Method for managing atmospheric water generation devices

Abstract

The invention relates to a method for controlling at least one atmospheric water generation device. The method comprises generating (GEN1) at least one control indicator according to an estimate of weather conditions (EST1), an estimate of infrastructure water consumption requirements (EST3), an estimate of the available electrical energy (EST2), and an estimate of the performance (EST4) of the atmospheric water generation device.

Description

[0001] Description

[0002] Title: METHOD FOR MANAGING ATMOSPHERIC WATER GENERATION DEVICES

[0003] Field of invention

[0004] The invention relates to a method for controlling the operation of an atmospheric water generation device or a network of devices intended to efficiently produce drinking water.

[0005] State of the art

[0006] Water is a vital natural resource that is constantly being depleted on a planet considered blue. With the world's population expected to reach nearly 10 billion by 2050, the global water crisis is one of the greatest threats humanity will face in the coming decades. Currently, billions of people around the world still lack adequate access to water, sanitation, and hygiene, according to a joint report by UNICEF and the World Health Organization (WHO). Despite its abundance on Earth, only 2.5% of the planet's available water is fresh, of which only 0.7% is accessible on the surface. Furthermore, climate change and global warming are affecting the health of lakes, rivers, and freshwater streams. Under the current climate change scenario, nearly half of the world's population will be living in regions subject to high water stress by 2030.Meanwhile, several factors are further exacerbating water stress, such as conflicts, increasing energy demand, demographic changes, pollution, and urbanization. Among current solutions, seawater desalination leaves behind potentially toxic brine. For areas with access to water, the bill paid for drinking water includes the cost of public water, sanitation services, and fees and taxes used to compensate for the pollution generated by water use. Thus, the recommended pricing to cover the cost of all these steps is tending to increase and will continue to rise given water scarcity and the frequent pollution of the resource.To solve the problems of water stress and rising water and energy prices, it is necessary to implement innovative solutions that offer attractive investment opportunities while ensuring an economic and environmental strategy, a pillar of sustainable development.

[0007] There are many known implementations of atmospheric water generation systems, that is, systems that extract water from the humidity contained in the ambient air. These atmospheric water generation systems have demonstrated, over the last decades, that they can be considered as emerging and promising solutions to overcome water scarcity in the world, especially for arid regions. Among these systems, two main methodologies are used including thermal cooling condensation systems including air conditioning, refrigeration, thermoelectric, thermo-acoustic systems, etc., and sorption systems. These thermal systems allow the extraction of water vapor contained in the ambient air by lowering the temperature below its dew point.Dew point, or dew point temperature, is the temperature to which humid air must be cooled for water vapor to condense. For example, refrigeration condensation systems such as vapor compression systems mainly consist of a condenser, an expansion valve, an evaporator, and a compressor using a refrigerant. The refrigerant passes through the compressor, which transforms it into high-pressure, high-temperature vapor, and then condenses through the condenser. Due to the pressure drop across the expansion valve, the refrigerant undergoes a drastic drop in temperature, allowing it to cool the ambient air as it passes through the evaporator and condense the moisture. This technology is considered energy-intensive due to the compressor's energy consumption.The second atmospheric water generation system is based on the sorption phenomenon using desiccant materials. These desiccant materials can be solid or liquid adsorbents, such as zeolite, silica gel, or lithium chloride and lithium bromide, etc. These desiccants are used to extract water vapor from the air through a hygroscopic process and then recover the water through regeneration. This technology works exceptionally well when used at relatively cool temperatures or when a low dew point is required. It should be noted that such technology remains expensive. A combination of these two technologies provides a hybrid system that can also be used to improve moisture extraction.

[0008] This type of atmospheric water generation device has certain drawbacks. For example, water availability, and therefore the amount of water that can be extracted through these devices, is closely linked to atmospheric conditions. This dependence is accentuated by the variability of these conditions in space and time. In addition, since these systems consume electricity and some technologies remain expensive, the price per liter produced is not very competitive.

[0009] In order to overcome these drawbacks, methods of atmospheric water generation systems are known which manage the operation of such devices and enable their performance to be improved.

[0010] Moreover, methods are known for atmospheric water generation systems configured to control one or more characteristic parameters of the ambient air to manage the starting and / or stopping of the water generation device on the basis of efficiency considerations.

[0011] Document US20128321061 is known, describing a method that allows predictive planning of automated irrigation of different plants. This document stipulates that water production is efficient when the air temperature and the dew point temperature are close. Estimates of the time or intervals of the operating time of the devices are made according to this criterion.

[0012] The method disclosed in this document only provides an estimate of the watering time based on the temperature for irrigation, it is therefore not suitable for the management of a drinking water network.

[0013] Also known is document US2021354080A1, describing a hybrid vapor compression and absorption system. The system includes a method that estimates the weather conditions over the next few hours and estimates the water demand in order to connect or not the vapor compression system. The planning of operating hours is carried out on the basis where the estimated ambient temperatures are close to the associated dew points. It is clear that these types of processes present a certain management of the operation of such atmospheric water generation devices, however, two major drawbacks emerge. Indeed, in the event of a succession of dry days, cloudy days, or seasonal climate with variability in weather conditions, user satisfaction in terms of water production and energy efficiency will not be guaranteed.

[0014] Indeed, this type of process presents a lack of anticipation of the variability of atmospheric data (pressure, wind speed, solar irradiation, temperature and humidity, for example). This type of process also does not solve the problem of the variability of users' water demand.

[0015] Furthermore, state-of-the-art solutions do not offer corrections to implemented forecasts and / or established orders.

[0016] Finally, it is known that by 2030, 660 million people will still be without electricity. Prior art methods do not allow for atmospheric water production in conditions of limited or non-existent access to electrical energy.

[0017] Summary of the invention

[0018] The invention aims to overcome the drawbacks of existing methods for controlling atmospheric water generation devices.

[0019] To this end, the invention relates to a method for controlling at least one atmospheric water generation device which comprises:

[0020] ■ First acquisition of a first set of meteorological data from:

[0021] - at least one source of meteorological data; and / or

[0022] - a history of time-stamped weather data; and / or

[0023] - a current date.

[0024] ■ First estimation of a second set of meteorological data over a first predefined period by means of a calculator and from the first set of meteorological data, said estimated meteorological data comprising: - A forecast of atmospheric temperature over a given period;

[0025] - A humidity forecast over a given period;

[0026] ■ Second acquisition of a set of energy quantification data from an energy data source of at least one electrical power supply equipment;

[0027] ■ Second estimation of a second set of energy quantification data, said set quantifying an energy available over the first predefined period;

[0028] ■ Third acquisition of a first set of consumption data from:

[0029] - at least one source of consumption data from a set of infrastructures and water consumption profiles;

[0030] ■ Third estimation of a second set of consumption data over the first predefined period, the second set of consumption data comprising at least one consumption threshold;

[0031] ■ Fourth acquisition of a first set of performance data from:

[0032] - At least one source of performance data from at least one atmospheric water generation device;

[0033] ■ Fourth estimation of a second set of performance data over the first predefined period, the second set of performance data comprising at least one forecast of a performance indicator, said performance indicator defining at least one ratio between a quantity of water produced and a unit of electrical energy consumed and / or a water production flow rate;

[0034] ■ Generation of a first control indicator over the first period of at least one atmospheric water generation device from the first, second, third and fourth estimates, said control indicator defining at least one control data item sent to the atmospheric water generator device, said control data item defining at least one electrical supply duration and / or at least one electrical supply power of said at least one atmospheric water generation device;

[0035] ■ Electrical control of said at least one atmospheric water generation device as a function of the first control indicator.

[0036] The control method according to the invention advantageously makes it possible to take into account a variety of different factors for controlling at least one atmospheric water generation device. The system takes into account meteorological data, energy quantification data, user consumption data and performance data of the atmospheric water generation devices. For each type of data, an estimate for a predefined period is made which makes it possible to define a performance index which is subsequently used to control the atmospheric water generator(s). Such a method of controlling the generator(s) is particularly advantageous because it makes it possible to reach a necessary production threshold according to the needs of the users while optimizing production, in particular over periods of time when the meteorological conditions are conducive to good atmospheric water production.

[0037] According to one embodiment, the at least one source of meteorological data is a source originating from:

[0038] ■ An atmospheric pressure sensor; and / or

[0039] ■ A humidity sensor; and / or

[0040] ■ A temperature sensor; and / or

[0041] ■ An anemometer; and / or

[0042] ■ A first monthly and / or daily meteorological database; and / or

[0043] ■ A second hourly weather database; and / or

[0044] ■ A third archived weather database.

[0045] According to one embodiment, the step of generating the first control indicator comprises a step of estimating a first performance indicator over the first period of at least one atmospheric water generation device from the first and second estimates, said performance indicator defining a ratio between the quantity of water produced and a unit of electrical energy consumed, said performance indicator defining a control time period, the control step being carried out as a function of the control time period.

[0046] According to one embodiment, the available energy data source of at least one electrical power equipment relates to:

[0047] ■ A first energy source from an energy storage component; and / or

[0048] ■ A second source of energy from an electrical energy generator; and / or

[0049] ■ A source of energy data available on an electrical network.

[0050] According to one embodiment, when a second energy source powers the atmospheric water generation device, the estimation of the quantity of energy available from the second energy source is carried out from the estimations of the calculated meteorological data.

[0051] According to one embodiment, a second performance indicator is generated, said second performance indicator defining a ratio between the quantity of water produced and a unit of electrical energy consumed from the second energy source.

[0052] According to one embodiment, a third performance indicator is generated, said third performance indicator defining a ratio between the quantity of water produced and a unit of electrical energy consumed from an electrical network.

[0053] According to one embodiment, the method comprises estimating a volume of water produced by a plurality of atmospheric water generating devices.

[0054] According to one embodiment, the method comprises a step of measuring by at least one sensor of the at least one atmospheric water generation device at least one water production flow rate and at least one electrical power consumed, the method further comprising a step of generating an alert indicator when the measured flow rate is lower than a predefined flow rate threshold.

[0055] According to one embodiment, the method makes it possible to maximize the performance of the machine by controlling components of the system such as the degree of opening and / or closing of an expansion valve, the frequency variation of a compressor, the frequency variation of a fan, the activation and / or deactivation of a defrosting system based on estimates of meteorological and / or atmospheric data.

[0056] According to one embodiment, the method comprises executing a first machine learning model trained from a first training data set comprising meteorological data, consumption data and energy quantity data and electrical regime data of at least one atmospheric water generation device, said machine learning model being configured to generate a performance indicator.

[0057] According to one embodiment, the method comprises the execution of a second machine learning model trained from a second set of training data comprising on the one hand estimates of the meteorological data, estimates of consumption data and estimates of energy quantity data and on the other hand real meteorological data, real consumption data and real energy quantity data, said machine learning model being configured to generate estimates from real data.

[0058] According to one embodiment, the method comprises executing a third machine learning model trained from a third training data set comprising meteorological data and performance data of the atmospheric water generation device. The third data set advantageously comprises control parameters such as the degree of opening and / or closing of an electronic expansion valve of the device, the frequency of a fan of the device, the frequency of a compressor of the device. Advantageously, the control parameters are associated with the meteorological data and the performance data. Advantageously, the third machine learning model is configured to generate a performance indicator.

[0059] According to one embodiment, the machine learning models are configured to assign scores based on a change in the performance indicator. Said machine learning models are advantageously trained based on the score corresponding to the change in the performance indicator. The use of the score advantageously allows supervised training of the machine learning models which make it possible to maximize the total sum of the scores over time by choosing the actions which improve the performance of the system.

[0060] According to one embodiment, the measurement of the production rate and the electrical power consumed by the or each atmospheric water generating device are included in the first training data set.

[0061] According to one embodiment, the measurement of the production rate and the electrical power consumed by the or each atmospheric water generating device are included in the third training data set.

[0062] According to another aspect, the invention relates to a central server configured to implement the steps of the control method.

[0063] According to another aspect, the invention relates to an atmospheric water generation device configured to implement the steps of the method.

[0064] According to another aspect, the invention relates to a computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the steps of the method.

[0065] According to another aspect, the invention provides a computer-readable recording medium comprising instructions which, when executed by a computer, cause the computer to carry out the steps of the method.

[0066] In another aspect, the invention relates to a non-transitory computer-readable medium in which the computer program product is recorded. By "non-transitory medium" is meant that the computer-readable medium is a tangible medium. It is not a transient signal per se.

[0067] Brief description of the figures

[0068] Other characteristics and advantages of the invention will emerge on reading the detailed description which follows, with reference to the appended figures, which illustrate: Fig. 1A: a block diagram presenting the architecture of an energy management system according to an embodiment of the invention;

[0069] Fig. 1 B: a block diagram showing the architecture of an energy management system according to one embodiment of the invention;

[0070] Fig. 2: a flowchart illustrating the central method according to one embodiment of the invention;

[0071] Fig. 3: a flowchart detailing a method for estimating meteorological data according to one embodiment of the invention;

[0072] Fig. 4: a flowchart detailing a method for estimating energy production at each of the sites according to one embodiment of the invention;

[0073] Fig. 5: a flowchart detailing a method for estimating the performance of each device according to one embodiment of the invention; Fig. 6: a flowchart detailing a method for estimating water consumption requirements according to one embodiment of the invention;

[0074] Fig. 7: a flowchart detailing a method for estimating operating hours according to one embodiment of the invention;

[0075] Fig. 8: a flowchart detailing a method of starting the work of the device according to one embodiment of the invention; and

[0076] Fig. 9: a flowchart illustrating the main steps of the method according to one embodiment of the invention.

[0077] Description of the invention

[0078] Referring now to Figures 1 to 9, where like numerals indicate like elements in the figures, an electronic control method for managing the performance of atmospheric water generation systems is presented.

[0079] According to the preferred embodiment of the invention but in a non-limiting manner, the invention relates to a central server 1000 providing centralized management of a device or a network of devices for generating atmospheric water. The invention also relates to a method for controlling at least one GEAi atmospheric water generation device. In the present description, the control method may also be called central method 100, or method 100. The central method 100 can be applied to any type of GEAi atmospheric water generation device or air dehumidification system. These devices are called “atmospheric water generation device” or “atmospheric water generator” or “atmospheric water generator devices” in the present application. According to one example, the atmospheric water generation device may include refrigeration or air conditioning systems.For example, the atmospheric water generation device may include an air cooler, a thermo-cooler, a vapor compression system or a sorption system.

[0080] In the present description, embodiments of the method 100 are described.

[0081] According to one embodiment, the central server 1000 is configured to implement the steps of the control method 100 according to any one of the embodiments, or according to any combination of embodiments.

[0082] According to one embodiment, the central server 1000 comprises at least one computer configured to implement steps of the method 100.

[0083] According to one embodiment, the central server 1000 comprises at least one memory in which an algorithm is recorded allowing the implementation of the steps of the method 100.

[0084] According to one embodiment, the central server 1000 comprises at least one communication interface allowing it to transmit data and / or receive data from at least one remote device. The remote device comprises, for example, at least one computer, for example at least one computer configured to implement steps of the method 100. The remote device comprises, for example, at least one atmospheric water generation device.

[0085] According to one embodiment, the central server 1000 is configured to automatically record data received from at least one remote device in a memory.

[0086] According to one embodiment, the central server 1000 comprises the memory.

[0087] According to one embodiment, the central server 1000 is configured to automatically implement steps of the method 100 in response to a reception of data from at least one remote device.

[0088] According to one embodiment, the central server 1000 is configured to automatically control at least one remote device, such as a computer or an atmospheric water generation device, in response to the reception of data or in response to the processing of received data, for example according to a result obtained by the implementation of an algorithm, for example an algorithm using as input data received from at least one remote device and / or data recorded in a memory.

[0089] According to one embodiment, at least one atmospheric water generation device comprises at least one communication interface enabling it to transmit data and / or receive data from at least one remote device. The remote device comprises, for example, at least one computer, for example at least one computer configured to implement steps of the method 100. The remote device comprises, for example, at least one atmospheric water generation device.

[0090] According to one embodiment, the method 100 is a computer-implemented method. The method is for example implemented by the central server. The method is for example implemented by means of a computer of the central server and / or by means of a computer with which the central server communicates via a communication interface. “Communicate” means transmitting and / or receiving data via at least one link, for example a wireless link, for example by means of a communication protocol, for example Wi-Fi, ZigBee, Bluetooth, LoRa, 3G, 4G, 5G, etc.

[0091] Figure 10 illustrates one embodiment of hardware configuration of the central server 1000.

[0092] The central server 1000 comprises, for example, a computer interacting with a computer program.

[0093] The central server 1000 comprises, for example, a computer 112. The computer 112 comprises, for example, one or more processors capable of interpreting instructions in the form of computer programs. The processing is, for example, executed by a processor sequentially or simultaneously, or according to another method, by one or more processors.

[0094] The calculator 112 comprises, for example, a data processing module for performing calculations, a memory 212 connected to the data processing module, a computer-readable medium and possibly a reader adapted to read the computer-readable medium.

[0095] The system 1000 comprises for example an input module, an output module and a communication interface 111.

[0096] Each function of the system 1000 is for example performed by causing the data processing module to read a predetermined program from a medium such as the memory 212, so that the data processing module performs calculations, controls the transmission of data through the communication interface and controls the reading or writing of data in the memory 212 and on the computer-readable medium.

[0097] The method 100 is for example executed on a computer or on a system distributed between several computers (for example cloud computing).

[0098] The memory 212 comprises, for example, a computer-readable storage medium. It comprises, for example, a random access memory or any other suitable storage medium. The memory 212 comprises, for example, an operating system for loading the program of the invention. The memory 212 comprises, for example, means for storing the parameter variables created and modified during the execution of the aforementioned program.

[0099] The program instructions are, for example, stored on a computer-readable storage medium including, for example, a removable medium, such as, but not limited to, a compact disc read-only memory, a removable disk, a database, a server, or any other suitable storage medium.

[0100] The program instructions come, for example, from an external source and are downloaded via a network. In this case, the program comprises, for example, a computer-readable data carrier on which the program instructions are stored or a data carrier on which the program instructions are encoded.

[0101] The system 1000 comprises, for example, a user interface 220 comprising an input device and an output device.

[0102] The input device comprises, for example, a keyboard and a pointing interface such as a mouse. The output device is, for example, adapted to return information to a user, electrically or sensorily, for example visually or audibly. The output device comprises, for example, a graphical user interface. The output interface may comprise the input device, for example in the case of a tablet.

[0103] The set of at least one communication interface allows for example communication between the elements of the system 1000 and, possibly, between at least one element of the system 1000 and an element external to the system 1000. The set of at least one communication interface establishes for example a physical link or a wireless link between the elements of the system 1000 and / or between at least one element of the system 1000 and at least one device external to the system 1000.

[0104] According to one embodiment, the method 100 allows the monitoring and control of one or more devices comprising air and water filtration components to produce water intended for human consumption meeting quality guidelines and standards.

[0105] According to one embodiment, each of said water generation devices is provided with an electronic control system. Each electronic control system is configured to control, via the central method, a water generation system installed on a defined site.

[0106] According to one embodiment, the location of the site can be entered by the user.

[0107] According to one embodiment, the location of each of the devices may be collected automatically when the site is connected to the internet, as illustrated later.

[0108] According to one embodiment, the central method generates a map comprising devices with the same geographical and / or atmospheric characteristics and controls all of the devices, forming the same zone, on the basis of the same estimate of atmospheric data.

[0109] Advantageously, each water generation device can be equipped with appropriate sensors and / or modules to feed back atmospheric and geographical data (for example, by sensors of temperature, humidity, wind speed and direction, pressure, solar irradiation, or weather station, GPS, WIFI, etc.). According to one embodiment, each atmospheric water generation device is equipped with appropriate sensors to evaluate its operating state. By operating state, we mean the state of the components constituting the atmospheric water generation device (for example, control system of the refrigeration, hydraulic and aeraulic circuits, on / off of the components, energy performance, thermodynamic performance, etc.).

[0110] According to one embodiment, the central method makes it possible to remotely control one or more electronic control systems and therefore one or more atmospheric water generation systems.

[0111] Advantageously, at least one database, originating from at least one water generation device, is collected, analyzed and controlled by said central method. Such databases may comprise instantaneous databases and / or parameter histories and / or predictive databases comprising meteorological, atmospheric and geographical conditions, user profiles and behaviors and operating performances associated with each atmospheric water generation device.

[0112] According to one embodiment, said water generating devices can be interconnected with each other through centralized computing environments, where instructions are executed by remote processing units, which are connected by a communication network.

[0113] According to one embodiment, each water generating device can be operated separately and independently of one another.

[0114] According to one embodiment, the electronic control system 50 relating to each atmospheric water generation device comprises a power supply unit 51, an acquisition and control unit 53, electrical components 59, measurement sensors / transducers 57, a control unit 58 and an HMI unit 55 (human machine interface) for control and interaction with the user. The power supply unit 51 provides power to the various power supply stages required for the optimal operation of the various elements of the device. For example, the power supply unit 51 provides a power supply of 220 V AC and a frequency of 50 Hz. The output of this unit passes through a filtering and protection unit and then through two converters. The first is an AC / DC converter which delivers a voltage of 24 V DC which supplies the control unit 58. This unit passes through a DC / DC regulator which will deliver an output voltage of 12 V.

[0115] According to one embodiment, the acquisition and control block 53 allows the acquisition of one or more data collected through one or more sensors (digital or analog) 57. Said data is transmitted to a microcontroller 301 which allows the reading of the different data collected and which will then transfer them to a control card for processing.

[0116] According to one embodiment, the acquisition and control block 53 allows the control of the device, such that the control part comprises a microprocessor 303. For example, said microprocessor 303 comprises the following functions:

[0117] • Recover the data from the 57 sensors acquired by the acquisition part,

[0118] • Communicate with the various 305 connectivity modules,

[0119] • Transfer this data to ensure control and monitoring of the machine in real time and remotely.

[0120] • Control the power supply of the various components (including, for example, compressor, fan(s), water pump(s), UV filter(s), electronic valve(s) through the acquisition card and relays).

[0121] • Connect and configure a touch screen.

[0122] According to one embodiment, the acquisition and control block 53 comprises:

[0123] • Connectivity modules 305 (i.e. 3G / 4G and WIFI modules, for example) to ensure the connectivity of the device to the internet which is necessary for the monitoring and remote control phase through the WEB APIs,

[0124] • A GPS module allowing the geographical location of the device to be traced,

[0125] • A BLE module that offers the possibility to control and monitor the status of the device through Bluetooth communication in case the device is installed in areas not accessible to the internet or in case of internet connection problems.

[0126] According to one embodiment, the acquisition and control block 53 comprises a display block 55 making it possible to facilitate the interaction of the device with the users.

[0127] Advantageously, the acquisition block 53, in addition to the control and monitoring of the device, allows the verification of the parameters and / or the re-parameterization such as the choice of the connection method, and the verification of the lifetimes of such components.

[0128] Thus, according to one embodiment, the acquisition block 53 comprises one or more sensors 57. According to one embodiment, at least one of the sensors 57 is located outside the GEAi atmospheric water generation device. The sensors 57 outside the water generation device are advantageously linked to the device by a link configured to transmit at least one data item measured by the sensor. Advantageously, this link comprises at least one wiring configured to transmit at least one electrical signal. Advantageously, the link comprises at least one wireless transmitter, for example a radio wave transmitter, configured to transmit a wireless signal. According to this example, the device comprises at least one wireless receiver configured to receive the wireless signal transmitted by the transmitter. The receiver is configured to transmit the received signal to the acquisition block 53 and / or is included by the acquisition block 53.These sensors 57 make it possible to measure data of meteorological and atmospheric characteristics (for example, temperature, humidity, pressure, wind direction and speed, solar irradiation). According to one example, the sensors 57 comprise at least one atmospheric pressure sensor Ci. According to one example, the sensors 57 comprise at least one humidity sensor C2. According to one embodiment, the sensors 57 comprise at least one temperature sensor C3. According to one example, the sensors 57 comprise at least one anemometer C4. According to one embodiment, the sensors 57 comprise at least one atmospheric pressure sensor.

[0129] According to one embodiment, the sensors 57 comprise at least one sensor for measuring geographic coordinates of the at least one atmospheric water generation device GEA1. According to one example, the sensors 57 comprise at least one GPS device configured to generate at least one geographic coordinate.

[0130] According to one embodiment, the sensors 57 comprise at least one sensor measuring operational parameter data of the GEAi atmospheric water generation device. According to one example, the device comprises at least one sensor configured to measure an amount of water produced, for example a water level sensor in a water tank of the device and / or a water flow rate sensor preferably arranged on a water outlet of the GEAi atmospheric water generation device.

[0131] According to one example, the sensors 57 comprise at least one sensor configured to measure a quality of the produced water. According to one example, the sensors 57 comprise at least one conductivity, and / or pH, and / or turbidity sensor.

[0132] According to one embodiment, the sensors 57 comprise at least one sensor configured to measure energy produced and / or energy consumed by the GEAi water generation device. According to one example, the sensors 57 comprise at least one sensor for measuring the electrical power supplying the atmospheric water generation device.

[0133] According to one embodiment, the sensors 57 comprise at least one sensor configured to measure at least one efficiency parameter of the device. According to one embodiment, the sensors 57 comprise at least one sensor for reporting the cooling capacity and / or at least one cooling temperature sensor. According to one embodiment, at least one sensor 57 is configured to measure an electrical power supply to the atmospheric water generator device GEAi. According to one embodiment, at least one of the sensors 57 is configured to measure an atmospheric water production flow rate. According to one embodiment, the method comprises a step of calculating a measured performance indicator INDPMI. Advantageously, the measured performance indicator comprises a ratio of the electrical power supply to the atmospheric water generator device GEAi and the flow rate of water produced by said device.

[0134] Thus, the collection, analysis and correction of these parameters constitute a large database allowing the automated and efficient operation of the device to be optimized in relation to external constraints (water availability, energy availability).

[0135] According to one embodiment, the central method 100 is configured to optimize the cost per liter of water produced by each of the devices by communicating with each of the associated electronic control systems. Without human intervention, control and command functions are implemented to execute the autonomous operation of each of the devices and facilitate analysis and decision-making by implementing an energy efficiency strategy. The central method is characterized in that it comprises:

[0136] • A procedure for weather forecasts 120. These forecasts are estimated then corrected over an optimized time interval;

[0137] • A procedure for estimating the quantity of energy available 130 (in electrical production and in storage, if available);

[0138] • A procedure for estimating the performance of each device in terms of water production and energy consumption 140 or in performance ratio. These performance indicators are estimated, analyzed, and corrected, if necessary;

[0139] • A procedure for estimating water consumption needs 150 taking into account atmospheric estimates as well as databases including user profiles and their consumption behavior;

[0140] • A procedure for estimating a threshold representative of a minimum quantity to be permanently available to the user;

[0141] • A procedure allowing, in view of the collected databases (atmospheric conditions, user behavior, available energy) and the constraints to be satisfied (user needs, device performance) to optimize the operating hours of each of the devices and the necessary operating period WPe of the water generation device.

[0142] The optimized time interval will sometimes be referred to in this application as the first predefined period. By first predefined period is meant a period of time during which the atmospheric water generation device(s) (AWG) are controlled to produce atmospheric water.

[0143] According to one example, the first predefined period lasts several hours, for example eight. This time corresponds, according to one example, to the duration of a night during which the atmospheric water generation devices (AWGs) will be controlled to produce water. According to one example, the first predefined period is a period of at least 24 hours. According to one embodiment, the predefined period is a period of at least several days, for example 7. This arrangement allows the atmospheric water generation device(s) to be controlled over a long period and therefore to spread the electricity consumption, as well as to be able to choose the production times during which the weather conditions allow the best efficiency to be achieved.

[0144] According to one embodiment, the predefined time period is a time period that is variable. According to one example, the predefined time period has a variable length that is advantageously scalable. According to one embodiment, the predefined time period. According to one embodiment, the first machine learning model, and / or the second machine learning model are configured to generate correction data for the predefined time period. According to one embodiment, the correction data advantageously allows an adaptation of the duration of the predefined time period. The adaptation of the predefined time period makes it possible to define a time period in which production can be optimized.

[0145] According to one embodiment, the method for controlling at least one atmospheric water generation device GEAi comprises at least one step of first acquisition ACQi of at least one first set of meteorological data ENSi. In the present application, the step of acquisition ACQi is sometimes called procedure for retrieving the weather forecasts 125. The acquisition ACQi of the first set of meteorological data ENSi is carried out from at least one source of meteorological data. According to one example, the source of meteorological data is one of the sensors 57 described previously, for example the temperature sensor, the atmospheric pressure sensor, the sunshine sensor, the relative air humidity sensor and / or the anemometer. According to one example, the source of meteorological data comprises at least one meteorological database of a national and / or regional meteorological service.According to one example, the meteorological data source comprises at least one meteorological measuring station configured to measure one or more meteorological quantities. The meteorological station is advantageously connected to a communication network and is configured to send the meteorological data or data over the communication network.

[0146] Advantageously, the first acquisition step ACQi of the first set of ENSi meteorological data is carried out from at least one current date. By current date, we mean the present date and / or time, or in other words the date of completion of the control process.

[0147] Advantageously, the first acquisition step ACQi of the first set of meteorological data ENSi is carried out from at least one history of time-stamped meteorological data. According to one example, the history of time-stamped meteorological data comprises a set of measured meteorological data associated with a date and / or a time of measurement. Advantageously, the history of time-stamped meteorological data comprises at least one data item corresponding to the measurement of a meteorological quantity measured one year before the current date. According to one example, the history of time-stamped meteorological data comprises at least one data item corresponding to the measurement of a meteorological quantity measured on dates close to the date corresponding to one year before the current date, for example, the data measured the week before and the week after the date corresponding to one year before the current date.In one example, the time-stamped weather data history includes at least one data item corresponding to the measurement of a meteorological quantity measured a few days before the current date. In one example, the history includes the data item corresponding to the measurement of the meteorological data item each day in the week preceding the current date.

[0148] The control method comprises a step of first estimation ESTi of a second set of meteorological data ENSi' over a first predefined period Pi by means of a calculator and from the first set of meteorological data ENSi. This step of first estimation ESTi is sometimes referred to in this application as a procedure for estimating the meteorological data 120. The step of estimation ESTi of the meteorological data advantageously makes it possible to generate a set of ENSi' which corresponds to a forecast of said meteorological data over the given period Pi.

[0149] Advantageously, the meteorological data of the second set ENSi' comprises at least one atmospheric temperature forecast over a given period. According to one example, the meteorological data of the second set ENSi' comprises at least one ambient temperature, and / or at least one wet bulb temperature, and / or at least one dew point temperature over the given period. The given period is, according to one example, a period of 24 hours following the current date. According to one example, the given period is a period of 48 hours following the current date. According to one example, the given period is a period of 7 days following the current date.

[0150] Advantageously, the meteorological data of the second ENSi' set includes at least one forecast of absolute and / or relative humidity of the ambient air over a given period.

[0151] Advantageously, the control method comprises a second acquisition step ACQ2 of a set of energy quantification data ENS2 from an energy data source of at least one electrical power supply equipment. In the present application, the acquisition step ACQ2 is sometimes referred to as a procedure for estimating the quantity of available energy 130. By electrical power supply equipment, we mean any device or infrastructure configured to store energy and / or to distribute energy. Advantageously, the electrical power supply equipment is an electrical accumulator and / or a battery of accumulators. According to one embodiment, the electrical power supply equipment is an electrical energy generator. By electrical energy generator, we mean any device configured to generate electrical energy, such as a generator, a power plant and / or a hydroelectric dam.According to one embodiment, the electrical energy generator comprises at least one renewable energy source. According to one example, the electrical energy generator is one or more solar panels. According to one example, the electrical energy generator is one or more wind turbines. According to one example, the electrical power equipment is an electrical grid.

[0152] According to one embodiment, the second set of quantification data of an energy ENS2 comprises at least one energy production history of at least one electrical power equipment. According to one example, the second set of quantification data of an energy ENS2 comprises several energy production histories of several electrical power equipment. According to one example, each production history comprises at least one quantified electrical production data, such as a produced power and / or a produced energy. According to one example, the quantified production data is associated with at least one temporal data, such as a production date and / or a production duration.

[0153] The control method comprises a second estimation step EST2 of a second set of data ENS2'. Advantageously, the second set of data ENS2' quantifies an energy available over the first defined period. According to one embodiment, the second set of data ENS2' comprises at least one data item defining an energy availability of at least one electrical power supply device.

[0154] In this application, the second estimation step EST2 is sometimes referred to as calculating the electrical energy estimates 132.

[0155] According to one embodiment, the second estimation step EST2 comprises a copy of the historical data from the energy quantification data set ENS2 into the second energy quantification data set ENS2'. Advantageously, the historical data comprises production data from an anniversary date of the current date. According to one example, the data inserted into the second energy quantification data set ENS2' comprises the energy production data of the equipment whose energy production is estimated on the anniversary date of the current date, for example the production data one year before the current date. According to one embodiment, the anniversary data corresponds to the production data of the equipment on a date preceding the current date by one day and / or one week.According to one embodiment, the estimation of energy availability is carried out by calculating a rolling average of the production of said equipment over a few days preceding the current date, for example, two days, 3 days, 4 days, five days, six days or 7 days.

[0156] According to one embodiment, the historical data comprises weather conditions associated with the energy availability data. According to one example, the second estimation step comprises a comparison between at least one meteorological data item estimated during the first estimation step and at least one meteorological data item associated with the energy availability data. In this way, the second estimation step is carried out by searching for energy availability data item corresponding to a date having weather conditions similar to the estimated weather conditions. According to one example, the second estimation is carried out by comparing the temperature forecast and / or the humidity forecast over the given period with the temperature and / or humidity data from the history.In this example, the date with the most similar weather conditions is selected, and the availability forecast is the availability value of the selected date in history.

[0157] According to one embodiment, the historical data comprises weather conditions associated with the energy efficiency data. According to one example, the fourth estimation step comprises a comparison between at least one weather data estimated during the first estimation step and at least one weather data associated with the energy efficiency data. In this way, the fourth estimation step is carried out by searching for an energy efficiency data corresponding to a date having weather conditions similar to the estimated weather conditions. According to one example, the fourth estimation is carried out by comparing the temperature forecast and / or the humidity forecast over the given period with the temperature and / or humidity data of the history.In this example, the date with the most similar weather conditions is selected, and the energy efficiency forecast is the energy efficiency value of the selected date in the history.

[0158] According to one embodiment, the ENS2 energy quantification data set comprises availability data provided by a manager of an energy distribution network and / or a manager of an energy production network.

[0159] According to one embodiment, the second set of quantification data of an ENS2' energy comprises at least one availability data of the first set of quantification data of an ENS2 energy. This arrangement makes it possible to directly take into account the data which are recovered from the manager of the distribution and / or energy production network.

[0160] According to one embodiment, the second estimation step EST2 of the second set of data quantifying an energy comprises an inclusion of the estimates of the meteorological data calculated in the first set of energy quantification data ENS2. According to one example, the second estimation EST2 is carried out by calculating one or more energy production data from the second set of estimated meteorological data ENS1. According to one embodiment, the second set of estimated meteorological data ENS1 comprises at least one data item relating to sunshine. According to this example, the at least one electrical supply equipment comprises at least one solar panel. A calculator calculates an estimate of the second set of energy quantification data ENS2' from the sunshine forecast. The methods of such a calculation are described below.

[0161] According to one embodiment, the second set of estimated meteorological data ENS1 comprises at least one piece of data relating to a wind intensity and / or a geographical direction of the wind. According to this example, the at least one piece of electrical power supply equipment comprises at least one wind turbine. The calculator calculates an estimate of the second set of quantification data of an energy ENS2', i.e. at least one electrical power supplied by the wind turbine and / or a quantity of electrical energy supplied by the wind turbine over the first predefined period from the forecast of the wind conditions. The methods of such a calculation are described below.

[0162] The control method comprises a third acquisition step AQC3 of a first set of consumption data. ENS3. The third acquisition step ACQ3 is carried out from at least one source of consumption data BDC1 of a set of infrastructures and water consumption profiles. Infrastructure means any building, equipment or structure intended to receive users.

[0163] According to one embodiment, the first set of consumption data BDC1 comprises at least one history of quantities of water consumed at the level of the infrastructure or set of infrastructures. According to one embodiment, the history of quantities of water consumed is a daily and / or weekly and / or monthly and / or annual history.

[0164] The control method comprises a third estimation step of a second set of consumption data ENS3'. The second set of consumption data comprises at least one consumption threshold. Advantageously, the consumption threshold corresponds to a forecast of water consumption at the infrastructure level. The third acquisition step ACQ3 is sometimes referred to as the user needs estimation procedure 150 in the present application.

[0165] According to one embodiment, the consumption threshold is calculated by the calculator from the first set of consumption data BDC1.

[0166] According to one embodiment, the third estimation step EST3 comprises a copy of the historical data from the first set of consumption data BDC1 into the second set of consumption data ENS3'. Advantageously, the historical data comprises water consumption data from the infrastructure on an anniversary date of the current date. According to one example, the data inserted into the second set of consumption data ENS3' comprises the consumption data from the infrastructure whose water consumption is estimated on the anniversary date of the current date, for example the consumption data one year before the current date. According to one embodiment, the anniversary data corresponds to the consumption data from the infrastructure on a date preceding the current date by one day and / or one week.According to one embodiment, the estimation of water consumption is carried out by calculating a rolling average of the water consumption of said infrastructure over a few days preceding the current date, for example, two days, 3 days, 4 days, five days, six days or 7 days.

[0167] According to one embodiment, the historical data comprises weather conditions associated with the consumption data. According to one example, the third estimation step comprises a comparison between at least one piece of weather data estimated during the first estimation step and at least one piece of weather data associated with the consumption data. In this way, the third estimation step is carried out by searching for consumption data corresponding to a date having weather conditions similar to the estimated weather conditions. According to one example, the third estimation is carried out by comparing the temperature forecast and / or the humidity forecast over the given period with the temperature and / or humidity data of the history.In this example, the date with the most similar weather conditions is selected, and the consumption forecast corresponds to the consumption value of the selected date in the history.

[0168] The control method comprises a fourth ACCU acquisition step of a first set of ENS4 performance data. The first set of ENS4 performance data is obtained from at least one source of performance data of at least one GEAi atmospheric water generation device. Advantageously, the performance data comprise water production histories of the GEAi atmospheric water generation device(s). According to one embodiment, the histories comprise at least one energy consumption data item and / or at least one water production flow rate data item of the device. Advantageously, the histories are time-stamped. Advantageously, the histories comprise at least one data item of water quantity produced.

[0169] The control method comprises a fourth estimation step EST4 of a second set of performance data ENS4'. This step is sometimes referred to as estimation of the energy efficiency parameters 141 in the present application. The second set of performance data comprises predictive data of the water production performance of the atmospheric water generation device(s) GEA1 over the first predefined period. The second set of performance data ENS4' comprises at least one performance indicator. The performance indicator defines at least one ratio between a quantity of water produced and a unit of electrical energy consumed over the first predefined period, or vice versa.

[0170] According to one embodiment, the fourth estimation step EST4 comprises a copy of the historical data from the first performance data set ENS4 into the second performance data set ENS4'. Advantageously, the historical data comprises performance data of the atmospheric water generation device on an anniversary date of the current date. According to one example, the data inserted into the second performance data set ENS4' comprises the performance data of the generation device GEA1 whose performance is estimated on the anniversary date of the current date, for example the performance data one year before the current date. According to one embodiment, the anniversary data corresponds to the performance data of the device on a date preceding the current date by one day and / or one week.According to one embodiment, the estimation of the performance is carried out by calculating a rolling average of the performance of said device over a few days preceding the current date, for example, two days, 3 days, 4 days, five days, six days or 7 days.

[0171] The method comprises a step of generating GEN1 a first control indicator PIL1 over the first period Pi of at least one atmospheric water generation device GEA1. This generation GEN1 is carried out from the first, second, third and fourth estimates. The control indicator defines at least one electrical supply duration of the atmospheric water generation device GEA1. The indicator defines at least one electrical supply power of the atmospheric water generation device GEAi.

[0172] According to one embodiment, the control data is generated based on the consumption threshold determined previously. The control data is generated based on the estimated consumption threshold so that water production reaches the threshold.

[0173] The method comprises a step of electrically controlling at least one atmospheric water generation device.

[0174] Advantageously, the electrical control comprises the control by the computer of the device of at least one electrical energy generator and / or at least one electrical power supply of said device. According to one embodiment, the control comprises an electrical control intensity or power and a power supply duration.

[0175] According to one embodiment, a comparison, at least hourly 190, of the estimated water production compared to the level of water actually produced, makes it possible to update the operating program developed and to correct it continuously.

[0176] According to one embodiment, said comparison is made every minute or fortnight to have more precise estimates.

[0177] According to an example, the planning, being intended to be implemented over a period WPe 200 equal to 54 hours for the production of a quantity of water estimated at 67 liters, will be reorganized over a shorter revised hourly period (WPe - 1) when the estimated value is lower than the value actually produced, and this until the satisfaction of the user need is achieved. Otherwise, the method stops the device for the so-called off-peak hours (i.e. hours which do not coincide with operating hours).

[0178] According to one embodiment, the analyses and decision-making use at least one artificial intelligence model based on artificial learning algorithms. According to one example, this model uses the technique of supervised reinforcement learning. Reinforcement learning means autonomous learning. This learning consists of the agent learning the actions to take, so as to optimize a quantitative reward over time, based on iterated experiments. Thus, the sum of the rewards over time is maximized. This method makes it possible to self-correct, minimize margins of error and accelerate corrective measures.

[0179] According to one embodiment, the central method optimally executes the interconnection of the different procedures, which may be successive, simultaneous or convergent.

[0180] According to one embodiment, the central method allows dynamic and scalable decision-making to execute the different predictive procedures, analyze them and revise them.

[0181] Moreover, the objectives of this process include economic (for example, optimizing the cost of the liter produced), energy (for example, rationalizing the energy consumed while ensuring user satisfaction) and environmental (reducing greenhouse gas emissions) goals, making it possible to provide pure, safe and drinkable water in a sustainable manner.

[0182] Another advantage of this solution is that water is produced by achieving a compromise between user needs, the performance of the atmospheric water generation system, water availability and the availability of energy sources, relative to each site. Each site can be subject to an optimized energy source of renewable (wind, solar, etc.), non-renewable or hybrid type.

[0183] To do this, according to one embodiment, the central method considers different scenarios in order to automate in an effective and efficient manner the operation of the water generation device depending on whether the connectivity of the site is established or not and whether the site is connected to the electricity network or not.

[0184] According to one embodiment, the geolocation system relating to each device makes it possible to automatically return the geographic coordinates of the specific site when it is connected to the Internet.

[0185] According to one embodiment, the user defines the geographic coordinates of his site when said site is not connected to the internet.

[0186] According to one embodiment, the processing of meteorological data relating to each site is different depending on whether the region is connected to the Internet network or not, as illustrated later.

[0187] According to one embodiment, when the connectivity test is valid, the electronic control system associated with the given atmospheric water generation device is connected to the Internet (for example, by SIM card, or by Wifi, etc.). Thus, the geographic coordinates are established. The availability of the API allows the collection of meteorological and / or atmospheric estimates 125 from the estimates established through the central method.

[0188] According to one embodiment, each device may be provided with one or more sensors (for example, digital or analog) of at least one characteristic of the ambient air making it possible to measure at least one parameter characteristic of the atmospheric conditions (for example, air temperature, humidity, dew point temperature, pressure, sunshine, wind direction and speed, etc.). The received and / or saved data 121 constitute a good database of the history of the weather conditions.

[0189] According to one embodiment, the central method uses historical weather and / or atmospheric conditions to estimate future weather databases using artificial intelligence by learning functions.

[0190] According to one embodiment, in order to quickly and reliably automate the operation of each atmospheric water generation device, it is necessary to estimate with increased precision the meteorological and / or atmospheric data 123 relative to the geographic database. According to one example, the method compares the estimates of the atmospheric data received by the APIs and those made from the histories 127 in order to make corrections to said estimates of the meteorological and / or atmospheric data.

[0191] Indeed, in order to improve the estimates of operational parameters (such as, for example, predictions in water production and user needs), the process uses at least two meteorological and / or atmospheric databases.

[0192] The first database provides monthly and / or daily histories over a given period, making it possible to compile statistics on trends in water availability, in other words, in water production potentials relative to this period. According to one example, the statistics are based on averages corresponding to previous years (for example, over the last year or the last 3, 5, 10 years). According to one embodiment of the invention, the method generates a typical meteorological year derived from a multi-year time series or collects such data from appropriate sites. The method uses said multi-year time series for the purpose of statistics on water production potential to be used to estimate these medium- and long-term potentials.

[0193] This first database presents daily or monthly averages of at least one of the characteristic parameters of the air, making it possible to analyze trends in daily or monthly water production potentials during an optimized period.

[0194] For example, this database can be:

[0195] • Collected from sensors installed in the atmospheric water generation system;

[0196] • Collected from databases provided by nearby atmospheric stations;

[0197] • Received by weather forecasting platforms;

[0198] • Stored in the system memory card.

[0199] According to one embodiment, each electronic control system 50 can collect and / or store the characteristics of the study site (i.e., the geographic coordinates and historical, instantaneous and forecast data of atmospheric conditions).

[0200] According to one embodiment, a second database provides more detailed hourly weather forecasts. This database includes the history of recent databases, in order to predict the next weather conditions, during an optimized period. According to one example, this database estimates the weather conditions for the next hours (48 h, 54 h, 72 h, 120 h or even 168 h) from the history of the last two weeks.

[0201] According to one embodiment, the weather forecasts informed from the APIs are compared to the forecasts from the hourly database 127, then are corrected 129, if necessary.

[0202] According to one embodiment, when the area is not connected to the Internet, the user can himself indicate the geographical coordinates of the site. The local (slave) method uses the history of at least one characteristic parameter of the air 121 of the specified site (such as ambient temperature, pressure, air humidity, dew point temperature, etc.) to estimate the associated atmospheric forecasts 123. The histories of the meteorological data can be measured by one or more sensors previously implanted in the atmospheric water generation device or databases stored in the memory card of the control system. The control system carries out the forecasts of the meteorological data 123 using artificial intelligence, for example by learning functions based on supervised artificial learning.Similarly, the local process can use statistics developed on daily and / or monthly histories in order to track trends in water production potential over the coming days and / or months.

[0203] According to one embodiment of the invention, the devices can be installed on sites equipped with an electrical network (connected to the national network, cogeneration system, for example) or located on isolated sites (non-electrified sites equipped with renewable energy sources).

[0204] Devices installed on sites connected to the local electricity grid can also be powered by hybrid energy sources integrating renewable energy sources (e.g. solar energy, geothermal energy, etc.).

[0205] According to one embodiment, for sites connected to the electricity network, no limit on the energy supplied is envisaged. Water production is then ensured while reducing energy consumption by automating the operation of each atmospheric water generation device. The autonomy of each of these devices is ensured by adjusting the operation to the best operating hours which allow a competitive price per liter produced. Therefore, for this type of site, the only constraint will be the availability of water since electrical energy is always available.

[0206] According to one embodiment, isolated sites require atmospheric water generation devices adopting autonomous energy systems such as renewable energy sources (solar energy, wind energy, etc.), non-renewable energy sources (generator sets, etc.) or hybrid sources. However, the intermittent nature of renewable energy sources generally requires additional storage systems to meet electrical load demands. According to one embodiment, the estimation of meteorological data (temperature, solar irradiation, wind speed and direction, for example) will be used, among other things, to provide feedback on the estimation of the characteristic parameters of the necessary electrical energy. According to one example, for an isolated site, the renewable energy source used is solar energy using a photovoltaic installation.The photovoltaic installation includes one or more photovoltaic modules, storage accumulators, i.e., batteries, energy regulator and a DC / AC conversion unit, if necessary.

[0207] The central method receives at least one meteorological parameter (solar irradiation data, for example) from at least one atmospheric water generation device and estimates the electrical energies 132 produced and stored, when batteries are used. In this case, the characteristics include estimates of solar irradiation, produced energy and stored energy.

[0208] According to one embodiment, a hybrid solution may be proposed for said off-grid regions, comprising a photovoltaic solar installation, a storage unit, i.e., battery and / or a generator, as a backup energy source.

[0209] According to one embodiment, as output variables, the method calculates the estimates of electrical energy produced 132, according to the nature of the site and the installed energy source, based on at least one database of estimates of meteorological data 120 associated with said site. The hourly estimate relating to said site, in particular, solar irradiation, uses either meteorological data via weather forecast platforms 125, or estimates 123 using the history 121 of the databases recovered by each of the devices.

[0210] According to one embodiment, the weather estimates may be instantaneous, hourly or daily.

[0211] According to one embodiment, the method calculates the energy estimates 132 by the photovoltaic modules, the energy stored by the storage units and / or the energy required as a backup by the use of a generator, for example. According to one embodiment, the calculation of the solar energy produced by the photovoltaic installation can be done, for example, from the following formula [Math 1]:

[0212] EEP = Pc * K * lrr

[0213] Or :

[0214] Pc: Installed peak power [kWc / kW. rrr 2 ]

[0215] EEP: Electrical energy produced [kWh / d]

[0216] Irr: Solar irradiation [kWh / m 2 j]

[0217] K: Conversion factor

[0218] According to one embodiment, the calculation of the solar energy stored by the batteries is done using the following formula [Math 2]:

[0219] Cbat: Battery capacity (kWh)

[0220] Ej: daily electrical energy requirements (kWh / d)

[0221] D: number of days of autonomy (day)

[0222] Kb: loss factor

[0223] According to one embodiment of the invention, the method is configured for estimating the performance 130 of each of the atmospheric water generation devices.

[0224] The performance of the system is understood to mean the ratio between water production and associated electricity consumption, or vice versa. In the following, the first ratio in liters produced per kilowatt-hours consumed will be used.

[0225] The method collects at least one database of the history 121 linked to the production of water coupled with the energy consumption and / or directly to the ratio of these two parameters of at least one atmospheric water generation device and delivers the estimates 131 of at least one of these characteristics over an optimized period.

[0226] According to one embodiment, the method performs an estimation of the databases 131 of the water production / electricity consumption pair from the established meteorological data estimations 120.

[0227] As illustrated previously, according to one embodiment, estimates of water production and user needs of each atmospheric water generation device are developed in the short, medium and long terms.

[0228] According to one embodiment, the method makes it possible to compare 133 the predicted database of the system performance with a reference model. The reference model may be a model resulting from an experimental characterization of the pair (water production, electricity consumption) carried out for a wide range of meteorological conditions. According to one example, the characterization is carried out for a wide range of air characteristics, for example for ambient air temperatures and relative humidity ranging, for information purposes, from T: ((-10) °C - 70 °C) and RH: (0% - 100%), respectively.

[0229] Typically, such characterization is performed at the time of development of the atmospheric water generation device.

[0230] An example of a reference model is represented by tables [Table 1] and [Table 2] for the couple (water production “p” and electricity consumption “q”), respectively, according to the meteorological conditions in (T, HR).

[0231] With :

[0232] HRi translates a relative humidity belonging to the interval of ]10% - 100% [, with an increasing step of 5%, for example.

[0233] Tj translates the ambient temperature belonging to the interval of ]-10 °C - 70 °C [ with an increasing step of 5 °C, for example.

[0234] For example, let T = 27°C and RH = 76% With pu = p(Ti, HRi) and qu = q(Ti , HRi), etc.

[0235] [Table 1]: Experimental characterization of produced water

[0236] [Table 2]: Experimental characterization of the energy consumed

[0237] According to one embodiment, the determination of the pair (water production, energy consumption) of the associated device, over the entire temperature / relative humidity range of the air, can be extended by bilinear interpolation according to the following formulas [Math 3] and [Math 4],

[0238] Calculation of water production

[0239] Calculation of electrical energy consumption

[0240] According to one embodiment, the reference model representing the performance of the system can be obtained by considering numerical simulations on the mass and enthalpy balances through the refrigeration and airflow circuits of such devices. In order to guarantee greater reliability of the process, the predictions of the performance of the associated device, for each site, are compared to the data provided by the reference model to the same weather forecast data.

[0241] According to one embodiment, a calculation of error 135 is provided using the following equation [Math 5]:

[0242] [Math 5]: error=|xref-xmodei| / xref

[0243] Or :

[0244] Xmodel and Xref are relative to the output parameters (produced water, consumed electrical energy) corresponding to the prediction model and the reference value, respectively.

[0245] The output parameters of the prediction model and the reference model are compared to each other to verify the operating status of each of the devices. The error calculation is decisive for judging the performance of the device. If the error value exceeds a certain threshold, the method alerts the users 137 about the possibility of breakdowns or malfunctions of the device in question. Otherwise, the method validates the value obtained 139 from the estimation of the performance of the device. According to an example, the aforementioned threshold corresponds to an error of 50%. According to an example, the aforementioned threshold corresponds to an estimated error of 20%. According to an example, the aforementioned threshold corresponds to an error of 80%.

[0246] According to one embodiment, the method compares the performance of several devices distributed in the same territory or in neighboring territories, and deduces, according to learning functions, the operating state of each of the devices.

[0247] According to one embodiment, the method is configured to predict user needs 150.

[0248] The method makes it possible to estimate water consumption 157 by gathering a database of the history of water consumption behavior of users 121 relative to the associated device.

[0249] According to one embodiment, the predictions of user needs relate to a database based on consumer profiles of each device.

[0250] According to one embodiment, the predictions take into account user consumption trends and profiles associated with each of the devices (e.g., number of users, user activity, frequency of use, ambient temperature, relative air humidity).

[0251] Since consumption habits vary, weather forecasts and therefore long-term forecasts of water production potential allow the process to accommodate variability in weather conditions and therefore variability in water production.

[0252] In this case, this embodiment is characterized in that it is configured to overcome potential water shortages for sites with variable and / or seasonal meteorological characteristics.

[0253] The coarse meteorological database (monthly and / or daily) allows for monthly and / or daily statistics and trends. These statistics allow for medium and long-term anticipation of user needs and therefore the quantity of water to be produced.

[0254] The hourly meteorological database makes it possible to estimate the short-term produced water potential.

[0255] Depending on the operating status of each device and the associated meteorological estimates, each forecast water production corresponds to a quantity of energy consumed.

[0256] The process is configured to generate statistics and estimate the missing quantities of water consumption in the medium and long term. These missing quantities of water quantity are relative to the months which have monthly averages in water production potential below the estimated value of user needs. Therefore, the process can anticipate future water needs and plan water storage when energy efficiency is guaranteed.

[0257] According to one embodiment, the method is configured to plan the mode of estimation of required water consumption according to at least two scenarios: the first mode addresses priority to the learning functions to estimate the water requirement of the users 157, otherwise to switch to a mode associated with a set value defined by each user 153.

[0258] According to one embodiment, the method is configured to plan the mode of estimation of required water consumption according to at least two scenarios: the first mode addresses priority to the learning functions of estimating the water needs of the users 157 (economic mode), otherwise to focus on maximizing the water production 153.

[0259] According to one embodiment, the method comprises a step of comparing the performance of the atmospheric water generation device according to the two scenarios. The method then comprises a step of notifying a user of a performance value according to each scenario. According to one embodiment, the notification comprises an indication of the scenario whose performance is the highest.

[0260] According to one embodiment, the method will alert the user to the non-satisfaction of the need in the short, medium or long term if the defined instruction 153 cannot be reached over the desired period.

[0261] According to one embodiment, the method is configured to automate the operation of each of the atmospheric water generation devices in complete autonomy. As mentioned previously, the starts and stops of each of these devices are governed by procedures comprising estimations of the meteorological and / or atmospheric parameters 120, the estimation of parameters linked to the performance of the device 130, the estimation of the needs of the users 150 and the estimation of the availability of the energy sources.

[0262] According to one embodiment, in order to meet user needs effectively and efficiently, the method gathers the various necessary databases 161, identifies the optimal period 163 for meeting user needs, sorts the minimum operating hours with decreasing energy efficiency ratio 165, and finally, plans the operating program of each water generation device 167.

[0263] The operating hours of each of the atmospheric water generation devices can be continuous or discontinuous over time.

[0264] According to one embodiment, the method will check whether the current time coincides with a scheduled operating time or not 182. If this time turns out to be an operating time, the procedure will check the availability of the different energy sources, for an isolated site, taking into account the successive availability of solar energy 183, the storage state of the batteries 185, or backup by a generator 186. The method is configured to start each of the devices 184 according to the availability of the energy sources, otherwise, to stop the device if the current time corresponds to a shutdown time 195.

[0265] According to one embodiment, the method will continuously verify and adjust the established instructions, that is, the different estimates resulting in the estimates in the automated operating program of each device.

[0266] According to one embodiment, the method will reiterate the forecast parameters 200 to execute a revised operating program if the water level actually produced in the reservoir is lower than the estimated water production level. The method will stop the operation of the associated device based on the planned off-peak hours when the water level actually produced is greater than or equal to the estimated water volume.

[0267] According to one embodiment, for sites without network connectivity, each electronic control system can itself control the associated atmospheric water generation device.

[0268] In one embodiment, each electronic control system uses edge computing to reduce latency and provide protection against internet outages. The edge computing provided by this control system allows data to be stored and processed closer to the users and applications that consume it.

[0269] Thus, in order to control the associated device via a dynamic and evolutionary analysis using learning functions, each process (slave) also includes the estimation procedures of:

[0270] • atmospheric and / or meteorological data 120;

[0271] • energy production data 130;

[0272] • performance of the atmospheric water generation device 140;

[0273] • water consumption 150;

[0274] • planning of operating hours 160;

[0275] • availability of energy source 180.

[0276] Therefore, the invention relates to a dynamic control and command method for efficiently and economically satisfying the atmospheric water supply, optimizing the cost of the produced liter as well as correcting the associated operational parameters based on the supervised artificial learning method.

[0277] According to one embodiment, the central server 1000 is configured to control a plurality of GEAi atmospheric water generators.

[0278] According to one embodiment, the server 1000 is configured to control a plurality of GEAi atmospheric water generators co-located in the same geographical area.

[0279] According to one embodiment, the central server 1000 can be configured by a user. The server can, for example, be configured by means of a configuration console comprising a user interface. The user interface comprises, for example, a screen displaying a configuration menu allowing a user to define control parameters for at least one GEAi atmospheric water generation device.

[0280] According to one embodiment, the central server 1000 is configured using a set of predefined parameters stored in a memory.

[0281] According to one embodiment, the predefined and / or user-configurable parameters comprise parameters for managing priorities for controlling a plurality of atmospheric water generators. According to one example, priorities for controlling a plurality of atmospheric water generators taken from a set of atmospheric water generators are automatically assigned based on data received and / or calculated and / or pre-recorded in a memory, such as data relating to water requirements, or even meteorological data.

[0282] This allows, for example, the allocation of higher control priority to an atmospheric water generator located in a high demand area.

[0283] According to one embodiment, at least one GEAi atmospheric water generation device comprises a voice recognition module.

[0284] According to one embodiment, at least one GEAi atmospheric water generation device is configured using an intelligent assistant.

[0285] The voice recognition module and / or the intelligent assistant are for example configured to decode and process data characteristic of a user's voice generated in response to receiving a voice command, and to generate a command in response to said decoding and said processing, for example a command to start or stop at least one GEAi atmospheric water generation device and / or display water production data on a display, and / or display energy production data on a user interface, and / or display performance data on a user interface, such as water production data over a given period, for example daily. The GEAi atmospheric water generation device comprises for example the user interface.In another example, the user interface is an interface of another device, such as a graphical interface displayed on a smartphone communicating with said GEAi atmospheric water generator.

[0286] According to one embodiment, at least one GEAi atmospheric water generation device comprises a loudspeaker. A command to emit a sound signal is for example automatically generated in response to the processing of the data characteristic of a voice.

[0287] This allows, for example, to provide audio responses to a user, such as suggestions for optimizing their use.

[0288] According to one embodiment, the calculator comprises the voice recognition module and / or the intelligent assistant.

[0289] According to one embodiment, the central system is configured to automatically generate and transmit at least one notification to a user in response to processing of received data. The processing of received data characterizes, for example, a state of at least one atmospheric water generator such as a low energy level, an imminent failure, or a need for maintenance. The transmitted notification comprises, for example, information on the state of at least one atmospheric water generator and / or preventive recommendations, such as recommendations on adjusting the operation of at least one atmospheric water generation device based on predicted future climatic conditions or an overload, or even performance statistics over a given period, for example daily, weekly or monthly. The notification is, for example, transmitted to remote equipment, for example communicating with a server.The notification is, for example, sent by email, displayed on a user interface, or issued in the form of an audio notification.

[0290] According to another aspect, the invention relates to a device comprising a user interface for controlling at least one atmospheric water generation device. The user interface comprises, for example, a graphical interface for displaying configuration menus or generating control requests for at least one atmospheric water generation device, or for displaying a community section for exchanging with other users or sponsoring other users or for displaying rewards received, such as rewards in the form of credits, discounts on consumables or free maintenance. The user interface comprises, for example, a menu for voice exchanges and / or data in the form of messages encoded or protected by encryption mechanisms via a community section.

[0291] This helps ensure user privacy while promoting a reliable and secure network.

[0292] Nomenclature:

[0293] GEAi: atmospheric water generation device

[0294] ACQi: acquisition of a first set of meteorological data

[0295] ACQ2: acquisition of a set of energy quantification data

[0296] ACQ3: acquisition of a first set of consumption data

[0297] ACQ4: acquisition of a first set of performance data ESTi: estimation of a second set of meteorological data

[0298] EST2: estimation of a second set of data quantifying available energy

[0299] EST3: estimation of a second set of consumption data

[0300] EST4: Estimation of a second set of performance data

[0301] GENi: Generation of a first performance indicator

[0302] PI Li: electrical control of the atmospheric water generation device

[0303] ENSi: first set of meteorological data

[0304] ENSi': second set of meteorological data

[0305] ENS2: energy quantification data set

[0306] ENS2': second set of data quantifying available energy

[0307] ENSs: first set of consumption data

[0308] ENS3': second set of consumption data

[0309] ENS4: first set of performance data

[0310] ENS'4: second set of performance data

[0311] BDC1: source of consumption data for a set of infrastructures

[0312] Pi: first predefined period

[0313] INDPI: first performance indicator

[0314] INDp2: second performance indicator

[0315] INDP3: third performance indicator

[0316] INDPMI: measured performance indicator

[0317] Ci: atmospheric pressure sensor

[0318] C2: humidity sensor

[0319] C3: temperature sensor

[0320] C4: anemometer

[0321] BD1: first monthly and / or daily weather database BD2: second hourly weather database

[0322] BAT1: first energy source from an energy storage component SOL1: second energy source from an electrical energy generator

[0323] RES1: energy data source available on an electrical network

[0324] MLi: first machine learning model

[0325] DATAi: first training dataset

[0326] DATA2: second training dataset

[0327] DATA3: third training dataset

[0328] 130: Amount of energy available

[0329] 50: electronic control system

[0330] 51: power supply

[0331] 53: command and acquisition block

[0332] 55: display block

[0333] 57: sensors

[0334] 58: control level

[0335] 59: electrical components

[0336] 100: central process

[0337] 120: Procedure for estimating meteorological data

[0338] 121: Parameter History

[0339] 123: Estimating weather data from historical data

[0340] 125: atmospheric forecast retrievals

[0341] 127: comparison of atmospheric data

[0342] 129: correction of atmospheric data

[0343] 130: procedure for estimating the electrical energy produced

[0344] 132: calculation of electrical energy estimates

[0345] 140: Device performance calculation procedure

[0346] 141: estimation of energy efficiency parameters

[0347] 143: comparison between estimated values ​​and reference values

[0348] 145: error test

[0349] 147: user alert

[0350] 149: validation of device performance estimation

[0351] 150: user needs estimation procedure

[0352] 151: Adjust user needs

[0353] 153: Definition of user needs

[0354] 155: user profile databases 157: user needs estimation

[0355] 160: Procedure for estimating operating hours

[0356] 161: recovery of estimates

[0357] 163: Optimization of the operating period

[0358] 165: Sorting operating hours

[0359] 167: development of the operating program

[0360] 180: start-up procedure depending on energy availability

[0361] 182: Operating hours test

[0362] 183: Solar Energy Availability Test

[0363] 184: Starting the device

[0364] 185: Stored Energy Availability Test

[0365] 186: availability of backup energy source

[0366] 187: increment test time

[0367] 190: test procedure

[0368] 195: device shutdown

[0369] 200: Operating period upgrade

[0370] 301: microcontroller

[0371] 303: microprocessor

[0372] 305: connectivity modules

[0373] API: Application Programming Interface

[0374] BLE: Bluetooth

[0375] EEC: Electrical energy consumed

[0376] EEP: Electrical energy produced

[0377] EEf: energy efficiency ratio

[0378] Irr: Solar irradiation

[0379] HRe: relative humidity

[0380] HUC: user consumption history

[0381] GPS: Global Positioning System

[0382] Tre: temperature

[0383] Pre: pressure

[0384] WPr: water production

[0385] WPe: optimal period

[0386] Wdi: wind direction

[0387] WSp: wind speed

Claims

CLAIMS 1. Method for controlling (100) at least one atmospheric water generation device (GEAi) characterized in that it comprises: ■ first acquisition (ACQi) of a first set of meteorological data (ENSi) from: ■ at least one source of meteorological data and / or, ■ a history of time-stamped weather data and / or, ■ a current date; ■ first estimation (ESTi) of a second set of meteorological data (ENSi') over a first predefined period (Pi) by means of a calculator and from the first set of meteorological data (ENSi), said estimated meteorological data comprising a forecast of atmospheric temperature over a given period and a forecast of humidity over a given period; ■ second acquisition (ACQ2) of a set of energy quantification data (ENS2) from an energy data source of at least one electrical power supply equipment; ■ second estimation (EST2) of a second set of energy quantification data (ENS2'), said set (ENS2') quantifying an energy available over the first predefined period (Pi); ■ third acquisition (ACQ3) of a first set of consumption data (ENS3) from at least one consumption data source (BDC1) of a set of infrastructures and water consumption profiles; ■ third estimation (EST3) of a second set of consumption data (ENS'3) over the first predefined period, the second set of consumption data comprising at least one consumption threshold; ■ fourth acquisition (ACQ4) of a first set of performance data (ENS4) from at least one source performance data from at least one atmospheric water generation device (AWG); ■ fourth estimate (EST4) of a second set of performance data (ENS'4) over the first predefined period, the second set of performance data comprising at least one forecast of a performance indicator, said performance indicator defining at least one ratio between a quantity of water produced and a unit of electrical energy consumed and / or a water production flow rate; ■ generation (GEN1) of a first control indicator (PIL1) over the first period (Pi) of at least one atmospheric water generation device (GEA1) from the first, second, third and fourth estimates (EST1, EST2, EST3, EST4), said control indicator (PIL1) defining at least one control data item sent to the atmospheric water generator device (GEA1), said control data item defining at least one electrical supply duration and / or at least one electrical supply power of said at least one atmospheric water generation device (GEA1); ■ electrical control (PIL1) of said at least one atmospheric water generation device (GEA1) as a function of the first control indicator (PIL1).

2. Method (100) according to claim 1, wherein at least one source of meteorological data is a source originating from: ■ an atmospheric pressure sensor (Ci) and / or; ■ a humidity sensor (C2); and / or ■ a temperature sensor (C3); and / or ■ an anemometer (C4); and / or ■ a first monthly and / or daily meteorological database (BD1); and / or ■ a second hourly weather database (BD2); and / or, ■ a third archived meteorological database (BD 3 ).

3. Method (100) according to any one of the preceding claims, wherein the available energy data source of at least one electrical supply equipment relates to: ■ a first energy source (BATi) coming from an energy storage component; and / or ■ a second energy source (SOLi) coming from an electrical energy generator; and / or ■ a source of energy data available on an electricity network (RESi).

4. Method (100) according to any one of the preceding claims, characterized in that when a second energy source (SOLi) supplies the atmospheric water generation device, the estimation of the quantity of energy available from the second energy source is carried out from the estimations of the calculated meteorological data (ESTi).

5. Method (100) according to any one of claims 3 to 4, comprising the generation of a second performance indicator (INDP2) defining a ratio between the quantity of water produced and a unit of electrical energy consumed from the second energy source (SOLi).

6. Method (100) according to any one of the preceding claims, comprising the generation of at least one third performance indicator (INDPS) defining a ratio between the quantity of water produced and a unit of electrical energy consumed from an electrical network (RESi).

7. A method (100) according to any preceding claim, comprising estimating a volume of water produced by a plurality of atmospheric water generating devices ({GEA N).

8. Method (100) according to any one of the preceding claims, comprising a step of measuring by at least one sensor of the at least one atmospheric water generation device (GEAi) at least one water production flow rate and at least one electrical power consumed, the method further comprising a step of generating an alert indicator when the measured flow rate is lower than a predefined flow rate threshold.

9. Method (100) according to any one of the preceding claims, comprising the execution of at least one first machine learning model (MLi) trained from a first training data set (DATAi) comprising meteorological data, consumption data and energy quantity data and electrical regime data of at least one atmospheric water generation device (GEAi), said machine learning model (MLi) being configured to generate a performance indicator (INDpi).

10. Method (100) according to claims 8 and 9, wherein the measurement of the production flow rate and the electrical energy consumed by the or each atmospheric water generator device (GEAi) are included in the first training data set (DATAi).

11. Method (100) according to any one of the preceding claims, characterized in that it comprises the execution of at least one second machine learning model (ML2) trained from a second set of training data (DATA2) comprising on the one hand estimates of meteorological data, estimates of consumption data and estimates of energy quantity data and on the other hand real meteorological data, real consumption data and real energy quantity data, said machine learning model (ML2) being configured to generate estimates from real data.

12. Method (100) according to any one of the preceding claims, characterized in that it comprises the execution of at least one third machine learning model (ML3) trained from a third training data set (DATA3) comprising the meteorological data and performance data of the atmospheric water generation device (GEA1), the third data set comprising control parameters, such as a degree of opening and / or closing of an electronic expansion valve of the water generation device (GEA1), a frequency of a fan of the water generation device GEA1, a frequency of a compressor of the water generation device (GEA1), the control parameters being associated with the meteorological data and the performance data, the third machine learning model being configured to generate a performance indicator.

13. Central server (1000) configured to implement the method (100) according to any one of the preceding claims.

14. Atmospheric water generation device (GEA1) comprising a computer configured to implement the method (100) according to any one of claims 1 to 12.

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