Reduced temperature water supply mode in water supply system

Abstract

A heater arrangement system for a water supply system, the water supply system is used to control the water supply provided to a water outlet, the water outlet is arranged to provide heated water to a user, the heater arrangement system includes: a water heating device and a control unit, the water heating device is arranged to be away from the water outlet; the control unit is coupled to the water heating device in a communication manner, the control unit is configured to: receive a request from a user to start a reduced temperature water supply mode, the reduced temperature water supply mode provides: heated water is supplied to the water outlet at a first temperature within a fixed time period, and then once the fixed time has passed, the temperature is reduced to a second temperature; and for the user whose request has been received, when it is detected that the user has opened the water outlet, heated water at a first temperature is provided within a first time period, and then after the first time period has passed, the temperature of the heated water is reduced from the first temperature to a second temperature, the second temperature being lower than the first temperature.

Description

Technical Field

[0001] The present disclosure generally relates to water / energy flow utility management in a water supply system, such as a heater arrangement system for a water supply system for supplying hot water to a plurality of water outlets (faucets) in a building. In particular, the present disclosure relates to implementing a reduced temperature water supply mode (or budget) in a water supply system for reducing the waste of water and / or energy flowing to the water outlets to save water and / or energy. Background Art

[0002] Whether in commercial or domestic environments, heated water is required in a year-round, all-day cycle. It goes without saying that providing heated water requires both clean water and a heat source. In order to provide heated water, a heating system is usually provided to a centralized water supply system to heat the water to a predetermined temperature, such as a predetermined temperature set by a user, and the heat source used is traditionally one or more electric heating elements or the combustion of natural gas. Typically, during periods of high energy (such as gas or electricity) demand, utility providers will implement peak electricity prices, which increase the unit cost of energy, partly to cover the additional cost of having to purchase more energy to supply to customers, and partly to prevent unnecessary energy use. Then, during periods of low energy demand, utility providers will implement off-peak electricity prices, which reduce the unit cost of energy to encourage customers to start using energy during off-peak periods rather than during peak periods, thereby achieving an overall more balanced energy consumption over time. However, such strategies will only be effective if customers are always aware of changes in electricity costs and make conscious efforts to change their energy consumption habits.

[0003] Clean water as a utility is currently receiving a lot of attention. As clean water becomes increasingly scarce, much effort has been put into educating the public about protecting clean water and developing systems and devices that reduce water consumption, such as aerated showers and faucets that reduce water flow, showers and faucets equipped with motion sensors that stop water flow when no motion is detected, etc. However, these systems and devices are limited to a single specific use and have limited impact on problematic water consumption habits.

[0004] With growing concern about the environmental impact of energy consumption, there has been a growing interest in using heat pump technology as a way to provide domestic heated water recently. A heat pump is a device that transfers thermal energy from a heat source to a heat reservoir. Although heat pumps require electricity to do the job of transferring thermal energy from a heat source to a heat reservoir, heat pumps are generally more efficient than resistance heaters (electric heating elements) because heat pumps typically have a coefficient of performance of at least 3 or 4. This means that for the same amount of electricity, a heat pump can provide 3 or 4 times more heat to a user than a resistance heater.

[0005] The heat transfer medium that carries the thermal energy is called a refrigerant. Thermal energy from the air (e.g., outdoor air, or air from a hot room in a house) or a ground source (e.g., a ground loop or a well filled with water) is extracted by a receiving heat exchanger and transferred to the contained refrigerant. The now higher energy refrigerant is compressed, causing its temperature to rise significantly, at which point the hot refrigerant exchanges the thermal energy to the hot water loop via a heat exchanger. In the case of heated water, the heat extracted by the heat pump can be transferred to water in an insulated tank that acts as a thermal energy storage, and the heated water can be used later when needed. The heated water can be delivered to one or more water outlets, such as faucets, showers, radiators, as needed. However, heat pumps generally require more time to bring the water to the desired temperature than electric resistance heaters.

[0006] Because different homes, workplaces, and commercial spaces have different requirements and preferences for heated water use, new ways of delivering heated water are needed to enable heat pumps to become a practical alternative to electric heaters. In addition, regulation of energy and clean water consumption may be required to conserve energy and water; however, regulation of utility consumption cannot simply limit usage.

[0007] It is therefore desirable to provide regulation of utility consumption in a water supply system. Summary of the invention

[0008] The present invention provides a heater arrangement for a water supply system as claimed in claim 1, the water supply system being used to control a water supply to a water outlet.

[0009] The invention also provides a method for controlling a water supply to a water outlet as claimed in claim 10 .

[0010] The present invention also provides a corresponding computer program product and a control module as claimed in claim 14 and claim 15. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Embodiments of the present disclosure will now be described with reference to the accompanying drawings, in which:

[0012] Figure 1 is a schematic system overview of an exemplary water supply system;

[0013] Figure 2 is a flow chart illustrating the steps involved in temporarily reducing the flow of energy and / or water in an exemplary water supply system; and

[0014] Figure 3 is a flow chart showing the steps involved in implementing a reduced temperature water supply mode. DETAILED DESCRIPTION

[0015] In view of the foregoing, the present disclosure provides various methods for providing heated water using a heat pump or assisted by a heat pump, and in some cases various methods for regulating the use of utilities including water and energy to reduce waste of water and energy. The method of the present invention can be implemented by using one or more machine learning algorithms (MLA), which are trained to control and regulate the water supply of the water supply system via a control module based on sensor data received from the water supply system. For example, during the training phase, the MLA can monitor the use of hot water by residents in a home environment and establish a normal usage pattern. The MLA can be trained to identify different types of water usage (such as showers, hand washing, heating, etc.) based on a variety of different inputs (such as time of day, day of the week, date, weather, etc.). In some embodiments, the MLA can collect additional data about, for example, the time when the system outlet is opened and closed when providing heated water to the user, the duration of use, the water temperature set by the user, and the actual water temperature, etc. In use, the MLA may use the learned usage patterns in a variety of different ways to improve the efficiency and effectiveness of using a heat pump or providing heated water with the assistance of a heat pump.

[0016] In some embodiments, the MLA can be trained to implement one or more energy conservation strategies when or before the water outlet is opened, and optionally to implement one or more interactive strategies to help change water and energy usage habits, such as to gradually reduce water and / or energy usage.

[0017] A brief overview of a number of different types of machine learning algorithms for implementations using one or more MLAs is given below. However, it should be noted that using MLAs to establish normal usage patterns is only one way to implement the present technology, but it is not required; in some implementations, the control module can be programmed with appropriate software functions to target specific heated water usage situations, such as excessive water flow, and respond in a predetermined manner.

[0018] Overview of MLA

[0019] There are many different types of MLAs known in the art. Broadly speaking, there are three types of MLAs: MLAs based on supervised learning, MLAs based on unsupervised learning, and MLAs based on reinforcement learning.

[0020] The supervised learning MLA process is based on a target-outcome variable (or dependent variable) which is to be predicted given a set of predictor variables (independent variables). Using these sets of variables, MLA (during training) generates a function that maps the input to the desired output. The training process continues until the MLA achieves the desired level of accuracy on the validation data. Examples of supervised learning based MLA include: regression, decision trees, random forests, logistic regression, etc.

[0021] Unsupervised learning MLA itself does not involve making predictions on the target or outcome variables. This type of MLA is used to cluster populations of values ​​into different groups, which is widely used to segment customers into different groups for specific interventions. Examples of unsupervised learning MLA include: Apriori algorithm, K-means.

[0022] A reinforcement learning MLA is trained to make specific decisions. During training, the MLA is exposed to a training environment where it trains itself through trial and error. The MLA learns from past experience and tries to acquire the best possible knowledge to make accurate decisions. An example of a reinforcement learning MLA is a Markov decision process.

[0023] It should be understood that different types of MLAs with different structures or topologies may be used for various tasks.One specific type of MLA includes an artificial neural network (ANN), also known as a neural network (NN).

[0024] Neural Network (NN)

[0025] Generally speaking, a given NN consists of a set of interconnected artificial "neurons" that process information using connectionist computational methods. NNs are used to model complex relationships between inputs and outputs (without actually understanding these relationships) or to find patterns in data. NNs are first conditioned in a training phase, in which a set of known "inputs" and information are provided to the NN to adapt the NN to generate appropriate outputs (for the given situation being tried to model). During this training phase, the given NN adapts to the situation being learned and changes its structure so that the given NN is able to provide reasonable predicted outputs for given inputs in new situations (based on what has been learned). Therefore, rather than trying to determine a complex statistical arrangement or mathematical algorithm for a given situation, the given NN aims to provide an "intuitive" answer based on a "feel" for the situation. Therefore, a given NN is considered a trained "black box" that can be used to determine reasonable answers for a given set of inputs when what happens in the "box" is not important.

[0026] NNs are often used in many situations where it is important to know the output based on the given input, but how the output is derived is of little or no importance. For example, NNs are often used to optimize network traffic distribution between servers and in data processing, including filtering, clustering, signal separation, compression, vector generation, etc.

[0027] Deep Neural Networks

[0028] In some non-limiting embodiments of the present technology, the NN may be implemented as a deep neural network. It should be understood that the NN may be classified into various types of NNs, and one of these types includes a recurrent neural network (RNN).

[0029] Recurrent Neural Networks (RNNs)

[0030] RNNs are suitable for processing input sequences using their "internal states" (stored memories). This makes RNNs well suited for tasks such as unsegmented handwriting recognition and speech recognition. These internal states of RNNs can be controlled and are called "gated" states or "gated" memories.

[0031] It should also be noted that RNN itself can also be classified into various subclasses of RNN. For example, RNN includes Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU), Bidirectional RNN (BRNN), etc.

[0032] LSTM networks are deep learning systems that can learn tasks that in a sense require "memory" of events that occurred earlier during very short and discrete time steps. The topology of LSTM networks can vary based on the specific tasks they are "learning" to perform. For example, LSTM networks can learn to perform tasks where relatively long delays occur between events, or where events occur simultaneously at low and high frequencies. RNNs with a specific gating mechanism are called GRUs. Unlike LSTM networks, GRUs lack an "output gate" and, therefore, have fewer parameters than LSTM networks. BRNNs may have "hidden layers" of neurons connected in opposite directions, which may allow information from past states and future states to be used.

[0033] Residual Neural Network (ResNet)

[0034] Another example of a NN that can be used to implement non-limiting embodiments of the present technology is a residual neural network (ResNet).

[0035] Deep networks naturally integrate low / mid / high-level features and classifiers in an end-to-end multi-layer manner, and the “level” of features can be enriched by the number of stacked layers (depth).

[0036] In summary, in the context of the present technology, at least partial implementation of one or more MLAs can be roughly divided into two phases - a training phase and a use phase. First, in the training phase, a given MLA is trained using one or more appropriate training data sets. Then, once a given MLA has learned what data to expect as input and what data to provide as output, the given MLA is run using the data in use in the use phase.

[0037] Water supply system

[0038] In an embodiment of the present technology, chilled and heated water is provided by a central water supply system to multiple water outlets, including faucets, showers, radiators, etc., in a building in a home or commercial environment. Figure 1 An exemplary water supply system according to an embodiment is shown. In this embodiment, the water supply system 100 includes a control module 110, which may include a machine learning algorithm 120. The control module 110 is coupled to various elements of the water supply system in a communicable manner and the control module 110 is configured to control various elements of the water supply system, including: a flow controller 130, for example, which is arranged in the form of one or more valves to control the water flow inside and outside the system; a (ground source or air source) heat pump 140, which is configured to extract heat from the surrounding environment and deposit the extracted heat in a thermal energy storage 150 for heating water; and one or more electric heating elements 160, which are configured to heat cold water directly to a desired temperature by controlling the amount of energy supplied to the electric heating element 160. Then, the heated water (whether heated by the thermal energy storage 150 or by the electric heating element 160) is directed to one or more water outlets as needed and when needed. In an embodiment, the heat pump 140 extracts heat from the surrounding environment into the thermal energy storage medium in the thermal energy storage 150 until the thermal energy storage medium reaches the operating temperature, and then, for example, cold water from the pipeline (main) can be heated to the required temperature by the thermal energy storage medium. The heated water can then be supplied to various water outlets in the system.

[0039] In the present embodiment, the control module 110 is configured to receive inputs from a plurality of sensors 170-1, 170-2, 170-3, ..., 170-n. The plurality of sensors 170-1, 170-2, 170-3, ..., 170-n may include, for example, one or more air temperature sensors, one or more water temperature sensors, one or more water pressure sensors, one or more timers, one or more motion sensors arranged indoors and / or outdoors, and may include other sensors that are not directly linked to the water supply system 100 (such as a GPS signal receiver, a calendar, a weather forecast application on a smartphone carried by a resident and communicating with the control module via a communication channel). In the present embodiment, the control module 110 is configured to use the received inputs to perform various control functions, such as controlling the water flow rate to the thermal energy storage 150 or the electric heating element 160 to heat the water through the flow controller 130.

[0040] Although heat pumps are generally more energy-efficient in heating water than resistance heaters, heat pumps require time to start because they will perform various checks and cycles before reaching normal operating conditions, and they require time to transfer sufficient amounts of thermal energy to the thermal energy storage medium before reaching the desired operating temperature. On the other hand, resistance heaters are generally able to provide heat more immediately. Therefore, heat pumps may take longer to heat the same amount of water to the same temperature than resistance heaters. In addition, in some embodiments, the heat pump 140 may, for example, use a phase change material (PCM) as a thermal energy storage medium, which changes from a solid to a liquid when heated. Therefore, if the PCM has been allowed to solidify, the heat pump may require additional time to first transfer a sufficient amount of heat to change the PCM from a solid to a liquid before further increasing the temperature of the liquefied thermal storage medium. Although this method of heating water is slower, it consumes less energy to heat the water than an electric heating element, so overall, energy is saved and the cost of providing heated water is reduced.

[0041] Phase change materials

[0042] In this embodiment, the phase change material can be used as a heat storage medium for the heat pump. One class of suitable phase change materials is paraffin wax, which has a solid-liquid phase transition at temperatures suitable for domestic hot water supply and use in conjunction with a heat pump. Of particular interest are paraffin waxes that melt at temperatures in the range of 40 to 60 degrees Celsius (°C), and within this range waxes can be found melting at different temperatures to suit specific applications. Typical latent heat capacities are between about 180 kJ / kg and 230 kJ / kg, and specific heat capacity in the liquid phase is approximately 2.27 Jg -1 K-1 , and the specific heat capacity in the solid phase is 2.1 Jg -1 K -1 As can be seen, very substantial amounts of energy can be stored using fusion latent heat. Even more energy can also be stored by heating the phase change liquid above its melting point. For example, during off-peak periods when electricity costs are relatively low, a heat pump can be run to "charge" the thermal energy storage to a higher than normal temperature to "superheat" the thermal energy storage.

[0043] A suitable wax may be one with a melting point of about 48°C (such as n-tricosane C 23 ) or paraffin C 20 -C 33 , which requires the heat pump to operate at a temperature of around 51°C and be able to heat water to a satisfactory temperature for ordinary domestic hot water of around 45°C, sufficient for use in, for example, kitchen taps, shower / bathroom taps. If necessary, cold water can be added to the water flow to lower the water temperature. The given temperature performance of the heat pump is taken into account. Generally, the maximum difference between the input temperature and the output temperature of the fluid heated by the heat pump is preferably kept in the range of 5°C to 7°C, although it can be as high as 10°C.

[0044] Although paraffin is a preferred material for use as a thermal energy storage medium, other suitable materials may also be used. For example, salt hydrates are also suitable for latent heat energy storage systems (such as existing latent heat energy storage systems). In this article, salt hydrates are mixtures of inorganic salts and water, and their phase change involves the loss of all or most of their water. During the phase change, the hydrate crystals are divided into anhydrous (or less water-containing) salts and water. The advantage of salt hydrates is that they have a much higher thermal conductivity than paraffin (2 to 5 times higher) and a much smaller volume change during the phase change. The suitable salt hydrate currently used is Na2S2O3·5H2O, which has a melting point of about 48°C to 49°C and a latent heat of 200kJ / kg to 220kJ / kg.

[0045] Overview of MLA

[0046] There are many different types of MLAs (machine learning algorithms) known in the art. There are many different types of MLAs (machine learning algorithms) known in the art. Broadly speaking, there are three types of MLAs: MLAs based on supervised learning, MLAs based on unsupervised learning, and MLAs based on reinforcement learning.

[0047] The supervised learning MLA process is based on a target-outcome variable (or dependent variable) which is to be predicted given a set of predictor variables (independent variables). Using these sets of variables, MLA (during training) generates a function that maps the input to the desired output. The training process continues until the MLA achieves the desired level of accuracy on the validation data. Examples of supervised learning based MLA include: regression, decision trees, random forests, logistic regression, etc.

[0048] Unsupervised learning MLA itself does not involve making predictions on the target or outcome variables. This type of MLA is used to cluster populations of values ​​into different groups, which is widely used to segment customers into different groups for specific interventions. Examples of unsupervised learning MLA include: Apriori algorithm, K-means.

[0049] A reinforcement learning MLA is trained to make specific decisions. During training, the MLA is exposed to a training environment where it trains itself through trial and error. The MLA learns from past experience and tries to acquire the best possible knowledge to make accurate decisions. An example of a reinforcement learning MLA is a Markov decision process.

[0050] It should be understood that different types of MLAs with different structures or topologies may be used for various tasks.One specific type of MLA includes an artificial neural network (ANN), also known as a neural network (NN).

[0051] Neural Network (NN)

[0052] Generally speaking, a given NN consists of a set of interconnected artificial "neurons" that process information using connectionist computational methods. NNs are used to model complex relationships between inputs and outputs (without actually understanding these relationships) or to find patterns in data. NNs are first conditioned in a training phase, in which a set of known "inputs" and information are provided to the NN to adapt the NN to generate appropriate outputs (for the given situation being tried to model). During this training phase, the given NN adapts to the situation being learned and changes its structure so that the given NN is able to provide reasonable predicted outputs for given inputs in new situations (based on what has been learned). Therefore, rather than trying to determine a complex statistical arrangement or mathematical algorithm for a given situation, the given NN aims to provide an "intuitive" answer based on a "feel" for the situation. Therefore, a given NN is considered a trained "black box" that can be used to determine reasonable answers for a given set of inputs when what happens in the "box" is not important.

[0053] NNs are often used in many situations where it is important to know the output based on the given input, but how the output is derived is of little or no importance. For example, NNs are often used to optimize network traffic distribution between servers and in data processing, including filtering, clustering, signal separation, compression, vector generation, etc.

[0054] Deep Neural Networks

[0055] In some non-limiting embodiments of the present technology, the NN may be implemented as a deep neural network. It should be understood that the NN may be classified into various types of NNs, and one of these types includes a recurrent neural network (RNN).

[0056] Recurrent Neural Networks (RNNs)

[0057] RNNs are suitable for processing input sequences using their "internal states" (stored memories). This makes RNNs well suited for tasks such as unsegmented handwriting recognition and speech recognition. These internal states of RNNs can be controlled and are called "gated" states or "gated" memories.

[0058] It should also be noted that RNN itself can also be classified into various subclasses of RNN. For example, RNN includes Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU), Bidirectional RNN (BRNN), etc.

[0059] LSTM networks are deep learning systems that can learn tasks that in a sense require "memory" of events that occurred earlier during very short and discrete time steps. The topology of LSTM networks can vary based on the specific tasks they are "learning" to perform. For example, LSTM networks can learn to perform tasks where relatively long delays occur between events, or where events occur simultaneously at low and high frequencies. RNNs with a specific gating mechanism are called GRUs. Unlike LSTM networks, GRUs lack an "output gate" and, therefore, have fewer parameters than LSTM networks. BRNNs may have "hidden layers" of neurons connected in opposite directions, which may allow information from past states and future states to be used.

[0060] Residual Neural Network (ResNet)

[0061] Another example of a NN that can be used to implement non-limiting embodiments of the present technology is a residual neural network (ResNet).

[0062] Deep networks naturally integrate low / mid / high-level features and classifiers in an end-to-end multi-layer manner, and the “level” of features can be enriched by the number of stacked layers (depth).

[0063] In summary, in the context of the present technology, at least partial implementation of one or more MLAs can be roughly divided into two phases - a training phase and a use phase. First, in the training phase, a given MLA is trained using one or more appropriate training data sets. Then, once a given MLA has learned what data to expect as input and what data to provide as output, the given MLA is run using the data in use in the use phase.

[0064] Figure 2 An embodiment of a method for controlling utility usage, for example in a home environment, is shown. The method begins at S2001 when a water outlet (e.g., a bathroom sink faucet) is activated or opened by a user to receive heated water supplied by a water heating system disposed remotely from the water outlet, the water heating system being Figure 1 The water heating system is controlled by a control module 110, and the water heating system is arranged far away from the water outlet and connected to a sensor (e.g., Figure 1 The control module 110 communicates with one of the sensors 170-n shown in the figure, which is used to sense the presence of an object (e.g., the hands of a person washing hands) below the water outlet. At S2002, the control module 110 receives a signal from the sensor, and at S2003, determines whether there is an object below the water outlet. If the control module determines that there is an object, the method returns to S2002, and the control module continues to monitor the signal from the sensor. If the control module determines that there is no object below the water outlet, at S2004, the control module controls the water heating system to reduce the temperature of the heated water supplied to the water outlet and / or reduce the flow rate of the heated water supplied to the water outlet. The method then returns to S2002, and the control module continues to monitor the signal from the sensor. In doing so, heated water can continue to be supplied to the water outlet, but is supplied to the water outlet with a reduced amount of energy and / or water used. For example, when a user is washing their hands with heated water under a faucet, when the user removes their hands from under the faucet to, for example, reach for soap, the control module can reduce the temperature and flow of the heated water to save energy and water, and then once the user returns their hands to the faucet, the temperature and flow of the heated water can be restored to the initial level.

[0065] There may be a situation where the user forgets to turn off the faucet. Therefore, in an alternative embodiment or in addition to the previous embodiment, when the water outlet is activated to supply heated water, a timer in communication with the control module is activated to record the elapsed time. At S2005, the control module receives a signal from the timer to determine the elapsed time T for continuously providing heated water from the water outlet. If the control module determines at S2006 that the elapsed time T does not exceed a predetermined first threshold value T1, the method returns to S2005, and the control module continues to monitor the signal from the timer. If the control module determines at S2006 that the elapsed time T exceeds the first threshold value T1, the control module initiates a warning sequence at S2007, which may include generating a sound or activating a light signal at or near the water outlet to warn the user: the water outlet has been continuously opened for a time T1, to prompt the user to close the water outlet if the heated water is no longer needed. At S2008, the control module determines whether the elapsed time T exceeds a predetermined second threshold value T2, which is higher than the first threshold value T1. If it is determined that the elapsed time T does not exceed the second threshold value T2, the method returns to S2005, and the control module continues to monitor the signal from the timer. If the control module determines at S2008 that the elapsed time T exceeds the second threshold value T2, the control module controls the water heating system at S2009 to stop providing heated water to the water outlet. In doing so, energy and water are not wasted when heated water is no longer needed. For example, if a user forgets to turn off the faucet after washing his hands, or if a child has left the faucet to play, the provision of heated water can be automatically stopped to save energy and water. In addition to the values ​​of the two thresholds T1 and T2 being predetermined values, the values ​​of the two thresholds T1 and T2 can also be determined by deriving predicted values ​​based on an artificial intelligence algorithm (such as deep learning or other MLA), which has been trained based on past water flow usage in a particular building, so that in the case of normal water usage in a particular household or commercial building, no warning is generated and the water is not turned off.

[0066] In an embodiment, the heated water usage data collected over time (S2010) may be used to generate a usage report (S2011) as a tool to prompt users to review and possibly modify their usage habits to reduce energy and water usage.

[0067] Figure 3An embodiment of a method for regulating the temperature of heated water based on a predetermined water supply mode or budget for reducing the temperature is shown. The method starts at S2201 by setting an energy mode and / or a heated water use mode. The mode can be set by a user through a user interface or by a software function or an artificial intelligence (AI) method that determines a cost-effective mode based on, for example, average use. When a user activates or opens a water outlet at S2202 to receive heated water, a control module determines the elapsed time T at S2203, such as described above. At S2204, if the control module determines that the elapsed time T exceeds a predetermined third threshold value T3, the control module controls the water heating system to reduce the temperature of the heated water supplied to the water outlet from the first temperature set by the user when opening the water outlet to a predetermined lower second temperature at S2206. Preferably, the reduction in temperature is a gradual reduction in temperature, rather than a sudden reduction, so as not to shock a user who has adapted to a higher temperature (e.g., in a shower). The rate of gradual reduction can be, for example, 1 degree Celsius per 5 seconds.

[0068] At S2204, if the control module determines that the elapsed time T does not exceed the third threshold value T3, the control module determines at S2205 whether the water outlet is still supplying heated water, for example based on the water flow rate from the water heating system. If it is determined that the water outlet is still supplying heated water, the method returns to S2203, and the control module continues to monitor the elapsed time T. If it is determined that the water outlet is no longer supplying heated water, the method ends. In this embodiment, the third threshold value T3 and the second temperature are set by the control module based on the energy budget and / or the heated water usage budget. Therefore, in this way, the use of heated water can be controlled and regulated to keep energy expenditures within the budget.

[0069] AI methods or algorithms (e.g. Figure 1 The MLA 120 in the embodiment predicts the values ​​of the first and second temperatures and the values ​​of the time periods, preferably after having been trained with training data based on past usage of the water supply system by users.

[0070] The control module 110 is programmed with software to perform the above-described and Figure 2 Alternatively, the control module is hardwired with hardware logic to perform the functions described above.

[0071] As will be appreciated by those skilled in the art, the present technology can be embodied as a system, method or computer program product.Therefore, the present technology can take the form of an all-hardware implementation, an all-software implementation or an implementation combining software and hardware.

[0072] In addition, the present technology may take the form of a computer program product embodied in a computer-readable medium having computer-readable program code embodied thereon. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device, or apparatus, or any suitable combination of the foregoing.

[0073] Computer program code for carrying out operations of the present technology may be written in any combination of one or more programming languages, including object oriented programming languages ​​and traditional procedural programming languages.

[0074] For example, program code for performing operations of the present technology may include source code, object code, or executable code in a conventional programming language (interpreted or compiled) such as C, or assembly code, code for setting up or controlling an ASIC (application specific integrated circuit) or FPGA (field programmable gate array), or code for a hardware description language such as Verilog™ or VHDL (very high speed integrated circuit hardware description language).

[0075] A code component may be embodied as a procedure, a method, etc., and may include sub-components, which may take the form of instructions or sequences of instructions at any level of abstraction, from direct machine instructions of a native instruction set to high-level compiled or interpreted language constructs.

[0076] It is also clear to those skilled in the art that all or part of the logic method according to the preferred embodiment of the present technology can be appropriately embodied in a logic device including logic elements for executing the steps of the method, and such logic elements can include components such as logic gates in a programmable logic array or a dedicated integrated circuit. Such a logic arrangement can also be embodied as an element that enables a logic structure to be temporarily or permanently established in such an array or circuit using, for example, a virtual hardware descriptor language, which can be stored and transmitted using a fixed or transmittable carrier medium.

[0077] The examples and conditional language recorded herein are intended to help the reader understand the principles of the present technology, rather than to limit its scope to such specifically recorded examples and conditions. It should be understood that a person skilled in the art may design various arrangements as follows, which, although not explicitly described or shown herein, embody the principles of the present technology and are included within the scope thereof defined by the appended claims.

[0078] In addition, to facilitate understanding, the above description may describe relatively simplified implementations of the present technology. Those skilled in the art will appreciate that various implementations of the present technology may have greater complexity.

[0079] In some cases, examples that are considered to be helpful for modifications of the present technology may also be set forth. This is done solely to aid understanding and, again, is not intended to limit the scope of the present technology or to set forth the limits of the present technology. These modifications are not exhaustive, and those skilled in the art may make other modifications while still remaining within the scope of the present technology. In addition, where no examples of modifications are set forth, it should not be construed that modifications are impossible and / or that what is described is the only way to implement elements of the present technology.

[0080] In addition, all statements recording the principles, aspects, and implementations of the technology and specific examples thereof herein are intended to encompass both structural and functional equivalents thereof, whether currently known or developed in the future. Thus, for example, it will be understood by those skilled in the art that any block diagram herein represents a conceptual view of an illustrative circuit that embodies the principles of the technology. Similarly, it should be understood that any flow chart, flow diagram, state transition diagram, pseudocode, etc. represent various processes that can be substantially represented in a computer-readable medium and executed by a computer or processor, whether or not such a computer or processor is explicitly shown.

[0081] The functions of the various elements shown in the figure, including any functional blocks marked as "processors", can be provided by using dedicated hardware and hardware capable of executing software in association with appropriate software. When provided by a processor, the function can be provided by a single dedicated processor, by a single shared processor, or by multiple separate processors, some of which can be shared. In addition, the explicit use of the term "processor" or "controller" should not be interpreted as exclusively referring to hardware capable of executing software, but may implicitly include, but is not limited to, digital signal processor (DSP) hardware, network processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), read-only memories (ROMs), random access memories (RAMs), and non-volatile memories for storing software. Other conventional and / or customized hardware may also be included.

[0082] Software modules or simple modules implicitly being software may be represented herein as any combination of flow chart elements or other elements indicating the execution of processing steps and / or textual descriptions. Such modules may be executed by hardware explicitly or implicitly shown.

[0083] It will be apparent to those skilled in the art that many improvements and modifications can be made to the foregoing exemplary embodiments without departing from the scope of the present technology.

Claims

1. A heater arrangement for a water supply system for controlling a supply of water to a water outlet arranged to provide heated water to a user, the heater arrangement comprising: a water heating device, the water heating device being arranged away from the water outlet; as well as a control unit communicatively coupled to the water heating device, the control unit being configured to: a) receiving a request from a user for initiating a reduced temperature water supply mode, said reduced temperature water supply mode providing: supplying heated water to said water outlet at a first temperature for a fixed first time period, and then reducing said temperature to a second temperature once said fixed first time period has elapsed; b) after the request has been received in a), detecting whether the user has opened the water outlet; c) in response to detecting that the user has opened the water outlet, activating a timer to provide an elapsed time; d) providing heated water at the first temperature to the water outlet; e) determining that the elapsed time exceeds a predetermined threshold, the predetermined threshold comprising a fixed first time period; and f) in response to determining that the elapsed time exceeds the predetermined threshold, reducing the temperature of the heated water provided to the water outlet from the first temperature to a second temperature, the second temperature being lower than the first temperature; wherein the first temperature and the second temperature are set by an artificial intelligence algorithm executed by the control unit; wherein the fixed first time period is set by an artificial intelligence algorithm executed by the control unit; wherein the artificial intelligence algorithm predicts the values ​​of the first temperature and the second temperature based on data associated with past usage of the water supply system; wherein the artificial intelligence algorithm predicts the value of the first fixed time period based on data associated with past usage of the water supply system; and Wherein, the predicted value of the first temperature, the predicted value of the second temperature and the predicted value of the fixed first time period are obtained by the artificial intelligence algorithm after training the algorithm with training data associated with past usage of the water supply system.

2. The heater arrangement system according to claim 1, wherein: The request at a) is initiated by the user through a user interface.

3. The heater arrangement system according to claim 1, wherein: After the fixed first time period has elapsed, the temperature is gradually reduced from the first temperature to the second temperature.

4. The heater arrangement system according to claim 3, wherein: The gradual decrease rate is 1 degree Celsius every 5 seconds.

5. The heater arrangement system according to claim 1, wherein: The water heating device comprises a heat pump and a thermal energy storage device.

6. The heater arrangement system according to claim 5, wherein: The thermal energy storage device is a phase change material device.

7. The heater arrangement system according to claim 6, wherein: The phase change material is paraffin wax.

8. The heater arrangement system according to claim 7, wherein: The paraffin wax melts at a temperature of 40°C to 60°C.

9. A heater arrangement according to claim 6, 7 or 8, wherein: The latent heat capacity of the phase change material is between 180 kJ / kg and 230 kJ / kg, and the specific heat capacity in the liquid phase is 2.27 Jg -1 K -1 , and the specific heat capacity in the solid phase is 2.1 Jg -1 K -1 .

10. A method of controlling a water supply to a water outlet in a water supply system, the water outlet being arranged to provide heated water from a water heating device to a user, the method comprising the steps of: a) receiving a request from a user for initiating a reduced temperature water supply mode, said reduced temperature water supply mode providing: supplying heated water to said water outlet at a first temperature for a fixed first time period, and then reducing said temperature to a second temperature once said fixed first time period has elapsed; b) after the request has been received in a), detecting whether the user has opened the water outlet; c) in response to detecting that the user has opened the water outlet, providing heated water at the first temperature for a fixed first time period; d) determining that the fixed first time period has elapsed, and in response to determining that the fixed first time period has elapsed, reducing the temperature of the heated water from the first temperature to a second temperature, the second temperature being lower than the first temperature; wherein the first temperature and the second temperature are set by an artificial intelligence algorithm executed by a control unit; wherein the fixed first time period is set by an artificial intelligence algorithm executed by the control unit; wherein the artificial intelligence algorithm predicts the values ​​of the first temperature and the second temperature based on data associated with past usage of the water supply system; wherein the artificial intelligence algorithm predicts the value of the first fixed time period based on data associated with past usage of the water supply system; and Wherein, the predicted value of the first temperature, the predicted value of the second temperature and the predicted value of the fixed first time period are obtained by the artificial intelligence algorithm after training the algorithm with training data associated with past usage of the water supply system.

11. The method according to claim 10, wherein: The request at a) is initiated by the user through a user interface.

12. The method according to claim 10, wherein: After the fixed first time period has elapsed, the temperature is gradually reduced from the first temperature to the second temperature.

13. The method according to claim 12, wherein: The gradual decrease rate is 1 degree Celsius every 5 seconds.

14. A computer readable medium comprising machine readable code which, when executed by a processor, causes the processor to perform the method according to any preceding method claim.

15. A control module, wherein the control module is configured to control the operation of a water supply system through a communication channel, the water supply system comprising a heating system, the heating system being configured to heat water from a pipe, and the heating system being controlled by the control module, the water supply system being configured to provide water heated by the heating system to a user at one or more water outlets, the control module comprising a processor having software executed thereon, or having pre-configured hardware logic components, the processor being configured to execute a method according to any one of the preceding method claims.

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