operation history and/or product information of the hot water system, and/or using data on the environmental conditions outside of the hot water system.
In some implementations, the machine learning model is continuously refined using data from operation of the hot water system. In some implementations, the machine learning model is refined based on occurrence of: a time condition; and/or a duration condition; and/or a processor usage condition, such as processor usage falling below a threshold value; and/or a product usage condition, such as product usage falling below a threshold value; an electricity and/or energy cost condition, such as electricity and/or energy cost falling below a threshold; and/or an event condition, such as an event notification received through the communication interface; and/or a condition related to the thermal comfort satisfaction level, such as the thermal comfort satisfaction level falling below a threshold value; and/or a condition related to the energy consumption level, such as the energy consumption level exceeding a threshold value; and/or a condition related to an environmental condition outside of the hot water system; and/or a deviation between the previous prediction result of the machine learning model and the current prediction result from the operation history of the hot water system.
In some implementations, the artificial intelligence model of the thermal comfort determination module is configured to predict the thermal comfort satisfaction level, based, at least in part, on the following inputs: the operation history of the hot water system; and/or the product information of the hot water system; and/or the actual or predicted environmental conditions outside of the hot water system; and/or commands to operate the hot water system. In some implementations, the artificial intelligence model of the energy consumption determination module is configured to predict the energy consumption level, based, at least in part, on the following inputs: the operation history of the hot water system; and/or the product information of the hot water system; and/or the actual or predicted environmental conditions outside of the hot water system; and/or commands to operate the hot water system. In some implementations, the artificial intelligence model of the environmental determination module is configured to predict environmental conditions outside of the hot water system, based, at least in part, on the following inputs: the operation history of the hot water system; and/or the product information of the hot water system.
In some implementations, the optimization operation uses an artificial intelligence model, more particularly a machine learning model. In some implementations, the optimization operation uses an artificial intelligence model, more particularly a machine learning model.
In some implementations, at least portions of the controller are cloud-based. For example, any or all of the thermal comfort determination module, the energy consumption determination module, the environmental determination module, the information module, and/or the optimization operation may be cloud-based or may be handled by a local controller. Similarly, the memory or processing associated with any of these modules, the optimization operation, and/or any other functions of the controller may be handled locally, or may be handled on a cloud-based system. Similarly, a physical or a virtual machine may be used to implement any of the functions of the controller, either locally or on a cloud-based system.
In some implementations, the disclosed technology provides a hot water system including a heat pump and an optional backup electrical heat source and/or an optional hydraulic backup heat source, the hot water system controlled by the controller of the disclosed technology.
In some implementations, the hot water system (200) is configured for use in cooling applications.
In the context of the present specification, unless expressly provided otherwise, the words "first", "second", "third", etc. have been used as adjectives only for the purpose of allowing for distinction between the nouns that they modify from one another, and not for the purpose of describing any particular relationship between those nouns.
In the context of the present specification, unless expressly provided otherwise, directions indicated by terms such as "top", "bottom", "upper", "lower", "above", "below", etc., are used in their usual sense - i.e., relative to a gravitational direction or axis.
Additional and/or alternative features, aspects and advantages of implementations of the present technology will become apparent from the following description, the accompanying drawings and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

In the figures, the subject-matter of the disclosure is schematically shown, wherein identical or similarly acting elements are usually provided with the same reference signs.

FIG. 1 is a block diagram of an example controller that could be used in some implementations of an energy management system.
FIG. 2 is a block diagram of an example hot water system.
FIG. 3 is a block diagram showing the structure of a control system for a hot water system, in accordance with the described technology.
FIG. 4 is a block diagram illustrating the training and refinement of a machine learning model for use in modules of the control system for a hot water system, in accordance with the described technology.

DETAILED DESCRIPTION

The examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the present technology and not to limit its scope to such specifically recited examples and conditions. It will be appreciated that those skilled in the art may devise various arrangements that, although not explicitly described or shown herein, nonetheless embody the principles of the present technology.
Furthermore, as an aid to understanding, the following description may describe relatively simplified implementations of the present technology. As persons skilled in the art would understand, various implementations of the present technology may be of a greater complexity.
In some cases, what are believed to be helpful examples of modifications to the present technology may also be set forth. This is done merely as an aid to understanding, and, again, not to define the scope or set forth the bounds of the present technology. These modifications are not an exhaustive list, and a person skilled in the art may make other modifications while nonetheless remaining within the scope of the present technology. Further, where no examples of modifications have been set forth, it should not be interpreted that no modifications are possible and/or that what is described is the sole manner of implementing that element of the present technology.
Moreover, all statements herein reciting principles, aspects, and implementations of the present technology, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof, whether they are currently known or developed in the future. Thus, for example, it will be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present technology.
With these fundamentals in place, we will now consider some nonlimiting examples to illustrate various implementations of aspects of the present disclosure.

Controller

FIG. 1 depicts an example controller 100, which may be any type of computer system or embedded controller. It will be recognized that some or all the components of the controller 100 may be virtualized and/or cloud-based. As depicted, the controller 100 may include one or more processors 102, a memory 110, a storage interface 120, and a communication interface 140. These system components may be interconnected via a bus 150, which may include one or more internal and/or external buses (not shown) (e.g. a PCI bus, universal serial bus, IEEE 1394 "Firewire" bus, SCSI bus, Serial-ATA bus, etc.), to which these various hardware components may be electronically coupled.
The memory 110, which may be a random-access memory or any other type of memory, may contain data 112, an optional operating system 114 (it will be understood that not all controllers require an operating system), and a program 116. The data 112 may be any data that serves as input to or output from any program in the controller 100. The operating system 114, which is optional, since not all control applications require use of an operating system, may be an operating system such as MICROSOFT WINDOWS, FreeRTOS, an operating system based on the LINUX kernel (e.g., UBUNTU, DEBIAN, FEDORA, ARCH, SUSE, etc.), or any other operating system suitable for use on a computer system or microcontroller. The program 116 may be any program or set of programs that include program instructions that may be executed by the processor to control actions taken by the controller 100. In particular, the program 116 may include program instructions that, when executed by the processor, cause the processor to carry out one or more of the methods described below.
The storage interface 120 may be used to connect storage devices, such as the depicted storage device 125, to the controller 100. The storage device 125 may be a solid-state drive using an integrated circuit assembly to store data persistently. Alternatively, the storage device 125 may be a hard drive using any of a variety of types of magnetic storage media to store and retrieve digital data. As another alternative, the storage device 125 may be an optical drive, or a card reader that receives a removable non-volatile semiconductor memory card. As still another alternative, the storage interface 120 may provide a universal serial bus connection to which the storage device 125 may be hot-pluggable, and the storage device 125 may be a flash memory device (e.g., a USB thumb drive). In some implementations, in which storage of data is not necessary, the storage interface 120 and storage device 125 may optionally be omitted.
In some implementations, the controller 100 may use well-known virtual memory addressing techniques that allow the programs of the controller 100 to behave as if they have access to a large, contiguous address space instead of access to multiple, smaller storage spaces, such as the memory 110 and the storage device 125. Therefore, while the data 112, the optional operating system 114, and the programs 116 are depicted as residing in the memory 110, those skilled in the art will recognize that these items may not necessarily be wholly contained in the memory 110 at the same time.
The one or more processors 102 may include one or more microprocessors and/or other integrated circuits able to execute program instructions stored in the memory 110. When the controller 100 starts up, the processor(s) 102 may initially execute program instructions of a boot routine and/or the program instructions that make up the operating system 114.
The communication interface 140 may be used to communicatively connect the controller 100 to other controllers, computer systems, or still other devices (not shown) via a communication channel 160. The communication channel 160 may be a serial or parallel connection, a wired, wireless, mesh or cellular network, or any other type of communication channel or combination of channels. Data and/or program instructions may be sent to the controller 100 as signals via the communication channel. The communication interface 140 may include a combination of hardware and software that enables communications on the communication channel 160. The software in the communication interface 140 may include software that uses one or more communication protocols to communicate over the communication channel 160, including and not limited to, network protocols such as TCP/IP (Transmission Control Protocol/Internet Protocol) or Modbus.
It will be understood that the depicted controller 100 is merely an example, and that the technology disclosed herein may be used with a wide variety of other controllers or computer systems, or still other computing devices having different configurations.

Hot Water System

FIG. 2 depicts an example hot water system 200. The hot water system 200 is generally used to provide domestic hot water to a domestic hot water installation, such as is used in a residential dwelling, but may be used in other heating and/or cooling applications, such as room heating or room cooling. However, it will be understood that the hot water system 200 may be used in other types of buildings, such as apartment buildings or other multi-dwelling buildings, office buildings, or any other type of building at which hot water systems are installed and/or controlled. It will also be understood that although referred to as a "hot water system", the hot water system 200 may also be used in cooling applications, in which the water is cooled, e.g., for use in room cooling.
As depicted, the hot water system 200 is a heat pump-based system with an optional electric resistive heating element providing backup heating. The system includes a heat pump 202 (the construction and operation of such a heat pump being well-known) and a hot water tank 204, which supplies hot water to a domestic hot water output 220, which may include (for example) hot water taps (not shown), showers (not shown), and other outlets or appliances that consume domestic hot water (not shown). The domestic hot water output 220 may mix the hot water from the hot water tank 204 with cold water to produce a range of water temperatures for domestic use.
A heat exchanger 206 in the hot water tank 204 transfers heat from primary water (or other heating fluids, such as a water-glycol mixture) warmed by the heat pump 202 to the sanitary domestic hot water in the hot water tank 204. An electric resistive heating element 208 may optionally be included in the hot water tank 204 to serve as backup heating, during periods in which the heat pump 202 may be unable to provide sufficient heating to meet the demand for domestic hot water. Other backup systems, such as electric, oil, hydrogen, wood, and/or gas-powered boilers, solar thermal systems, additional heat pumps, and/or other known heating systems may optionally be used in addition to the heat pump 202 to heat the primary water (or other heating fluids) that heat the domestic hot water in the hot water tank 204. These additional heat sources may be referred to herein as hydraulic heat sources or hydraulic backup heat sources.
A controller 210, which may be similar to the example controller 100, described above, controls the heat pump 202, the optional electric resistive heating element 208, any optional hydraulic heat sources (not shown), and/or other devices and actuators that control the flow and mixing of domestic hot water through the hot water system 200. The controller 210 may be connected to various sensors (not shown) that provide information on, e.g., water temperatures at various points in the system, ambient air temperature, and other measurements that may be used (as described below) to control the hot water system 200. The controller 210 may also be connected to a cloud-based system 250, which may provide access to databases, updates, commands from users, remote commands or information, external services, cloud-based storage, or other information sources and services that may be accessed, e.g., over the Internet. For example, the cloud-based system 250 may be used by the controller 210 to access weather predictions that may be used by the controller 210. In some implementations, most of the control of the hot water system 200 may be carried out in the cloud-based system 250, with the controller 210 serving primarily to communicate commands to the hot water system 200, and as a backup controller if there are communication problems that prevent communications between the cloud-based system 250 and the controller 210.
The controller 210 may communicate with various system components in a variety of ways. For example, the controller 210 may communicate wirelessly with sensors, actuators, hydraulic heat sources, and/or other system components using a wireless communication protocol, such as WIFI, Bluetooth, or Zigbee. In some implementations, they may communicate over a wired bus, using protocols such as OpenTherm. Some devices, such as sensors, may be directly connected to the controller 210. For some devices, including older boilers, etc., the controller 210 may be limited to controlling the device using on/off signals. In some implementations, the controller 210 may use numerous types of communication, depending on the devices that are being controlled.
In some implementations, the heat pump 202 may be built within a single housing (not shown) that also houses the hot water tank 204, the controller 210, and the optional electric resistive heating element 208, to provide a self-contained water heating appliance. In some implementations, such a self-contained water heating appliance may also include an optional backup hydraulic heat source, such as a boiler within the single housing or as an attachment to the housing. In some implementations, the heat pump 202 may more directly heat the domestic hot water in the hot water tank 204, without use of primary water or the heat exchanger 206 (e.g., the water in the hot water tank 204 is heated through heat exchange with the refrigerant circuit of the heat pump 202).
It will be understood that although the example hot water system 200 shown in FIG. 2 is configured to provide domestic hot water, other configurations could be used to provide hot water for heating and/or cooling applications (e.g., through radiators, underfloor or ceiling heating or cooling loops, etc.). Such systems may include, e.g., various load circuits and pumps, to provide hot or cool water to heating or cooling zones in a house. Other hot water systems 200 may provide hot water for both heating/cooling and domestic hot water uses. Such systems may include load circuits that include domestic heating, such as through radiators, and at least one load circuit that includes a hot water tank for domestic hot water, such as is shown in FIG. 2. While the technology is discussed primarily in terms of control for providing domestic hot water, it will be understood that substantially the same optimization techniques, balancing thermal comfort satisfaction with energy consumption, could be used in domestic heating systems, and/or in systems that provide both domestic heating/cooling and domestic hot water.

intelligent Control

FIG. 3 shows the structure of an "intelligent" control system for a hot water system such as is shown in FIG. 2. The modules and other elements that make up the intelligent control system described with reference to FIG. 3 may be operated as a program on the controller 210 shown in FIG. 2, may be operated on the cloud-based system 250, may be operated on separate controllers, which may be hardware-based, virtual, or cloud-based (not shown in FIG. 2), or may be distributed between any combination of these.
As shown in FIG. 3, the intelligent control system 300 includes a thermal comfort determination module 302, an energy consumption determination module 304, an environmental determination module 306, an information module 308, and a single user-determined parameter 310. An optimizer 320 uses information and predictions provided by these modules to generate instructions to control the hot water system 200 to achieve a balance of thermal comfort and energy consumption influenced by the preferences of the system's user, as specified using the single user-determined parameter 310.
The intelligent control system 300 is structured as several determination modules and an optimization system that attempts to balance thermal comfort with energy consumption using information provided by these determination modules. Generally, thermal comfort and energy consumption are linked and dependant. It is necessary to consume energy to produce thermal comfort, but there may be flexibility with respect to how and when to consume energy to produce heat (e.g., in terms of which heat producers to use, performance for heat pumps based on weather conditions, prices for various types of energy, CO₂ impact, etc.). Thus, the aim of the intelligent control system 300 is to: balance thermal comfort and energy consumption to meet a user's objective and preferences; determine which heating device to use; and determine how to use the available heat producers, depending on their energy consumption (taking into account their energy performances, their energy costs, weather conditions that may affect their performance, and hot water usages).
The intelligent control system 300 is driven by the optimizer 320, which attempts to determine "optimal" commands for controlling the hot water system 200. The degree to which a set of commands is "optimal" depends on balancing thermal comfort and energy consumption, as described below. This involves generating a prediction of the thermal comfort satisfaction level and the energy consumption level that will result from any given set of commands, by sending potential commands, as well as other inputs to the thermal comfort determination module 302, to obtain a prediction of the thermal comfort satisfaction level that will result from the commands, and to the energy consumption determination module 304, to obtain a prediction of the energy consumption level that will result from the commands. A function that balances these predictions may then be applied to determine the "fitness" of the potential commands. This fitness function is influenced by the single user-determined parameter 310, which specifies, in a single parameter, the how much importance the user wants to give to thermal comfort satisfaction level vs. energy consumption level in determining this balance. The optimization can proceed by testing commands to find a set of commands that provides the best fitness score according to this fitness function.
In some implementations, the single user-determined parameter 310 may be normalized to a value between 0 and 1, where a value of 0 gives maximum importance to thermal comfort satisfaction level, and a value of 1 gives maximum importance to energy consumption level (i.e., consuming as little energy, or energy cost as possible). The predicted values for the thermal comfort satisfaction level provided by the thermal comfort determination module 302 and the energy consumption level provided by the energy consumption determination module 304 may also be normalized to valued between 0 and 1. For example, a value of 0 for the thermal comfort satisfaction level would represent minimal thermal comfort, while a value of 1 for thermal comfort satisfaction level would represent "perfect" thermal comfort satisfaction. Similarly, an energy consumption level of 0 would represent maximum energy inefficiency or cost, while an energy consumption level of 1 would represent minimal energy inefficiency or cost. In such a system, one possible fitness function would be:
Where: x is the single user-determined parameter 310, Comfort is the predicted thermal comfort satisfaction level, and Consumption is the predicted energy consumption level. While this provides a useful example of a fitness function for use by the optimizer 320, other fitness functions could be used. It will also be understood that the single user-determined parameter 310 may be used in a variety of ways in such fitness functions, and need not always be used as a mathematical weight in the function.
The single user-determined parameter 310 provides an easy to use means for a user of the system to choose and orient the behavior and aims of the intelligent control system 300, to determine the desired balance between energy consumption/savings and thermal comfort. This convenient single parameter simplifies the configuration/parametrization of the product control for the user, and also quantitatively expresses the desired balance between these two concurrent objectives.
In some implementations, both thermal comfort and energy consumption may be expressed in terms of energy, so that they may be more readily compared and balanced. In some implementations, both levels can be converted to a notion relative to an energy, such as an energy cost. This may provide measures that are practical for a user who is interested in obtaining a balance between thermal comfort and energy cost. Expressing a notion such as thermal comfort in terms of energy is discussed further below.
The optimizer 320 may use any known optimization techniques or algorithms to generate commands for the hot water system 200. For example, techniques including, e.g., simplex methods, evolutionary algorithms, genetic algorithms, greedy algorithms, heuristic and meta-heuristic methods, and/or rule-based algorithms may be used. For example, a "greedy" or iterative optimization algorithm may iteratively generate command sets, generate predicted thermal comfort satisfaction levels and predicted energy consumption levels for those command sets to use the fitness function to evaluate the command sets, and then alter a command set in the next iteration in a way that is expected to improve the fitness of command set in the next iteration. This will lead to finding at least local optima for the command sets when the alterations are no longer improving the fitness.
In addition to these (or other) known optimization techniques or algorithms, known artificial intelligence and/or machine learning techniques may be used to generate command sets. For example, an artificial neural network may be trained to accept weather conditions, operation conditions, and product information on the hot water system as inputs, and generate commands as outputs. The fitness function and prediction modules could be used both to train the artificial neural network, and to refine its training as the system is used. Other artificial intelligence and/or machine learning methods (supervised and/or unsupervised), such as reinforcement learning, hidden Markov models, linear or non-linear regression, random trees, support vector machines, and/or Kalman filters could also be applied to find command sets that provide a good "fitness". As with artificial neural networks, some of these artificial intelligence and/or machine learning models may be trained, and may improve over time as that training is refined during use of the model.
In some implementations, the optimizer 320 may access predictions relating to environmental conditions such as weather information and predictions from the environmental determination module 306. This information may be passed on from the optimizer 320 to other modules, since the environmental information may affect both thermal comfort determinations and energy consumption determinations. Additionally, the information may be used by the optimizer 320 itself, for generating commands, as input to an artificial intelligence or machine learning model, and/or for training or refining such artificial intelligence or machine learning models.
The optimizer 320 may also access product information about the hot water system and operation history information on the hot water system from the information module 308. The product information may include information such as the tank volume, heat pump power, maximum flow rated, location of the installation, etc. Operation history information may include recorded history information on air temperatures, water inlet temperatures, water tank temperatures, water outlet temperatures, outlet flow rates, ambient temperatures, thermal tank energy, consumed energy, producer injected energy, which heating devices were used, the commands that were used, etc. This information may be passed from the optimizer 320 to the other modules, since this product and historic information may be used to predict both thermal comfort satisfaction levels and energy consumption levels. Additionally, the optimizer 320 may itself make use of this information, to generate commands, as input to an artificial intelligence or machine learning model, and/or for training or refining such artificial intelligence or machine learning models.
It will be understood that the intelligent control system 300 shown in FIG 3 represents only one possible organization of the modules that make up the control system 300, and that other possible organizations of these modules may be used. For example, in some implementations, the optimizer 320 may use only a thermal comfort determination module 302 and an energy consumption determination module 304, with information on the system and environmental conditions being considered in these modules, rather than being handled by the optimizer 320. For example, the thermal comfort determination module 302 and/or the energy consumption determination module 304 may access information on environmental conditions, product information, and/or operation history directly. In implementations in which these modules are based on artificial intelligence and/or machine learning models, predictions of environmental conditions, product information, and/or operation history may be built into the model, e.g., through training.
The thermal comfort determination module 302 predicts a thermal comfort satisfaction level delivered by the hot water system or water heating device. Thermal comfort is often understood in terms of the delivered temperature, but may also be expressed as the ability of the system to deliver a desired volume of hot water at a desired temperature, at a desired time. This is conventionally expressed in terms of "V40", which is the volume of water at 40° C that can be delivered by a system by mixing all water above 40° C with the inlet/domestic cold water. Using this metric, one can easily understand how much hot water can be delivered by the system. This V40 measure is conventionally used, e.g., by consumers to determine if a system is able to meet the hot water demands of a household. It should be noted that calculation of the V40 artificially "dilutes" water above 40° C so that it reaches 40° C. For example, a thermodynamic water heater delivering 200L of water at 55° C with a domestic cold temperature (DCW) of 10° C has a V40 of 300L. The value of 40° C is used because it corresponds to a standard maximum operating temperature in a domestic water system, though for purposes of comfort and other uses, other temperatures could be used (e.g., V50, using 50° C).
While V40 is a useful measure of comfort, it may not be adequate for purposes of predicting a comfort satisfaction level. Such a calculation needs to take into account that different amounts of hot water may be needed at different times, or that different minimal hot water temperature (e.g. a V50 is not comparable with a V40) may be needed at different times. For example, during certain morning periods, when much of a household is showering, or during evening periods when dishes are being washed, greater amounts of hot water may be needed than during mid-day periods, when members of the household are away at work, or during periods in the middle of the night, when household members are asleep.
Additionally, because the disclosed technology is directed to balancing thermal comfort satisfaction with energy consumption, it is useful to express thermal comfort in terms of energy, rather than in terms of a temperature or in terms of a volume of water at a particular temperature. The water volume at a particular temperature (compared to a base temperature) can be expressed as an amount of energy. Thus, thermal comfort may be understood as the ability to deliver the desired energy of hot water at the desired time.
For purposes of the thermal comfort determination module 302, there is perfect thermal comfort if all present and future actual usages of hot water are thermally satisfied - i.e., the user is able to draw the amount of thermal energy from the tank to provide the desired volume of water at the desired temperature up to a given flowrate. The closer the system is able to meet this full amount of thermal energy, the higher the thermal comfort satisfaction level. Because the perceived level of comfort is inexact, a high but imperfect thermal comfort satisfaction level may be good enough to satisfy most users.
Based on the above, to predict the thermal comfort satisfaction level at the present or at a future time, the patterns of hot water usage may be used or predicted, as well as the time to provide the needed thermal energy to the hot water tank (which will depend on, e.g., the water heating that is used, loss of heat from the tank, the ambient temperature of heat pump's source medium (air, water), etc.). The patterns of hot water usage will depend on the household in which the system is installed. These patterns of usage can be learned over time by, e.g., an artificial intelligence or machine learning-based model. The ability of the system to provide particular amounts of thermal energy as hot water over a particular period of time will depend on factors such as product information (e.g., tank volume, heat pump power), environmental conditions outside of the hot water system (e.g., ambient temperature, outdoor temperature, cold water temperature), and commands that have been sent to, e.g., set up operational parameters of the heating device(s), such as the power at which the heat pump is set, the availability and power at which any backup heat sources are set, and so on.
Thus, the thermal comfort determination module 302 may be understood to take inputs including operation history information, product information, external environmental conditions, and commands to operate the hot water system. The thermal comfort determination module 302 then uses these inputs to predict a present or future thermal comfort satisfaction level that will be achieved using these commands. This prediction may be made using an artificial intelligence or machine learning model that has been trained on, e.g. usage patterns and/or other information, such as product information, operation history information, environmental conditions, etc. While an artificial intelligence and/or machine learning-based approach to predicting the thermal comfort satisfaction level may be used, the thermal comfort determination module 302 may also make use of more conventional models of the performance of heating systems.
It will be understood that the thermal comfort determination module 302 as described above produces a measure of thermal comfort satisfaction that is appropriate for hot water systems that provide domestic hot water. A hot water system that provides hot water for use in home heating systems including, e.g., radiators and underfloor or ceiling heating loops, may use somewhat different measures of thermal comfort satisfaction. Similarly, a cool water system that provides cool water for use in home cooling systems including, e.g. radiators or underfloor or ceiling cooling loops, may use somewhat different measures of thermal satisfaction. While the levels may be somewhat different, the same methods as described above may be applied. Thermal comfort will can still be measured in terms of availability of sufficient volumes of hot/cool water, at sufficient temperatures, at the correct times to meet user demand. This measure can still be expressed in terms of energy, to make it easier to balance against energy consumption. Additionally, this can still be predicted using, e.g., machine learning models, albeit with somewhat different training than would be used for a system predicting thermal comfort satisfaction levels for domestic hot water. It will also be understood that a single model that is capable of handling thermal comfort measures for multiple applications, such as domestic hot water, heating, and/or cooling could also be trained and used, applying substantially the same methods as are described above.
The energy consumption determination module 304 predicts a present or future energy consumption level for the hot water system. The energy consumption will depend on the characteristics of the heat pump, external conditions that affect the efficiency of the heat pump, such as weather or ambient temperature, the mix of backup heating sources that are used, such as an electric resistive heating element, a gas boiler, etc. It should be noted that the energy consumption may come in various forms. For example, a heat pump and an electric resistive heating element will use electricity, while a gas boiler will use gas (e.g., propane) to generate heat. In some implementations, energy costs are used to combine the energy consumption of these various forms of energy into a single energy consumption value. This has the advantage of providing energy consumption values that reflect the cost of the energy, so that optimization of energy consumption effectively optimizes cost.
Thus, the energy consumption determination module 304 takes inputs such as the operation history of the hot water system, product information on the hot water system, actual or predicted environmental conditions outside of the hot water system, and/or commands to operate the hot water system. Based on these inputs, the energy consumption determination module 304 predicts a current or future energy consumption for use in optimization.
Because of the various factors that go into predicting the present and/or future energy consumption, in some implementations, an artificial intelligence or machine learning model may be used, trained on past energy consumption of the system, given the operation history of the hot water system, product information on the hot water system, actual or predicted environmental conditions outside of the hot water system, and/or commands to operate the hot water system. Conventional models of the energy performance of heating systems may also be used for predicting the energy consumption level.
It will also be understood that the energy consumption level predicted by the energy consumption determination module 304 may represent slightly varying measures of energy consumption. For example, in some implementations, the energy consumption level may represent energy efficiency. In some implementations, the energy consumption level may represent energy cost or energy savings.
The environmental determination module 306 predicts present or future environmental conditions outside of the hot water system. These environmental conditions may include local weather, such as temperature, humidity, solar irradiance, wind, and so on. These conditions may affect the operation and efficiency of various heating systems. For example, the efficiency of an air-source heat pump will depend on the temperature of the source air. If this air is being drawn from outdoors, this will depend on the outdoor temperature. Solar irradiance will affect the operation of a solar thermal system that may be used as a heat source in the system. Of course, in some systems, such as heat pump water heaters located in a basement or utility room and drawing air from the room in which they are installed, the ambient temperature of the basement or utility room may affect the efficiency of the heat pump and the heat loss from the hot water tank.
For many of these environmental conditions, predictions may be obtained from cloud-based weather services, if the location of the system is known. For other environmental conditions, such as ambient temperature in a basement or utility room, the environmental conditions can be obtained by sensors connected to the hot water system or from a home automation system. The data may be collected immediately for present environmental conditions, or may be based on data collected from the site of the system over time, for future environmental condition predictions. In some cases, an artificial intelligence or machine learning model trained on the conditions surrounding the system over time may be used in the environmental determination module 306.
Generally, the environmental determination module 306 may accept inputs such as the time, date, location of the hot water system, and/or other information on the operation history and/or product information on the hot water system, and may output current or predicted future environmental conditions that may affect the operation of the hot water system.
The information module 308 stores and provides product information on the hot water system 200, and on its operation history. This information is stored by the information module 308 during system installation and operation, and may be provided by the information module 308 as the information is requested, e.g., by the optimizer 320 and/or by other modules of for other purposes. The product information may include information such as the tank volume, heat pump power, maximum flow rated, location of the installation, etc. Operation history information may include recorded history information on air temperatures, water inlet temperatures, water tank temperatures, water outlet temperatures, outlet flow rates, ambient temperatures, thermal tank energy, consumed energy, producer injected energy, which heating devices were used, the commands that were used, etc.
It will be understood that this information may require considerable storage capacity, depending on how much information is stored by the information module 308, and for how long a period of time. In some implementations, the information module 308 may use local storage to store the information. In some implementations, the information may be stored in a cloud-based system. In some implementations, the information module 308 may itself be cloud-based, such that both the information storage and the functions of the information module 308 are provided in the cloud.
As described above, the thermal comfort determination module 302, the energy consumption determination module 304, and the environmental determination module 306 may use artificial intelligence and/or machine learning models to make their predictions. For example, to determine a thermal comfort satisfaction level in the thermal comfort determination module 302, an artificial neural network may be trained to accept weather conditions, operation conditions, product information on the hot water system, and proposed commands or instructions for operating the hot water system as inputs, and generate a predicted thermal comfort satisfaction level as an output. Actual measured results from applying commands may be used to train such an artificial neural network. Other artificial intelligence and/or machine learning methods (supervised and/or unsupervised), such as reinforcement learning, hidden Markov models, linear or non-linear regression, random trees, support vector machines, and/or Kalman filters could also be applied to predict a thermal comfort satisfaction level, an energy consumption level, or environmental conditions. As with artificial neural networks, some of these artificial intelligence and/or machine learning models may be trained, and may improve over time as that training is refined during use of the model. It will also be understood that more conventional models may also be used, in which, e.g., a predicted energy consumption level is based on mathematical models or recorded data regarding the performance of heating appliances used in the hot water system.

Model Refinement

As is discussed above, the models used to predict the thermal comfort satisfaction level, the energy consumption level, and environmental conditions may be based on known machine learning techniques. Similarly, the optimizer that generates commands to operate the hot water system to balance thermal comfort with energy consumption may be based on known machine learning techniques. In many such machine learning systems, the system is initially trained, and then may be fine-tuned during operation. Such fine-tuning or refinement may be helpful in systems where the model may vary considerably from installation to installation. For example, while an initially trained model may suffice for predicting thermal comfort satisfaction levels in some households, there may be households that have demands or schedules that are outside of "normal" expectations. If the model is to achieve good results in such households, it may be useful to fine-tune or refine the model as it is used, so that it will adjust to the thermal comfort needs of such a household over time.
FIG. 4 is a diagram showing the training and refinement of the machine learning models that may be used in the determination modules and optimizer of the intelligent control system 300 of the present disclosure. As shown in FIG. 4, there are three phases to the training and refinement of these machine learning models: an initial training phase 402, an operation phase 404, and a refinement phase 406.
The initial training phase 402 involves the initial training of the model. In some implementations, this may be done, e.g., by the manufacturer of the intelligent control system 300 and/or hot water system 200. For example, the manufacturer may use a large data set built up in its own testing to train a machine learning model, such as an artificial neural network, to predict a thermal comfort satisfaction level and/or an energy consumption level, as discussed above. This training may be done once, for a "standard" installation of the system, and the resulting model may be deployed on systems either prior to or at the time of installation. While a model with such initial training may function reasonably well in a wide variety of installations, it may be possible to improve its functionality through fine-tuning or refinement.
In some implementations, a system may be installed using a "generic" control system, and the initial training phase 402 may occur after the system has been installed. In such systems, the generic control system may operate the hot water system 200 during an initial period, while data are recorded to be used in the initial training phase 402. Once sufficient training data have been recorded (which may take weeks or months, depending on the installation), machine learning models may be trained using the recorded data. This training may be done locally, by a controller on the hot water system, or may be handled by a cloud-based service. Once the training of the machine learning models has been completed, the trained machine learning models may be deployed (e.g., through download from a cloud-based service), and the intelligent control system 300 may take over control of the hot water system 200. This permits the initial training to be customized for each installation of the hot water system, which could provide better performance after the initial training phase 402 is completed, and may reduce the need for refinement of the machine learning model.
During the operation phase 404, the intelligent control system 300 operates as described above. During operation, data may be collected to evaluate how well the modules of the system and the optimizer are operating. These data may be used to determine when fine-tuning or refinement of the machine learning models should be performed. The data may also be used in refining the machine learning models, on either a continuous basis, or when it is determined that such refinement may be beneficial.
The refinement phase 406 uses known machine learning fine-tuning or refinement techniques to refine the models used by the intelligent control system 300. These refinement techniques will depend on the nature of the machine learning models that are being used. In some implementations, known incremental learning techniques, such as online gradient descent or online boosting, are used to update the models without requiring that they be completely retrained.
In some implementations, refinement of the machine learning models is a continuous process - i.e., the refinement phase 406 occurs essentially as a part of normal operation of the system. Continuous model refinement is an ongoing process aimed at maintaining and improving the performance of a machine learning model after it has been deployed. This ensures that the model remains accurate and relevant as new data become available and as the underlying system or environment changes.
In some implementations refinement of the machine learning model may be triggered by various events. The refinement phase 406 occurs when refinement is triggered. Examples of events that may result in the refinement phase 406 operating to refine one or more of the machine learning models employed by the intelligent control system 300 include a time condition and/or a duration condition - i.e., refinement occurs at predetermined times and/or after predetermined durations.
In some implementations, refinement may be triggered by a processor usage condition, such as processor usage falling below a threshold value and/or a product usage condition, such as product usage falling below a threshold value. These conditions recognize that refinement of the machine learning models may consume considerable processing resources, so may be best undertaken during periods of low processor usage, low product usage, and/or low electricity/energy cost.
In some implementations, refinement may be triggered by an event condition, such as an event notification received through the communication interface. These event notifications may be sent, for example, from a heating device, from an external control system, from a thermostat, from a user device (such as a hand-held device or smart phone), from a cloud-based service (operated, e.g., by the system manufacturer, installer, or maintainer), or from other authorized external sources.
In some implementations, refinement may be triggered by a performance-based measure, such as a deviation between the previous prediction result of the machine learning model and the current prediction result from the operation history. Other examples of such performance-based measures that may trigger model refinement may include a condition related to the thermal comfort satisfaction level, such as the thermal comfort satisfaction level falling below a threshold value, and/or a condition related to the energy consumption level, such as the energy consumption level exceeding a threshold value.
In some implementations, refinement of one or more of the machine learning models may be triggered by a condition related to an environmental condition, such as weather or temperature. Such a trigger may result from, e.g., discrepancies between a machine learning model's prediction of environmental conditions and actual environmental conditions (e.g., as reported by a weather service, or by sensors).
It will be understood that, although the embodiments and/or implementations presented herein have been described with reference to specific features and structures, various modifications and combinations may be made without departing from the disclosure. For example, it is contemplated that in some implementations, the features described above may be used in different arrangements, or in other combinations. The specification and drawings are, accordingly, to be regarded simply as an illustration of the discussed implementations or embodiments and their principles as defined by the appended claims, and are contemplated to cover any and all modifications, variations, combinations or equivalents that fall within the scope of the present disclosure.

REFERENCE SIGNS

100: controller
102: processors
110: memory
112: data
114: operating system
116: program
120: storage interface
125: storage device
140: communication interface
150: bus
160: communication channel
200: hot water system
202: heat pump
204: hot water tank
206: heat exchanger
208: electric resistive heating element
210: controller
220: hot water output
250: cloud-based system
300: intelligent control system
302: thermal comfort determination module
304: energy consumption determination module
306: environmental determination module
308: information module
310: single user-determined parameter
320: optimizer
402: initial training phase
404: operation phase
406: refinement phase

Claims

A controller (100, 210) configured to control at least a hot water system (200), the hot water system comprising a heat pump (202) and an optional electrical backup heat source (208) and/or an optional hydraulic backup heat source, the controller (100, 210) comprising:
a memory (110);

a processor (102) configured to access data and instructions from the memory (110); and

a communication interface (140) communicatively coupled to the processor (102) and to the hot water system (200), the communication interface configured to communicate commands and data between the controller (100, 210) and the hot water system (200);
characterized in that:
the controller (100, 210) further comprises a thermal comfort determination module (302) configured to predict a thermal comfort satisfaction level delivered by the hot water system (200) and an energy consumption determination module (304) configured to predict an energy consumption level of the hot water system; and in that

the memory (110) comprises instructions that when executed by the processor (102) cause the processor (102) to:
receive a prediction of the thermal comfort satisfaction level from the thermal comfort determination module (302);

receive a prediction of the energy consumption level from the energy consumption determination module (304);

perform an optimization operation (320) based, at least in part, on the prediction of the thermal comfort satisfaction level and the prediction of the energy consumption level; and

determine commands to operate the hot water system (200) based, at least in part, on a result of the optimization operation (320).
The controller (100, 210) of claim 1, further characterized in that the controller (100, 210) further comprises an environmental determination module (306) configured to predict environmental conditions outside of the hot water system (200), and in that the memory (110) comprises instructions that when executed by the processor (102) cause the processor (102) to receive predicted environmental conditions from the environmental determination module (306) and to perform the optimization operation (320) further based, at least in part, on the predicted environmental conditions.
The controller (100, 210) of claim 1 or claim 2, further characterized in that the controller further comprises an information module (308) configured to provide operation history information and/or product information on the hot water system, and in that the memory (110) comprises instructions that when executed by the processor (102) cause the processor (102) to receive operation history information and/or product information from the information module (308) and perform the optimization operation (320) further based, at least in part, on the operation history information and/or product information.
The controller (100, 210) of any one of the preceding claims, further characterized in that the optimization operation (320) is influenced by a single parameter (310) set by a user, the single parameter (310) providing a user-selected balance between the thermal comfort satisfaction level and the energy consumption level in the optimization operation (320).
The controller (100, 210) of any one of the preceding claims, further characterized in that the thermal comfort satisfaction level and the energy consumption level are both provided in terms of energy.
The controller (100, 210) of any one of the preceding claims further characterized in that the thermal comfort determination module (302), and/or the energy consumption determination module (304), and/or the environmental determination module (306) uses an artificial intelligence model, more particularly a machine learning model, to generate its predictions.
The controller (100, 210) of claim 6, further characterized in that the machine learning model is trained using data on the operation history and/or product information of the hot water system (200), and/or using data on the environmental conditions outside of the hot water system (200).
The controller (100, 210) of claim 6 or claim 7, further characterized in that the machine learning model is continuously refined using data from operation of the hot water system (200).
The controller (100, 210) of claim 6 or claim 7, further characterized in that the machine learning model is refined based on occurrence of:
a time condition; and/or

a duration condition; and/or

a processor usage condition, such as processor usage falling below a threshold value; and/or

a product usage condition, such as product usage falling below a threshold value; and/or

an electricity and/or energy cost condition, such as electricity and/or energy cost falling below a threshold; and/or

an event condition, such as an event notification received through the communication interface (140); and/or

a condition related to the thermal comfort satisfaction level, such as the thermal comfort satisfaction level falling below a threshold value; and/or

a condition related to the energy consumption level, such as the energy consumption level exceeding a threshold value; and/or

a condition related to an environmental condition outside of the hot water system (200); and/or

a deviation between a previous prediction result of the machine learning model and a current prediction result from the operation history of the hot water system (200).
The controller (100, 210) of any one of claims 6 to 9, further characterized in that:
the artificial intelligence model of the thermal comfort determination module (302) is configured to predict the thermal comfort satisfaction level, based, at least in part, on the following inputs:
the operation history of the hot water system (200); and/or

the product information of the hot water system (200); and/or

the actual or predicted environmental conditions outside of the hot water system (200); and/or

commands to operate the hot water system (200);

and/or the artificial intelligence model of the energy consumption determination module (304) is configured to predict the energy consumption level, based, at least in part, on the following inputs:
the operation history of the hot water system (200); and/or

the product information of the hot water system (200); and/or

the actual or predicted environmental conditions outside of the hot water system (200); and/or

commands to operate the hot water system (200);

and/or the artificial intelligence model of the environmental determination module (306) is configured to predict environmental conditions outside of the hot water system (200), based, at least in part, on the following inputs:
the operation history of the hot water system (200); and/or

the product information of the hot water system (200).
The controller (100, 210) of any one of the preceding claims, further characterized in that the optimization operation (320) uses an artificial intelligence model, more particularly a machine learning model.
The controller (100, 210) of any one of the preceding claims, further characterized in that an artificial intelligence model, more particularly a machine learning model, is used to determine commands to operate the hot water system (200).
The controller (100, 210) of any one of the preceding claims, further characterized in that at least portions of the controller (100, 210) are cloud-based.
A hot water system (200) comprising a heat pump (202) and an optional backup electrical heat source (208) and/or an optional hydraulic backup heat source, the hot water system (200) controlled by the controller (100, 210) of any one of claims 1 to 13.
The hot water system (200) of claim 14, further characterized in that the hot water system (200) is configured for use in cooling applications.