Method and system for predictive preparation of water supply systems

Abstract

The present disclosure relates to a computer-implemented method for predictively preparing a water supply system installed in a building, the water supply system including a heat pump configured to transfer thermal energy from an ambient environment to a thermal energy storage medium and including a control module 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 users of the building at one or more water outlets, the method being performed by the control module and including: receiving a current location of the user; estimating an expected arrival time of the user to the building based on the current location; and determining an expected utilization of the building based on the expected arrival time.

Description

Technical Field

[0001] The present disclosure relates generally to utility management. In particular, the present disclosure 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 needed all day, all year round, in both commercial and domestic environments. It goes without saying that providing hot water requires both clean water and a heat source. To provide hot water, a heating system is usually provided to a centralized water supply system to heat the water to a predetermined temperature, such as set by the user, and the heat source used is usually one or more electric heating elements or burning natural gas. Generally speaking, during periods of high energy (such as natural gas or electricity) demand, utility providers will implement peak rates, which increase the unit cost of energy, in part to cover the additional cost of having to purchase more energy to supply to customers, and in part to discourage unnecessary energy use. Then, during periods of low energy demand, utility providers will implement off-peak rates, thereby reducing the unit cost of energy to encourage customers to use energy during these off-peak hours rather than peak hours, thereby achieving an overall more balanced energy consumption over time. However, such strategies will only be effective if customers are always aware of rate changes and make conscious efforts to change their energy consumption habits.

[0003] Clean water as a public utility is currently receiving a lot of attention. As clean water becomes increasingly scarce, great efforts have been made to educate the public about protecting clean water and to develop systems and devices that reduce water usage, 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 habits.

[0004] With growing concern about the environmental impact of energy consumption, 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 a heat pump requires electricity to do the job of transferring thermal energy from a heat source to a heat reservoir, it is generally more efficient than a resistance heater (electric heating element) because it typically has a coefficient of performance of at least 3 or 4. This means that for the same amount of electricity used, a heat pump can provide up to 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 from a ground source (e.g., a ground loop or a borehole filled with water) is transferred to the contained refrigerant, for example, via a heat exchanger. The now higher energy refrigerant is compressed, causing its temperature to rise significantly, and this now hot refrigerant then exchanges the thermal energy to the hot water circuit via a heat exchanger. In the case of providing hot water, the heat extracted by the heat pump can be transferred to water in an insulated tank that acts as a thermal energy store, and the hot water can be used later when needed. As needed, the heated water can be transferred to one or more outlets, such as taps, showers, radiators. However, heat pumps generally require more time to raise the water to the desired temperature than electric resistance heaters, partly because heat pumps are generally slow to start.

[0006] Because different homes, workplaces, and commercial spaces have different requirements and preferences for hot water use, new ways of hot water delivery are needed to make heat pumps a practical alternative to electric heaters. In addition, to conserve energy and water, it may be necessary to regulate energy and clean water consumption; however, regulating utility consumption cannot simply limit usage.

[0007] It would therefore be desirable to provide improved methods and systems for providing heated water. Summary of the invention

[0008] One aspect of the present technology provides a computer-implemented method for predictively preparing a water supply system installed in a building, the water supply system including a heat pump configured to transfer thermal energy from an ambient environment to a thermal energy storage medium and including a control module 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 including: receiving a current location of the user; estimating an expected arrival time of the user to the building based on the current location; determining an expected occupancy of the building based on the expected arrival time; and enabling the heat pump to begin storing thermal energy in the thermal energy storage medium for a predetermined length of time before the expected arrival time, thereby storing thermal energy in the thermal energy storage medium.

[0009] According to the present embodiment, the control module is able to estimate when a user is expected to arrive at a building based on the user's current location (e.g., a GPS signal received from the user's smartphone). This information can be used in a number of different ways, such as to prepare the building for use, such as heating the building on cold days, cooling the building on hot days, and storing heat in a thermal energy storage medium in advance so that the stored thermal energy can be extracted for later use. Compared to traditional methods of preparing the hot water system in advance (e.g., to provide hot water to central heating) based on a preset schedule (which cannot take into account daily, weekly or one-time changes) or based on manual remote activation by the user (which may not be practical, for example, if the user is driving), determining or estimating the arrival time based on the current location of one or more users provides a more accurate indication of the expected use of the building, which in turn enables the water supply system to be operated in a more energy-efficient manner, such as by pre-charging the thermal energy storage medium to enable the heat pump to be used more efficiently as a heating method for water or the building, but without unnecessarily running the water heating system (e.g., for central heating) when it is not needed. By pre-charging the thermal energy storage medium in advance of an expected rise in utilization, the delay in using the heat pump as a means of heating water, which is normally caused by the thermal energy storage medium needing to reach a certain operating temperature, can be reduced, thereby enabling the heat pump to be used as an efficient means of heating water.

[0010] In some embodiments, the method may be performed at least in part by a first machine learning algorithm MLA executed on the control module, the first MLA having been trained to predict utilization of the building based on the user's current location.

[0011] In some embodiments, receiving the user's current location may include communicating with an electronic device previously registered with the control module, including one or more of a smartphone, a tablet computer, and / or a satellite navigation system.

[0012] In these embodiments, a user may register one or more electronic devices with the control module as part of an initial setup process. For example, the registration process may enable the control module to identify the one or more electronic devices when the one or more electronic devices attempt to wirelessly communicate with the control module via a communication channel to transmit information such as a current location, current traffic conditions, and / or an expected arrival time.

[0013] In some implementations, estimating the expected arrival time may include obtaining the expected arrival time from the electronic device.

[0014] In some implementations, the expected arrival time may be estimated based on current traffic conditions at the current location.

[0015] In some embodiments, the predetermined time length may be set based on expected utilization, wherein the predetermined time length is set longer for higher expected utilization. Higher utilization may require more hot water, such as for heating and general use (e.g., washing hands, showering), and in order to meet the higher demand for hot water, the thermal energy storage medium may be pre-charged to a higher operating temperature by running the heat pump for a longer time before the user is expected to arrive at the building.

[0016] In some embodiments, the predetermined length of time may be set based on expected hot water usage estimated using utility usage patterns established by the second MLA for the water supply system based on sensor data obtained from the water supply system. In some cases, low utilization does not mean low demand for hot water, for example, when users frequently shower after arriving at the building or use hot water to prepare meals before other users arrive. By pre-charging the thermal energy storage medium for a length of time based on expected hot water usage, it is possible to ensure that sufficient hot water is provided by the heat pump and reduce the use of, for example, electric heating elements.

[0017] In some embodiments, utility usage patterns may include: expected cold water usage with respect to time, week, and / or date; expected hot water usage with respect to time, week, and / or date; expected energy usage with respect to time, week, and / or date; or a combination of the above.

[0018] In some embodiments, the method may further include transferring a portion of the thermal energy extracted by the heat pump to heat the building from a current indoor temperature to a first indoor temperature, wherein the predetermined length of time is set based on the current indoor temperature and the first indoor temperature. A portion of the thermal energy extracted by the heat pump may be used directly to heat the building in cold weather, or may be used to heat the building in cold weather by circulating hot water in a central heating system. The amount of thermal energy required to heat the building to the preset first indoor temperature will depend on the current indoor temperature. Therefore, by pre-charging the thermal energy storage medium for a length of time based on the current indoor temperature and the preset first temperature, it is possible to ensure that the building reaches the first temperature when the user arrives, while storing a sufficient amount of energy in the thermal energy storage medium to provide hot water.

[0019] In some implementations, the predetermined length of time may be manually set by a user.

[0020] In some embodiments, it may be desirable for the thermal energy storage medium to be pre-charged to an optimal operating temperature before a user arrives. Thus, in some embodiments, the predetermined length of time may be set based on the time for the thermal energy storage medium to reach the optimal operating temperature.

[0021] In some embodiments, the time at which the thermal energy storage medium reaches the optimal operating temperature may be estimated based on an average of a plurality of previous times at which the thermal energy storage medium reaches the optimal operating temperature.

[0022] While the average time to pre-charge the thermal energy storage medium over multiple previous occasions is a good indication of the length of time required when conditions are similar, there may be situations where, for example, weather conditions change suddenly, the average time becomes a less accurate indication. Therefore, in some embodiments, the time when the thermal energy storage medium reaches the optimal operating temperature can be determined by a third MLA that is trained to determine the time when the thermal energy storage medium reaches the optimal operating temperature based on sensor data obtained from the water supply system.

[0023] In some embodiments, the sensor data may include indoor temperature, outdoor temperature, the time when the heat pump was last activated, or a combination thereof.

[0024] Another aspect of the present technology provides a computer readable medium including machine readable code, which when executed by a processor causes the processor to perform the method as described above.

[0025] Another aspect of the present technology provides a control module configured to control a water supply system, the control module comprising a processor having a machine learning algorithm running thereon, the machine learning algorithm being trained to perform the method described above.

[0026] In some embodiments, the control module may further include a communication interface configured to communicate with an electronic device registered on the control module via a communication channel, wherein the electronic device includes one or more of a smart phone, a tablet computer, and / or a satellite navigation system.

[0027] Each embodiment of the present technology 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 technology generated by attempting to achieve the above-mentioned purpose may not meet the purpose and / or may meet other purposes not specifically described herein.

[0028] Additional and / or alternative features, aspects, and advantages of embodiments of the present technology will become apparent from the following description, drawings, and appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Embodiments of the present disclosure will now be described with reference to the accompanying drawings, in which:

[0030] Figure 1 is a schematic system overview of an exemplary water supply system;

[0031] Figure 2schematically illustrates an exemplary training phase of an MLA for establishing usage patterns;

[0032] Figure 3 schematically illustrates exemplary data processing performed by the MLA to output a utilization prediction;

[0033] Figure 4 schematically illustrates exemplary data processing performed by the MLA to pre-charge a thermal storage;

[0034] Figure 5 schematically illustrates exemplary data processing by the MLA to enable a heat pump;

[0035] Figure 6 schematically illustrates exemplary data processing performed by the MLA to initiate a heat pump defrost cycle;

[0036] Figure 7 is a flow chart of an exemplary method of changing a user's water usage habits according to an embodiment;

[0037] Figure 8 is a flow chart of an exemplary method of regulating water usage according to an embodiment;

[0038] Fig. 9 is a flow chart of another exemplary method of regulating water usage according to an embodiment; and

[0039] Fig.10 Exemplary data processing by the MLA to output a leakage alert is schematically illustrated. DETAILED DESCRIPTION

[0040] In view of the foregoing, the present disclosure provides various methods for providing hot water using a heat pump or assisted by a heat pump, and in some cases a method for regulating the use of utilities including 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 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 in a home environment and establish a normal usage pattern. The MLA can be trained to identify different types of water use (e.g., showering, washing hands, heating, etc.) based on a variety of different inputs, such as a time of day, a day of the week, a date, weather, etc. In some embodiments, the MLA can collect additional data, such as the time when the system outlet is opened and closed, the duration of use, the water temperature set by the user, and the actual water temperature when the hot water is provided to the user. In use, the MLA can use the learned usage patterns in various different ways to improve the efficiency and effectiveness of using a heat pump or assisted by a heat pump to provide hot water.

[0041] 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 implement one or more interactive strategies to help change water and energy usage habits, such as gradually reducing water and / or energy usage.

[0042] A brief overview of a number of different types of machine learning algorithms for embodiments 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 embodiments, the control module can be programmed with appropriate software functions to target specific hot water usage, such as excessive water flow, and respond in a predetermined manner.

[0043] MLA Overview

[0044] There are many different types of MLA known in the art. Broadly speaking, MLA is divided into three types: MLA based on supervised learning, MLA based on unsupervised learning, and MLA based on reinforcement learning.

[0045] 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 input to the desired output. The training process is continued 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.

[0046] Unsupervised learning MLA itself does not involve predicting the target or outcome variable. This type of MLA is used to cluster value groups into different groups and is widely used to segment customers into different groups for specific interventions. Examples of unsupervised learning MLA include: Apriori algorithm, K-means.

[0047] Reinforcement learning MLAs are trained to make specific decisions. During training, the MLA is exposed to a training environment where it continuously 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.

[0048] 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).

[0049] Neural Network (NN)

[0050] In general, 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 those relationships) or to find patterns in data. NNs are first conditioned in a training phase, where the neural network is provided with a known set of "inputs" and information to adapt the NN to generate appropriate outputs (for a given situation that it is trying to model). During this training phase, the given NN adapts to the situation that is 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 it has learned). Therefore, rather than trying to determine a complex statistical arrangement or mathematical algorithm for a given situation, the given NN provides an "intuitive" answer based on a "feeling" of the situation. Therefore, a given NN is considered a trained "black box" and a given NN can be used to determine reasonable answers for a given set of inputs in situations where it does not matter what happens in the "box".

[0051] Neural networks are often used in many such situations where it is only necessary to know the output given the input, but how the output is derived is of little or no importance. For example, NNs are often used to optimize the distribution of network traffic between servers and to optimize data processing, including filtering, clustering, signal separation, compression, vector generation, etc.

[0052] Deep Neural Networks

[0053] In some non-limiting embodiments of the present technology, the NN can be implemented as a deep neural network. It should be understood that the NN can be divided into various categories of NNs, and one of these categories includes a recurrent neural network (RNN).

[0054] Recurrent Neural Networks (RNNs)

[0055] RNNs are adapted to process sequences of inputs using their "internal states" (stored memory). 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.

[0056] 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 (GRU), Bidirectional RNNs (BRNNs), etc.

[0057] LSTM networks are deep learning systems that can learn tasks that, in a sense, require "memorizing" previous events that occurred in very short and discrete time steps. The topology of LSTM networks may 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 learn to perform tasks where events occur together at both 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 and future states to be used.

[0058] Residual Neural Network (ResNet)

[0059] Another example of a NN that can be used to implement non-limiting embodiments of the present technology is a residual neural network (ResNet).

[0060] Deep networks naturally integrate low / mid / high-level features and classifiers in an end-to-end multi-layer manner, and the “levels” of features can be enriched by the number of stacked layers (depth).

[0061] In summary, the implementation of at least a portion of one or more MLAs in the context of the present technology can be roughly divided into two phases - a training phase and an in-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 will be run using the data being used in the in-use phase.

[0062] Water supply system

[0063] In an embodiment of the present technology, cold and hot water is provided by a central water supply system to multiple water outlets in a building in a home or commercial environment, including taps, showers, radiators, etc. 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. The control module 110 is communicatively coupled to the various elements of the water supply system and is configured to control the various elements of the water supply system, the various elements of the water supply system including a flow control member 130, for example in the form of one or more valves, 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 member 130 is arranged to control the water flow inside and outside the system, 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 one or more electric heating elements 160 are configured to directly heat cold water to a desired temperature by controlling the amount of energy supplied to the electric heating element 160. The heated water, whether heated by the thermal energy storage 150 or by the electric heating element 160, is then directed to one or more water outlets as needed and when needed. In an embodiment, the heat pump 140 extracts heat from the environment (e.g., ambient air for an air source heat pump, geothermal energy for a ground source heat pump, or heat from a body of water for a water source heat pump), which is absorbed by the refrigerant and then transferred from the refrigerant to a working fluid, which in turn transfers the heat to a thermal energy storage medium within the thermal energy storage 150, where the heat is preferably stored as latent heat. The energy from the thermal energy storage medium can then be used to heat cooler water, such as cold water from a water source (which may be a tap water source), to a desired temperature. The heated water can then be supplied to various water outlets in the system.

[0064] 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, for example, 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 linked to the water supply system 100, such as a GPS signal receiver, a calendar, a weather forecast application located on, for example, a smartphone carried by a user and communicating with the control module through 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 flow of water through the flow control member 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 executed 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 based on, for example, time of day, day of the week, date (e.g., seasonal variations, public holidays), utilization, etc. The learned usage patterns may then be used to determine and, in some cases, improve various control functions performed by the control module 110.

[0065] While heat pumps are generally more energy efficient at heating water than electrical resistance heaters, it takes time to start the heat pump (typically a minute or more), and it also takes time to transfer a sufficient amount of thermal energy to the thermal energy storage medium so that the storage medium reaches the desired operating temperature and can then be used to heat the water; therefore, from an initial starting 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 when heated. In this case, if it has been allowed to solidify, additional time may be required for the PCM to change from a solid to a liquid before the thermal energy extracted by the heat pump has the effect of raising the temperature of the thermal storage medium (until this point the energy is stored as latent heat). 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.

[0066] Phase change materials

[0067] In this embodiment, the phase change material can be used as a heat storage medium for a heat pump. One class of suitable phase change materials is paraffin wax, which has a solid-liquid phase change at temperatures of interest for domestic hot water supply and which can be used 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 waxes can be found to melt at different temperatures within this range to suit specific applications. Typical latent heat capacities are between about 180 kJ / kg and 230 kJ / kg, and specific heat capacities are about 2.27 Jg in the liquid phase. -1 K -1 , in the solid phase is 2.1 Jg -1 K -1 As can be seen, very large amounts of energy can be stored using the latent heat of fusion. Even more energy can also be stored by heating the phase change liquid above its melting point. For example, during off-peak hours when electricity costs are relatively low, a heat pump can be operated to "charge" the thermal energy storage to a higher than normal temperature to "superheat" the thermal energy storage.

[0068] Suitable waxes may be waxes having 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 about 51°C and be able to heat water to a temperature of about 45°C, which is satisfactory for general domestic hot water, and water of about 45°C is sufficient for kitchen taps, shower / bathroom taps. If necessary, cold water can be added to the water flow to reduce the water temperature. The temperature performance of the heat pump is taken into account. Generally, the maximum temperature difference between the input and output temperatures of the fluid heated by the heat pump is preferably maintained in the range of 5°C to 7°C, although it can be as high as 10°C.

[0069] 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 thermal energy storage systems, such as existing latent thermal energy storage systems. In this article, salt hydrates are mixtures of inorganic salts and water, whose phase change involves the loss of all or most of the 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 their thermal conductivity is much higher than that of paraffin (up to 2 to 5 times higher) and the volume change during the phase change is much 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.

[0070] Usage Mode

[0071] Figure 2 The training phase of the MLA 2200 (eg, the MLA 120 ) is shown to establish a baseline utility usage pattern, according to an embodiment.

[0072] In this embodiment, the MLA 2200 receives inputs from multiple sensors and other sources over a period of time to understand, for example, usage patterns of users of a house. For example, a control module (e.g., control module 110) on which the MLA 2200 executes may include a clock, and the MLA 2200 may receive a time of day 2101 and a date and day of the week 2102 from the clock. The house may be installed with multiple motion sensors, and the MLA 2200 may receive utilization data 2103 from the motion sensors. In another embodiment described below, utilization may also be predicted based on multiple factors. The control module may communicate with one or more outdoor temperature sensors for the MLA 2200 to receive input of current weather 2104. The control module may also communicate with one or more indoor temperature sensors for the MLA 2200 to receive indoor temperature 2105. A plurality of water temperature, pressure and flow sensors may be provided at various locations in the water supply system, such as at the mains water inlet to measure the mains water inlet temperature 2106, mains flow 2107 and mains flow pressure 2108, which may be input to the MLA 2200. Sensors may be provided at one or more or each outlet (or valve controlling the flow of water to an outlet) to detect when the corresponding outlet is open and when it is closed, and the temperature of the water at the outlet, and data relating to hot / cold water usage time and temperature 2109 and hot / cold water usage volume 2110 may be input to the MLA 2200. The MLA 2200 may also collect data on the energy usage 2111 of the water supply system, such as time of use, amount of energy used, and, if the control module is in communication with an energy supplier, current rates. The MLA 2200 may also collect data on heat pump usage 2112, such as time of use, length of use, etc. It should be noted that the MLA does not necessarily receive, collect and / or use all of the input sensor data described herein, and the list of input sensor data described herein is not exhaustive, and the MLA may also receive, collect and / or use other input data as needed. Specifically, in embodiments where the control module communicates with, for example, one or more smart devices (e.g., smartphones) or one or more users' personal computers, the MLA may receive and use other personal or public data obtained from these devices.

[0073] During the training phase, the MLA 2200 establishes water and energy usage patterns for the user 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, utilization patterns, and usage patterns that provide a baseline for expected usage based on, for example, time of day, day of the week, date, utilization level, etc.

[0074] Leverage predictions

[0075] Figure 3 Schematically illustrated is an implementation of an MLA 3200 executing on a control module (e.g., control module 110) that processes a set of input data to output a utilization forecast for, for example, a house. MLA 3200 may be the same MLA as MLA 2200 or may be a different MLA. MLA 3200 may be trained using an appropriate training data set, such as based on utilization levels and projections of users arriving at the house over a year.

[0076] The MLA 3200 receives input data specific to the premises and its users from a number of sources, including one or more sensors arranged around the premises, one or more user interfaces (e.g., control panels around the premises that communicate with the control module, smart devices, personal computers, etc.), one or more software programs, one or more public and private databases, etc., through the control module. In this embodiment, the MLA 3200 receives inputs of the current time 3101, date 3102, and day of the week 3103, for example, from a clock and calendar function running on the control module or remotely running through a communication network. The MLA 3200 also receives inputs of any special events or public holidays 3104, for example, from the users of the premises through the user interface, automatically obtained from a calendar application on the user's smart device, or obtained from the public domain through the communication network. The MLA 3200 then determines the expected utilization level based on the input data and outputs a utilization forecast 3300. By determining the expected utilization of the building, the likely utility (e.g., energy and water) demand can be estimated or predicted.

[0077] In another embodiment, when it is determined that the user is not in the house, the MLA 3200 receives input of the current location 3105 of one or more users. For example, the user may register one or more smart devices (such as smart phones) with a GPS function to the control module or a server in communication with the control module, and then the MLA 3200 may receive the current location of each user by acquiring the GPS signal received on the registered smart device corresponding to each user via the communication network. Then, based on the user'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 user to arrive at the house. The expected arrival time 3106 of each user can 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. The MLA 3200 can then use the expected arrival time 3106 to output a utilization forecast 3300 for the house (future utilization level instead of the current utilization level).

[0078] When the control module performs various control functions of the water supply system, the use of prediction 3300 is a useful indicator. For example, hot water can be directed to the radiator of the central heating system installed in the house before the expected arrival of the user. Another example is to start the heat pump before the expected arrival of the user to begin storing thermal energy in the thermal energy storage; further, the heat pump can be started at a time based on the expected arrival time 3106 of the user, so that the thermal energy storage is "filled" (reaching a certain degree of liquefaction) before the expected arrival of the user.

[0079] Pre-charging thermal energy storage

[0080] In the conventional method, heat extracted from the outside air and from the compression of the refrigerant by the heat pump is transferred directly from the working fluid of the heat pump to water (e.g., from the mains), such as water stored in an insulated storage tank, from which hot water is supplied to the various water outlets as needed. One disadvantage of this conventional method is the time required for the heat pump to transfer a sufficient amount of heat from the outside air (or whatever energy source the heat pump extracts energy from) to the water in the tank to reach the desired temperature. As a result, heat pump water heaters are often installed in conjunction with conventional electric resistance water heaters, which sometimes heat the water to the desired temperature when the water has not yet been sufficiently heated by the heat pump.

[0081] According to an embodiment of the present technology, the thermal energy storage medium in the thermal energy storage 150 is configured to store heat extracted by the heat pump 140, and the stored heat can be used to heat water when needed. In this embodiment, before the demand for hot water occurs, the thermal energy storage medium can be pre-charged by operating the heat pump to transfer heat to the thermal energy storage. This may be desirable in situations where the demand for hot water and / or the demand for electricity fluctuates throughout the day, such that, for example, operating the heat pump and / or the electric resistance water heater when the demand for hot water is high may not be cost-effective and may place additional pressure on the energy network during times of high demand.

[0082] Figure 4 Schematically illustrated is an embodiment of an MLA 4200 executing on a control module (e.g., control module 110) that processes a set of input data to output a decision to pre-charge a thermal energy storage medium to raise its temperature to a desired operating temperature. MLA 4200 may be the same MLA as MLA 2200 and / or MLA 3200, or it may be a different MLA. MLA 4200 may be trained using an appropriate training data set, for example, based on a house's hot water demand.

[0083] The MLA 4200 receives input data specific to the house and its users 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. In this embodiment, the MLA 4200 receives input of the current time and date 4101, for example, from a clock and / or calendar on the control module, and obtains energy demand data, such as current rates 4102, for example, from an energy supplier that supplies energy to the house, which specifies the unit cost of energy during off-peak hours when the unit cost of energy is lower.

[0084] Alternatively or additionally, the MLA 4200 may derive energy demand data from the utility usage patterns 2300 established as described above and the utilization forecasts 3300. For example, if the current energy usage of the house is below the average level over a period of time (e.g., a day), then the current energy usage may be considered low; conversely, if the current energy usage of the house is above the average level, then the current energy usage may be considered high.

[0085] Then, based on the received rate information 4102 obtained from the energy supplier (and any other energy demand data), the MLA 4200 can determine the current energy demand level and, when the current energy demand is deemed low, enable the heat pump to pre-charge the thermal energy storage 4300 in preparation for providing hot water before the demand for hot water arises (e.g., when a user is expected to arrive at the house and / or when hot water demand is expected to rise in the evening).

[0086] In addition, using the received time / date 4101 and the utility usage pattern 2300 and utilizing the forecast 3300, the MLA 4200 may predict one or more parameters, such as an expected level of hot water usage and an expected level of energy usage. The MLA 4200 may then determine an amount of thermal energy to be stored in the thermal energy storage medium based on the predicted parameters. For example, if the level of expected hot water usage is expected to be high and remain high for an extended period of time, the MLA 4200 may operate the heat pump for a sufficient period of time in advance of the expected rise in demand in order to pre-charge the thermal energy storage medium to a temperature above the normal operating temperature set, for example, by the user of the installer, thereby storing a sufficient amount of energy for continued hot water usage.

[0087] 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 a rise in demand, the present embodiment enables the heat pump to be used in situations where it might not otherwise be able to respond adequately if enabled only at the time of demand. Furthermore, with current tariffs as input, the heat pump can be operated to pre-charge the thermal energy store during periods of low energy demand when the unit cost of energy is low, and relieve pressure on the energy network by shifting energy use from times of high demand to times of low demand. The present embodiment is also applicable to self-sufficient households, as the demand for hot water and electricity typically rises and falls throughout the day. Therefore, shifting the use of electricity used to operate the heat pump to times when electricity demand is low can enable the self-sufficient household to run more smoothly. Overall, the present embodiment enables a more efficient form of hot water provision, namely a heat pump, to be used at a lower cost, and with few of the disadvantages caused by the delay in heating the water to the desired temperature.

[0088] Hot water demand forecasting

[0089] Figure 5 An embodiment of an MLA 5200 executed on a control module (e.g., control module 110) is shown as an example, and the MLA 5200 is trained to determine whether to enable a heat pump based on the use of cold water. MLA 5200 can be the same MLA as MLA2200 and / or MLA3200 and / or MLA4200, or it can be a different MLA.

[0090] The MLA 5200 receives premises-specific input data from a plurality of inputs through a control module, including one or more sensors arranged around the premises, one or more user interfaces (e.g., control panels around the premises that communicate with a control module, a smart device, a personal computer, etc.), one or more software programs, one or more public and private databases, etc. During a training phase, the MLA 5200 may be trained to recognize correlations between subsequent hot water usage and cold water usage. For example, the MLA 5200 may be trained to recognize correlations between cold water usage in a bathroom (e.g., a water pitcher to fill a toilet) and subsequent hot water demand from a faucet in the bathroom (e.g., for hand washing). Thus, during a training phase, the MLA 5200 may use sensor data associated with hot water usage following cold water usage to establish a degree of correlation between the two events. The sensor data may, for example, include the time elapsed 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, a time of day, a day of the week, but this list is not exhaustive.

[0091] In this embodiment, the MLA 5200 receives input of the enabled cold water outlet 5101, and based on the current cold water usage associated with the established utility usage pattern 2300 and the utilization forecast 3300, the MLA 5200 can determine the likelihood of a hot water demand based on the degree of correlation between the current cold water usage and the hot water usage after the current cold water usage. If an expected demand for hot water is determined, the MLA 5200 can instruct the control module to enable the heat pump 5300 in the event of an expected demand.

[0092] In order to determine whether the likelihood of a demand for hot water following current cold water usage is high enough to warrant expending energy to operate a heat pump to pre-charge the thermal energy storage medium, the MLA 5200 may establish a threshold value during the training phase that indicates when the likelihood warrants enabling the heat pump. In one embodiment, the threshold value may be manually established by a user or installer manually inputting instances when such predictive enabling of the heat pump is desired. In another embodiment, the threshold value may be established by the MLA 5200 based on utility usage patterns 2300 and / or utilizing predictions 3300.

[0093] In another embodiment, the MLA 5200 determines whether to enable the heat pump may also be based on input such as current rates 5102 obtained from an energy provider. In this embodiment, the threshold may be determined based on rate information obtained from the energy provider during a training phase. Alternatively or additionally, the threshold may be modified during runtime based on the current rates. For example, if the current rates 5102 indicate an off-peak rate, meaning that the electric heating element 160 may operate at a low cost, and the MLA 5200 determines a low correlation between current cold water usage and expected demand for hot water, the MLA 5200 may determine that there is no need to enable the heat pump to pre-charge the thermal energy storage because there is unlikely to be a demand for hot water; if there is a demand for hot water, the electric heating element 160 may be used to heat the water. On the other hand, if the current rate 5102 indicates a peak rate when the unit cost of energy is high, which means that heating water using the electric heating element 160 will be expensive, and if the MLA 5200 determines that there is only a low correlation between the current cold water usage and the expected hot water demand, the MLA 5200 may determine that it is more cost-effective to enable the heat pump to pre-charge the thermal energy storage to prepare for the hot water demand (although the correlation is low), 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 enabled in the latter example even though the probability of both cases is the same.

[0094] By preparing the water supply system before hot water is needed, delays in hot water supply can be reduced, thereby reducing the waste of clean water by reducing the time the water outlet remains open while the user waits for the water to be heated. In addition, by predicting the expected demand for hot water and predictively preparing the storage heat source by operating the heat pump before hot water is needed, the heat pump can be utilized as a reliable form of hot water supply, thereby reducing or even eliminating the inherent delays.

[0095] Predictive defrost

[0096] 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 outside air or ground and transfers it directly to an indoor unit, either to the interior of a building to heat it, or to a thermal energy storage medium for storage for later use. The process of extracting thermal energy from the outdoor air cools the heat exchanger coil in the outdoor unit, and moisture in the air condenses on the cool outdoor coil. In cold outdoor conditions, such as when the outdoor air is 5°C, the outdoor coil cools below freezing and frost forms on the outdoor coil. As 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 desirable to run the heat pump in a defrost cycle periodically and when frost accumulates to remove frost from the heat exchanger coil in the outdoor unit of the heat pump.

[0097] 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, an older system 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 coil.

[0098] During the defrost cycle, the heat pump runs in reverse, sending warm refrigerant to the outdoor unit to defrost the heat exchanger coil. The heat pump can run the defrost cycle until, for example, the coil reaches about 15°C. Once the heat exchanger coil is thawed, the heat pump can resume the normal heating cycle. Obviously, when the heat pump is running a 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 necessary to prepare the building accordingly before the heat pump defrost cycle begins.

[0099] Figure 6Schematically illustrates an implementation of an MLA 6200 executed on a control module (e.g., control module 110) that processes a set of input data to predict the next defrost cycle of a heat pump (e.g., heat pump 140). MLA 6200 may be the same MLA as MLA 2200 and / or MLA 3200 and / or MLA 4200 and / or MLA 5200, or it may be a different MLA.

[0100] The MLA 6200 receives house-specific input data from a plurality of inputs 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 to establish a time scale and average energy requirement for operating the heat pump during a defrost cycle, based, for example, on weather forecasts, current weather conditions, indoor temperature, and data collected from previous defrost cycles, as well as an understanding of the performance of the heat pump (e.g., the average thermal energy output of the heat pump, the efficiency or coefficient of performance of the heat pump, and any other information or quantity related to the performance of the heat pump).

[0101] In this embodiment, the MLA 6200 receives the following inputs, namely, a weather forecast 6101 obtained, for example, from the public domain or a weather application on a smart device registered with the control module, current weather conditions 6102 such as temperature and humidity obtained, for example, from the public domain or from one or more sensors arranged around the house, indoor temperature 6103 obtained, for example, from one or more temperature sensors arranged inside the house, and data related to the last defrost cycle 6104 when the heat pump was most recently defrosted. On this basis, the MLA 6200 can. Based on the weather forecast, current weather conditions and indoor temperature, the MLA 6200 can predict when the next defrost cycle can be performed 6301, for example, when there is a long period of low temperature and high humidity, a defrost cycle may need to be performed more quickly, and the length of time required to defrost the heat pump can be estimated. In addition, using established utility usage patterns 2300 and utilizing forecasts 3300, the MLA 6200 can estimate expected energy and hot water demand during the time when a defrost cycle is predicted to occur and prepare the water supply system 6302 in anticipation of the predicted defrost cycle, such as by storing additional thermal energy in a thermal energy reservoir (in addition to latent heat in the PCM, by storing energy as sensible heat), heating the house to a temperature above a preset temperature, etc.

[0102] Additionally or alternatively, the MLA 6200 may also predict when demand for energy and hot water (e.g., for faucets, showers, and / or central heating) is low and determine appropriate timing for defrosting the heat pump, the determined appropriate timing, for example, causing less disruption to the supply of hot water to users. Using these inputs, the MLA 6200 may determine time periods when water and energy demand is low (e.g., at night) and / or utilization is low (e.g., during school and work hours) and adjust the expected start time of the next defrost cycle to the determined low demand times and / or low utilization times. The MLA 6200 may then instruct the control module to operate the heat pump to begin a defrost cycle at the adjusted start time 6301. For example, if the MLA 6200 predicts that a defrost cycle may be required in the evening hours when energy and hot water demand is expected to be high, the MLA 6200 may pre-charge the thermal energy storage medium by operating the heat pump to store more heat, such as by raising the temperature of the thermal energy storage medium to a higher operating temperature, and divert some of the heat to pre-heat the building before the predicted defrost cycle, and / or the MLA 6200 may adjust the defrost cycle start time 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 utilization forecasts and / or usage patterns that the next defrost cycle will be during a time period when energy and hot water demand is low, such as when utilization is expected to be low or zero, and the MLA 6200 determines that no preparation or adjustment is required.

[0103] By predicting the next defrost cycle of the heat pump based on, for example, the performance of the heat pump, weather forecast, current weather conditions, current indoor temperature, expected utilization and hot water demand, and predictively preparing the water supply system before the defrost cycle begins, the present embodiment allows the necessary heat pump defrost cycle to be performed in a manner that causes less disruption to the supply of hot water, thereby enabling the heat pump to be used as an efficient way to provide hot water.

[0104] Cold water recommendations

[0105] In one embodiment, methods and systems are provided to monitor and interactively change a user's water usage habits. The method can be implemented by MLA 7200. 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 a training phase, data related to water usage by, for example, a user of a house is provided to MLA 7200 to establish a normal water usage pattern, as described above. In addition, MLA 7200 can be trained to recognize or identify instances in a normal usage pattern, i.e., the water outlet is opened to provide hot water at a temperature T1 set by the user, but then the water outlet is closed until the water is heated to T1. This is particularly relevant when a heat pump is used to provide hot water, because there may be instances where, when the heat pump is started, the energy extracted by the heat pump must first heat the thermal energy storage medium to the desired operating temperature and then fully heat the water by the thermal energy storage medium. If the heat pump is activated in response to a demand for hot water, but the water outlet is closed until the water is heated to the desired temperature, the energy (electricity) used to operate the heat pump is wasted because the user does not actually receive hot water. In view of the foregoing, the MLA 7200 can be trained to employ one or more energy reduction strategies when such a short duration instance is determined.

[0106] Reference Figure 7 , at S7001, the user 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 opened, for example, by using one or more sensors to detect changes in water pressure or water flow at a water source supplied to the water supply system, and at S7003, the control module runs MLA 7200 to monitor changes in water temperature at the water outlet. Subsequently, at S7004, the control module determines that the water outlet is closed, and at S7005, MLA 7200 determines whether the water temperature reaches 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.

[0107] At S7005, if it is determined that the water temperature has not reached T1 during the time period when the water outlet is opened, the MLA 7200 may adopt one or more energy saving strategies. In an embodiment, at S7006, the MLA 7200 initiates a software function to generate a notification to inform the user that the water has not reached the preset temperature before the water outlet is closed. At S7007, the MLA 7200 optionally records the event.

[0108] Subsequently, the user can set the water temperature of the same water outlet to T1 again and open the water outlet. Before or when determining that the water outlet is opened, in this embodiment, 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 MLA7200 starts the software function to generate a prompt signal, prompting the user to set the water temperature to a lower temperature T2 or use cold water instead of hot water. The prompt signal can 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. MLA 7200 can determine such short-duration instances based on established usage patterns, or use one or more indications to identify such short-duration instances. For example, MLA 7200 can use the location of the water outlet or the time when hot water is needed as an indication. As another example, the MLA 7200 may predetermine a correlation between instances of cold water usage preceding such short duration hot water usage instances (e.g., when a toilet is flushed and then refilled) and subsequent hot water demand for hand washing, and use such cold water usage as an indication.

[0109] Thus, according to the present embodiment, the user is made aware of instances where they need hot water but have not used water long enough for the water to be heated to a certain temperature. Furthermore, in the next instance, when the duration may be shorter, the user is prompted to use lower temperature water or cold water instead of hot water, so that the user may choose to avoid wasting energy by requiring hot water from the water supply system when it is not necessary. Thus, the present embodiment can interactively change the user's hot water usage habits to reduce energy usage.

[0110] exist Figure 8 In another supplementary or alternative embodiment shown, at S8001, the user again sets the water temperature to T1 and opens the water outlet. Before or when the control module determines that the water outlet is opened at S8002, the MLA 7200 identifies the water use instance as a short duration instance and adopts an additional or alternative energy reduction strategy by causing the control module to change the temperature setting of the water outlet from T1 to a lower temperature T2. Temperature T2 can be a lower temperature than T1 but still heated, or T2 can represent unheated cold water from the mains. At S8003, under the control of the control module, the water supply system outputs water at temperature T2 to the water outlet.

[0111] Therefore, according to the present embodiment, when the MLA 7200 identifies a short duration instance, the control module proactively lowers the water temperature. By lowering the water temperature, less energy is required to heat the water. In doing so, the present embodiment reduces energy consumption in situations where hot water is not necessary.

[0112] exist Fig. 9In another supplementary or alternative embodiment shown, at S9001, the user sets the water temperature to T1 again and opens the water outlet. Before or when the control module determines that the water outlet is opened at S9002, the MLA 7200 identifies the water usage instance as a short duration instance and adopts an additional or alternative energy reduction strategy by causing the control module to adjust the flow rate of the water outlet to a lower flow rate. At S9003, under the control of the control module, the water supply system outputs water to the water outlet at a lower flow rate.

[0113] Therefore, according to this embodiment, when the MLA identifies a short duration 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, so that by reducing the water flow, less water needs to be heated and less energy is required to heat the amount of water used. In doing so, this embodiment reduces water and energy consumption.

[0114] Leakage Alert

[0115] Fig.10 Schematically illustrates an implementation of an MLA 1200 executed on a control module (e.g., control module 110), which processes a set of sensor data to output a leak alarm for a given 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 it may be a different MLA.

[0116] The MLA 1200 receives premises-specific input data from a plurality of inputs through the control module, including one or more sensors disposed about the premises, one or more user interfaces (e.g., control panels about the premises 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 the current time and date 1101, for example, from a clock and calendar function running on the control module or remotely running through a communication network, and then using the established utility usage patterns 2300 and utilizing the forecasts 3300, the MLA 1200 can estimate the expected water usage for the current time and date. In addition, the MLA 1200 receives inputs such as the mains water inlet temperature 1102, the mains water flow rate 1103, and the mains water pressure 1104 measured by appropriate sensors at the premises' mains water inlet, and determines real-time water usage. The MLA 1200 can 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 alert 1300. The MLA 1200 can be pre-trained to recognize whether the current water usage exceeding the expected usage is associated with a water leak in the system or is associated with an unexpected increase in demand (e.g., weather changes or increased utilization). In some embodiments, the MLA 1200 can be provided with a threshold, and water usage levels above the threshold that exceed the expected usage are considered leaks. The MLA 1200 can alternatively establish such thresholds during a training phase, or adjust the thresholds at the time of use, for example based on user feedback.

[0117] By establishing utility usage patterns and monitoring current water usage against that pattern, potential water leaks in the system can be detected and early warnings given to customers before the leaks become more serious so that remedial or corrective action can be taken.

[0118] The various MLAs described above may refer to the same or different MLAs. If multiple MLAs are implemented, one, some, or all of the MLAs may be executed on the control module 110, and one, some, or all of the MLAs may be executed on a server (e.g., a cloud server) that communicates with the control module 110 through a suitable communication channel. It should be understood by those skilled in the art that the above embodiments may be implemented in any combination, parallel, or alternative strategies as needed.

[0119] 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.

[0120] Furthermore, the present technology may take the form of a computer program product 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 electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, apparatus or device, or any suitable combination of the foregoing.

[0121] 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.

[0122] 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 in a hardware description language, such as Verilog™ or VHDL (very high speed integrated circuit hardware description language).

[0123] The program code may be executed entirely on the user's computer, partially on the user's computer and partially 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 that 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 structures.

[0124] 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 an application-specific integrated circuit. Such a logic arrangement can further be embodied as an enabling element that temporarily or permanently establishes a logic structure 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.

[0125] The examples and conditional language cited herein are intended to help the reader understand the principles of the present technology, 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 technology and are included within the scope defined by the appended claims.

[0126] 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.

[0127] In some cases, useful examples that are considered modifications to the present technology may also be described. This is done only to aid understanding and, again, is not intended to limit the scope of the present technology or to describe the boundaries of the present technology. These modifications are not an exhaustive list, and those skilled in the art may still make other modifications within the scope of the present technology. In addition, where no modification examples are described, it should not be interpreted that modifications are impossible and / or that what is described is the only way to implement the element of the present technology.

[0128] In addition, all statements describing the principles, aspects, and implementations of the technology and specific examples thereof herein are intended to cover their structural and functional equivalents, whether they are 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., represents various processes that can be substantially represented in a computer-readable medium and thus executed by a computer or processor, whether or not such a computer or processor is explicitly shown.

[0129] 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, and 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.

[0130] Software modules or simple modules implicitly being software may be represented herein as flow chart elements or any combination of other elements indicating the execution of processing steps and / or text descriptions. Such modules may be executed by hardware explicitly or implicitly shown.

[0131] It will be apparent to those skilled in the art that various improvements and modifications can be made to the foregoing exemplary embodiments without departing from the scope of the present technology.

Claims

1. A computer-implemented method for predictively preparing a water supply system, the computer-implemented method for predictively preparing a water supply system for predictively preparing a water supply system installed in a building, the water supply system comprising a heat pump configured to transfer thermal energy from an ambient environment to a thermal energy storage medium to store the thermal energy and comprising a control module configured to control the operation of the heat pump, the water supply system being configured to heat water using the thermal energy stored in the thermal energy storage medium and then supplying the heated water to users of the building at one or more water outlets, the method being performed by the control module and comprising: receiving a current location of the user; estimating an expected arrival time of the user to the building based on the current location; determining an expected utilization of the building based on the expected arrival time; determining, based on an expected increase in utilization of the building, to precharge the thermal energy storage medium to increase the temperature of the thermal energy storage medium to a desired operating temperature; as well as Prior to the expected arrival time, the heat pump is enabled to begin storing thermal energy in the thermal energy storage medium for a predetermined length of time, thereby precharging thermal energy in the thermal energy storage medium to the desired operating temperature, wherein the predetermined length of time is set based on the expected utilization, wherein for higher expected utilization, the predetermined length of time is set to be longer.

2. The method according to claim 1, wherein: The method is performed at least in part by a first machine learning algorithm running on the control module, the first machine learning algorithm having been trained to predict utilization of the building based on a current location of the user.

3. The method according to claim 1 or 2, wherein: Receiving the user's current location includes communicating with an electronic device previously registered with the control module, the electronic device including one or more of a smartphone, a tablet computer, and / or a satellite navigation system.

4. The method according to claim 3, wherein: Estimating the expected time of arrival includes obtaining the expected time of arrival from the electronic device.

5. The method according to claim 1 or 2, wherein: The expected arrival time is estimated based on current traffic conditions at the current location.

6. The method according to claim 1 or 2, wherein: The predetermined length of time is set based on expected hot water usage, the expected hot water usage being estimated using utility usage patterns established for the water system by a second machine learning algorithm based on sensor data obtained from the water system.

7. The method according to claim 6, wherein: The utility usage patterns include: expected chilled water usage with respect to time, week and / or date; expected hot water usage with respect to time, week and / or date; expected energy usage with respect to time, week and / or date; or a combination of the above.

8. The method according to claim 1 or 2, further comprising: A portion of the thermal energy extracted by the heat pump is diverted to heat the building from a current indoor temperature to a first indoor temperature, wherein the predetermined length of time is set based on the current indoor temperature and the first indoor temperature.

9. The method according to claim 1 or 2, wherein: The predetermined length of time is manually set by the user.

10. The method according to claim 1 or 2, wherein: The predetermined length of time is set based on the time it takes for the thermal energy storage medium to reach an optimum operating temperature.

11. The method according to claim 10, wherein: The time at which the thermal energy storage medium reaches the optimal operating temperature is estimated based on an average of a plurality of previous times at which the thermal energy storage medium reaches the optimal operating temperature.

12. The method according to claim 10, wherein: The time when the thermal energy storage medium reaches the optimal operating temperature is determined by a third machine learning algorithm, which is trained to determine the time when the thermal energy storage medium reaches the optimal operating temperature based on sensor data obtained from the water supply system.

13. The method according to claim 12, wherein: The sensor data includes: indoor temperature, outdoor temperature, time when the heat pump was last activated, or a combination thereof.

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 one of claims 1 to 13.

15. A control module, configured to control a water supply system, the control module comprising a processor, a machine learning algorithm running on the processor, the machine learning algorithm being trained to perform the method according to any one of claims 1 to 13.

16. The control module according to claim 15, further comprising a communication interface, wherein the communication interface is configured to communicate with an electronic device registered on the control module through a communication channel, wherein: The electronic device includes one or more of a smart phone, a tablet computer and / or a satellite navigation system.

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