WO2022168040A1 - Procédés et systèmes de détection de fuite d'eau - Google Patents

Procédés et systèmes de détection de fuite d'eau Download PDF

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Publication number
WO2022168040A1
WO2022168040A1 PCT/IB2022/051071 IB2022051071W WO2022168040A1 WO 2022168040 A1 WO2022168040 A1 WO 2022168040A1 IB 2022051071 W IB2022051071 W IB 2022051071W WO 2022168040 A1 WO2022168040 A1 WO 2022168040A1
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WO
WIPO (PCT)
Prior art keywords
water
mla
usage
provision system
control module
Prior art date
Application number
PCT/IB2022/051071
Other languages
English (en)
Inventor
Peter KONOWALCZYK
Original Assignee
Octopus Energy Group Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from GBGB2101678.7A external-priority patent/GB202101678D0/en
Application filed by Octopus Energy Group Limited filed Critical Octopus Energy Group Limited
Publication of WO2022168040A1 publication Critical patent/WO2022168040A1/fr

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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H15/00—Control of fluid heaters
    • F24H15/10—Control of fluid heaters characterised by the purpose of the control
    • F24H15/12—Preventing or detecting fluid leakage
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
    • F24D19/00—Details
    • F24D19/10—Arrangement or mounting of control or safety devices
    • F24D19/1006—Arrangement or mounting of control or safety devices for water heating systems
    • F24D19/1051—Arrangement or mounting of control or safety devices for water heating systems for domestic hot water
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H15/00—Control of fluid heaters
    • F24H15/10—Control of fluid heaters characterised by the purpose of the control
    • F24H15/172—Scheduling based on user demand, e.g. determining starting point of heating
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H15/00—Control of fluid heaters
    • F24H15/20—Control of fluid heaters characterised by control inputs
    • F24H15/238—Flow rate
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H15/00—Control of fluid heaters
    • F24H15/20—Control of fluid heaters characterised by control inputs
    • F24H15/265—Occupancy
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H15/00—Control of fluid heaters
    • F24H15/30—Control of fluid heaters characterised by control outputs; characterised by the components to be controlled
    • F24H15/305—Control of valves
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H15/00—Control of fluid heaters
    • F24H15/30—Control of fluid heaters characterised by control outputs; characterised by the components to be controlled
    • F24H15/305—Control of valves
    • F24H15/31—Control of valves of valves having only one inlet port and one outlet port, e.g. flow rate regulating valves
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H15/00—Control of fluid heaters
    • F24H15/30—Control of fluid heaters characterised by control outputs; characterised by the components to be controlled
    • F24H15/395—Information to users, e.g. alarms
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
    • F24D2220/00—Components of central heating installations excluding heat sources
    • F24D2220/04—Sensors
    • F24D2220/044—Flow sensors
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
    • F24D2220/00—Components of central heating installations excluding heat sources
    • F24D2220/04—Sensors
    • F24D2220/046—Pressure sensors
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H15/00—Control of fluid heaters
    • F24H15/20—Control of fluid heaters characterised by control inputs
    • F24H15/242—Pressure

Definitions

  • the present disclosure relates generally to utility management.
  • the present disclosure relates to methods and systems that can be used to help modify hot water usage habit of a user.
  • heated water is required throughout the day all year round. It goes without saying that the provision of heated water requires both clean water and a source of heat.
  • a heating system is provided to an often centralised water provision system to heat water up to a predetermined temperature e.g. set by a user, and the heat source used is conventionally one or more electric heating elements or burning of natural gas.
  • the heat source used is conventionally one or more electric heating elements or burning of natural gas.
  • utilities providers would implement a peak tariff which increases the unit cost of energy, partly to cover the additional cost of having to purchase more energy to supply to customers and partly to discourage unnecessary energy usage.
  • Clean water as a utility is currently receiving much attention.
  • As clean water becomes scarcer there has been much effort to educate the public on the conservation of clean water as well as development of systems and devices that reduce water consumption, such as aerated showers and taps to reduce water flow, showers and taps equipped with motion sensors that stop the flow of water when no motion is detected, etc.
  • systems and devices are restricted to a single specific use and only have limited impact on problematic water consumption habits.
  • a heat pump is a device that transfers thermal energy from a source of heat to a thermal reservoir.
  • a heat pump requires electricity to accomplish the work of transferring thermal energy from the heat source to the thermal reservoir, it is generally more efficient than electrical resistance heaters (electrical heating elements) as it typically has a coefficient of performance of at least 3 or 4. This means under equal electricity usage 3 or 4 times the amount of heat can be provided to users via heat pumps compared to electrical resistance heaters.
  • the heat transfer medium that carries the thermal energy is known as a refrigerant.
  • Thermal energy from the air e.g. outside air, or air from a hot room in the house
  • a ground source e.g. ground loop or water filled borehole
  • the now higher energy refrigerant is compressed, causing it to raise temperature considerably, where this now hot refrigerant exchanges thermal energy via a heat exchanger to a heating water loop.
  • heat extracted by the heat pump can be transferred to water in an insulated tank that acts as a thermal energy storage, and the heated water may be used at a later time when needed.
  • the heated water may be diverted to one or more water outlets, e.g. a tap, a shower, a radiator, as required.
  • a heat pump generally requires more time compared to electrical resistance heaters to get water up to the desired temperature.
  • An aspect of the present technology provides a computer-implemented method of detecting a leak in a water provision system, the water provision system comprising a heating system configured to heat water from the mains and a control module configured to control operation of the heating system, the water provision system being configured to provide water heated by the heating system to a user at one or more water outlets, the method being performed by the control module and comprising: determining an expected water usage of the water provision system based on an occupancy prediction; receiving sensor data indicating a current water usage of the water provision system measured by a sensor disposed at a mains inlet; determining whether the current water usage corresponds to a possible leakage in the water provision system based on the expected water usage of the water provision system; and upon determining that the current water usage corresponds to a possible leakage, initiating a warning to notify the user.
  • possible leakage in the water provision system is determined on the basis of current water usage with respect to an expect water usage.
  • the present approach is capable of early detection of a leakage by noticing changes in water usage before the leakage becomes serious enough to be directly detectable. It is therefore possible to provide early warning for a user to take remedial or corrective measures before the leakage becomes more serious, e.g. causes flooding.
  • the method may be performed by a first machine learning algorithm, MLA, executing on the control module, the first MLA having been trained to recognize a leakage in the water provision system based on sensor data obtained from the water provision system.
  • MLA machine learning algorithm
  • the method may further comprise determining, by a second MLA executing on the control module, the expected water usage of the water provision system based on a current time and/or date and a previously established utility usage pattern of the water provision system, wherein the second MLA has been trained to establish the utility usage pattern for the water provision system based on sensor data received by the control module.
  • the utility usage pattern may comprise an expected cold water usage in respect of time, day and/or date, an expected heated water usage in respect of time, day and/or date, an expected energy usage in respect of time, day and/or date, an expected occupancy in respect of time, day and/or date, or a combination thereof.
  • determining the expected water usage of the water provision system based on an occupancy prediction is performed by a third MLA executing on the control module, wherein the third MLA has been trained to provide the occupancy prediction based on sensor data received by the control module.
  • the sensor data indicating a current water usage may comprise a water temperature at the mains inlet, a water flow rate at the mains inlet, or a flow pressure at the mains inlet, or a combination thereof.
  • the warning may comprise a light signal, an audio signal, a verbal or multimedia warning, or a combination thereof.
  • the method may further comprise providing on a display an option for the user to switch off the mains inlet.
  • the method may further comprise automatically switching off the mains inlet if the user has not responded within a predetermined time.
  • determining whether the current water usage corresponds to a possible leakage may comprise determining whether the current water usage exceeds the expected water usage.
  • the method may further comprise determining an extent of the current water usage exceeding the expected water usage and comparing the extent with a leakage threshold.
  • control module may determine that the current water usage corresponds to a possible leakage when the extent of the current water usage exceeding the expected water usage is equalled to or above the leakage threshold.
  • the method may further comprise, upon determining that the current water usage does not exceed the expected water usage, continue monitoring the current water usage of the water provision system.
  • the method may further comprise adjusting the leakage threshold during run-time based on sensor data obtained from the water provision system and input from the user.
  • the leakage threshold By allowing the leakage threshold to be adjusted during run-time, it is possible to account for fluctuations in water usage due for example to an increase in occupancy, an unexpected increase or decrease in outdoor temperature, or any other factor that may temporarily or permanently change the normal water usage level.
  • the leakage threshold may be established by the first MLA during a training phase based on sensor data obtained from the water provision system, or the leakage threshold is set by a manufacturer or an installer of the water provision system.
  • the method may further comprise bypassing the step of initiating a warning to notify the user when it is determined that the current water usage meets one or more exception conditions.
  • the one or more exception conditions may comprise a drop in outdoor temperature exceeding a predetermined threshold within a predetermined period of time, or an increase in occupancy exceeding a predetermined number, or a combination thereof.
  • the heating system may comprise a heat pump configured to transfer thermal energy from the surroundingto a thermal energy storage medium, operation of the heat pump being controlled by the control module.
  • a further aspect of the present technology provides a computer-readable medium comprising machine-readable code which, when executed by a processor, causes the processor to perform the methods described above.
  • a yet further aspect of the present technology provides a control module configured to control operation of a water provision system over a communication channel, the water provision system comprising a heating system configured to heat water from the mains and controlled by the control module, the water provision system being configured to provide water heated by the heating system to a user at one or more water outlets, the control module comprising a processor having a machine learning algorithm executing thereon for performing the methods described above.
  • Implementations of the present technology each have at least one of the above- mentioned objects and/or aspects, but do not necessarily have all of them. It should be understood that some aspects of the present technology that have resulted from attempting to attain the above-mentioned object may not satisfy this object and/or may satisfy other objects not specifically recited herein.
  • Fig. 1 is a schematic system overview of an exemplary water provision system
  • Fig. 2 schematically shows an exemplary training phase of an MLA to establish a usage pattern
  • Fig. 3 schematically shows exemplary data processing by an MLA to output an occupancy prediction
  • Fig. 4 schematically shows exemplary data processing by an MLA to pre-charge a heat storage
  • Fig. 5 schematically shows exemplary data processing by an MLA to activate a heat pump
  • Fig. 6 schematically shows exemplary data processing by an MLA to initiate a heat pump defrost cycle
  • Fig. 7 is a flow diagram of an exemplary method of modifying water usage habit of a user according to an embodiment
  • Fig. 8 is a flow diagram of an exemplary method of modulating water usage according to an embodiment
  • Fig. 9 is a flow diagram of another exemplary method of modulating water usage according to an embodiment.
  • Fig. 10 schematically shows exemplary data processing by an MLA to output a leakage warning.
  • the present disclosure provides various approaches for the provision of heated water using or assisted by a heat pump, and in some cases for modulating the use of utilities including water and energy to reduce water and energy wastage.
  • the present approaches may be implemented through the use of one or more machine learning algorithm (MLA) trained to control and modulate water provision for a water provision system via a control module based on sensor data received from the water provision system.
  • MLA machine learning algorithm
  • the MLA may monitor heated water usage of a household in a domestic setting and establish a normal usage pattern.
  • the MLA may be trained to recognise different types of water usage (e.g.
  • the MLA may collect additional data, for example, on the time when a water outlet of the system is turned on and off, the duration of use, the water temperature set by the user and the actual water temperature when heated water is provided to the user.
  • the MLA may use the learned usage pattern in a variety of different ways to improve the efficiency and effectiveness of heated water provision using or assisted by a heat pump.
  • the MLA may be trained to implement one or more energysaving strategies when or before a water outlet is turned on, and optionally to implement one or more interactive strategies to help modify water and energy usage habits e.g. to gradually reduce water and/or energy usage.
  • the following gives a brief overview of a number of different types of machine learning algorithms for embodiments in which one or more MLAs are used.
  • a control module may be programmed with appropriate software functions to target specific heated water usage, e.g. excessive water flow, and to respond in a predetermined manner.
  • MLAs There are many different types of MLAs known in the art. Broadly speaking, there are three types of MLAs: supervised learning-based MLAs, unsupervised learning-based MLAs, and reinforcement learning based MLAs.
  • Supervised learning MLA process is based on a target - outcome variable (or dependent variable), which is to be predicted from a given set of predictors (independent variables). Using these set of variables, the MLA (during training) generates a function that maps inputs to desired outputs. The training process continues until the MLA achieves a desired level of accuracy on the validation data. Examples of supervised learning-based MLAs include: Regression, Decision Tree, Random Forest, Logistic Regression, etc.
  • Unsupervised learning MLA does not involve predicting a target or outcome variable per se. Such MLAs are used for clustering a population of values into different groups, which is widely used for segmenting customers into different groups for specific intervention. Examples of unsupervised learning MLAs include: Apriori algorithm, K-means.
  • Reinforcement learning MLA is trained to make specific decisions. During training, the MLA is exposed to a training environment where it trains itself continually using trial and error. The MLA learns from past experience and attempts to capture the best possible knowledge to make accurate decisions.
  • An example of reinforcement learning MLA is a Markov Decision Process.
  • MLAs having different structures or topologies may be used for various tasks.
  • MLAs includes artificial neural networks (ANN), also known as neural networks (NN). Neural Networks (NN)
  • a given NN consists of an interconnected group of artificial "neurons", which process information using a connectionist approach to computation.
  • NNs are used to model complex relationships between inputs and outputs (without actually knowing the relationships) or to find patterns in data.
  • NNs are first conditioned in a training phase in which they are provided with a known set of "inputs” and information for adapting the NN to generate appropriate outputs (for a given situation that is being attempted to be modelled).
  • the given NN adapts to the situation being learned and changes its structure such that the given NN will be able to provide reasonable predicted outputs for given inputs in a new situation (based on what was learned).
  • the given NN aims to provide an "intuitive" answer based on a "feeling" for a situation.
  • the given NN is thus regarded as a trained "black box", which can be used to determine a reasonable answer to a given set of inputs in a situation when what happens in the "box" is unimportant.
  • NNs are commonly used in many such situations where it is only important to know an output based on a given input, but exactly how that output is derived is of lesser importance or is unimportant.
  • NNs are commonly used to optimize the distribution of webtraffic between servers and in data processing, including filtering, clustering, signal separation, compression, vector generation and the like.
  • the NN can be implemented as a deep neural network. It should be understood that NNs can be classified into various classes of NNs and one of these classes comprises recurrent neural networks (RNNs).
  • RNNs recurrent neural networks
  • RNNs Recurrent Neural Networks
  • RNNs are adapted to use their "internal states" (stored memory) to process sequences of inputs. This makes RNNs well-suited for tasks such as unsegmented handwriting recognition and speech recognition, for example. These internal states of the RNNs can be controlled and are referred to as “gated” states or “gated” memories.
  • RNNs themselves can also be classified into various subclasses of RNNs.
  • RNNs comprise Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Bidirectional RNNs (BRNNs), and the like.
  • LSTM Long Short-Term Memory
  • GRU Gated Recurrent Unit
  • BRNNs Bidirectional RNNs
  • LSTM networks are deep learning systems that can learn tasks that require, in a sense, "memories” of events that happened during very short and discrete time steps earlier. Topologies of LSTM networks can vary based on specific tasks that they "learn” to perform. For example, LSTM networks may learn to perform tasks where relatively long delays occur between events or where events occur together at low and at high frequencies. RNNs having particular gated mechanisms are referred to as GRUs. Unlike LSTM networks, GRUs lack “output gates” and, therefore, have fewer parameters than LSTM networks. BRNNs may have "hidden layers" of neurons that are connected in opposite directions which may allow using information from past as well as future states.
  • Residual Neural Network (ResNet)
  • NN Another example of the NN that can be used to implement non-limiting embodiments of the present technology is a residual neural network (ResNet).
  • ResNet residual neural network
  • Deep networks naturally integrate low/mid/high-level features and classifiers in an end- to-end multilayer fashion, and the "levels" of features can be enriched by the number of stacked layers (depth).
  • the implementation of at least a portion of the one or more MLAs in the context of the present technology can be broadly categorized into two phases - a training phase and an in-use phase.
  • the given MLA is trained in the training phase using one or more appropriate training data sets.
  • the given MLA learned what data to expect as inputs and what data to provide as outputs, the given MLA is run using in-use data in the in-use phase.
  • cold and heated water is provided by a centralized water provision system to a plurality of water outlets, including taps, showers, radiators, etc., for a building in a domestic or commercial setting.
  • An exemplary water provision system according to an embodiment is shown in Fig. 1.
  • the water provision system 100 comprises a control module 110.
  • the control module 110 is communicatively coupled to, and configured to control, various elements of the water provision system, including flow control 130 for example in the form of one or more valves arranged to control the flow of water internal and external to the system, a (ground source or air source) heat pump 140 configured to extract heat from the surroundings and deposit the extracted heat in a thermal energy storage 150 to be used to heat water, and one or more electric heating elements 160 configured to directly heat cold water to a desired temperature by controlling the amount of energy supplied to the electric heating elements 160. Heated water, whether heated by the thermal energy storage 150 or heated by the electric heating elements 160, is then directed to one or more water outlets as and when needed.
  • the heat pump 140 extracts heat from the surroundings into a thermal energy storage medium within the thermal energy storage 150.
  • the thermal energy storage medium may in addition be heated by other sources.
  • the thermal energy storage medium is heated until it reaches a desired operation temperature, then cold water e.g. from the mains can be heated by the thermal energy storage medium to the desired temperature.
  • the heated water may then be supplied to various water outlets in the system.
  • the control module 110 is configured to receive input 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 disposed indoor and/or outdoor, one or more water temperature sensors, one or more water pressure sensors, one or more timers, one or more motion sensors, and may include other sensors not directly linked to the water provision system 100 such as a GPS signal receiver, calendar, weather forecasting app on e.g. a smartphone carried by an occupant and in communication with the control module via a communication channel.
  • the control module 110 is configured, in the present embodiment, to use the received input to perform a variety of control functions, for example controlling the flow of water through the flow control 130 to the thermal energy storage 150 or electric heating elements 160 to heat water.
  • a machine learning algorithm (MLA) 120 is used, which may execute on a processor (not shown) of the control module 110 or execute on a server which communicates with the processor of the control module 110 over a communication channel.
  • the MLA 120 may be trained using the input sensor data received by the control module 110 to establish a baseline water and energy usage pattern based e.g. on the time of the day, the day of the week, the date (e.g. seasonal changes, public holiday), occupancy, etc.
  • the learned usage pattern may then be used to determine, and in some cases improve, the various control functions performed by the control module 110.
  • a heat pump While a heat pump is generally more energy efficient for heating water compared to an electrical resistance heater, a heat pump requires time to start up as it performs various checks and cycles before reaching a normal operation state, and time to transfer sufficient amount of thermal energy into a thermal energy storage medium before reaching the desired operation temperature.
  • an electrical resistance heater is generally able to provide heat more immediately.
  • a heat pump can take longer to heat the same amount of water to the same temperature compared to an electrical resistance heater.
  • the heat pump 140 may for example use a phase change material (PCM), which changes from a solid to a liquid upon heating, as a thermal energy storage medium.
  • PCM phase change material
  • phase change material may be used as a thermal storage medium for the heat pump.
  • phase change materials are paraffin waxes which have a solid-liquid phase change at temperatures of interest for domestic hot water supplies and for use in combination with heat pumps.
  • paraffin waxes that melt at temperatures in the range 40 to 60 degrees Celsius (°C), and within this range waxes can be found that melt at different temperatures to suit specific applications.
  • Typical latent heat capacity is between about 180kJ/kg and 230kJ/kg and a specific heat capacity of perhaps 2.27Jg -1 K 1 in the liquid phase, and 2.1Jg -1 K 1 in the solid phase. It can be seen that very considerable amounts of energy can be stored taking using the latent heat of fusion.
  • More energy can also be stored by heating the phase change liquid above its melting point.
  • the heat pump may be operated to "charge” the thermal energy storage to a higher-than-normal temperature to "overheat" the thermal energy storage.
  • a suitable choice of wax may be one with a melting point at around 48°C, such as n- tricosane C23, or paraffin C20-C33, which requires the heat pump to operate at a temperature of around 51°C, and is capable of heating water to a satisfactory temperature of around 45°C for general domestic hot water, sufficient for e.g. kitchen taps, shower/bathroom taps. Cold water may be added to a flow to reduce water temperature if desired. Consideration is given to the temperature performance of the heat pump. Generally, the maximum difference between the input and output temperature of the fluid heated by the heat pump is preferably kept in the range of 5°C to 7°C, although it can be as high as 10°C.
  • salt hydrates are also suitable for latent heat energy storage systems such as the present ones.
  • Salt hydrates in this context are mixtures of inorganic salts and water, with the phase change involving the loss of all or much of their water. At the phase transition, the hydrate crystals are divided into anhydrous (or less aqueous) salt and water.
  • Advantages of salt hydrates are that they have much higher thermal conductivities than paraffin waxes (between 2 to 5 times higher), and a much smaller volume change with phase transition.
  • a suitable salt hydrate for the current application is Na 2 S2O3-5H2O, which has a melting point around 48°C to 49°C, and latent heat of 200-220 kJ/kg.
  • Fig. 2 illustrates a training phase of an MLA 2200, such as the MLA 120, to establish a baseline utility usage pattern according to an embodiment.
  • the MLA 2200 receives inputs from a plurality of sensors and other sources over a period of time to learn the usage pattern e.g. of the occupant(s) of a house.
  • a control module e.g. control module 110, on which the MLA 2200 executes may comprise a clock and the MLA 2200 may receive a time of the day 2101 and a date and day of the week 2102 from the clock.
  • the house may have a plurality of motion sensors installed and the MLA 2200 may receive occupancy data 2103 from the motion sensors.
  • occupancy may also be predicted based on a plurality of factors.
  • the control module may be in communication with one or more outdoor temperature sensors for the MLA 2200 to receive an input of the current weather 2104.
  • the control module may also be in communication with one or more indoor temperature sensors for the MLA 2200 to receive an indoor temperature 2105.
  • a plurality of water temperature, pressure and flow sensors may be disposed at various locations of the water provision system, e.g. at the mains water inlet to measure the mains water inlet temperature 2106, mains flow rate 2107 and mains flow pressure 2108, which can be input to the MLA 2200.
  • a sensor may be disposed at one or more or each water outlet (or a valve controlling water flow to the water outlet) to detect when the respective water outlet is turned on and when it is turned off, and the temperature of the water at the water outlet, and data relating to hot/cold water usage time and temperature 2109 and hot/cold water usage volume 2110 can be input to the MLA 2200.
  • the MLA 2200 may also collect data on energy usage 2111 by the water provision system, for example time of use, amount of energy used, and, in cases where the control module is in communication with the energy provider, current tariff.
  • the MLA 2200 may also collect data on heat pump usage 2112 such as time of use, length of use, etc.
  • the MLA may receive, collect and/or use all the input sensor data described herein, and that the list of input sensor data described herein is not exhaustive, other input data may also be received, collected and/or used by the MLA as desired.
  • the control module is in communication with e.g. one or more smart devices (e.g. a smartphone) or personal computers of one or more occupants
  • the MLA may receive and use other personal or public data obtained from these devices.
  • the MLA 2200 establishes a water and energy usage pattern for the occupants based on the received input data.
  • the usage pattern 2300 may include a pattern of heated water usage, a pattern of cold-water usage, a patten of energy usage, a pattern of heat pump usage, an occupancy pattern that provide a baseline of expected usage based on e.g. the time of the day, the day of the week, the date, occupancy level, etc.
  • Fig. 3 schematically shows an embodiment of an MLA 3200 executing on a control module (e.g. control module 110) processing a set of input data to output an occupancy prediction e.g. for a house.
  • the MLA 3200 may be the same MLA as the MLA 2200 or it may be a different MLA.
  • the MLA 3200 may be trained using an appropriate training data set, for example based on occupancy level and occupant(s) scheduling of arrival to the house over the course of a year.
  • the MLA 3200 receives input data specific to the house and its occupants, through the control module, from a plurality of sources, including one or more sensors disposed around the house, one or more user interfaces (e.g. control panels around the house in communication with the control module, smart devices, personal computers, etc.), one or more software program, one or more public and private databases, etc.
  • the MLA 3200 receives inputs of the current time 3101, date 3102 and day of the week 3103 e.g. from a clock and calendar function running on the control module or remotely over a communication network.
  • the MLA 3200 further receives inputs of any special events or public holidays 3104 e.g.
  • the MLA 3200 determines the expected occupancy level based on the input data and outputs an occupancy prediction 3300. By determining the expected occupancy of the building, it is possible to estimate or predict the likely utility (e.g. energy and water) demand.
  • the likely utility e.g. energy and water
  • the MLA 3200 receives an input of the current locations 3105 of one or more occupants when the occupants are determined not to be in the house.
  • the occupants may register one or more smart devices (e.g. smartphones) with GPS capability with the control module or a server in communication with the control module, then the MLA 3200 may receive the current location of each occupant by obtaining a GPS signal received on a registered smart device corresponding to each occupant over a communication network. Then, based on the occupants' current locations 3105 and optionally other information such as traffic conditions obtained from the public domain, the MLA 3200 determines an expected arrival time 3106 at the house for each occupant.
  • the MLA 3200 determines an expected arrival time 3106 at the house for each occupant.
  • the expected arrival time 3106 of each occupant may also be determined based on other inputs such as current time 3101, date 3102, day of the week 3103 and event day 3104.
  • the MLA 3200 can then use the expected arrival time 3106 to output an occupancy prediction 3300 (occupancy level in the future rather than current occupancy level) for the house.
  • the occupancy prediction 3300 is a useful indicator for the control module when performing various control functions for the water provision system. For example, heated water may be directed to radiators of a central heating system installed in the house before the occupants are expected to arrive. Another example is to activate the heat pump to begin storing thermal energy in the thermal energy storage before the occupants are expected to arrive, and moreover the heat pump may be activated at a time based on the expected arrival time 3106 of the occupants such that the thermal energy storage is "fully-charged" (reached operation temperature) before the occupants are expected to arrive.
  • heated water may be directed to radiators of a central heating system installed in the house before the occupants are expected to arrive.
  • Another example is to activate the heat pump to begin storing thermal energy in the thermal energy storage before the occupants are expected to arrive, and moreover the heat pump may be activated at a time based on the expected arrival time 3106 of the occupants such that the thermal energy storage is "fully-charged" (reached operation temperature) before the occupant
  • heat extracted from the outside air by a heat pump is transferred directly to water from the mains, e.g. stored in an insulated tank, and the heated water is diverted to various water outlets when needed.
  • One drawback of such conventional approaches is the time required for the heat pump to transfer a sufficient amount of heat from the outside air to the water in the tank for the water to reach the desired temperature.
  • a heat pump water heater is generally installed in addition to a conventional electrical resistance water heater that brings the water up to the desired temperature at times when the water has not been heated sufficiently by the heat pump.
  • thermal energy storage medium in a thermal energy storage 150 is provided for storing heat extracted by the heat pump 140, and the stored heat can be used for heating water when required.
  • the thermal energy storage medium may be pre-charged by operating the heat pump to transfer heat into the thermal energy storage before demands for heated water arise. This may be desirable where demands for heated water and/or demands for electricity fluctuate throughout the day, such that, for example, operating the heat pump and/or the electrical resistance water heater when demands for heated water are high may not be cost- effective and may put additional pressure on the energy network at a time of high demands.
  • Fig. 4 schematically shows an embodiment of an MLA 4200 executing on a control module (e.g. control module 110) processing a set of input data to output a decision to precharge the thermal energy storage medium to raise its temperature to a desired operation temperature.
  • the MLA 4200 may be the same MLA as the MLA 2200 and/or MLA 3200, or it may be a different MLA.
  • the MLA 4200 may be trained using an appropriate training data set, for example based on heated water demands of the house.
  • the MLA 4200 receives input data specific to the house and its occupants, through the control module, from a plurality of sources, including one or more sensors disposed around the house, one or more user interfaces (e.g. control panels around the house in communication with the control module, smart devices, personal computers, etc.), one or more software program, one or more public and private databases, etc.
  • the MLA 4200 receives an input of a current time and date 4101 e.g. from a clock and/or calendar on the control module, and energy demand data such as a current tariff 4102 which specifies a unit cost of energy e.g. obtained from the energy provider that supplies energy to the house, during off-peak period when the unit cost of energy is lower
  • the MLA 4200 can derive energy demand data from the utility usage pattern 2300 established as described above and an occupancy prediction 3300. For example, if the current energy usage of the house is lower than an average level over the period of e.g. a day, then the current energy usage may be considered low; in contrast, if the current energy usage of the house is higher than average, then the current energy usage may be considered high.
  • the MLA 4200 can determine the current level of energy demand and activates the heat pump to pre-charge the thermal energy storage 4300 when the current energy demand is deemed to be low, in preparation for provision of heated water before demands for heated water arise e.g. when the occupants are expected to arrive at the house and/or when demands for heated water is expected to rise in the evening.
  • the MLA 4200 can predict one or more parameters such as an expected level of heated water usage and an expected level of energy usage. Then, based on the predicted parameters, the MLA 4200 can determine an amount of thermal energy to be stored in the thermal energy storage medium. For example, if the expected level of heated water usage is expected to be high and remain high for a long period of time, the MLA 4200 may operate the heat pump for a period of time sufficiently long before the expected rise of demand in order to pre-charge the thermal energy storage medium to a temperature higher than the normal operating temperature set e.g. by an occupant of the installer in order to store a sufficient amount of energy for sustained heated water usage.
  • the MLA 4200 may operate the heat pump for a period of time sufficiently long before the expected rise of demand in order to pre-charge the thermal energy storage medium to a temperature higher than the normal operating temperature set e.g. by an occupant of the installer in order to store a sufficient amount of energy for sustained heated water usage.
  • the present embodiment enables a heat pump to be utilised when it may otherwise not be sufficiently responsive if it is only activated at the time of demand. Moreover, by using the current tariff as an input, it is possible to operate the heat pump to pre-charge the thermal energy storage during a low-energy-demand period when the unit cost of energy is lower and relieve the pressure on the energy network by shifting energy usage from a high-demand time to a low- demand time.
  • the present embodiment is equally applicable to a self-sustaining home, in that demands for heated water and electricity often rise and fall in parallel throughout the day.
  • the present embodiment enables the use of a more efficient form of providing heated water, i.e. a heat pump, at a lower cost and with little drawback as a result of delays in heating water up to the desired temperature.
  • FIG. 5 schematically shows an embodiment of an MLA 5200 executing on a control module (e.g. the control module 110) trained to determine whether to activate the heat pump based on cold water usage.
  • the MLA 5200 may be the same MLA as the MLA 2200 and/or MLA 3200 and/or MLA 4200, or it may be a different MLA.
  • the MLA 5200 receives input data specific to the house, through the control module, from a plurality of inputs, including one or more sensors disposed around the house, one or more user interfaces (e.g. control panels around the house in communication with the control module, smart devices, personal computers, etc.), one or more software program, one or more public and private databases, etc.
  • the MLA 5200 can be trained to recognise a correlation between heated water usage that follows from cold water usage.
  • the MLA 5200 can be trained to recognise a correlation between the use of cold water in a bathroom (e.g. to fill the water tank of a toilet) followed by a demand for heated water from a tap in the bathroom (e.g. for handwashing).
  • the MLA 5200 may use sensor data relating to heated water usage that follows from cold water usage to establish a degree of correlation between the two events.
  • the sensor data may for example include an elapse time between receiving the first sensor data and receiving the second sensor data, a location of the second water outlet in relation to the first water outlet, a frequency of receiving the second sensor data subsequent to receiving the first sensor data, a time of the day, a day of the week, but the list is not exhaustive.
  • the MLA 5200 receives inputs of a cold-water outlet being activated 5101, and based on the current cold water usage in relation to the established utility usage pattern 2300 and an occupancy prediction 3300, the MLA 5200 can determine the probability of a demand for heated water that may follow from the current cold water usage according to the degree of correlation for the current cold water usage. If an expected demand for heated water is determined, the MLA 5200 can instruct the control module to activate the heat pump 5300 in anticipation for the demand.
  • the MLA 5200 may establish a threshold during the training phase that indicates when the probability merits an activation of the heat pump.
  • the threshold may be established manually by an occupant or an installer manually inputting instances when such a predictive activation of the heat pump is desirable.
  • the threshold may be established by the MLA 5200 based on the utility usage pattern 2300 and/or an occupancy prediction 3300.
  • the determination by the MLA 5200 of whether to activate the heat pump may moreover be based on an input of the current tariff 5102 e.g. obtained from the energy provider.
  • the threshold may be determined based on tariff information obtained from the energy provider during the training phase.
  • the threshold may be revised during run time based on the current tariff.
  • the MLA 5200 may determine that it is not necessary to activate the heat pump to pre-charge the thermal energy storage since there is unlikely to be a demand for heated water; should there be a demand for heated water, the electrical heating elements 160 can be used to heat water.
  • the MLA 5200 may determine that it is more cost-effective to activate the heat pump to precharge the thermal energy storage in preparation for a demand for heated water despite the low correlation, so as to avoid the more costly option of using the electrical heating elements 160 to provide heated water.
  • the MLA 5200 may revise the threshold so that it is lower than the threshold in the former example, such that the heat pump may be activated in the latter example even if the probabilities in both cases are the same.
  • the process of extracting thermal energy from the outside air cools the heat exchanger coils in the outdoor unit, and moisture from the air condenses on the cool outdoor coils.
  • the outdoor coils can cool below freezing, and frost can form on the outdoor coils.
  • frost accumulates on the outdoor coils, the heat pump becomes less efficient, requiring a greater temperature difference with the outside air to output the same power compared to frost-free coils. It is therefore desirable to operate a heat pump in a defrost cycle, regularly and when frost accumulates, to remove frost from the heat exchanger coils in the outdoor unit of the heat pump.
  • a heat pump operates a defrost cycle whenever frost forms on the outdoor heat exchanger coils.
  • a heat pump is operated in reverse, in that warm refrigerant is sent to the outdoor unit to thaw the heat exchanger coil.
  • a heat pump may operate a defrost cycle until, for example, the coil reaches around 15°C. Once the heat exchanger coil is thawed, the heat pump can resume the normal heating cycle.
  • a heat pump While a heat pump is operating a defrost cycle, it will not be able to performs its normal function of transferring heat to the indoor unit (e.g. into the thermal energy storage 150) until the defrost cycle is complete. It may therefore be desirable to prepare the building before a heat pump defrost cycle begins.
  • Fig. 6 schematically shows an embodiment of an MLA 6200 executing on a control module (e.g. the control module 110) processing a set of input data to predict the next defrost cycle of a heat pump (e.g. the heat pump 140).
  • the MLA 6200 may be the same MLA as the MLA 2200 and/or MLA 3200 and/or MLA 4200 and/or MLA 5200, or it may be a different MLA.
  • the MLA 6200 receives input data specific to the house, through the control module, from a plurality of inputs, including one or more sensors disposed around the house, one or more user interfaces (e.g. control panels around the house in communication with the control module, smart devices, personal computers, etc.), one or more software program, one or more public and private databases, etc.
  • the MLA 6200 may be trained to recognise when a defrost cycle is required, and establish a timescale and an average energy requirement for operating the heat pump in a defrost cycle, based for example on weather forecasts, current weather conditions, indoor temperatures and data collected from previous defrost cycle(s), with knowledge of the performance of the heat pump (e.g. an average thermal energy output of the heat pump, a heat pump efficiency or coefficient of performance, and any other information or quantities relating to the performance of the heat pump).
  • the performance of the heat pump e.g. an average thermal energy output of the heat pump, a heat pump efficiency or coefficient of performance, and any other information or quantities relating
  • the MLA 6200 receives inputs of weather forecast 6101 e.g. obtained from the public domain or a weather app on a smart device registered on the control module, current weather condition 6102 such as temperature and humidity e.g. obtained from the public domain or one or more sensors disposed around the house, indoor temperature 6103 e.g. obtained from one or more temperature sensors disposed inside the house, and data relating to the last defrost cycle(s) 6104 when the heat pump was last defrosted. Based on, the MLA 6200 can. Based on the weather forecast, the current weather conditions and the indoor temperature, the MLA 6200 can predict when the next defrost cycle may be expected 6301, e.g.
  • the MLA 6200 can estimate an expected energy and heated water demand during the time when a defrost cycle is predicted, and prepares the water provision system in anticipation of the predicted defrost cycle 6302, for example by storing additional thermal energy in the thermal energy storage (to raise the thermal energy storage medium to a higher operating temperature), heating the house to a temperature higher than the pre-set temperature, etc. Additionally or alternatively, the MLA 6200 may moreover anticipate when demands for energy and heated water (e.g.
  • the MLA 6200 can determine a time period when water and energy demands are low (e.g. overnight) and/or when occupancy is low (e.g. during school and working hours), and adjust the expected start time of the next defrost cycle to the determined low-demand time and/or low-occupancy time. The MLA 6200 may then instruct the control module to operate the heat pump to begin a defrost cycle 6301 at the adjusted start time.
  • the MLA 6200 may pre-charge the thermal energy storage medium by operating the heat pump to store more heat, e.g. by raising the temperature of the thermal energy storage medium to a higher operating temperature, as well as diverting some of the heat to warm up 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 demands are expected to be lower, .
  • the MLA 6200 may determine, based on an occupancy prediction and/or the usage pattern, that the next defrost cycle is during a period of time when energy and heated water demands are low e.g. when occupancy is expected to be low or zero, and determine that no preparation or adjustment is required.
  • the present embodiment allows necessary heat pump defrost cycles to be performed in a manner that is less disruptive to the provision of heated water and thereby enables a heat pump to be utilised as an effective way of providing heated water.
  • methods and systems are provided to monitor and interactively modify water usage habits of the occupants.
  • the methods may be implemented by an MLA 7200.
  • the MLA 7200 may be the same MLA as the MLA 2200 and/or MLA 3200 and/or MLA 4200 and/or MLA 5200 and/or MLA 6200, or it may be a different MLA.
  • the MLA 7200 is provided with data relating to water usage e.g. by occupants of the house to establish a normal water usage pattern, as described above.
  • the MLA 7200 may be trained to recognize or identify instances in the normal usage pattern when a water outlet is turned on for provision of heated water at a temperature T1 set by an occupant, but the water outlet is subsequently turned off before the water is heated to Tl. This is particularly relevant when a heat pump is used to provide heated water, since there may be instances when, upon activating the heat pump, energy extracted by the heat pump must first heat a thermal energy storage medium up to a desired operating temperature before water can be heated sufficiently by the thermal energy storage medium. In cases where the heat pump is activated in response to a demand for heated water, but the water outlet is turned off before water is heated to the desired temperature, the energy (electricity) used to operate the heat pump is wasted since the occupant did not in fact receive heated water. In view of the foregoing, the MLA 7200 may be trained to employ one or more energyreduction strategies when one such short-duration instances is determined.
  • an occupant sets the water temperature at a water outlet to Tl and opens the water outlet.
  • the control module determines that the water outlet is turned on, e.g. by detecting, using one or more sensors, a change in water pressure or water flow at a water source supplying to the water provision system, and the control module at S7003 runs the MLA 7200 to monitor changes in the water temperature at the water outlet.
  • the control module at S7004 determines that the water outlet is turned off, and the MLA 7200 at S7005 determines whether, during the time period when the water outlet is turned on, the water temperature has reached Tl set by the user. If so, the method ends with no further action.
  • the MLA 7200 may employ one or more energy-reduction strategies.
  • the MLA 7200 initiates a software function to generate a notification to notify the occupant, at S7006, that the water has not reached the pre-set temperature before the water outlet was turned off.
  • the MLA 7200 may optionally log the event at S7007.
  • an occupant may again set the water temperature at the same water outlet to T1 and turned on the water outlet.
  • the MLA 7200 identifies this water usage instance as a short duration instance in which the water temperature is unlikely to reach T1 before the user turns off the water outlet, and then initiates a software function to generate a prompt signal to prompt the user to set the water temperature at a lower temperature T2 or to use cold water instead of heated water.
  • the prompt signal may for example be flashing light at or nearthe water outlet, production of a predetermined sound ortone, a verbal and/orvisual prompt (e.g. playing a message or an image), etc.
  • the MLA 7200 may determine such short- duration instances based on the established usage pattern, or use one or more indicators to identify such short-duration instances.
  • the MLA 7200 may use the location of the water outlet or the time when heated water is demanded as an indicator. As another example, the MLA 7200 may previously determine a correlation between a cold-water usage instance prior to such short-duration heated water usage instance, such as when a toilet is flushed then refilled, and the subsequent heated water demand for handwashing, and use such cold-water usage as an indicator.
  • the occupant is made aware of instances when they demand heated water but have not used the water for a sufficiently long time for the water to be heated to temperature. Moreover, the user is prompted to use lower temperature or cold water instead of heated water at the next instance when it is likely to be of short duration, such that the user has a choice to avoid wasting energy by demanding heated water from the water provision system when it may not be necessary.
  • the present embodiment therefore enables interactive modification of heated water usage habits of occupants to reduce energy usage.
  • an occupant again sets the water temperature to T1 at S8001 and turns on the water outlet.
  • the MLA 7200 identifies this water usage instance as a short-duration instance and employs 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 may be a lower temperature than T1 but still heated, or T2 may represent unheated cold water from the mains.
  • the water provision system outputs water at the temperature T2 to the water outlet at S8003.
  • the control module proactively reduces the water temperature when the MLA 7200 identifies a short duration instance. By reducing the water temperature, less energy is required to heat the water. In doing so, the present embodiment reduces energy consumption when heated water is not necessary.
  • an occupant again sets the water temperature to T1 at S9001 and turns on the water outlet.
  • the MLA 7200 identifies this water usage instance as a short-duration instance and employs 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.
  • the water provision system outputs water at the lower flow rate to the water outlet at S9003.
  • the control module proactively reduces the water flow when the MLA identifies a short duration instance.
  • This embodiment is particularly relevant when water is heated e.g. by electric heating elements, such that, by reducing water flow, less water is required to be heated and less energy is required to heat the amount of water being used. In doing so, the present embodiment reduces both water and energy consumption.
  • Fig. 10 schematically shows an embodiment of an MLA 1200 executing on a control module (e.g. the control module 110) processing a set of sensor data to output a leakage warning for a given building.
  • the MLA 1200 may be the same MLA as the 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.
  • the MLA 1200 receives input data specific to the house, through the control module, from a plurality of inputs, including one or more sensors disposed around the house, one or more user interfaces (e.g. control panels around the house in communication with the control module, smart devices, personal computers, etc.), one or more software program, one or more public and private databases, etc.
  • the MLA 1200 receives the current time and date 1101 e.g. from a clock and calendar function running on the control module or remotely over a communication network, then using the established utility usage pattern 2300 and an occupancy prediction 3300, the MLA 1200 can estimate an expected water usage for the current time and date.
  • the MLA 1200 receives inputs of the mains water inlet temperature 1102, flow rate of the mains water 1103 and water pressure of the mains water 1104 e.g. measured by appropriate sensor(s) at the mains water inlet to the house, and determines a real-time water usage.
  • the MLA 1200 can then determine, based on the expected usage and real-time usage, whether the current water usage is as expected or not, and if the current water usage exceeds the expected usage the MLA 1200 outputs a water leakage warning 1300.
  • the MLA 1200 can be previously trained to recognise whether instances when the current water usage exceeds the expected usage correlate to a water leakage in the system or to an unexpected increase in demand, for example a change in the weather or an increase in occupancy.
  • the MLA 1200 may be provided with a threshold above which the level of water usage exceeding the expected usage is deemed a leak.
  • the MLA 1200 may alternatively establish such a threshold during a training phase, or adjust the threshold while in use e.g. based on user feedback.
  • the various MLAs described above may refer to the same or different MLA. If multiple MLAs are implemented, one or some or all of the MLAs may be executed on the control module 110, and one or some or all of the MLAs may be executed on a server (e.g. a cloud server) in communication with the control module 110 via a suitable communication channel. It will be understood by those skilled in the art that the embodiments above may be implemented in any combinations, in parallel or as alternative strategies as desired.
  • the present techniques may be embodied as a system, method or computer program product. Accordingly, the present techniques may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware.
  • the present techniques may take the form of a computer program product embodied in a computer readable medium having computer readable program code embodied thereon.
  • the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
  • a computer readable medium may be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
  • Computer program code for carrying out operations of the present techniques may be written in any combination of one or more programming languages, including object-oriented programming languages and conventional procedural programming languages.
  • program code for carrying out operations of the present techniques may comprise source, object or executable code in a conventional programming language (interpreted or compiled) such as C, or assembly code, code for setting up or controlling an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array), or code for a hardware description language such as VerilogTM or VHDL (Very high-speed integrated circuit Hardware Description Language).
  • a conventional programming language interpreted or compiled
  • ASIC Application Specific Integrated Circuit
  • FPGA Field Programmable Gate Array
  • VerilogTM or VHDL Very high-speed integrated circuit Hardware Description Language
  • the program code may execute entirely on the user's computer, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected to the user's computer through any type of network.
  • Code components may be embodied as procedures, methods or the like, and may comprise sub-components which may take the form of instructions or sequences of instructions at any of the levels of abstraction, from the direct machine instructions of a native instruction set to high-level compiled or interpreted language constructs.
  • a logical method may suitably be embodied in a logic apparatus comprising logic elements to perform the steps of the method, and that such logic elements may comprise components such as logic gates in, for example a programmable logic array or application-specific integrated circuit.
  • Such a logic arrangement may further be embodied in enabling elements for temporarily or permanently establishing logic structures in such an array or circuit using, for example, a virtual hardware descriptor language, which may be stored and transmitted using fixed or transmittable carrier media.
  • processor any functional block labeled as a "processor”
  • functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software.
  • the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared.
  • processor or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read-only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • ROM read-only memory
  • RAM random access memory
  • non-volatile storage Other hardware, conventional and/or custom, may also be included.

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Abstract

La présente invention concerne un procédé de détection d'une fuite mis en oeuvre par ordinateur, dans un système de fourniture d'eau, le système d'approvisionnement en eau comprenant un système de chauffage configuré pour chauffer l'eau provenant du réseau et un module de commande configuré pour commander le fonctionnement du système de chauffage, le système d'approvisionnement en eau étant configuré pour fournir de l'eau chauffée par le système de chauffage à un utilisateur au niveau d'un ou de plusieurs points de sortie d'eau, le procédé étant mis en oeuvre par le module de commande et consistant en : la réception de données de détecteurs indiquant une utilisation actualisée de l'eau du système d'approvisionnement en eau mesurée par un détecteur disposé au niveau d'une entrée du réseau; la détermination si l'utilisation actuelle de l'eau correspond à une fuite éventuelle dans le système d'approvisionnement en eau sur la base d'une utilisation d'eau prévue du système d'approvisionnement en eau; et lors de la détermination que l'utilisation actuelle de l'eau correspond à une fuite éventuelle, le déclenchement d'un avertissement pour notifier l'utilisateur.
PCT/IB2022/051071 2021-02-07 2022-02-07 Procédés et systèmes de détection de fuite d'eau WO2022168040A1 (fr)

Applications Claiming Priority (18)

Application Number Priority Date Filing Date Title
GB2101678.7 2021-02-07
GBGB2101678.7A GB202101678D0 (en) 2021-02-07 2021-02-07 Methods and systems and apparatus to support reduced energy and water usage
GB2109598.9A GB2603552B (en) 2021-02-07 2021-07-02 Energy storage arrangements and installations
GB2109598.9 2021-07-02
GB2109597.1 2021-07-02
GB2109599.7A GB2603553B (en) 2021-02-07 2021-07-02 Energy storage arrangement and installations
GB2109593.0 2021-07-02
GB2109600.3A GB2603824B (en) 2021-02-07 2021-07-02 Methods and systems and apparatus to support reduced energy and water usage
GB2109596.3 2021-07-02
GB2109594.8 2021-07-02
GB2109599.7 2021-07-02
GB2109593.0A GB2603976B (en) 2021-02-07 2021-07-02 Methods of configuring and controlling hot water supply installations
GB2109597.1A GB2603551B (en) 2021-02-07 2021-07-02 Energy storage arrangements and installations including such energy storage arrangements
GB2109596.3A GB2603550B (en) 2021-02-07 2021-07-02 Energy storage arrangement and installations
GB2109600.3 2021-07-02
GB2109594.8A GB2604668B (en) 2021-02-07 2021-07-02 Methods and systems and apparatus to support reduced energy and water usage
GB2111071.3A GB2604946B (en) 2021-02-07 2021-08-02 Methods and systems for detecting water leakage
GB2111071.3 2021-08-02

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Cited By (1)

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CN115147028A (zh) * 2022-09-05 2022-10-04 武汉盛科达科技有限公司 基于物联网的智慧水务管理方法、系统、终端及存储介质

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