Method and system for performing a heat pump defrost cycle

Abstract

The present invention provides a computer-implemented method for defrosting a heat pump of a water supply system installed in a building, the water supply system comprising a heat pump and a control module, the heat pump being configured to transfer thermal energy from outside the building to a thermal energy storage medium inside the building, the control module being configured to control the operation of the heat pump, the water supply system being configured to provide water heated by the thermal energy storage medium to occupants of the building at one or more water outlets, the method being executed by the control module and comprising: determining an expected start time of a next defrost cycle based on the performance of the heat pump; and preparing the water supply system before the expected start time of the next defrost cycle.

Description

Technical Field

[0001] The present invention relates generally to energy utility management. In particular, the present invention relates to methods and systems that can be used to help change a user's hot water usage habits. Background Art

[0002] Hot water is essential throughout the year, both in commercial and domestic settings. It goes without saying that providing hot water requires both clean water and a heat source. To provide hot water, a centralized water supply is usually provided with a heating system to heat the water to a predetermined temperature, for example set by the user; one or more electric heating elements or burning natural gas are usually used as the heat source. Usually during periods of high demand for energy (such as natural gas or electricity), energy utility suppliers will implement peak prices, thereby increasing the unit cost of energy, partly to cover the additional costs of purchasing more energy to supply consumers, and partly to encourage the reduction of unnecessary energy use. Then, during periods of low energy demand, energy utility suppliers will implement off-peak prices, thereby reducing the unit cost of energy, in order to incentivize consumers to use energy during off-peak hours rather than peak hours, thereby achieving a more balanced energy consumption in the long run. However, this strategy will only work if consumers are always aware of energy price changes and make a conscious effort to change their energy consumption habits.

[0003] Clean water as an energy utility is currently receiving a lot of attention. As clean water becomes increasingly scarce, great efforts have been made to educate the public to conserve clean water and to develop systems and devices that reduce water consumption, such as aerated showers and faucets that reduce water output; showers and faucets equipped with motion sensors that stop water output when no activity is detected, etc. However, these systems and devices are limited to specific uses and have limited impact on bad water habits.

[0004] With increasing concern about the impact of energy consumption on the environment, there has been a recent increase in interest in using heat pump technology as a way to provide domestic hot water. A heat pump is a device that transfers thermal energy from a heat source to a heat reservoir. Although heat pumps require electricity to transfer the heat from the heat source to the heat reservoir, they are generally more efficient than resistance heaters (electric heating elements) because they 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 the user than a resistance heater.

[0005] The heat transfer medium that carries away the heat energy is called a refrigerant. Heat energy from the air (e.g. outdoor air or air from a hot room in the house) or from a ground source (e.g. an underground loop or an underground borehole filled with water) is extracted by a heat receiving exchanger and transferred to the contained refrigerant. The now high-energy refrigerant is compressed, causing its temperature to rise significantly, and the now hot refrigerant exchanges heat energy into the hot water loop through a heat exchanger. In the case of hot water provision, the heat extracted by the heat pump can be transferred to water in an insulated water tank that acts as a thermal energy storage, and the hot water can be used later when needed. As needed, the hot water can be delivered to one or more water outlets, such as taps, showers, radiators. However, compared to electric resistance heaters, heat pumps usually take more time for the water to reach the required temperature, partly because heat pumps are usually slow to start.

[0006] Since different homes, workplaces, and commercial spaces have different requirements and preferences for hot water use, new ways of hot water supply are needed to make heat pumps a practical alternative to electric heaters. In addition, in order to save energy and water resources, it is necessary to regulate the consumption of energy and clean water; however, energy utility consumption cannot be regulated simply by limiting usage.

[0007] It would therefore be desirable to provide an improved method and system for providing hot water. Summary of the invention

[0008] On the one hand, the present invention provides a computer-implemented method for defrosting a heat pump of a water supply system installed in a building, the water supply system comprising a heat pump and a control module, the heat pump being configured to transfer heat energy from outside the building to a thermal energy storage medium inside the building, the control module being configured to control the operation of the heat pump, and the water supply system being configured to provide water heated by the thermal energy storage medium to occupants of the building at one or more water outlets, the method being executed by the control module and comprising: determining an expected start time of a next defrost cycle based on the performance of the heat pump; pre-charging the thermal energy storage medium by running the heat pump for a predetermined period of time before the expected start time of the next defrost cycle to store heat energy in the thermal energy storage medium so that the thermal energy storage medium reaches a first temperature before the expected start time, thereby preparing the water supply system before the expected start time of the next defrost cycle.

[0009] According to the present embodiment, the expected start time of the next defrost cycle of the heat pump is determined based on the performance of the heat pump, and then the control module predictively prepares the water supply system before the defrost cycle starts. In doing so, the present embodiment allows the necessary heat pump defrost cycle to be performed in a manner that interferes less with the supply of hot water, so that the heat pump can efficiently provide hot water. By pre-charging the thermal energy storage medium so that it reaches the required temperature before the expected start time of the next defrost cycle, when the operation of the heat pump is to be disturbed, it can be ensured that sufficient thermal energy is stored to reduce the interference with the hot water supply.

[0010] In some embodiments, the method may be performed at least in part by a first machine learning algorithm MLA implemented on the control module, the first MLA having been trained to predict the next defrost cycle based on weather data.

[0011] In some embodiments, the performance of the heat pump may include an average heat output of the heat pump, an efficiency of the heat pump, a coefficient of performance of the heat pump, or a combination thereof.

[0012] The time when the heat pump needs to perform a defrost cycle is affected by external factors such as outdoor temperature and humidity. Therefore, in some embodiments, the method may further include receiving weather data, wherein the expected start time of the next defrost cycle is further determined based on the weather data.

[0013] In some embodiments, the weather data may include one or more of: a weather forecast, current weather conditions, an indoor temperature of a building, or a combination thereof.

[0014] In some embodiments, the method further includes collecting data associated with one or more previous defrost cycles of the heat pump, wherein the expected start time of the next defrost cycle is further determined based on the collected data. Using the data collected from previous defrost cycles, such as the time interval between consecutive defrost cycles under different weather conditions or different indoor temperatures, the time required to complete each defrost cycle, etc., the control module or the first MLA can more accurately determine the expected start time of the next defrost cycle.

[0015] In some embodiments, the predetermined time period may be set based on the first temperature and the performance of the heat pump and / or weather data.

[0016] In some embodiments, the first temperature may be higher than a preset operating temperature set by an occupant. By precharging the thermal storage medium to a temperature higher than the normal operating temperature, more stored thermal energy may be used during the next defrost cycle, thereby further reducing the disruption to hot water supply caused by the next defrost cycle.

[0017] During the defrost cycle, the heat pump operation is interrupted and cannot heat the building. Therefore, in some embodiments, the water supply system may include a central heating system for raising the indoor temperature of the building, wherein preparing the water supply system includes operating the heat pump to supply hot water to the central heating system to raise the indoor temperature of the building before the expected start time.

[0018] In some embodiments, the water supply system may include one or more resistance heating elements, wherein increasing the indoor temperature of the building includes operating the one or more resistance heating elements to supply hot water to the central heating system.

[0019] In some embodiments, increasing the indoor temperature of the building may include increasing the indoor temperature of the building from a current temperature to a second temperature.

[0020] In some embodiments, the second temperature can be higher than a preset indoor temperature set by an occupant. By raising the indoor temperature of the building to a temperature higher than the preset temperature set by the occupant (e.g., one or two degrees higher) before the expected start time of the next defrost cycle, it can be ensured that the indoor temperature of the building remains within a comfortable range when the heat pump operates during the next defrost cycle.

[0021] In some embodiments, precharging the thermal energy storage medium and / or raising the indoor temperature of the building is performed based on an expected demand for hot water determined by an energy facility usage pattern established by a second MLA for the water supply system based on sensor data obtained from the water supply system.

[0022] In some embodiments, the first temperature may be determined by the second MLA based on energy facility usage patterns.

[0023] In some embodiments, the energy facility usage pattern includes expected chilled water usage associated with a time, day, and / or date, expected hot water usage associated with a time, day, and / or date, expected energy usage associated with a time, day, and / or date, or a combination thereof.

[0024] In some embodiments, the method may further include determining a low demand time around an expected start time of a next defrost cycle based on the energy utility usage pattern when the expected demand for hot water is low.

[0025] In some embodiments, precharging the thermal energy storage medium and / or raising the indoor temperature of the building may be performed based on an expected occupancy of the building as determined by a third MLA for the water system based on sensor data obtained from the water system.

[0026] In some embodiments, the method further includes determining a low occupancy time around an expected start time of a defrost cycle based on the expected occupancy when the expected occupancy of the building is low.

[0027] In some embodiments, the sensor data includes time of day, day of the week, date, water flow rate and / or pressure at one or more outlets, time elapsed since the outlet was opened, mains water temperature, water temperature at one or more outlets, energy consumption amount and / or rate, the user's current location, or a combination thereof.

[0028] In some cases, the expected start time of the next defrost cycle may be particularly disruptive to the normal hot water supply of the water supply system. For example, the expected start time may be a period of time when the expected demand for hot water or energy is high, such as in the evening. Therefore, in some embodiments, the method may also include adjusting the expected start time of the defrost cycle to the adjusted start time based on low demand time and / or low occupancy time. In this way, it is possible to reduce the disruption caused by the next defrost cycle.

[0029] In some embodiments, the method may further include operating the heat pump to initiate the next defrost cycle at the adjusted start time.

[0030] Another aspect of the present invention provides a computer readable medium comprising machine readable codes. When the machine readable codes are executed by a processor, the processor is caused to execute the method as described above.

[0031] Another aspect of the present invention provides a control module for controlling a water supply system, the control module comprising a processor having a machine learning algorithm trained to execute the above method.

[0032] Each embodiment of the present invention has at least one of the above-mentioned purposes and / or aspects, but not necessarily all of them. It should be understood that some aspects of the present invention generated by attempting to achieve the above-mentioned purposes may not meet a certain purpose and / or may meet other purposes not specifically listed herein.

[0033] Additional and / or alternative features, aspects and advantages of embodiments of the present technology will be apparent from the following description, drawings and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Embodiments of the present invention will now be described with reference to the accompanying drawings, in which:

[0035] Figure 1 is a system overview schematic diagram of an exemplary water supply system;

[0036] Figure 2 schematically illustrates an exemplary training phase of an MLA to establish a usage pattern;

[0037] Figure 3 Schematically illustrates exemplary data processing for MLA output occupancy forecasts;

[0038] Figure 4 Schematically illustrates an exemplary data process for pre-charging thermal energy storage with an MLA;

[0039] Figure 5 Schematically illustrates an exemplary data process for an MLA activated heat pump;

[0040] Figure 6 schematically illustrates exemplary data processing for an MLA to initiate a heat pump defrost cycle;

[0041] Figure 7 is a flow chart of an exemplary method of one embodiment for changing a user's water usage habits.

[0042] Figure 8 is a flow chart of an exemplary method of one embodiment of regulating water usage;

[0043] Fig. 9 is a flow chart of another exemplary method of one embodiment of regulating water usage; and

[0044] Fig.10 Exemplary data processing for an MLA output leak warning is schematically illustrated. DETAILED DESCRIPTION

[0045] As previously described, the present invention provides various methods for providing hot water using a heat pump or with the assistance of a heat pump, and in some cases can also adjust the use of energy facilities such as water and energy to reduce the waste of water and energy. The method can be implemented by using one or more machine learning algorithms (MLA), which are trained to control and adjust the water supply of the water supply system through 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 domestic hot water in a home environment and establish a normal usage pattern. The MLA can be trained to identify different types of water use (such as showering, washing hands, 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, such as the opening and closing time of the system outlet, the duration of use, the water temperature set by the user, and the actual water temperature when hot water is provided to the user. When used, the MLA can use the learned usage patterns in a variety of different ways to improve the efficiency and effectiveness of using a heat pump or providing hot water with the assistance of a heat pump.

[0046] In some embodiments, the MLA may be trained to implement one or more energy conservation strategies upon or before opening a water outlet, and optionally to implement one or more interactive strategies to help change water and energy usage habits, such as gradually reducing water and / or energy usage.

[0047] The following is a brief overview of several different types of machine learning algorithms that may be used in embodiments using one or more MLAs. However, it should be noted that using MLAs to establish normal usage patterns is only one way to implement the present technology and is not required; in some embodiments, the control module can be programmed using appropriate software functions to achieve specific target hot water usage, such as when water flow is too large, and react in a predetermined manner.

[0048] MLA Overview

[0049] There are many different types of MLA known in the art. Broadly speaking, there are three types of MLA: MLA for supervised learning, MLA for unsupervised learning, and MLA for reinforcement learning.

[0050] The supervised learning MLA process is based on a target outcome variable (or dependent variable) which is to be predicted based on a given set of predictor variables (independent variables). Using these sets of variables, MLA (during training) generates a function that maps the inputs to the desired outputs. The training process continues until the MLA achieves the desired accuracy on the validation data. Examples of supervised learning based MLA include: regression, decision trees, random forests, logistic regression, etc.

[0051] Unsupervised learning MLA does not involve predicting the target or outcome variable itself. This type of MLA is used to cluster a set of values ​​into different groups. This method is widely used to segment consumers into different groups for specific interventions. Examples of unsupervised learning MLA include: Apriori algorithm, K-means.

[0052] Reinforcement learning MLAs are trained to make specific decisions. During training, the MLA is exposed to a training environment and continuously trains itself through trial and error. The MLA learns from past experience and tries to capture the best knowledge to make accurate decisions. An example of a reinforcement learning MLA is a Markov decision process.

[0053] It should be understood that different types of MLAs having 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).

[0054] Neural Network (NN)

[0055] In general, a given neural network consists of a set of interconnected artificial "neurons" that use a connectionist computational approach to process information. Neural networks are used to simulate complex relationships between inputs and outputs (without actually knowing those relationships) or to discover patterns in data. The neural network is first conditioned during a training phase, where a set of known "inputs" and information is provided to the neural network to debug the neural network so that it generates appropriate outputs (for the specific situation it is trying to simulate). During this training phase, the given neural network adapts to the situation it is learning and changes its structure so that the given neural network can provide reasonable predicted outputs for given inputs in new situations (based on what it has learned). Therefore, rather than trying to determine a complex statistical arrangement or mathematical algorithm for a given situation, the given neural network provides an "intuitive" answer based on a "feeling" of the situation. Therefore, a given neural network is considered a trained "black box" that can be used to determine reasonable answers for a given set of inputs in situations where it is not important what happens in the "box".

[0056] Neural networks are often used in many situations where it is only necessary to know the output given the input, and the exact way the output was derived is less important or unimportant. For example, neural networks are often used to optimize the distribution of network traffic between servers, as well as data processing including filtering, clustering, signal separation, compression, vector generation, etc.

[0057] Deep Neural Networks

[0058] In some non-limiting embodiments of the present invention, the neural network can be implemented as a deep neural network. It should be understood that neural networks can be divided into various types, one of which includes recurrent neural networks (RNNs).

[0059] Recurrent Neural Networks (RNNs)

[0060] RNNs are suitable for processing input sequences using their "internal state" (stored memory). This makes RNNs well suited for tasks such as unsegmented handwriting recognition and speech recognition. These internal states of the RNN can be controlled and are called "gated" states or "gated" memories.

[0061] It should also be noted that RNNs themselves can also be divided into various subclasses of RNNs. For example, RNNs include Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Bidirectional RNNs (BRNNs), etc.

[0062] LSTM networks are deep learning systems that can learn tasks that require, in a sense, "memorizing" events that occurred very short discrete time steps in the past. The topology of LSTM networks can vary depending on the specific task 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, so that information from both past and future states can be used.

[0063] Residual Neural Network (ResNet)

[0064] Another embodiment of a neural network that can be used to implement non-limiting embodiments of the present invention is a residual neural network (ResNet).

[0065] 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 (depth) of stacked layers.

[0066] In summary, in the context of the present invention, the implementation of at least a portion of one or more MLAs can be roughly divided into two phases - a training phase and a use phase. First, a given MLA is trained using one or more appropriate training data sets in the training phase. 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 on the data being used in the use phase.

[0067] Water supply system

[0068] In an embodiment of the present technology, in a building in a home or commercial environment, cold and hot water are provided by a central water supply system to multiple water outlets, including faucets, showers, radiators, etc. Figure 1An exemplary water supply system based on an embodiment is shown. In this embodiment, the water supply system 100 includes a control module 110. The control module 110 is communicatively connected to various elements of the water supply system and is configured to control these elements, including a flow control 130, a (ground source or air source) heat pump 140, a thermal energy storage 150, and one or more electric heating elements 160. The flow control 130 is in the form of one or more valves for controlling the water flow inside and outside the system, for example, the heat pump 140 is configured to extract heat from the surrounding environment and store the extracted heat in the thermal energy storage 150 for heating water, and the electric heating element 160 is configured to directly heat cold water to a desired temperature by controlling the energy provided to the electric heating element 160. Whether the hot water is heated by the thermal energy storage 150 or the electric heating element 160, it is directed to one or more water outlets when needed. In an embodiment, the heat pump 140 extracts heat from the environment (e.g., an air source heat pump extracts heat from the ambient air, a ground source heat pump extracts heat from the ground, and a water source heat pump extracts heat from a body of water), which heat is absorbed by the refrigerant and then transferred from the refrigerant to the working fluid, which in turn transfers the heat to the thermal energy storage medium in the thermal energy storage 150, where the heat is preferably stored as latent heat. The energy of the thermal energy storage medium can then be used to heat colder water, such as heating cold water from a water supply system, which may be a tap water system, to a desired temperature. The heated water can then be supplied to various water outlets in the system.

[0069] 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, for example, may include 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 disposed indoors and / or outdoors, and may include other sensors that are not directly connected 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 through a communication channel, etc. In the present embodiment, the control module 110 is configured to perform various control functions using the received inputs, such as controlling the flow of water through the flow controller 130 to the thermal energy storage 150 or the electric heating element 160 to heat the water. In the present embodiment, a machine learning algorithm (MLA) 120 is used, which may be implemented on a processor (not shown) of the control module 110, or on a server in communication with the processor of the control module 110 via a communication channel. The MLA 120 may be trained using input sensor data received by the control module 110 to establish baseline water and energy usage patterns of occupants based on, for example, time of day, day of the week, date (e.g., seasonal changes, public holidays), occupancy, etc. The learned usage patterns may then be utilized to determine and, in some cases, improve the various control functions performed by the control module 110.

[0070] While heat pumps are generally more efficient at heating water than electrical resistance heaters, it takes time to start a heat pump because the heat pump must be checked / cycled before reaching full power, and it also takes time to transfer a sufficient amount of thermal energy to the thermal energy storage medium to bring the storage medium to a desired operating temperature before it can be used to heat water; therefore, from an initial startup point, a heat pump generally takes longer to heat the same amount of water to the same temperature than an electrical resistance heater. 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 state when heated. In this case, if the PCM has already become solid before the thermal energy extracted by the heat pump has the effect of raising the temperature of the thermal storage medium (prior to which the energy was stored as latent heat), it may take more time for the PCM to change from a solid state to a liquid state. Although this method of heating water is slower, it consumes less energy to heat the water than an electric heating element, thereby saving energy overall and reducing the cost of providing hot water.

[0071] Phase change materials

[0072] In this embodiment, a phase change material can be used as the thermal energy storage medium for the heat pump. One suitable type of phase change material is paraffin wax, which undergoes a solid-liquid phase change at the relevant temperature in the domestic hot water supply and when used in conjunction with a heat pump. Of particular interest is that paraffin wax melts in the range of 40 to 60 degrees Celsius (°C), and it has been found that paraffin wax can be melted at different temperatures within this range for specific applications. Typical latent heat capacities are between about 180 kJ / kg and 230 kJ / kg, and liquid specific heat capacities are about 2.27 Jg -1 K -1 , solid phase is about 2.1Jg -1 K -1 As can be seen, a large amount of energy can be stored using the latent heat of fusion. Even more energy can 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 temperature than normal, making the thermal energy storage "overheated."

[0073] The melting point of a suitable wax is about 48°C, for example, 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 around 45°C for typical domestic hot water, e.g. kitchen taps, shower / bathroom taps. If necessary, cold water can be added to the water flow to lower the water temperature. This is subject to the temperature performance of the heat pump. Typically, the maximum difference between the input and output temperatures of the water flow heated by the heat pump is preferably kept in the range of 5°C to 7°C, but can be as high as 10°C.

[0074] 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 in the present system are also suitable for latent heat storage systems. In the present invention, salt hydrates are a mixture of inorganic salts and water, which lose all or most of their water during the phase change process. During the phase change process, the hydrate crystals are divided into anhydrous (or less water-containing) salts and water. The advantage of salt hydrates is that their thermal conductivity is much higher than that of paraffin (2 to 5 times higher), and the volume change during the phase change is smaller. 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 200-220kJ / kg.

[0075] Usage Mode

[0076] Figure 2 The training phase of an MLA 2200 (eg, MLA 120 ) is shown to establish baseline energy facility usage patterns according to one embodiment.

[0077] In this embodiment, the MLA 2200 receives inputs from multiple sensors and other sources over a period of time to learn, for example, usage patterns of the residents of a house. For example, a control module implementing the MLA 2200, such as the control module 110, may include a clock, and the MLA 2200 may receive the time of day 2101, the date, and the day of the week 2102 from the clock. The house may be equipped with multiple motion sensors, and the MLA 2200 may receive occupancy data 2103 from the motion sensors. In another embodiment described below, occupancy may also be predicted based on multiple factors. The control module may communicate with one or more outdoor temperature sensors to enable the MLA 2200 to receive input of the current weather 2104. The control module may also communicate with one or more indoor temperature sensors to enable the MLA 2200 to receive the indoor temperature 2105. A plurality of water temperature, pressure and flow sensors may be provided at different locations of the water supply system, such as measuring the tap water inlet temperature 2106, tap water flow 2107 and tap water pressure 2108 at the tap water inlet, and these data may be input into the MLA 2200. A sensor may be provided at one or more or each water outlet (or a valve controlling the flow of water to the water outlet) to detect when each water outlet is opened and closed, and data related to the water temperature at the water outlet, the time and temperature of hot / cold water use 2109, and the amount of hot / cold water used 2110 may be input into the MLA 2200. The MLA 2200 may also collect energy use 2111 data of the water supply system, such as the time of use, the amount of energy used, and the current price when the control module communicates with the energy supplier. The MLA 2200 may also collect data on the use 2112 of the heat pump, such as the time of use, the length of use, etc. It should be noted that it is not necessary for the MLA to receive, collect and / or use all the input sensor data described in the present invention, and the data list of the input sensors described in the present invention is not exhaustive, and the MLA may also receive, collect and / or use other input data as needed. In particular, in embodiments where the control module communicates with, for example, one or more smart devices (e.g., smartphones) or one or more occupants' personal computers, the MLA may obtain and use other personal data or public data from these devices.

[0078] During the training phase, the MLA 2200 establishes water and energy usage patterns for occupants based on the received input data. For example, the usage patterns 2300 may include hot water usage patterns, cold water usage patterns, energy usage patterns, heat pump usage patterns, occupancy patterns, which provide a baseline of expected usage based on, for example, time of day, day of the week, date, occupancy level, etc.

[0079] Occupancy rate forecast

[0080] Figure 3One embodiment of MLA 3200 implemented on a control module (e.g., control module 110) is schematically shown, which processes a set of input data to output, for example, an occupancy prediction for a house. MLA 3200 can be the same MLA as MLA 2200, or it can be a different MLA. MLA 3200 can be trained using an appropriate training data set, for example, based on occupancy levels of houses and the time schedule of occupants arriving at the houses over the course of a year.

[0081] The MLA 3200 receives input data specific to the house and its occupants from a number of sources through the control module, including one or more sensors disposed around the house, one or more user interfaces (e.g., control panels around the house that communicate with the control module, smart devices, personal computers, etc.), one or more software programs, one or more public and private databases, etc. In this embodiment, the MLA 3200 receives inputs of the current time 3101, date 3102, and day of the week 3103, such as from a clock and calendar function running on the control module or remotely received through a communication network. The MLA 3200 also receives inputs of any special events or public holidays 3104, such as obtained from the house occupants through the user interface, automatically obtained from a calendar application on the occupant's smart device, or obtained from the public domain through a communication network. The MLA 3200 then determines an expected occupancy level based on the input data and outputs an occupancy forecast 3300. By determining the expected occupancy of the building, the likely demand for energy utilities (e.g., energy and water) can be estimated or predicted.

[0082] In another embodiment, when it is determined that the occupant is not in the house, the MLA 3200 receives input of the current location 3105 of one or more occupants. For example, the occupant may register one or more smart devices (e.g., smart phones) with a control module or a server in communication with the control module, and then the MLA 3200 may obtain the GPS signal received on each occupant's registered smart device through the communication network, thereby receiving the current location of each occupant. Then, based on the occupant's current location 3105 and optional other information, such as traffic conditions obtained from the public domain, the MLA 3200 determines the expected arrival time 3106 of each occupant to arrive at the house. The expected arrival time 3106 of each occupant may also be determined based on other inputs, such as the current time 3101, the date 3102, the day of the week 3103, and the event day 3104. Then, the MLA 3200 may use the expected arrival time 3106 to output the occupancy prediction 3300 of the house (the future occupancy level instead of the current occupancy level).

[0083] The occupancy prediction 3300 is a useful indicator when the control module performs various control functions of the water supply system. For example, hot water can be directed to the radiators of the central heating system installed in the house before the expected arrival of the occupants. Another example is to activate a heat pump before the expected arrival of the occupants to begin storing thermal energy in the thermal energy storage. In addition, the heat pump can be activated based on the expected arrival time 3106 of the occupants so that the thermal energy storage is "filled" (reaches a certain level of liquefaction) before the expected arrival of the occupants.

[0084] Pre-charged thermal energy storage

[0085] In the traditional method, the heat extracted by the heat pump from the environment (such as the outside air) and from the compression process of the refrigerant is transferred directly from the working fluid of the heat pump to water (such as tap water), such as stored in an insulated water storage tank, and then the hot water in the water storage tank is supplied to various water outlets when needed. One disadvantage of this traditional method is that the heat pump needs time to transfer a sufficient amount of heat to the water in the water tank to bring the water to the required temperature. Therefore, heat pump water heaters are generally installed together with traditional electric resistance water heaters, which can heat the water to the required temperature when the heat pump does not heat the water sufficiently.

[0086] According to an embodiment of the present invention, the thermal energy storage medium in the thermal energy storage 150 is used to store heat extracted by the heat pump 140, and the stored heat can be used to heat water when needed. In this embodiment, the thermal energy storage medium can be pre-charged by running the heat pump to transfer heat to the thermal energy storage before the demand for hot water occurs. This may be desirable in situations where the demand for hot water and / or the demand for electricity fluctuates throughout the day. For example, running the heat pump and / or the electric resistance water heater when the demand for hot water is high may be cost-prohibitive and may cause additional pressure on the energy network when the demand is high.

[0087] Figure 4 One embodiment of an MLA 4200 implemented on a control module (e.g., control module 110) is schematically shown, which processes a set of input data to output a decision to pre-charge a thermal energy storage medium to increase its temperature to a desired operating temperature. MLA 4200 can be the same MLA as MLA 2200 and / or MLA 3200, or a different MLA. MLA 4200 can be trained using an appropriate training data set, such as based on a house's hot water demand.

[0088] The MLA 4200 receives input data specific to the house and its occupants from a variety of sources through the control module, including one or more sensors arranged around the house, one or more user interfaces (e.g., control panels around the house that communicate with the control module, smart devices, personal computers, etc.), one or more software programs, one or more public and private databases, etc. In this embodiment, the MLA 4200 receives input of the current time and date 4101, such as from a clock and / or calendar on the control module; and energy demand data, such as the current energy cost 4102 that specifies the unit price of energy, such as data obtained from the energy supplier that provides energy to the house during off-peak periods when the unit cost of energy is lower.

[0089] Alternatively or in addition, the MLA 4200 may derive energy demand data from the energy utility usage patterns 2300 and occupancy forecasts 3300 established as described above. For example, if the current energy usage of the house is lower than the average level over a period of time, such as a day, the current energy usage may be considered low; conversely, if the current energy usage of the house is higher than the average level, the current energy usage may be considered high.

[0090] Then, based on the energy cost information 4102 received from the energy supplier (and any other energy demand data), the MLA 4200 can determine the current energy demand level and activate the heat pump when the current energy demand is deemed low, pre-charging the thermal energy storage 4300 in preparation for providing hot water in advance of increased demand for hot water, for example, when occupants are expected to arrive at the house and / or when hot water demand is expected to rise in the evening.

[0091] In addition, using the received time / date 4101 and the energy utility usage pattern 2300 and occupancy forecast 3300, the MLA 4200 can predict one or more parameters, such as expected hot water usage levels and expected energy usage levels. The MLA 4200 can then determine the amount of heat that needs to be stored in the thermal energy storage medium based on the predicted parameters. For example, if the expected hot water usage level is expected to be high and last for a long time, the MLA 4200 can operate the heat pump for a sufficient period of time in advance of the expected increase in demand to pre-charge the thermal energy storage medium to a temperature above the normal operating temperature setting (such as set by the occupants or installer) to store sufficient energy for continued hot water use.

[0092] This embodiment utilizes a heat pump by enabling the water supply system to predict the expected demand for hot water so that a stored heat source is prepared in advance of increased demand, which otherwise may not respond quickly enough if the heat pump is activated only when demand arises. In addition, using current energy costs as input data, the thermal energy storage can be pre-charged during low energy demand periods when unit energy costs are low, and the pressure on the energy network can be relieved by shifting energy use from high demand periods to low demand periods. This embodiment is also suitable for self-sufficient households because the demand for hot water and electricity tends to rise and fall in parallel throughout the day. Therefore, shifting the electricity used to run the heat pump to times when electricity demand is low can make the self-sufficient household run more smoothly. Overall, this embodiment is able to use a more efficient form of hot water supply, namely a heat pump, at a lower cost, and with almost no disadvantages of delays caused by heating the water to the required temperature.

[0093] Hot water demand forecasting

[0094] Figure 5 Schematically illustrates an embodiment of an MLA 5200 implemented on a control module (e.g., control module 110) that is trained to determine whether to activate a heat pump based on the use of cold water. MLA 5200 may be the same MLA as MLA 2200 and / or MLA 3200 and / or MLA 4200, or may be a different MLA.

[0095] The MLA 5200 receives input data specific to the house and its occupants from a plurality of sources through the control module, including one or more sensors arranged around the house, one or more user interfaces (e.g., control panels around the house that communicate with the control module, smart devices, personal computers, etc.), one or more software programs, one or more public and private databases, etc. During the training phase, the MLA 5200 can be trained to recognize the correlation between the usage of hot water and the usage of cold water. For example, the MLA 5200 can be trained to recognize the correlation between the use of cold water in the bathroom (e.g., toilet tank water use) followed by the demand for hot water in the bathroom faucet (e.g., hand washing). Thus, during the training phase, the MLA 5200 can use sensor data related to the use of hot water immediately following the use of cold water to establish the degree of correlation between the two events. For example, the sensor data can include the elapsed time between receiving the first sensor data and receiving the second sensor data, the position of the second water outlet relative to the first water outlet, the frequency of receiving the second sensor data after receiving the first sensor data, the time of day, the day of the week, but this list is not exhaustive.

[0096] In this embodiment, the MLA 5200 receives an input 5101 of an activated cold water outlet, and based on the current cold water usage associated with the established energy utility usage pattern 2300 and the occupancy forecast 3300, the MLA 5200 can determine the probability of a hot water demand that may be caused by the current cold water usage based on the degree of correlation of the current cold water usage. If an expected hot water demand is determined, the MLA 5200 can instruct the control module to activate the heat pump 5300 to meet the demand.

[0097] In order to determine whether the probability of a hot water demand that may occur with current cold water usage is high enough to justify expending energy to operate a heat pump to pre-charge the thermal energy storage medium, the MLA 5200 may establish a threshold during the training phase that indicates when the probability justifies activating the heat pump. In one embodiment, the threshold may be manually established by an occupant or installer, with manual input of the circumstances that require a prediction of activating the heat pump. In another embodiment, the threshold may be established by the MLA 5200 based on the energy utility usage pattern 2300 and / or the occupancy forecast 3300.

[0098] In another embodiment, the MLA 5200's determination of whether to activate the heat pump may also be based on input of current energy costs 5102, such as input obtained from an energy supplier. In this embodiment, the threshold may be determined based on energy costs obtained from an energy supplier during a training phase. Alternatively or in addition, during operation, the threshold may be modified based on current energy costs. For example, if the energy cost 5102 indicates an off-peak cost, meaning that the electric heating element 160 may operate at a low cost, and the MLA 5200 determines that there is a low correlation between current cold water usage and the expected demand for hot water, the MLA 5200 may determine that there is no need to activate the heat pump to pre-charge the thermal energy storage because hot water is unlikely to be needed; if hot water is needed, the electric heating element 160 may be used to heat the water. On the other hand, if the current energy cost 5102 indicates a peak rate when the unit energy cost is high, which means that it is more expensive to heat water using the electric heating element 160, and if the MLA 5200 determines that the correlation between the current cold water usage and the expected demand for hot water is low, the MLA 5200 may determine that activating the heat pump to pre-charge the thermal energy storage to prepare for the hot water demand is less expensive despite the lower correlation, thereby avoiding the more expensive option of providing hot water using the electric heating element 160. In the latter example, the MLA 5200 may modify the threshold value so that it is lower than the threshold value in the former example, so that the heat pump may be activated in the latter example even though the probability of the two cases is the same.

[0099] By preparing the water supply system in advance of the need for hot water, delays in hot water delivery can be reduced, thereby reducing the waste of clean water by reducing the time occupants have to turn on the tap while waiting for the water to heat. In addition, by predicting the expected demand for hot water and operating the heat pump in advance of the need for hot water, and predictively preparing the storage heat source, it is possible to utilize heat pumps as a reliable form of hot water delivery, reducing or even eliminating the inherent delays.

[0100] Defrost prediction

[0101] As described above, a heat pump such as heat pump 140 includes an outdoor unit having a heat exchanger coil that extracts heat from the outdoor air or ground and transfers it to an indoor unit, either directly to the interior of a building for heating, or to a thermal energy storage medium for storage for later use. The process of extracting heat energy from the outdoor air causes the heat exchanger coil in the outdoor unit to cool, and moisture in the air condenses on the cooled outdoor coil. In cold outdoor conditions, such as when the outdoor air temperature is 5°C, the outdoor coil may cool below freezing and frost may form on the outdoor coil. When frost accumulates on the outdoor coil, the efficiency of the heat pump decreases, requiring a greater temperature difference with the outdoor air to output the same power compared to a frost-free coil. Therefore, it is best to run the heat pump for a defrost cycle regularly when frost accumulates in order to remove frost from the heat exchanger coil of the heat pump outdoor unit.

[0102] Many factors affect when a heat pump will need a defrost cycle, such as the outdoor temperature and humidity, the heat pump's power output, and the condition of the heat pump (for example, older systems may be less efficient and require more frequent defrosts). Generally speaking, a heat pump will run a defrost cycle whenever frost forms on the outdoor heat exchanger coils.

[0103] During the defrost cycle, the heat pump operates in reverse, sending hot refrigerant to the outdoor unit to defrost the heat exchanger coil. For example, the heat pump may operate the defrost cycle until the coil temperature reaches about 15°C. Once the heat exchanger coil is thawed, the heat pump may resume the normal heating cycle. Obviously, when the heat pump is operating the defrost cycle, it will not be able to perform its normal function of transferring heat to the indoor unit (e.g., to the thermal energy storage 150) until the defrost cycle is completed. Therefore, it may be desirable to prepare the water supply system and / or the building before the heat pump defrost cycle is initiated.

[0104] Figure 6An embodiment of an MLA 6200 implemented on a control module (e.g., control module 110) is schematically shown, which processes a set of input data to predict the next defrost cycle of a heat pump (e.g., heat pump 140). MLA 6200 can be the same MLA as MLA 2200 and / or MLA 3200 and / or MLA 4200 and / or MLA 5200, or can be a different MLA.

[0105] The MLA 6200 receives house specific input data from a plurality of input sources through the control module, including one or more sensors arranged around the house, one or more user interfaces (e.g., control panels around the house that communicate with the control module, smart devices, personal computers, etc.), one or more software programs, one or more public and private databases, etc. During the training phase, the MLA 6200 can be trained to recognize when a defrost cycle is required and establish a schedule and average energy demand for operating the heat pump during a defrost cycle based on, for example, weather forecasts, current weather conditions, indoor temperature, and data collected from previous defrost cycles, combined with knowledge of the performance of the heat pump (e.g., the average thermal output of the heat pump, the efficiency or coefficient of performance of the heat pump, and any other relevant information or quantity related to the performance of the heat pump).

[0106] In this embodiment, the MLA 6200 receives inputs of a weather forecast 6101, such as may be obtained from the public domain or from a weather application on a smart device registered with the control module; current weather conditions 6102, such as inputs of temperature and humidity, such as may be obtained from the public domain or from one or more sensors disposed around the house; indoor temperature 6103, such as may be obtained from one or more temperature sensors disposed indoors; and data relating to the last defrost cycle 6104 when the heat pump was last defrosted. Based on the weather forecast, current weather conditions, and indoor temperature, the MLA 6200 may predict the expected time of the next defrost cycle 6301, such as when a prolonged period of low temperatures and high humidity occurs, a defrost cycle may be required soon, and may estimate the length of time required to defrost the heat pump. In addition, using the established energy utility usage patterns 2300 and occupancy forecasts 3300, the MLA 6200 can estimate the expected energy and hot water demand during the predicted defrost cycle and prepare the water supply system 6302 in advance of the predicted defrost cycle, such as by storing additional thermal energy in the thermal energy storage (in addition to the latent heat in the PCM, energy can also be stored as sensible heat), heating the house to a temperature above a preset temperature, etc. Specifically, the control module can start operating the heat pump to store thermal energy in the thermal energy storage sufficient time in advance of the expected start time of the next defrost cycle based on the prediction of the MLA 6200, to pre-charge the thermal energy storage until the thermal energy storage is completely filled (reaching a predetermined or optimal operating temperature). In addition, because the MLA 6200 can estimate the length of time required for the heat pump to defrost, the water supply system can be prepared by pre-charging the thermal energy storage to ensure that sufficient thermal energy is stored for hot water supply during the duration of the defrost cycle.

[0107] Alternatively or in addition, the MLA 6200 can also predict when demand for energy and hot water (e.g., for faucets, showers, and / or central heating) is low and determine an appropriate time to defrost a heat pump, such as a time when there is less disruption to the supply of hot water to occupants. Using these inputs, the MLA 6200 can determine time periods when water and energy demand is low (e.g., nighttime) and / or time periods when occupancy is low (e.g., school and work hours) and adjust the expected start time of the next defrost cycle to the determined low demand times and / or low occupancy times. The MLA 6200 can then instruct the control module to run the heat pump to start a defrost cycle 6301 at the adjusted start time. For example, if the MLA 6200 predicts that a defrost cycle may be required in the early evening hours when energy and hot water demand are expected to be high, the MLA 6200 may pre-charge the thermal energy storage medium by operating a heat pump to store more energy, such as by raising the temperature of the thermal energy storage medium to a higher operating temperature, and diverting some heat to pre-heat the building prior to the predicted defrost cycle, and / or the MLA 6200 may adjust the start time of the defrost cycle to later in the evening when demand is expected to be lower. In another example, if a defrost cycle is expected to occur during the day, the MLA 6200 may determine, based on occupancy forecasts and / or usage patterns, that the next defrost cycle is during a time period when energy and hot water demand are low, such as when occupancy is expected to be low or zero, and thus determine that no preparation or adjustment is required.

[0108] By predicting the next defrost cycle of the heat pump and predictively preparing the water supply system before the defrost cycle is initiated based on, for example, the performance of the heat pump, the weather forecast, current weather conditions, current indoor temperature, expected occupancy rate and demand for hot water, the present embodiment allows the necessary heat pump defrost cycle to be performed in a manner that less disrupts the hot water supply, thereby enabling the heat pump to be used as an efficient way to provide hot water.

[0109] Cold water recommendations

[0110] In one embodiment, a method and system for monitoring and interactively modifying occupants' water usage habits are provided. These methods can be implemented by MLA7200. MLA 7200 can be the same MLA as MLA 2200 and / or MLA 3200 and / or MLA 4200 and / or MLA 5200 and / or MLA 6200, or it can be a different MLA. During the training phase, MLA 7200 obtains data related to water use, such as the water consumption of the occupants of the house, to establish a normal water use pattern. In addition, MLA 7200 can also be trained to recognize or identify instances in normal usage patterns: opening the water outlet to provide hot water at a temperature T1 set by the occupant, but then closing the water outlet before the water is heated to T1. This is particularly relevant when hot water is provided by a heat pump, because there may be a situation where, when the heat pump is activated, the energy extracted by the heat pump must first heat the thermal energy storage medium to the required operating temperature before the thermal energy storage medium can fully heat the water. If the heat pump activates in response to a demand for hot water, but the water outlet is shut off before the water is heated to the desired temperature, the energy (electricity) used to run the heat pump is wasted because the occupants do not actually get hot water. Given this, the MLA7200 can be trained to employ one or more energy-saving strategies when such a short-term instance is determined.

[0111] Reference Figure 7 , at S7001, the occupant sets the water temperature of the water outlet to T1 and opens the water outlet. At S7002, the control module determines that the water outlet is open, for example, by using one or more sensors to detect changes in water pressure or water flow at a water source supplying water to the water supply system, and the control module at S7003 runs MLA 7200 to monitor changes in water temperature at the water outlet. Subsequently, the control module at S7004 determines that the water outlet is closed, and the MLA 7200 at S7005 determines whether the water temperature has reached T1 set by the user during the time period when the water outlet is opened. If so, the method ends and no further action is taken.

[0112] If it is determined at S7005 that the water temperature has not reached T1 during the time period when the water outlet is open, the MLA 7200 may employ one or more energy saving strategies. In one embodiment, the MLA 7200 activates a software function at S7006 to generate a notification to notify the occupant that the water has not reached the preset temperature before the water outlet is closed. The MLA 7200 may choose to record the event at S7007.

[0113] Subsequently, the occupant may again set the water temperature of the same water outlet to T1 and open the water outlet. Before or when determining to open the water outlet, in this embodiment, the MLA 7200 identifies the water use instance as a short-term instance in which the water temperature cannot reach T1 before the user closes the water outlet, and then activates the software function to generate a prompt signal to prompt the user to set the water temperature to a lower temperature T2 or use cold water instead of hot water. The prompt signal may be, for example, a flash at or near the water outlet, the generation of a predetermined sound or tone, a verbal and / or visual prompt (e.g., playing a message or image), etc. The MLA 7200 may determine such a short-term instance based on the established usage pattern, or use one or more indicators to identify such a short-term instance. For example, the MLA 7200 may use the location of the water outlet or the time when hot water is needed as an indicator. As another example, the MLA 7200 may predetermine the correlation between the cold water usage before such a short-term hot water usage instance, such as when the toilet is flushed and then refilled, and the subsequent hot water demand for hand washing, and use such cold water usage as an indicator.

[0114] Therefore, according to this embodiment, when the occupant requests hot water, but the hot water is not used long enough to heat the water to the corresponding temperature, the occupant will be reminded. In addition, when the next use time may be shorter, the user will be prompted to use lower temperature water or cold water instead of hot water, so that the user can avoid requesting hot water from the water supply system when it may not be necessary, thereby wasting energy. Therefore, this embodiment can interactively modify the occupant's hot water usage habits to reduce energy usage.

[0115] exist Figure 8 In another supplementary or optional embodiment shown, the occupant sets the water temperature to T1 again and opens the water outlet at S8001. Before or when the control module determines that the water outlet is open at S8002, the MLA 7200 identifies the water use instance as a short-term instance and adopts an additional or optional energy-saving strategy by causing the control module to change the temperature setting of the water outlet from T1 to a lower temperature T2. Temperature T2 may be lower than T1, but still heatable, or T2 may represent unheated cold water from the tap. Under the control of the control module, the water supply system outputs water with a temperature of T2 to the water outlet at S8003.

[0116] Therefore, according to this embodiment, when the MLA 7200 identifies a short-term instance, the control module will actively reduce the water temperature. By reducing the water temperature, the energy required to heat the water is reduced. In doing so, this embodiment reduces energy consumption when hot water is not needed.

[0117] exist Fig. 9In another supplementary or optional embodiment shown, the occupant sets the water temperature to T1 again and opens the water outlet at S9001. Before or when the control module determines that the water outlet is open at S9002, the MLA 7200 identifies the water usage instance as a short-term instance and adopts an additional or optional energy-saving strategy by causing the control module to adjust the flow rate of the water outlet to a lower flow rate. Under the control of the control module, the water supply system outputs water to the water outlet at a lower flow rate at S9003.

[0118] Therefore, according to this embodiment, when the MLA identifies a short-term instance, the control module actively reduces the water flow. This embodiment is particularly relevant when the water is heated, for example, by an electric heating element, because by reducing the water flow, the amount of water that needs to be heated is reduced, and the energy required to heat the amount of water used is also reduced. In this way, this embodiment reduces both water consumption and energy consumption.

[0119] Water leak warning

[0120] Fig.10 Schematically illustrates an embodiment of an MLA 1200 implemented on a control module (e.g., control module 110) that processes a set of sensor data to output a water leak warning for a specific building. MLA 1200 may be the same MLA as MLA 2200 and / or MLA 3200 and / or MLA 4200 and / or MLA 5200 and / or MLA 6200 and / or MLA 7200, or may be a different MLA.

[0121] The MLA 1200 receives house specific input data from multiple input sources through the control module, including one or more sensors arranged around the house, one or more user interfaces (e.g., control panels around the house that communicate with the control module, smart devices, personal computers, etc.), one or more software programs, one or more public and private databases, etc. In this embodiment, the MLA 1200 receives input of the current time and date 1101, such as from a clock and calendar function running on the control module or remotely received through a communication network, and then by using the established energy facility usage pattern 2300 and occupancy rate forecast 3300, the MLA 1200 can estimate the expected water consumption for the current time and date. In addition, the MLA 1200 receives inputs of tap water inlet temperature 1102, tap water flow rate 1103, and tap water pressure 1104, such as measured by sensors at the tap water inlet of the house, and determines the real-time water consumption. The MLA 1200 may then determine whether the current water usage is in line with expectations based on the expected usage and the real-time usage, and if the current water usage exceeds the expected usage, the MLA 1200 outputs a water leak warning 1300. The MLA 1200 may be pre-trained to identify whether the current water usage exceeding the expected water usage is related to a leak in the system or an unexpected increase in demand, such as a change in weather or an increase in occupant demand. In some embodiments, the MLA 1200 may be provided with a threshold, and if the difference between the water usage and the expected usage exceeds the threshold, it is considered a water leak. Optionally, the MLA 1200 may establish such a threshold during a training phase, or adjust the threshold during use, for example based on user feedback.

[0122] By establishing patterns in energy utility usage and monitoring current water usage based on that pattern, potential leaks in the system can be detected and early warnings given to occupants before the leaks become more severe so that remedial or corrective action can be taken.

[0123] The various MLAs described above may refer to the same or different MLAs. If there are multiple MLAs, one or some or all of the MLAs may be implemented on the control module 110, and one or some or all of the MLAs may be implemented on a server (e.g., a cloud server) that communicates with the control module 110 through a communication channel. It should be understood by those skilled in the art that the above embodiments may be implemented in any combination, parallel, or optional strategies as needed.

[0124] Those skilled in the art should understand that the present technology can be embodied as a system, method or computer program product. Accordingly, the present technology can be in the form of complete hardware, complete software or a combination of hardware and software.

[0125] In addition, the present technology can also be in the form of a computer program product, embedded in a computer-readable medium with a computer-readable program code. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or apparatus, or any suitable combination of the above media.

[0126] 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.

[0127] For example, the program code used to execute the operation of the present technology may include source code, object code or executable code in a conventional programming language (interpreted or compiled) such as C language, or include assembly code, or include code for setting or controlling ASIC (application-specific integrated circuit) or FPGA (field programmable gate array), or hardware description language code such as VerilogTM or VHDL (very high speed integrated circuit hardware description language).

[0128] The program code may be implemented entirely on the user's computer; partly on the user's computer and partly on a remote computer; or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer through any type of network. Code components may be embodied as procedures, methods, etc., and may include subcomponents, which may take the form of instructions or sequences of instructions at any level of abstraction, from direct machine instructions of the native instruction set to high-level compiled or interpreted language structures.

[0129] It is also clear to those skilled in the art that the logic method according to the preferred embodiment of the present technology can be embodied in whole or in part in a logic device, which includes 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 device can also be embodied as an enabling element for temporarily or permanently establishing a logic structure in such an array or circuit using a virtual hardware description language, which can be stored and transmitted using a fixed or transmittable carrier medium.

[0130] The examples and conditional language described herein are intended to help the reader understand the principles of the present invention, rather than to limit its scope to such specifically cited examples and conditions. It should be understood that a person skilled in the art may design various arrangements that, although not explicitly described or shown herein, embody the principles of the present invention and are included within the scope defined by the appended claims.

[0131] In addition, to aid understanding, the above description may describe relatively simplified embodiments of the present invention. As will be appreciated by those skilled in the art, various embodiments of the present invention may be more complex.

[0132] In some cases, useful examples of modifications to the present technology may also be set. This is done only to aid understanding, and once again, it is emphasized that this does not limit the scope of the invention or set forth the limits of the invention. These modifications are not exhaustive, and those skilled in the art may still make other modifications within the scope of the invention. In addition, where no modification examples are listed, it should not be interpreted as impossible to modify and / or that what is described is the only way to implement this element of the invention.

[0133] In addition, all statements of the principles, aspects, and implementations of the technology described herein and specific examples thereof are intended to encompass structural and functional equivalents thereof, whether currently known or developed in the future. Thus, for example, those skilled in the art will appreciate that any block diagram herein is a conceptual view of an illustrative circuit that embodies the principles of the present invention. 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 implemented by a computer or processor, whether or not the computer or processor is explicitly shown.

[0134] The functions of the various elements shown in the figure, including any functional blocks marked as "processors", can be implemented by using dedicated hardware as well as hardware capable of implementing software together with appropriate software. When provided by a processor, the functions can be provided by a single dedicated processor, a single shared processor, or 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 implementing software, which 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.

[0135] 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 process steps and / or text descriptions. Such modules may be implemented by hardware explicitly or implicitly shown.

[0136] 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 method for performing a defrost cycle of a heat pump, the heat pump being included in a water supply system installed in a building, the water supply system comprising: The heat pump, a thermal energy storage medium, at least one valve and a control module, wherein the heat pump is configured to transfer thermal energy from outside a building to the thermal energy storage medium inside the building, wherein the control module includes at least one processor that executes software instructions to control the operation of the heat pump, the thermal energy storage medium and the at least one valve, and provide water heated by the thermal energy storage medium to occupants of the building at one or more water outlets, such that the heat energy of the heat pump heats the water only indirectly through the thermal energy storage medium. The method for performing a heat pump defrost cycle includes: determining, by at least one processor of the control module, an expected start time of a next heat pump defrost cycle based on the performance of the heat pump, wherein the expected start time is a time predicted by at least one processor in the control module before the next defrost cycle starts; and The thermal energy storage medium is precharged to store thermal energy in the thermal energy storage medium by operating the heat pump for a predetermined period of time prior to an expected start time of a next defrost cycle of the heat pump such that the thermal energy storage medium reaches a first temperature above its normal operating temperature prior to the expected start time. 2 . The method of claim 1 , wherein the method is performed at least in part by a first machine learning algorithm MLA implemented on the control module, the first machine learning algorithm MLA having been trained to predict a next defrost cycle.

3. The method according to claim 1 or 2, wherein the information related to the performance of the heat pump includes data related to the average thermal energy output of the heat pump, the efficiency of the heat pump, the coefficient of performance of the heat pump, or a combination thereof. 4 . The method of claim 1 , further comprising receiving weather data, wherein the expected start time of the next defrost cycle is further determined based on the weather data. 5 . The method of claim 4 , wherein the weather data comprises one or more of: a weather forecast, current weather conditions, an indoor temperature of the building, or a combination thereof.

6. The method of claim 1 or 2, further comprising collecting data related to one or more previous defrost cycles of the heat pump, wherein the expected start time of the next defrost cycle is further determined based on the collected data.

7. The method of claim 1, wherein the water supply system comprises a central heating system for raising the indoor temperature of the building, and wherein preparing the water supply system comprises raising the indoor temperature of the building by operating the heat pump to supply hot water to the central heating system before the expected start time.

8. The method of claim 7, wherein the water supply system includes one or more electrical resistance heating elements, and wherein increasing the indoor temperature of the building includes operating the one or more electrical resistance heating elements to supply hot water to the central heating system.

9. The method according to claim 7 or 8, wherein increasing the indoor temperature of the building comprises increasing the indoor temperature of the building from a current temperature to a second temperature.

10. The method of claim 9, wherein the second temperature is higher than a preset indoor temperature set by an occupant.

11. A method according to claim 7 or 8, wherein precharging the thermal energy storage medium and / or raising the indoor temperature of the building is performed based on an expected demand for hot water determined by an energy facility usage pattern established by a second machine learning algorithm MLA for the water supply system based on sensor data obtained from the water supply system.

12. The method of claim 11, wherein the energy utility usage pattern comprises expected chilled water usage associated with time, day of the week and / or date, expected hot water usage associated with time, day of the week and / or date, expected energy usage associated with time, day of the week and / or date, or a combination thereof.

13. The method of claim 11, further comprising determining a low demand time around an expected start time of the next defrost cycle based on the energy utility usage pattern when expected demand for hot water is low.

14. A method according to claim 7 or 8, wherein precharging the thermal energy storage medium and / or increasing the indoor temperature of the building is performed based on an expected occupancy rate of the building, and the expected occupancy rate is determined by a third machine learning algorithm MLA for the water supply system based on sensor data acquired by the water supply system.

15. The method of claim 14, further comprising determining a low occupancy time around an expected start time of the defrost cycle based on the expected occupancy when an expected occupancy of the building is low.

16. The method of claim 11, wherein the sensor data comprises time of day, day of the week, date, water flow rate and / or pressure at the one or more water outlets, time elapsed since the water outlet was opened, tap water temperature, water temperature at the one or more water outlets, energy consumption amount and / or rate, current location of the user, or a combination thereof. 17 . The method of claim 13 , further comprising adjusting an expected start time of the defrost cycle to an adjusted start time based on the low demand time. 18 . The method of claim 15 , further comprising adjusting an expected start time of the defrost cycle to an adjusted start time based on the low occupancy time.

19. The method of claim 17 or 18, further comprising operating the heat pump at the adjusted start time to initiate a next defrost cycle.

20. A computer readable medium comprising machine readable code, which, when executed by a processor, causes the processor to perform the method of any one of claims 1 to 19.

21. A control module configured for controlling a water supply system, the control module comprising a processor having a machine learning algorithm trained to implement the method of any one of claims 1 to 19.

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