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WO2019059514A1 - Système de réglage automatique de la température pour immeuble d'appartements - Google Patents

Système de réglage automatique de la température pour immeuble d'appartements Download PDF

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Publication number
WO2019059514A1
WO2019059514A1 PCT/KR2018/008639 KR2018008639W WO2019059514A1 WO 2019059514 A1 WO2019059514 A1 WO 2019059514A1 KR 2018008639 W KR2018008639 W KR 2018008639W WO 2019059514 A1 WO2019059514 A1 WO 2019059514A1
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WO
WIPO (PCT)
Prior art keywords
temperature
switch
information
unit
household
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/KR2018/008639
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English (en)
Korean (ko)
Inventor
송석진
정재목
김용우
김철홍
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Dasan Global New Tech Group Co Ltd
Original Assignee
Dasan Global New Tech Group Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Dasan Global New Tech Group Co Ltd filed Critical Dasan Global New Tech Group Co Ltd
Publication of WO2019059514A1 publication Critical patent/WO2019059514A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/50—Control or safety arrangements characterised by user interfaces or communication
    • F24F11/56—Remote control
    • F24F11/59—Remote control for presetting
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/65—Electronic processing for selecting an operating mode
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J9/00—Circuit arrangements for emergency or stand-by power supply, e.g. for emergency lighting
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J9/00—Circuit arrangements for emergency or stand-by power supply, e.g. for emergency lighting
    • H02J9/005—Circuit arrangements for emergency or stand-by power supply, e.g. for emergency lighting using a power saving mode
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/10—Temperature
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/20—Humidity
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/50—Air quality properties
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/50—Air quality properties
    • F24F2110/65—Concentration of specific substances or contaminants
    • F24F2110/70—Carbon dioxide
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2120/00—Control inputs relating to users or occupants
    • F24F2120/10—Occupancy
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2140/00—Control inputs relating to system states
    • F24F2140/60—Energy consumption

Definitions

  • the present invention relates to a home temperature automatic temperature control system for controlling the temperature of each household having an AI switch in a apartment house.
  • a thermostat is a device that automatically adjusts the temperature of a specific place to keep it at the required constant.
  • the thermostat can sense the temperature itself and can heat and cool it.
  • the automatic thermostat has the advantage of being able to efficiently manage the energy by automatically detecting the presence of occupants in the room and automatically adjusting the room temperature.
  • Korean Patent No. 10-0725925 which is related to this automatic thermostat, discloses a thermostat.
  • the conventional automatic thermostat has provided a function of automatically controlling the room temperature by sensing the presence or absence of occupants in the room. However, if the user is in rest or sleeping and there is no movement, Unnecessary energy waste was also generated.
  • the present invention is to provide an automatic temperature control system of apartment house which combines the functions of a cold / hot temperature controller with a conventional standby power cut-off switch.
  • IoT technology intelligent sensor based AI switch, integrated energy meter reading and smart home appliance interworking, we can provide convenience and comfort in apartment house by applying machine learning algorithm based on big data in smart home, saving energy
  • the present invention provides an automatic thermostat control system for a residential home that provides operation. And to provide a home automatic temperature control system that automatically learns a user's life pattern through a machine learning algorithm and automatically controls a room temperature and a lighting according to a user's life pattern.
  • the automatic room temperature control system of the apartment house which eliminates unnecessary waste of energy through the detection of the room occupant's room and the inflow of outside air to prevent unnecessary energy waste. It is to be understood, however, that the technical scope of the present invention is not limited to the above-described technical problems, and other technical problems may exist.
  • an energy management system including an energy management server for managing energy of a dwelling house, an AI switch installed for each household, and a cloud server for providing a machine learning algorithm
  • the energy management server comprises: a data collector for collecting at least one piece of information collected from the AI switch; a data transmitter for transmitting the collected at least one piece of information to the cloud server; And an AI switch control unit for generating control information for controlling the AI switch to adjust the temperature of each of the plurality of generations based on the machine learning algorithm receiver and the machine learning algorithm, The machine learning algorithm is generated and updated And a machine learning algorithm transmission unit for transmitting the machine learning algorithm to the energy management server
  • the AI switch comprises: an environmental sensor including a temperature sensor for measuring a room temperature of the household; And a temperature control unit for controlling the temperature of the household based on the control information.
  • the present invention also provides a home temperature automatic thermostat system comprising:
  • any one of the above-mentioned objects of the present invention it is possible to provide a system for automatically controlling the temperature of the apartment house, which combines the functions of the hot / cold temperature controller with the existing standby power cut-off switch.
  • IoT technology intelligent sensor based AI switch, integrated energy meter reading and smart home appliance interworking, we can provide convenience and comfort in apartment house by applying machine learning algorithm based on big data in smart home, saving energy
  • the apartment house automatic temperature control system which provides the operation. It is possible to provide a home dwelling automatic temperature control system that learns user's life pattern through a machine learning algorithm and automatically controls room temperature and lighting according to a user's life pattern. It is possible to provide an automatic home temperature control system for a apartment house that eliminates unnecessary energy wastage by preventing the unnecessary energy from being wasted by detecting the occupant's room and detecting the inflow of outside air.
  • FIG. 1 is a block diagram of a system for automatically controlling a apartment house according to an exemplary embodiment of the present invention. Referring to FIG. 1
  • FIG. 2 is a configuration diagram of an energy management server according to an embodiment of the present invention.
  • FIG. 3 is a flowchart of a method for managing energy of a apartment house in an energy management server according to an embodiment of the present invention.
  • FIG. 4 is a configuration diagram of a cloud server according to an embodiment of the present invention.
  • FIG. 5 is a flowchart illustrating a method of providing a machine learning algorithm for controlling temperature of each household having an AI switch in a multi-family house in a cloud server according to an exemplary embodiment of the present invention.
  • FIG. 6 is a configuration diagram of an AI switch according to an embodiment of the present invention.
  • FIG. 7 is a block diagram of a system for automatically controlling a apartment house according to another embodiment of the present invention. Referring to FIG.
  • &quot part " includes a unit realized by hardware, a unit realized by software, and a unit realized by using both. Further, one unit may be implemented using two or more hardware, or two or more units may be implemented by one hardware.
  • FIG. 1 is a block diagram of a system for automatically controlling a apartment house according to an exemplary embodiment of the present invention.
  • the apartment house automatic temperature control system 1 may include an energy management server 110, a cloud server 120, and an AI switch 130.
  • Each component of the apartment house thermostatic control system 1 of FIG. 1 is generally connected via a network.
  • the energy management server 110 may be connected to the AI switch 130 and the cloud server 120 of the apartment house 100 at the same time or at intervals.
  • the network refers to a connection structure in which information can be exchanged between each node such as terminals and servers.
  • An example of such a network is 3G, 4G, 5G, Wi-Fi, Bluetooth, , A LAN (Local Area Network), a Wireless LAN (Local Area Network), a WAN (Wide Area Network), a PAN (Personal Area Network), and the like.
  • the energy management server 110 may be connected to the AI switch 130 of the apartment house 100 via a first network.
  • the first network means a communication method between the energy management server 110 and the AI switch 130 of the apartment house 100 and can be connected only to the network (internal network).
  • the energy management server 110 may be connected to the cloud server 120 via a second network.
  • the second network means a communication method between the energy management server 110 and the cloud server 120, and the second network can be an internet network (external network).
  • the energy management server 110 can manage the energy of the apartment house 100.
  • the energy management server 110 collects at least one piece of information gathered from the AI switch 130 and transmits the collected at least one piece of information to the cloud server 130 immediately or at least periodically 120).
  • the energy management server 110 may refine at least one piece of collected information into learning data through a preprocessing process of a machine learning algorithm. For example, the energy management server 110 may purify the broken data or the data that is not normally collected for at least one piece of collected information, and refine it into learning data such as calculating an instantaneous value as an average value.
  • the at least one piece of information may include, for example, power consumption, heating information of each room of each household, lighting information of each room of each household, and outlet information of each room of each household.
  • the energy management server 110 may receive the machine learning algorithm from the cloud server 120 and generate control information that controls the AI switch 130 to adjust the temperature of each generation based on the machine learning algorithm. To this end, the energy management server 110 learns a generation pattern for each household based on at least one piece of information, generates a temperature control schedule based on the generation pattern, and generates a temperature control schedule based on the generated temperature control schedule, Can be controlled.
  • the energy management server 110 may adjust the temperature of each generation based on one of the plurality of modes received from the user terminal (not shown).
  • the plurality of modes may include, for example, a power saving mode, a normal mode, and a comfortable mode.
  • the energy management server 110 can estimate the amount of electricity used and the estimated charge after a predetermined time based on the monitored power usage amount and the temperature control schedule. In addition, the energy management server 110 can estimate the amount of electric power used and the estimated charge after a predetermined time for each mode for a plurality of modes from a user terminal (not shown).
  • the cloud server 120 may provide a machine learning algorithm.
  • the cloud server 120 may generate a machine learning algorithm based on at least one piece of information, periodically update the generated machine learning algorithm, and generate and update the machine learning algorithm to the energy management server 110 Lt; / RTI >
  • the energy management server 110 and the cloud server 120 are included in the apartment house thermostat system 1, the energy management server 110 manages the energy of the apartment house 100 ,
  • the cloud server 120 may provide a machine learning algorithm to the energy management server 110.
  • the energy management server 110 may collect at least one piece of information from the AI switch 130, send it to the cloud server 120, receive control information from the cloud server 120, Can be controlled.
  • the energy management server 110 may control the AI switch 130 by receiving control commands from a user terminal (not shown). In addition, the energy management server 110 may transmit at least one piece of information received from the AI switch 130 to a user terminal (not shown).
  • the cloud server 120 may invoke external weather information to learn and establish a control plan accordingly, and other functions may be performed in the energy management server 110.
  • Management of the energy of the apartment house 100 and creation of a machine learning algorithm in the server 110 and the cloud server 120 may provide only the update information of the machine learning algorithm to the energy management server 110.
  • the energy management server 110 can collect at least one piece of information from the AI switch 130, invoke external weather information to learn directly to generate control information, and then control the AI switch 130 have. At this time, the energy management server 110 can receive only the update information of the machine learning algorithm from the cloud server 120 without transmitting at least one piece of information collected from the AI switch 130 to the cloud server 120 .
  • the energy management server 110 may control the AI switch 130 by receiving control commands from a user terminal (not shown). In addition, the energy management server 110 may transmit at least one piece of information received from the AI switch 130 to a user terminal (not shown).
  • the energy management server 110 performs all the functions to be performed in one embodiment, but may also perform a function in the cloud server 120.
  • the energy management server 110 when the energy management server 110 is included in the apartment house thermostat system 1 and operates without communication with the cloud server 120, And the energy management server 110 does not receive the update information of the machine learning algorithm from the cloud server 120.
  • the energy management server 110 when the energy management server 110 is included in the apartment house thermostat system 1 and operates without communication with the cloud server 120, And the energy management server 110 does not receive the update information of the machine learning algorithm from the cloud server 120.
  • the energy management server 110 performs all the functions performed in the other embodiments, but does not collect update information of the machine learning algorithm from the cloud server 120.
  • the AI switch 130 can measure the room temperature of the household through the temperature sensor included in the environmental sensor.
  • the environmental sensor may include, for example, an ambient temperature sensor, a CO 2 sensor, a humidity sensor, and an air cleanliness measurement sensor.
  • the AI switch 130 can detect whether or not the user is inside the household. For example, when the user's body is detected through the body detection sensor, the AI switch 130 can track the body of the user and detect the presence or absence of the body.
  • the AI switch 130 may monitor the power usage of the household.
  • the AI switch 130 can control the temperature of the generation based on the control information.
  • the AI switch 130 may control the AI switch 130 based on the temperature control schedule.
  • the AI switch 130 can control the lighting of the household based on the control information or the user's control.
  • the AI switch 130 may periodically collect instantaneous power per outlet in the household to monitor the standby power and shut off standby power. For example, the AI switch 130 determines a power value corresponding to the standby power for each outlet based on the collected instant power for each outlet, and if the instantaneous power is equal to or lower than the power value corresponding to the standby power for a preset time, The instantaneous power can be determined as the standby power.
  • the AI switch 130 may control the ventilation device within the household. For example, the AI switch 130 may operate the ventilator when the CO 2 concentration collected from the CO 2 sensor exceeds a predetermined value. As another example, the AI switch 130 may operate the ventilator if the humidity collected from the humidity sensor exceeds a predetermined value.
  • the AI switch 130 can notify the crime prevention information based on the information collected from the crime prevention sensor installed in the predetermined area in the household.
  • the AI switch 130 can receive the demand management information from the demand management server (not shown) and participate in the demand response by controlling the heating / cooling apparatus in the household based on the demand management information.
  • the AI switch 130 can sense the inflow of outside air into the household.
  • the AI switch 130 may monitor the CO 2 concentration per unit time from the information collected from the CO 2 sensor and sense the inflow of ambient air based on the CO 2 concentration per unit time.
  • the AI switch 130 can output the temperature of the generation, the user's status, the amount of power consumption, the state of the lighting, and the like. In addition, the AI switch 130 may output a power consumption amount and an expected charge after a predetermined time.
  • the AI switch 130 can be configured as a temperature-integrated type in which one device provides temperature control and illumination control.
  • the AI switch 130 uses a polling method of RS-485 serial communication as a home network, Communication can be performed.
  • the serial communication method includes a polling method and an event-driven method.
  • the polling method is a method in which one server (monthly pad) requests data from a plurality of clients (apparatuses) at predetermined intervals. Each device responds to a received data request and transmits data or control commands, You can not have more than one server.
  • the event-driven method transmits data only when an event occurs, so that a plurality of apparatuses for requesting and receiving data may be configured.
  • the wall pad serves as a server, and a polling method of RS-485 serial communication is mainly used as a home network.
  • the monthly pad acts as a server, and it manages the protocol according to all communication methods, data formats, and functions.
  • the AI switch 130 of the conventional temperature integrated type has difficulty in meeting the reply timing because the server to issue the command has two moon pads (lighting control) and two temperature valve controllers (temperature control) There is a disadvantage that the probability of occurrence of errors increases as the data loss due to communication collision is concerned.
  • the command control signal must be unified to the wall pad or unified to the valve controller.
  • the conventional wall pad since the conventional wall pad has to operate various devices, it does not perform the function of temperature control and is developed in such a manner that both the temperature control and the light control signal transmission are performed in the valve controller.
  • the temperature-controlled and light-controlled AI switch 130 which is proposed in the present invention, is connected to a valve controller (not shown), and a temperature controller and a lighting controller are connected to the valve controller Can be relayed by a month pad.
  • a valve controller (not shown) receives the control command or status inquiry of the month pad, and sends it to each temperature controller to receive or control the status value.
  • the valve controller (not shown) can distinguish between temperature-related communication and illumination-related communication by the ID value specified in the protocol.
  • the AI switch 130 performs a temperature-related operation when a temperature-related signal is included on the ID, and performs an illumination-related operation when an illumination-related signal is included.
  • the AI switch of each room which is a lower AI switch of the AI switch 130, accepts various functions such as temperature control, illumination, outlet control, energy measurement, occupancy detection, ventilation control, ventilation sensor measurement information, Can be accommodated.
  • AI switch of each room can use standard protocol.
  • the wall pad only carries out data communication only, and the valve controller transmits the corresponding signal to the lower AI switch, and when receiving data from the lower switch, the received data can be returned to the wall pad.
  • the valve controller includes a temperature control function such as storing the temperature control state value of each room temperature control switch, it is possible to process not only simple communication but also calculation according to the state value.
  • the remote meter reading server collects cumulative usage amounts from five kinds of meters such as electric power, water, hot water, and heating through a remote meter installed for each household of the apartment house 100, for example, Can be stored.
  • the remote meter reading server can calculate the charge by calculating the energy usage on a daily basis and on a monthly basis.
  • the remote meter reading server (not shown) can transmit the energy usage to the AI switch 130 so that the AI switch 130 can monitor the power consumption of the household of the apartment house 100.
  • a user terminal may request automatic operation to the AI switch 130 according to the user's pattern by using an application associated with the AI switch 130.
  • the user terminal may request the AI switch 130 to perform pre-cooling and heating according to the time schedule, analysis of the user's interference according to the set temperature, and automatic operation according to the room temperature.
  • a user terminal may present a user mode and display usage and charge estimates for each mode on a display.
  • User terminal When any one of a plurality of modes is selected in the user mode, information on the selected mode can be transmitted to the energy management server 110.
  • the plurality of modes may include, for example, a power saving mode, a normal mode, and a comfortable mode.
  • the energy management server 110 includes a data collecting unit 210, a data transmitting unit 220, a machine learning algorithm receiving unit 230, a user pattern learning unit 240, a temperature control schedule generating unit 250 A power usage predicting unit 260 and an AI switch control unit 270.
  • the data collection unit 210 may collect at least one piece of information collected from the AI switch 130.
  • the at least one piece of information may include, for example, power consumption, heating information of each room of each household, lighting information of each room of each household, and outlet information of each room of each household.
  • the data transmission unit 220 may transmit the collected at least one information to the cloud server 120.
  • the machine learning algorithm receiving unit 230 may receive the machine learning algorithm from the cloud server 120.
  • the user pattern learning unit 240 can learn the generation pattern for each generation based on at least one piece of information.
  • the temperature control schedule generator 250 can generate the temperature control schedule based on the generation pattern.
  • the power consumption predicting unit 260 can predict the power consumption and the estimated charge after a predetermined time based on the monitored power capacity and the temperature control schedule. Also, the power usage predicting unit 260 may estimate the power usage amount and the estimated cost after the predetermined time for each mode for the power saving mode, the general mode, and the comfort mode. For example, the power consumption predicting unit 260 may derive the usage amount based on the temperature control schedule based on the current usage amount of the expected energy (for example, electricity, heating, gas) usage. When the reference date is 5 days, the predicted usage of 15 days can be calculated as' usage up to 14 days + (heating / cooling equipment operation plan based on temperature control schedule * energy consumption per use time) System, and expected power usage.
  • the expected energy for example, electricity, heating, gas
  • the AI switch control unit 270 can generate control information that controls the AI switch 130 to adjust the temperature of each generation based on the machine learning algorithm.
  • the AI switch control unit 270 can control the AI switch 130 based on the temperature control schedule.
  • the AI switch control unit 270 can adjust the temperature of each household based on one of the plurality of modes received from the user terminal (not shown).
  • the plurality of modes may include a power saving mode, a normal mode, and a comfortable mode.
  • FIG. 3 is a flowchart of a method for managing energy of a apartment house in an energy management server according to an embodiment of the present invention.
  • the method for managing the energy of the apartment house performed by the energy management server 110 shown in FIG. 3 is performed in a time-series manner in the apartment house automatic temperature control system 1 according to the embodiment shown in FIGS. 1 and 2 / RTI > Therefore, the present invention is also applicable to a method of managing the energy of the apartment house performed by the energy management server 110 according to the embodiment shown in Figs.
  • the energy management server 110 may collect at least one piece of information collected from the AI switch 130 in step S310.
  • step S320 the energy management server 110 may transmit at least one collected information to the cloud server 120.
  • the energy management server 110 may receive the machine learning algorithm from the cloud server 120 in step S330.
  • step S340 the energy management server 110 may generate control information that controls the AI switch to adjust the temperature of each generation based on the machine learning algorithm.
  • steps S310 to S340 may be further divided into further steps or combined into fewer steps, according to an embodiment of the present invention.
  • some of the steps may be omitted as necessary, and the order between the steps may be switched.
  • the cloud server 120 may include an algorithm generation unit 410 and an algorithm transmission unit 420.
  • the algorithm generation unit 410 may generate and update a machine learning algorithm based on at least one information.
  • the algorithm transmitting unit 420 may transmit the machine learning algorithm to the energy management server 110.
  • FIG. 5 is a flowchart illustrating a method of providing a machine learning algorithm for controlling temperature of each household having an AI switch in a multi-family house in a cloud server according to an exemplary embodiment of the present invention.
  • a method of providing a machine learning algorithm for adjusting the temperature of each household having an AI switch in a multi-family house performed in the cloud server 120 shown in FIG. 5 is described in detail with reference to FIGS. And thermally processed in a home thermostat system (1). Therefore, even if the following description is omitted, it is possible to provide a machine learning algorithm for adjusting the temperature of each household having the AI switch in the apartment house performed by the cloud server 120 according to the embodiment shown in FIGS. 1 to 4 Method.
  • the cloud server 120 may periodically update the machine learning algorithm based on at least one information.
  • step S520 the cloud server 120 may send the updated machine learning algorithm to the energy management server 110.
  • steps S510 to S520 may be further divided into further steps or combined into fewer steps, according to an embodiment of the present invention.
  • some of the steps may be omitted as necessary, and the order between the steps may be switched.
  • the AI switch 130 includes an environment sensor 610, a moving body detection sensor 620, a room sensing unit 630, a power consumption monitoring unit 640, a temperature control unit 650, a lighting control unit 660 A standby power interruption unit 670, a ventilation control unit 680, a crime prevention unit 690, a demand reaction participation unit 700, and an output unit 710.
  • the environmental sensor 610 may include a temperature sensor for measuring the room temperature of the household 600 of the apartment house 100.
  • the environmental sensor 610 may include a CO 2 sensor, a humidity sensor, and an air cleanliness measurement sensor.
  • the moving body detection sensor 620 can detect the body of the user 603.
  • the moving body detecting sensor 620 is configured with a high sensitivity so that it can detect the movement of the user 603 even when the user 603 moves a little.
  • the occupant detection unit 630 can detect whether or not the user 603 is re-occupied in the household 600. Specifically, the occupancy sensing unit 630 may track the body of the user 603 and sense whether the occupant is re-occupied. For example, when the user 603 enters the large room through the living room and sleeps in the household 600, the room detecting unit 630 detects the living room of the user 603 from the living room detection-> The user can sense the body of the user 603 in the form of non-sensing -> large sensing and living sensing. At this time, if the user 603 detects that there is no movement of the user 603 after the user 603 moves to the large room through the living room, it can determine that there is a room occupant in the large room .
  • the power consumption monitoring unit 640 can monitor the power consumption of the household 600.
  • the power usage monitoring unit 640 can receive and monitor the cumulative usage amount per unit time of electricity, heating, gas, etc. from the database of the remote meter reading server.
  • the temperature control unit 650 can control the temperature of the generation based on the control information.
  • the illumination control unit 660 can control the illumination of the generation 600 based on the control information or the user's control.
  • the standby power cutoff unit 670 periodically collects the instantaneous power for each outlet in the generation 600 to monitor the standby power and shut off the standby power. Specifically, the standby power cut-off unit 670 determines the power value corresponding to the standby power for each outlet based on the collected instant power for each outlet, and if the instantaneous power is equal to or lower than the power value corresponding to the standby power for a preset time , It is possible to determine the instantaneous power as the standby power.
  • the standby power can be set by receiving the standby power cutoff value storage button and storing the power consumption at that time. Thereafter, if the electric power consumption is less than the stored value and the electric power consumption is maintained for a predetermined time, the AI switch 130 can cut off the electric power.
  • the conventional AI switch 130 requires the user 603 to artificially create a standby power state, it is difficult to use and there is a lot of inconvenience because the user must reset the home appliances connected to the outlet.
  • the standby power cut-off unit 670 periodically collects the instantaneous power per outlet, monitors the lowest value excluding 0 in terms of 24 hours, and keeps the lowest value for a predetermined time (for example, one hour) , It can be determined that standby power has been generated. Accordingly, even if the household appliances connected to the outlet are changed, the standby power cut-off unit 670 can detect the standby power by checking the minimum value every 24 hours, Power interruption can be provided.
  • the ventilation control unit 680 can control the ventilation device in the household 600.
  • the ventilation control unit 680 activates the ventilator and when the CO 2 concentration becomes lower than a predetermined value ,
  • the ventilator can be operated off.
  • the ventilation control unit 680 may operate the ventilation apparatus when the humidity collected from the humidity sensor exceeds a predetermined value.
  • the ventilation control unit 680 can perform on / off control, timer control, and intensity control on the ventilator.
  • the crime prevention unit 690 can notify the crime prevention information based on the information collected by the crime prevention sensor installed in the predetermined area in the household 600.
  • the demand reaction participating unit 700 can receive the demand management information from the demand management server (not shown) and participate in the demand response by controlling the heating / cooling unit in the household 600 based on the demand management information . That is, the AI switch 130 can be utilized as an actuator of demand management resources.
  • Demand management means that when a power supply company receives power from a power plant, it is supplied from an expensive energy source at peak demand (maximum power demand). In this case, when demand is low, As a result, it is possible to lower the cost of purchasing electricity, which means that the customer is allowed to use the energy at the peak time and return the profit to the customer.
  • the demand response participating unit 700 can perform demand management on the basis of the total energy of the apartment house 100 and can be used as a useful means since peak control is possible. For example, in the case of a 100-generation apartment, when air conditioners are used sequentially for 30 seconds every 5 minutes and 10 households, individual households do not heat up significantly, It is possible to effectively manage demand.
  • the outside air inflow detecting unit may detect inflow of outside air into the household 600.
  • the ambient air inflow sensing unit can monitor the CO 2 concentration per unit time from the information collected from the CO 2 sensor and sense the inflow of the ambient air based on the CO 2 concentration per unit time. For example, when the window of the household 600 is opened and the outside air is introduced, the outside air inflow detecting unit can detect that the outside air is introduced when the CO 2 per unit time and the temperature change exceed the threshold value.
  • the output unit 710 can output the temperature of the generation 600, the reuse status of the user 603, the amount of power consumption, and the state of illumination. In addition, the output unit 710 may output the amount of electric power used after the predetermined time, the estimated charge, and the like.
  • FIG. 7 is a block diagram of a system for automatically controlling a apartment house according to another embodiment of the present invention. Referring to FIG.
  • the apartment house automatic temperature control system may include a cloud server 120 and an AI switch 130.
  • the cloud server 120 can directly manage the energy of the apartment house 100.
  • the cloud server 120 may collect at least one piece of information collected from the AI switch 130, refine the collected at least one piece of information into training data through a preprocessing process of the machine learning algorithm, And generate control information that controls the AI switch 130 to adjust the temperature of each generation based on the machine learning algorithm.
  • the cloud server 120 learns a generation pattern for each household based on at least one piece of information, generates a temperature control schedule based on the generation pattern, and controls the AI switch 130 based on the generated temperature control schedule .
  • the cloud server 120 directly manages the energy of the apartment house 100, the energy cost of the apartment house can be managed and the energy of the apartment house can be saved.
  • FIG. 1 to 7 may be implemented in the form of a computer program stored in a medium executed by a computer or a recording medium including instructions executable by the computer.
  • the apartment house automatic temperature control method described with reference to Figs. 1 to 7 may also be implemented in the form of a computer program stored in a medium executed by a computer.
  • Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media.
  • the computer-readable medium may also include computer storage media.
  • Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

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Abstract

L'invention concerne un système de réglage automatique de la température, qui est destiné à un immeuble d'appartements et règle la température de chaque appartement avec un commutateur AI dans l'immeuble d'appartements, comprenant : un serveur de gestion d'énergie qui gère l'énergie de l'immeuble d'appartements ; les commutateurs AI qui sont installés dans chacun des appartements ; et un serveur en nuage qui fournit un algorithme d'apprentissage automatique, ledit serveur de gestion d'énergie comprenant : une unité de collecte de données qui collecte au moins un élément d'informations collecté à partir des commutateurs AI ; une unité de transmission de données qui transmet le ou les éléments d'informations collectés au serveur en nuage ; une unité de réception d'algorithme d'apprentissage automatique qui reçoit l'algorithme d'apprentissage automatique en provenance du serveur en nuage ; et une unité de commande de commutateur AI qui produit des informations de commande permettant de commander les commutateurs AI de façon à régler la température de chacun des appartements sur la base de l'algorithme d'apprentissage automatique, ledit serveur en nuage comprenant : une unité de production d'algorithme qui produit et met à jour l'algorithme d'apprentissage automatique sur la base du ou des éléments d'informations ; et une unité de transmission d'algorithme d'apprentissage automatique qui transmet l'algorithme d'apprentissage automatique au serveur de gestion d'énergie, et ledit commutateur AI comprenant : un capteur d'environnement qui comprend un capteur de température qui mesure la température intérieure de l'appartement correspondant ; une unité de détecteur de mouvements qui détecte si un utilisateur occupe l'appartement correspondant ; et une unité de commande de température qui commande la température de l'appartement correspondant sur la base des informations de commande.
PCT/KR2018/008639 2017-09-20 2018-07-30 Système de réglage automatique de la température pour immeuble d'appartements Ceased WO2019059514A1 (fr)

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