KR102130030B1 - System for automatically controlling temperature of apartment - Google Patents

Abstract

The apartment house automatic temperature control system that controls the temperature of each household with an AI switch in the apartment house includes an energy management server that manages the energy of the apartment house, an AI switch installed in each household, and a valve controller installed in each household. However, the energy management server uses the data collection unit to collect at least one piece of information collected from the AI switch and control information generated from the machine learning algorithm based on the at least one piece of information to enable the AI switch to generate the It includes an AI switch control unit for controlling to adjust the temperature, the AI switch is an environmental sensor including a temperature sensor for measuring the indoor temperature of the household and a temperature control unit for controlling the temperature of the household based on the control information Included, the AI switch is connected to the valve controller of the corresponding generation, and the valve controller connected to the AI switch relays control of the temperature of the corresponding generation between the AI switch and the wall pad of the corresponding generation.

Description

{SYSTEM FOR AUTOMATICALLY CONTROLLING TEMPERATURE OF APARTMENT}

The present invention relates to a multi-unit automatic temperature control system for controlling the temperature of each household equipped with an AI switch in the multi-family house.

Thermostat refers to a device that automatically adjusts the temperature of a certain place to maintain the required constant value. The automatic temperature control device can detect and heat the temperature itself. The automatic temperature control device has an advantage of efficiently managing energy by detecting the presence or absence of occupants in the room and automatically adjusting the indoor temperature.

Korean Patent No. 10-0725925, which is a prior art related to such an automatic temperature control device, discloses an automatic temperature control device.

Conventional automatic temperature control devices provide the function to automatically adjust the room temperature by detecting the presence or absence of occupants in the room, but when the user is in a rest or sleep state and there is no movement, the human body against an innocent person cannot be detected. Some unnecessary energy was wasted.

We intend to provide an automatic climate control system for apartment houses that combines the functions of a cooling/heating thermostat with an existing standby power cut-off switch. Provides convenience and comfort in apartment houses by applying machine learning algorithms based on big data in smart homes through the interlocking of AI switches with integrated smart sensors using IoT technology, integrated energy meter reading, and smart home appliances, and saving energy. We would like to provide an apartment thermostat that provides driving. The purpose of the present invention is to provide an automatic climate control system for a multi-family house by learning a user's life pattern through a machine learning algorithm and automatically controlling room temperature and lighting according to the user's life pattern. We intend to provide an automatic climate control system for apartment houses to prevent unnecessary waste of energy by removing the elements of unnecessary energy waste through detecting the presence of an inactive person and detecting the inflow of outside air. However, the technical problems to be achieved by the present embodiment are not limited to the technical problems as described above, and other technical problems may exist.

As a means for achieving the above technical problem, an embodiment of the present invention includes an energy management server for managing the energy of the apartment house, an AI switch installed for each household and a valve controller installed for each household, The energy management server controls the temperature of each generation by the AI switch by using a data collection unit collecting at least one information collected from the AI switch and control information generated from a machine learning algorithm based on the at least one information. It includes an AI switch control unit to control, the AI switch includes an environmental sensor including a temperature sensor for measuring the indoor temperature of the household and a temperature control unit for controlling the temperature of the household based on the control information, The AI switch is connected to the valve controller of the corresponding household, and the valve controller connected to the AI switch relays control of the temperature of the corresponding household between the AI switch and the wall pad of the corresponding household. System.

Another embodiment of the present invention includes a cloud server, an AI switch installed for each generation, and a valve controller installed for each generation, wherein the cloud server collects data collecting at least one information collected from the AI switch and , Using the control information generated from the machine learning algorithm based on the at least one information to control the AI switch to adjust the temperature of each generation, the AI switch includes a temperature sensor for measuring the indoor temperature of the household And a temperature controller for controlling the temperature of the corresponding generation based on the environmental sensor and the control information, wherein the AI switch is connected to the valve controller of the corresponding generation, and the valve controller connected to the AI switch is connected to the AI switch. It is possible to provide an automatic climate control system for a multi-family house that relays control of the temperature of the corresponding household between wall pads of the corresponding household.

The above-described problem solving means are merely examples and should not be construed as limiting the present invention. In addition to the exemplary embodiments described above, there may be additional embodiments described in the drawings and detailed description of the invention.

According to any one of the above-described problem solving means of the present invention, it is possible to provide an automatic temperature control system for a multi-family house combining a function of a cooling/heating temperature controller with an existing standby power cut-off switch. Provides convenience and comfort in apartment houses by applying machine learning algorithms based on big data in smart homes through the interlocking of AI switches with integrated smart sensors using IoT technology, integrated energy meter reading, and smart home appliances, and saving energy. It is possible to provide an automatic climate control system for apartment houses providing driving. By learning the user's life pattern through a machine learning algorithm, it is possible to provide an automatic climate control system for a multi-family house that automatically controls room temperature and lighting according to the user's life pattern. It is possible to provide an automatic climate control system for an apartment house to prevent unnecessary waste of energy by removing elements of unnecessary waste of energy through the detection of the presence of an inactive person and the inflow of outside air.

1 is a block diagram of an automatic climate control system for an apartment house according to an embodiment of the present invention.
2 is a block diagram of an energy management server according to an embodiment of the present invention.
3 is a flow chart of a method for managing energy in an apartment house in an energy management server according to an embodiment of the present invention.
4 is a configuration diagram of a cloud server according to an embodiment of the present invention.
5 is a flow chart of a method for providing a machine learning algorithm for controlling the temperature of each generation equipped with an AI switch in an apartment house in a cloud server according to an embodiment of the present invention.
6 is a configuration diagram of an AI switch according to an embodiment of the present invention.
7 is a block diagram of an automatic climate control system for an apartment house according to another embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art to which the present invention pertains can easily practice. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. In addition, in order to clearly describe the present invention in the drawings, parts irrelevant to the description are omitted, and like reference numerals are assigned to similar parts throughout the specification.

Throughout the specification, when a part is "connected" to another part, this includes not only "directly connected" but also "electrically connected" with other elements in between. . Also, when a part is said to "include" a certain component, it means that the component may further include other components, not exclude other components, unless specifically stated otherwise. However, it should be understood that the existence or addition possibilities of numbers, steps, actions, components, parts or combinations thereof are not excluded in advance.

In the present specification, the term “unit” includes a unit realized by hardware, a unit realized by software, and a unit realized by using both. Further, one unit may be realized by using two or more hardware, and two or more units may be realized by one hardware.

Some of the operations or functions described in this specification as being performed by the terminal or device may be performed instead on a server connected to the corresponding terminal or device. Similarly, some of the operations or functions described as being performed by the server may be performed in a terminal or device connected to the corresponding server.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram of an automatic climate control system for an apartment house according to an embodiment of the present invention. Referring to FIG. 1, the automatic climate control system 1 for a multi-family house may include an energy management server 110, a cloud server 120, and an AI switch 130.

Each of the components of the apartment thermostat 1 in FIG. 1 is generally connected via a network. For example, as shown in FIG. 1, the energy management server 110 may be connected to the AI switch 130 and the cloud server 120 of the apartment house 100 simultaneously or at a time interval.

The network means a connection structure capable of exchanging information between each node such as terminals and servers, and an example of such a network is 3G, 4G, 5G, Wi-Fi, Bluetooth, and the Internet. , Local Area Network (LAN), Wireless Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), and the like.

For example, the energy management server 110 may be connected to the AI switch 130 of the apartment house 100 through 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 may be connected to a complex network (internal network). The energy management server 110 may be connected to the cloud server 120 through 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 may be an Internet network (external network).

The energy management server 110 may manage energy of the apartment house 100.

The energy management server 110 collects at least one piece of information collected from the AI switch 130, and collects at least one piece of information from the AI switch 130 immediately or periodically when the at least one piece of information is generated. 120).

The energy management server 110 may refine the collected at least one information into learning data through a pre-processing process of a machine learning algorithm. For example, the energy management server 110 may refine broken data or data that is not normally collected for at least one piece of information collected, 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 a 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 generation based on at least one piece of information, generates a temperature control schedule based on the generation pattern, and generates an AI switch 130 based on the generated temperature control schedule. ) Can be controlled.

The energy management server 110 may adjust the temperature of each household based on information of one of a plurality of modes received from a 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 may predict power usage and estimated charges after a preset time based on the monitored power usage and temperature control schedule. In addition, the energy management server 110 may predict power usage and estimated charges after a preset 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. For example, the cloud server 120 generates a machine learning algorithm based on at least one piece of information, periodically updates the generated machine learning algorithm, and generates and updates the machine learning algorithm to the energy management server 110 Can transmit.

According to an embodiment, when the energy management server 110 and the cloud server 120 are included in the automatic housing temperature control system 1, the energy management server 110 manages the energy of the shared housing 100 and , The cloud server 120 may provide a machine learning algorithm to the energy management server 110.

For example, the energy management server 110 collects at least one information from the AI switch 130 and transmits it to the cloud server 120, and receives the control information from the cloud server 120 to receive the AI switch 130. Can be controlled.

For another example, the energy management server 110 may control the AI switch 130 by receiving a control command from a user terminal (not shown). Also, the energy management server 110 may transmit at least one information received from the AI switch 130 to a user terminal (not shown).

That is, according to an embodiment, the cloud server 120 retrieves and learns external weather information, establishes a control plan accordingly, and other functions may be performed in the energy management server 110.

According to another embodiment, when the energy management server 110 and the cloud server 120 is included in the apartment house thermostat 1, and the energy management server 110 is equipped with a machine learning engine, the energy management server The energy management of the apartment house 100 and generation of a machine learning algorithm are performed at 110, and the cloud server 120 can provide only the update information of the machine learning algorithm to the energy management server 110.

For example, the energy management server 110 collects at least one piece of information from the AI switch 130, loads external weather information, learns it directly, generates control information, and then controls the AI switch 130. have. At this time, the energy management server 110 does not transmit at least one information collected from the AI switch 130 to the cloud server 120, and can receive only the update information of the machine learning algorithm from the cloud server 120. .

For another example, the energy management server 110 may control the AI switch 130 by receiving a control command from a user terminal (not shown). Also, the energy management server 110 may transmit at least one information received from the AI switch 130 to a user terminal (not shown).

That is, according to another embodiment, the energy management server 110 performs all functions performed in one implementation, but may also perform functions performed by the cloud server 120.

According to another embodiment, when the energy management server 110 is included in the automatic climate control system 1 of the apartment house and operates without communication with the cloud server 120, the energy management server 110 includes the apartment house 100 Energy management and machine learning algorithm generation, and the energy management server 110 does not receive update information of the machine learning algorithm from the cloud server 120.

That is, according to another embodiment, the energy management server 110 performs all functions performed in other embodiments, but does not collect update information of the machine learning algorithm from the cloud server 120.

The AI switch 130 may measure the indoor temperature of the corresponding household through a temperature sensor included in the environmental sensor. The environment 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 may detect whether the user is present within the corresponding household. For example, when the user's body is detected through the body detection sensor, the AI switch 130 may detect the presence of the user by tracking the user's body.

The AI switch 130 may monitor power consumption of the corresponding household.

The AI switch 130 may control the temperature of the corresponding generation based on the control information. For example, the AI switch 130 may control the AI switch 130 based on a temperature control schedule.

The AI switch 130 may control lighting of a corresponding generation based on control information or user control.

The AI switch 130 periodically collects instantaneous power for each outlet in a corresponding household to monitor standby power and cut off standby power. For example, the AI switch 130 determines a power value corresponding to standby power for each outlet based on the collected instantaneous power for each outlet, and when the current instantaneous power is equal to or less than the power value corresponding to standby power for a predetermined time, Current instantaneous power can be determined as standby power.

AI switch 130 may control the ventilation device in the household. For example, the AI switch 130 may activate the ventilation device when the CO ₂ concentration collected from the CO ₂ sensor exceeds a predetermined value. For another example, the AI switch 130 may activate the ventilation device when the humidity collected from the humidity sensor exceeds a preset value.

The AI switch 130 may notify the crime prevention information based on the information collected from the crime prevention sensor installed in a predetermined zone in the corresponding household.

The AI switch 130 may participate in a demand response by receiving demand management information from a demand management server (not shown) and controlling air conditioning and heating devices in the corresponding household based on the demand management information.

The AI switch 130 can detect the inflow of outside air into the corresponding generation.

The AI switch 130 may monitor the CO ₂ concentration per unit time from information collected from the CO ₂ sensor, and detect the inflow of outside air based on the CO ₂ concentration per unit time.

The AI switch 130 may output the temperature of the corresponding household, whether the user is present, power consumption, and the state of lighting. In addition, the AI switch 130 may output power usage and estimated charges after a predetermined time.

The AI switch 130 may be configured as a temperature-integrated type in which one device provides temperature control and lighting control, and uses a polling method of RS-485 serial communication as a home network, with a wall pad and a number of electronic products. Communication can be performed.

In general, the serial communication method includes a polling method and an event-driven protocol. The polling method is a method in which one server (wall pad) requests data every predetermined period to a plurality of clients (devices), and each device responds to the received data request and transmits data or control commands, so requesting data There cannot be more than two servers. The event-driven method transmits data only when an event occurs, so a plurality of devices for requesting and receiving data may be configured.

In the conventional temperature control system, the wall pad serves as a server, and a polling method of RS-485 serial communication is mainly used as a home network. Wallpad acts as a server and supervises protocols for all communication methods, data formats, and functions.

In the conventional room temperature control system, when the server, the wall pad, requests a control command or status information from the temperature valve controller, which is one client, the wall valve recognizes only the temperature valve controller as a client, so that the temperature valve controller is connected to the temperature controller of each room. It was constructed in a way that conveys status and control commands.

In this way, in the conventional temperature-integrated AI switch 130, it is difficult to set the timing of the reply, even if the server to issue the command has two wall pads (light control) and a temperature valve controller (temperature control). It has the disadvantage that the probability of an error increases as data loss due to communication collision is concerned. To solve this, the command control signal should be unified with the wall pad or unified with the valve controller. However, since the conventional wall pad needs to operate a variety of devices, it does not perform the function of temperature control, and was developed in such a way that it performs both temperature control and light control signal transmission from the valve controller.

Therefore, the temperature-integrated AI switch 130 capable of temperature control and lighting control proposed in the present invention is connected to a valve controller (not shown), and temperature control and lighting control are provided to the valve controller by a valve controller (not shown). Can be relayed to the wall pad. The valve controller (not shown) may receive a control command or status inquiry of the wall pad and transmit it to each temperature controller to receive or control the status value. The valve controller (not shown) may distinguish whether it is temperature-related communication or lighting-related communication by an ID value designated on the protocol. When the temperature-related signal is included on the ID, the AI switch 130 performs temperature-related performance, and when the light-related signal is included, performs the lighting-related performance.

For example, the AI switch of each room, which is a lower AI switch of the AI switch 130, accommodates or selects various functions such as temperature control, lighting, outlet control, power measurement, occupancy detection, ventilation control, and ventilation sensor measurement information. I can accept it. At this time, the AI switch of each room can use a standard protocol. The wall pad only proceeds with data communication, and the valve controller transmits a corresponding signal to the lower AI switch and, when receiving data from the lower switch, may return the received data to the wall pad. When the valve controller includes a temperature control function such as storing a temperature control state value of each room temperature control switch, it is possible to process not only simple communication transmission but also calculation according to the state value.

The remote meter reading server (not shown) collects cumulative usage from 5 types of meters of electricity, water, hot water, and heating, for example, in units of 1 hour, through a remote meter installed for each household of the apartment house 100, and stores them in the database. Can be saved. In addition, the remote meter reading server (not shown) can calculate the charge by calculating the energy consumption on a daily and monthly basis.

The remote meter reading server (not shown) may transmit energy usage to the AI switch 130 so that the AI switch 130 can monitor the power usage of the corresponding household of the apartment house 100.

The user terminal (not shown) may use the application associated with the AI switch 130 to request automatic operation to the AI switch 130 according to the user's pattern. For example, the user terminal (not shown) may request the AI switch 130 to perform pre-heating according to a time schedule, analysis of the user's interference according to a set temperature, and automatic operation according to presence or absence.

The user terminal (not shown) may present a user mode, and display usage and charge prediction for each mode on a display. When any one of a plurality of modes is selected as a user terminal (not shown) user mode, information on the selected mode may 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.

2 is a block diagram of an energy management server according to an embodiment of the present invention. Referring to FIG. 2, the energy management server 110 includes a data collection unit 210, a data transmission unit 220, a machine learning algorithm reception unit 230, a user pattern learning unit 240, and a temperature control schedule generation unit 250 ), the power usage prediction unit 260 and the AI switch control unit 270.

The data collection unit 210 may collect at least one 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 may learn generation patterns for each generation based on at least one piece of information.

The temperature control schedule generation unit 250 may generate a temperature control schedule based on the generation pattern.

The power usage predicting unit 260 may predict power usage and estimated charges after a preset time based on the monitored power capacity and temperature control schedule. In addition, the power usage prediction unit 260 may predict power usage and estimated charges after a preset time for each mode for the power saving mode, the normal mode, and the comfort mode. For example, the power usage prediction unit 260 may derive the usage amount based on the temperature control schedule based on the current usage amount of the expected energy (eg, electricity, heating, gas) usage. If the standard day for meter reading is 5 days, the predicted usage amount for 15 days can be calculated as'use up to 14 days + (operation plan for air-conditioning and heating equipment according to temperature control schedule * energy usage per hour of use)'. It can be calculated based on the system and estimated power consumption.

The AI switch control unit 270 may 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 may control the AI switch 130 based on the temperature control schedule.

The AI switch control unit 270 may adjust the temperature of each household based on information of one of a plurality of modes received from a user terminal (not shown). The plurality of modes may include a power saving mode, a normal mode and a comfortable mode.

3 is a flow chart of a method for managing energy in an 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 time-seriesly processed by the apartment house automatic temperature control system 1 according to the embodiment illustrated in FIGS. 1 and 2 Includes steps. Therefore, even if it is omitted below, it is also applied to a method of managing energy of an apartment house performed by the energy management server 110 according to the embodiment illustrated in FIGS. 1 and 2.

In step S310, the energy management server 110 may collect at least one information collected from the AI switch 130.

In step S320, the energy management server 110 may transmit the collected at least one information to the cloud server 120.

In step S330, the energy management server 110 may receive a machine learning algorithm from the cloud server 120.

In 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.

In the above description, steps S310 to S340 may be further divided into additional steps or combined into fewer steps, according to an embodiment of the present invention. In addition, some steps may be omitted if necessary, and the order between the steps may be switched.

4 is a configuration diagram of a cloud server according to an embodiment of the present invention. Referring to FIG. 4, the cloud server 120 may include an algorithm generator 410 and an algorithm transmitter 420.

The algorithm generator 410 may generate and update a machine learning algorithm based on at least one piece of information.

The algorithm transmission unit 420 may transmit the machine learning algorithm to the energy management server 110.

5 is a flow chart of a method for providing a machine learning algorithm for controlling the temperature of each household equipped with an AI switch in an apartment house in a cloud server according to an embodiment of the present invention. A method of providing a machine learning algorithm for controlling the temperature of each household equipped with an AI switch in a common house performed by the cloud server 120 shown in FIG. 5 is a joint according to the embodiments shown in FIGS. 1 to 4. It includes steps that are processed in time series in the home thermostat system 1. Accordingly, even if omitted, the machine learning algorithm for controlling the temperature of each household equipped with an AI switch in the apartment house performed by the cloud server 120 according to the embodiment illustrated in FIGS. 1 to 4 is provided. Method also applies.

In step S510, the cloud server 120 may periodically update the machine learning algorithm based on at least one piece of information.

In step S520, the cloud server 120 may transmit the updated machine learning algorithm to the energy management server 110.

In the above description, steps S510 to S520 may be further divided into additional steps or combined into fewer steps, according to an embodiment of the present invention. In addition, some steps may be omitted if necessary, and the order between the steps may be switched.

6 is a configuration diagram of an AI switch according to an embodiment of the present invention. Referring to FIG. 6, the AI switch 130 includes an environmental sensor 610, a motion detection sensor 620, a presence detection unit 630, a power usage monitoring unit 640, a temperature control unit 650, and a lighting control unit 660 ), a standby power cut-off unit 670, a ventilation control unit 680, a crime prevention unit 690, a demand response participation unit 700 and an output unit 710.

The environmental sensor 610 may include a temperature sensor that measures the indoor temperature of the corresponding household 600 of the apartment house 100. For example, 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 may detect the moving body of the user 603. The fuselage detection sensor 620 is configured with high sensitivity, and can detect the movement of the user 603 even when the user 603 moves even a little.

The presence detector 630 may detect whether the user 603 is present within the corresponding generation 600. Specifically, the occupancy sensor 630 may detect the presence of the user by tracking the body of the user 603. For example, when the user 603 enters and sleeps in a large room through the living room within the corresponding generation 600, the presence detector 630 detects the presence of the user 603 in the living room->big room detection and living room The body of the user 603 may be detected in the form of undetected->not detected in the large room and not detected in the living room. At this time, the presence detector 630 may detect that there is no movement of the user 603 after the user 603 moves to the large room through the living room, and may determine that the presence of the occupant exists in the large room. .

The power usage monitoring unit 640 may monitor power usage of the corresponding household 600. For example, the power usage monitoring unit 640 may receive and monitor the cumulative usage for each hour, such as electricity, heating, and gas, from the database of the remote meter reading server.

The temperature controller 650 may control the temperature of the corresponding household based on the control information.

The lighting control unit 660 may control lighting of the corresponding generation 600 based on control information or user control.

The standby power blocking unit 670 periodically collects instantaneous power for each outlet in the corresponding household 600 to monitor standby power and cut off 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 instantaneous power for each outlet, and when the current instantaneous power is less than or equal to the standby power for a predetermined time , It is possible to determine the current instantaneous power as standby power.

Conventionally, in order to cut off standby power by using the AI switch 130, in a state in which the user 603 has turned off all household appliances (consuming only standby power), the user of the AI switch 130 from the user It was possible to set the standby power by receiving the standby power cutoff value storage button and storing the power consumption at that time. Subsequently, if a certain period of time continues in a state in which the power consumption is less than the stored value, the AI switch 130 can cut off the power. However, the conventional AI switch 130 is difficult to use because the user 603 has to artificially create a standby power state, and there is a lot of inconvenience because the household appliances connected to the outlet need to be reset every time they change.

However, in the present invention, the standby power cut-off unit 670 periodically collects instantaneous power for each outlet, monitors the lowest value excluding 0 based on 24 hours, and the minimum value is maintained for a preset time (for example, 1 hour) or more. In this case, it may be determined that standby power is generated. Through this, the standby power cut-off unit 670 can detect standby power and induce standby power detection notification and automatic setting every day by checking the lowest value every 24 hours even if the household appliances connected to the outlet are different. It is possible to provide power cutoff.

The ventilation control unit 680 may control a ventilation device in the corresponding household 600. For example, when the CO ₂ concentration collected from the CO ₂ sensor exceeds a preset value, the ventilation control unit 680 operates the ventilation device on and when the CO ₂ concentration becomes lower than the preset value. , It is possible to operate the ventilation device off. For another example, the ventilation control unit 680 may operate the ventilation device when the humidity collected from the humidity sensor exceeds a predetermined value. The ventilation control unit 680 may perform on/off control, timer control, and intensity control for the ventilation device.

The crime prevention unit 690 may notify the crime prevention information based on information collected from the crime prevention sensor installed in a predetermined area in the corresponding household 600.

The demand response participation unit 700 may participate in demand response by receiving demand management information from a demand management server (not shown) and controlling air conditioning and heating devices in the corresponding household 600 based on the demand management information. . That is, the AI switch 130 may be used as an actuator of demand management resources.

Demand management means that when a power supply company receives power from a power plant, it receives a supply from an expensive energy source in the peak demand (maximum power demand) section. In this case, when demand decreases, it does not receive power from an expensive energy source. Therefore, as it becomes possible to lower the cost of electric power purchase, it means driving the vehicle in a manner that returns the profit to the consumer by refraining from using energy during peak hours at the consumer.

In the case of the multi-family house 100, demand management was not applied due to an increase in public relations expenses, unguaranteed participation rate, etc., because many houses must refrain from or use electricity at the time. However, if permission is obtained from the customer in advance, if the air conditioning and heating devices can be directly controlled, such as the AI switch 130, management is possible as a demand resource, so the AI switch 130 can be used as an actuator for demand management resources. .

The demand response participant 700 can manage demand on the basis of the total energy of the apartment house 100, so that peak control becomes possible and can be used as a useful means. For example, in the case of an apartment of 100 households, if the air conditioner is used sequentially for 5 seconds, 30 seconds, and 10 households, the individual households are not heated very much, but the whole apartment is 1/10 of the energy simultaneously used by 100 households. It is reduced to enable effective demand management.

The outside air inflow detection unit (not shown) may detect the inflow of outside air into the corresponding generation 600. Specifically, the outside air inflow detection unit may monitor the CO ₂ concentration per unit time from information collected from the CO ₂ sensor, and detect the inflow of outside air based on the CO ₂ concentration per unit time. For example, when the window of the corresponding household 600 is opened and the outside air flows in, when the CO ₂ per unit time and the temperature change exceeds a threshold value, the outside air inflow detection unit may detect that the outside air flows in.

The output unit 710 may output the temperature of the corresponding generation 600, whether the user 603 is present, the power consumption, and the state of lighting. In addition, the output unit 710 may output power usage and estimated charges after a preset time.

7 is a block diagram of an automatic climate control system for an apartment house according to another embodiment of the present invention.

Referring to FIG. 7, the apartment house thermostat system may include a cloud server 120 and an AI switch 130.

The cloud server 120 may directly manage the energy of the apartment house 100. For example, the cloud server 120 collects at least one piece of information collected from the AI switch 130, and refines the collected at least one piece of information into learning data through a pre-processing process of the machine learning algorithm, thereby learning the machine learning algorithm. And generating and updating the control information to control the AI switch 130 to adjust the temperature of each generation based on the machine learning algorithm. The cloud server 120 learns generation patterns for each generation based on at least one piece of information, generates a temperature control schedule based on the generation patterns, and controls the AI switch 130 based on the generated temperature control schedule. You can.

That is, since the cloud server 120 directly manages the energy of the apartment house 100, the energy cost of the apartment house can be managed, thereby reducing the energy of the apartment house.

The method for automatically controlling the temperature of an apartment house 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 or a recording medium including instructions executable by the computer. In addition, the method for automatically controlling the temperature of an apartment house described with reference to FIGS. 1 to 7 may 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. In addition, computer readable media may 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.

The above description of the present invention is for illustration only, and a person having ordinary knowledge in the technical field to which the present invention pertains can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the above detailed description, and it should be interpreted that all changes or modified forms derived from the meaning and scope of the claims and equivalent concepts thereof are included in the scope of the present invention. do.

100: apartment house
110: energy management server
120: cloud server
130: AI switch
210: data collection unit
220: data transmission unit
230: machine learning algorithm receiver
240: user pattern learning unit
250: temperature control schedule generation unit
260: power consumption prediction unit
410: algorithm generation unit
420: algorithm transmission unit
600: apartment house generation
601: air conditioning
602: lighting
610: temperature sensor
620: fuselage detection sensor
630: presence detector
640: power usage monitoring unit
650: temperature control
660: lighting control
670: standby power cut-off
680: ventilation control
690: crime prevention department
700: Demand response part
710: output unit

Claims (13)

In the apartment house automatic temperature control system to control the temperature of each household with an AI switch in the apartment house,
Energy management server for managing the energy of the apartment house;
AI switches installed in each generation; And
Includes a valve controller that is installed for each generation,
The energy management server
A data collection unit collecting at least one information collected from the AI switch; And
And an AI switch control unit controlling the AI switch to adjust the temperature of each generation using control information generated from the machine learning algorithm based on the at least one information.
The AI switch
Environmental sensor including a temperature sensor for measuring the indoor temperature of the household; And
It includes a temperature control unit for controlling the temperature of the generation based on the control information,
The AI switch is connected to the valve controller of the corresponding generation,
The valve controller connected to the AI switch relays control of the temperature of the corresponding generation between the AI switch and the wall pad of the corresponding generation,
Further comprising a lower AI switch of the AI switch,
The lower AI switch,
A standby power cut-off unit that periodically collects instantaneous power for each outlet in the household to monitor standby power and cut off the standby power.
Including,
The standby power cut-off unit determines a power value corresponding to standby power for each outlet based on the instantaneous power for each outlet, and when the current instantaneous power is less than or equal to the power value corresponding to the standby power for a predetermined time, the current Apartment thermostat system that judges instantaneous power as standby power.
According to claim 1,
The at least one piece of information includes at least one of power consumption of each household, 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. Thermostat system.
According to claim 2,
The energy management server includes a user pattern learning unit that learns generation patterns for each generation based on the at least one information; And
Temperature control schedule generation unit for generating a temperature control schedule based on the generation pattern
It further comprises, the thermostat of a multi-family house.
The method of claim 3,
The AI switch control unit is to control the AI switch on the basis of the temperature control schedule, apartment automatic climate control system.
According to claim 1,
The apartment house thermostat system further includes a cloud server,
The energy management server
A data transmission unit transmitting the collected at least one information to the cloud server; And
Further comprising a machine learning algorithm receiving unit for receiving the machine learning algorithm from the cloud server,
The cloud server
An algorithm generator for generating and updating the machine learning algorithm for each generation based on at least one information received from the energy management server; And
Machine learning algorithm transmission unit for transmitting the machine learning algorithm generated and updated for each generation to the energy management server
Included, the apartment house thermostat system.
According to claim 1,
The lower AI switch,
A presence detector that detects whether a user is present within the household.
It further comprises, the thermostat of a multi-family house.
The method of claim 6,
The lower AI switch,
A lighting control unit controlling lighting of the corresponding generation based on the control information or the control of the user;
An output unit that outputs at least one of the temperature, power consumption, and the lighting state of the corresponding household; And
Ventilation control unit for controlling the ventilation device in the household
It further comprises, the thermostat of a multi-family house.
delete The method of claim 6,
The occupancy sensing unit tracks the user's fuselage and detects whether the user is present or not.
The method of claim 7,
The ventilation control unit is to operate the ventilation device when the concentration of CO ₂ collected from the CO ₂ sensor exceeds a predetermined value, the automatic climate control system for apartment houses.
The method of claim 7,
The ventilation control unit is to operate the ventilation device when the humidity collected from the humidity sensor exceeds a predetermined value, automatic climate control system for apartment houses.
According to claim 1,
The AI switch control unit adjusts the temperature of each household based on information of one of a plurality of modes received from the user terminal,
The plurality of modes includes at least one of a power saving mode, a normal mode and a comfortable mode, the automatic climate control system for apartment houses.
In the apartment house automatic temperature control system to control the temperature of each household with an AI switch in the apartment house,
Cloud server;
AI switches installed in each generation; And
Includes a valve controller that is installed for each generation,
The cloud server
Collect data to collect at least one information collected from the AI switch,
Using the control information generated from the machine learning algorithm based on the at least one information to control the AI switch to adjust the temperature of each generation,
The AI switch
Environmental sensor including a temperature sensor for measuring the indoor temperature of the household; And
It includes a temperature control unit for controlling the temperature of the generation based on the control information,
The AI switch is connected to the valve controller of the corresponding generation,
The valve controller connected to the AI switch relays control of the temperature of the corresponding generation between the AI switch and the wall pad of the corresponding generation,
Further comprising a lower AI switch of the AI switch,
The lower AI switch,
A standby power cut-off unit that periodically collects instantaneous power for each outlet in the household to monitor standby power and cut off the standby power.
Including,
The standby power cut-off unit determines a power value corresponding to standby power for each outlet based on the instantaneous power for each outlet, and when the current instantaneous power is less than or equal to the power value corresponding to the standby power for a predetermined time, the current Apartment thermostat system that judges instantaneous power as standby power.

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