KR102209210B1 - System for automatically controlling temperature of apartment - Google Patents

Abstract

The apartment house automatic temperature control system that controls the temperature of each household equipped with an AI switch in the apartment house includes an energy management server that manages energy in the apartment house, an AI switch installed for each household, and a controller installed for each of the households. , The energy management server uses a data collection unit for collecting at least one piece of information collected from the AI switch and control information generated from a machine learning algorithm based on the at least one piece of information, and the AI switch sets the temperature of each household. The AI switch includes an environment sensor including a temperature sensor for measuring the indoor temperature of the household and a temperature controller for controlling the temperature of the household based on the control information. And, the AI switch is connected to the controller of the corresponding household, and the controller connected to the AI switch relays the temperature control of the corresponding household between the AI switch and the wall pad of the corresponding household.

Description

Apartment house automatic temperature control system {SYSTEM FOR AUTOMATICALLY CONTROLLING TEMPERATURE OF APARTMENT}

The present invention relates to an automatic temperature control system for an apartment house for controlling the temperature of each household equipped with an AI switch in the apartment house.

Thermostat refers to a device that automatically adjusts the temperature of a specific place to a required constant value. The automatic thermostat can detect the temperature itself and heat and cool it. The automatic temperature control device has the advantage of being able to efficiently manage energy by automatically adjusting the room temperature by detecting the presence or absence of occupants in the room.

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

Conventional automatic temperature control devices provide a function to automatically adjust the indoor temperature by detecting the presence of occupants in the room, but when the user is in a rest or sleeping state and there is no movement, the human body to the inactive person cannot be detected. There was also an unnecessary waste of energy.

It intends to provide an automatic temperature control system for apartment houses that combines the functions of a cooling/heating thermostat with the existing standby power cut-off switch. Through the AI switch with built-in smart sensor using IoT technology, integrated energy meter reading, and linkage of smart home appliances, a machine learning algorithm based on big data is applied in the smart home to provide convenience and comfort in apartment houses, and energy savings. It is intended to provide a multi-family thermostat system that provides driving. It is intended to provide an automatic temperature control system for apartment houses that automatically controls indoor temperature and lighting according to the user's life pattern by learning the user's life pattern through a machine learning algorithm. It is intended to provide an automatic temperature control system for apartment houses that prevents unnecessary waste of energy by removing unnecessary elements of energy waste through detection of the occupancy and inflow of outside air. However, the technical problem to be achieved by the present embodiment is not limited to the technical problems as described above, and other technical problems may exist.

As a means for achieving the above-described technical problem, an embodiment of the present invention includes an energy management server for managing energy in a multi-family house, an AI switch installed for each household, and a controller installed for each household, wherein the energy The management server allows the AI switch to control the temperature of each household using control information generated from a data collection unit for collecting at least one information collected from the AI switch and a machine learning algorithm based on the at least one information. The AI switch includes an environmental sensor including a temperature sensor for measuring the indoor temperature of the household and a temperature controller for controlling the temperature of the corresponding household based on the control information, and the The AI switch is connected to the controller of the corresponding household, and the controller connected to the AI switch relays the control of the temperature of the corresponding household between the AI switch and the wall pad of the corresponding household. can do.

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

The above-described problem solving means are merely exemplary and should not be construed as limiting the present invention. In addition to the above-described exemplary embodiments, 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 an apartment house in which the function of a cooling/heating temperature controller is combined with an existing standby power cut-off switch. Through the AI switch with built-in smart sensor using IoT technology, integrated energy meter reading, and linkage of smart home appliances, a machine learning algorithm based on big data is applied in the smart home to provide convenience and comfort in apartment houses, and energy savings. It can provide a multi-family thermostat system that provides driving. By learning a user's life pattern through a machine learning algorithm, it is possible to provide an automatic temperature control system for an apartment house that automatically controls indoor temperature and lighting according to the user's life pattern. It is possible to provide an automatic temperature control system for apartment houses that prevents unnecessary waste of energy by removing unnecessary elements of energy waste through detection of the occupancy and inflow of outside air.

1 is a block diagram of an automatic temperature 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 a multi-family 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 flowchart of a method of providing a machine learning algorithm for controlling the temperature of each household equipped with an AI switch in a multi-family house in a cloud server according to an embodiment of the present invention.
6 is a block diagram of an AI switch according to an embodiment of the present invention.
7 is a block diagram of an automatic temperature 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 of ordinary skill in the art can easily implement the present invention. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and similar reference numerals are assigned to similar parts throughout the specification.

Throughout the specification, when a part is said to be "connected" to another part, this includes not only "directly connected" but also "electrically connected" with another element interposed therebetween. . In addition, when a part "includes" a certain component, it means that other components may be further included, and one or more other features, not excluding other components, unless specifically stated to the contrary. It is to be understood that it does not preclude the presence or addition of any number, step, action, component, part, or combination thereof.

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

In the present specification, some of the operations or functions described as being performed by the terminal or device may be performed instead by a server connected to the terminal or device. Likewise, some of the operations or functions described as being performed by the server may also be performed by a terminal or device connected to the server.

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

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

Each of the components of the multi-family thermostat system 1 of FIG. 1 is generally connected through 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 at the same time or at a time interval.

Network refers to a connection structure that enables information exchange between nodes such as terminals and servers, and examples of such networks are 3G, 4G, 5G, Wi-Fi, Bluetooth, and Internet. , LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), and the like are included, but are not limited thereto.

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 refers to a communication method between the energy management server 110 and the AI switch 130 of the apartment house 100, and may be connected through 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 refers to 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 the energy of the apartment house 100.

The energy management server 110 collects at least one information collected from the AI switch 130, and uses the collected at least one information as soon as at least one information is generated from the AI switch 130 or periodically a cloud server ( 120).

The energy management server 110 may refine the collected at least one piece of information into learning data through a preprocessing process of a machine learning algorithm. For example, the energy management server 110 may refine broken data or data not normally collected for at least one piece of collected information 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 usage, heating information of each room of each household, lighting information of each room of each household, information about an outlet of each room of each household, and the like.

The energy management server 110 may receive a machine learning algorithm from the cloud server 120 and generate control information for controlling 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 on 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 an estimated charge after a preset time based on the monitored power usage and temperature control schedule. In addition, the energy management server 110 may predict power consumption and an estimated charge 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 transfers the generated and updated machine learning algorithm to the energy management server 110. Can be transmitted.

According to an embodiment, when the energy management server 110 and the cloud server 120 are included in the apartment housing automatic temperature control system 1, the energy management server 110 manages the energy of the apartment house 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 piece of information from the AI switch 130 and transmits it to the cloud server 120, and receives control information from the cloud server 120 to enable 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). 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).

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

According to another embodiment, when the energy management server 110 and the cloud server 120 are included in the apartment automatic temperature control system 1, and the energy management server 110 is equipped with a machine learning engine, the energy management server In 110, energy management of the apartment 100 and generation of a machine learning algorithm may be performed, and the cloud server 120 may provide only update information of the machine learning algorithm to the energy management server 110.

For example, the energy management server 110 may collect at least one piece of information from the AI switch 130, retrieve external weather information, and directly learn to generate control information, and then control the AI switch 130. have. In this case, the energy management server 110 may not transmit at least one information collected from the AI switch 130 to the cloud server 120, and may 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). 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).

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

According to another embodiment, when the energy management server 110 is included in the apartment house automatic temperature control system 1 and operates without communication with the cloud server 120, the energy management server 110 is the apartment house 100 Energy management and machine learning algorithm generation, etc. are performed, 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 the 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 a corresponding household through a temperature sensor included in the environmental sensor. The environmental sensor may include, for example, an outdoor temperature sensor, a CO ₂ sensor, a humidity sensor, and an air quality measurement sensor.

The AI switch 130 may detect whether a user is occupied within a corresponding household. For example, when the user's moving body is detected through the moving object detection sensor, the AI switch 130 may track the user's moving body and detect whether the user is occupied.

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

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

The AI switch 130 may periodically collect instantaneous power for each outlet within 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 less than or equal to the power value corresponding to standby power for a preset time, Current instantaneous power can be determined as standby power.

The AI switch 130 may control ventilation devices within the household. For example, the AI switch 130 may operate the ventilation device when the concentration of CO ₂ collected from the CO ₂ sensor exceeds a preset value. For another example, the AI switch 130 may operate the ventilation device when the humidity collected from the humidity sensor and a preset value are exceeded.

The AI switch 130 may notify security information based on information collected from a security sensor installed in a preset area within a 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 the cooling and heating devices in the corresponding household based on the demand management information.

The AI switch 130 may detect 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 detect the inflow of outside air based on the CO ₂ concentration per unit time.

The AI switch 130 may output a temperature of a corresponding household, whether a user is occupied, power consumption, and lighting status. In addition, the AI switch 130 may output power usage and an estimated charge after a preset time.

This AI switch 130 may be configured as a temperature integrated type in which one device provides temperature control and lighting control, and by using a polling method of RS-485 serial communication as a home network, a wall pad and a plurality 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 from multiple clients (devices) at a fixed period, and each device responds to the received data request and transmits data or control commands. There cannot be more than two servers. Since the event-driven method transmits data only when an event occurs, a plurality of devices that request and receive data may be configured.

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

In the conventional temperature control system for each room, when the wall pad, which is a server, requests control command or status information from the temperature valve controller, which is a single client, the wall pad recognizes only the temperature valve controller as a client. It is structured in a way that conveys status and control commands.

As described above, in the conventional temperature-integrated AI switch 130, as the server to issue the command has two wall pads (light control) and a temperature valve controller (temperature control), it is difficult to match the response timing, even if the response timing is set. It has the disadvantage of increasing the probability of an error occurring as data loss due to communication collision is concerned. In order to solve this problem, the command control signal must be unified by the wall pad or the valve controller. However, since the conventional wall pad has to operate various devices, it does not perform a function of temperature control, and has been developed in a way that both temperature control and light control signal transmission are performed by a 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 performed by the valve controller (not shown). It can be relayed to the wall pad. The valve controller (not shown) may receive a control command or a status inquiry of the wall pad, and transmit it to each temperature controller to receive or control a status value. The valve controller (not shown) can classify whether it is a temperature-related communication or a 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, it performs 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 all various functions such as temperature control, lighting, outlet control, power measurement, occupancy detection, ventilation control, and ventilation sensor measurement information. Can be accommodated. At this time, the AI switch in each room can use the standard protocol. The wall pad only performs data communication, and the valve controller transmits the signal to the lower AI switch, and when receiving data from the lower switch, the received data can be returned to the wall pad. In the case of including a temperature control function such as storing the temperature control state value of each room temperature control switch in the valve controller, it is possible to process not only simple communication transmission but also an operation according to the state value.

The remote meter reading server (not shown) collects the cumulative usage from five meters of electricity, water, hot water, and heating through a remote meter installed for each household of the apartment house 100, for example, in an hour Can be saved. In addition, the remote meter reading server (not shown) may calculate a charge by calculating energy usage 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 in the apartment house 100.

The user terminal (not shown) may request automatic driving from the AI switch 130 according to a user's pattern by using an application associated with the AI switch 130. For example, the user terminal (not shown) may request the AI switch 130 for pre-cooling and heating according to a time schedule, an interference analysis of the user according to a set temperature, and automatic operation according to whether or not to occupy the room.

The user terminal (not shown) may present a user mode and display the usage and charge prediction for each mode on the display. User terminal (not shown) When any one of a plurality of modes is selected as a 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 receiving unit 230, a user pattern learning unit 240, and a temperature control schedule generation unit 250. ), a power usage prediction unit 260 and an AI switch control unit 270 may be included.

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 usage, heating information of each room of each household, lighting information of each room of each household, information about an outlet of each room of each household, and the like.

The data transmission unit 220 may transmit the collected at least one piece of information to the cloud server 120.

The machine learning algorithm receiver 230 may receive a machine learning algorithm from the cloud server 120.

The user pattern learning unit 240 may learn a generation pattern for each generation based on at least one piece of information.

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

The power consumption prediction unit 260 may predict power consumption and an estimated charge after a preset time based on the monitored power sha capacity and temperature control schedule. In addition, the power usage predictor 260 may predict power usage and an estimated charge after a preset time for each mode for the power saving mode, the normal mode, and the comfortable mode. For example, the power consumption prediction unit 260 may derive the amount of use based on the temperature control schedule based on the current amount of energy (eg, electricity, heating, gas) consumption. If the meter reading reference date is 5 days, the predicted usage for 15 days can be calculated as ``use up to 14 days + (air conditioner operation plan by temperature control schedule * energy consumption per hour of use)''. System, can be calculated from the estimated power consumption.

The AI switch controller 270 may generate control information for controlling the AI switch 130 to adjust the temperature of each household based on a machine learning algorithm.

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

The AI switch controller 270 may adjust the temperature of each household based on information on 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 a multi-family house in an energy management server according to an embodiment of the present invention. The energy management method of the apartment house performed by the energy management server 110 shown in FIG. 3 is processed in a time series by the apartment house automatic temperature control system 1 according to the embodiment shown in FIGS. 1 and 2. Includes steps. Therefore, even if 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 exemplary embodiment shown in FIGS. 1 and 2.

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

In step S320, the energy management server 110 may transmit the collected at least one piece of 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 for controlling the AI switch to adjust the temperature of each household based on a 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 as necessary, and the order between 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 generating unit 410 and an algorithm transmitting unit 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 flowchart of a method of providing a machine learning algorithm for controlling the temperature of each household equipped with an AI switch in a multi-family 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 common method according to the embodiment shown in FIGS. 1 to 4. It includes steps that are processed in time series in the house thermostat system 1. Therefore, even if omitted below, a 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 shown in FIGS. 1 to 4 is provided. The 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 as necessary, and the order between steps may be switched.

6 is a block diagram of an AI switch according to an embodiment of the present invention. 6, the AI switch 130 includes an environment sensor 610, a moving body detection sensor 620, an occupancy detection unit 630, a power consumption 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 quality measurement sensor.

The moving body detection sensor 620 may detect the moving body of the user 603. The moving body 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 occupancy detector 630 may detect whether the user 603 is occupied within the corresponding household 600. Specifically, the occupancy detector 630 may detect the occupancy by tracking the body of the user 603. For example, when the user 603 enters a large room through the living room within the household 600 and sleeps, the occupancy detector 630 detects the occupancy of the user 603 in the living room -> detects the large room and The moving body of the user 603 can be detected in the form of undetected -> large room undetected and living room undetected. At this time, the occupancy detector 630 may determine that the occupant is present in the large room when it is detected that the user 603 does not move after the user 603 moves to the large room through the living room. .

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

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

The lighting controller 660 may control the lighting of the corresponding household 600 based on control information or user control.

The standby power cut-off unit 670 may periodically collect instantaneous power for each outlet within the household 600 to monitor standby power and cut off standby power. Specifically, the standby power cut-off unit 670 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 less than or equal to the power value corresponding to standby power for a preset time , The current instantaneous power may be determined as standby power.

Conventionally, in a state in which the user 603 turns off all home appliances (a state in which only standby power is consumed) in order to cut off standby power using the AI switch 130, the AI switch 130 It was possible to set the standby power by receiving the standby power cutoff value save button and storing the power consumption at that time. Thereafter, when a certain amount of time continues in a state in which the power consumption is less than the stored value, the AI switch 130 may 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 it has to be reset whenever the home appliance connected to the outlet changes.

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 lowest value lasts longer than a preset time (for example, 1 hour). If so, it can be determined that standby power has been generated. Through this, the standby power cut-off unit 670 detects standby power by checking the minimum value every 24 hours, even if the home appliance connected to the outlet is different, and can induce standby power detection notification and automatic setting every day. It is possible to provide power cutoff.

The ventilation control unit 680 may control a ventilation device within the 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 the CO ₂ concentration is lower than the preset value. , The ventilation device can be turned off. For another example, the ventilation control unit 680 may operate the ventilation device when the humidity collected from the humidity sensor exceeds a preset value. The ventilation control unit 680 may perform on/off control, timer control, and strength and weakness control for the ventilation device.

The security unit 690 may notify security information based on information collected from a security sensor installed in a preset area within the household 600.

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

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

In the case of the apartment house 100, since a number of houses should refrain from or reduce the use of electricity during the time period, demand management was not applied for reasons such as an increase in public relations costs and an unguaranteed participation rate. However, in the case of obtaining permission from the customer in advance, if the heating and cooling equipment can be directly controlled, such as the AI switch 130, management is possible as a demand resource, so that the AI switch 130 can be utilized as an actuator of the demand management resource. .

The demand response participant 700 can manage the demand of the total energy of the apartment house 100 as a unit, so that peak control is 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 30 seconds every 5 minutes and 10 households in sequence, the individual households are not very hot and the whole apartment is 1/10 of the energy used by 100 households at the same time. As a result, effective demand management becomes possible.

The outside air inflow detector, not shown, may detect the inflow of outside air into the corresponding household 600. Specifically, the outdoor 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 outside air is introduced, the outdoor air inflow detector may detect that outside air has been introduced when CO ₂ per unit time and temperature change exceed a threshold value.

The output unit 710 may output the temperature of the household 600, whether the user 603 is occupied, power consumption, and lighting conditions. In addition, the output unit 710 may output power usage and an estimated charge after a preset time.

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

Referring to FIG. 7, the automatic temperature control system for an apartment house 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 information collected from the AI switch 130 and refines the collected at least one information into learning data through a preprocessing process of a machine learning algorithm, It is possible to generate and update control information to control the AI switch 130 to adjust the temperature of each generation based on a machine learning algorithm. The cloud server 120 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 controls the AI switch 130 based on the generated temperature control schedule. I can.

That is, since the cloud server 120 directly manages the energy of the apartment house 100, it is possible to reduce the energy of the apartment house by managing the energy cost of the apartment house.

The automatic temperature control method for 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 or a recording medium including instructions executable by a computer. In addition, the automatic temperature control method for an apartment house described through 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. Further, the computer-readable medium may include a computer storage medium. 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 illustrative purposes only, and those of ordinary skill in the art to which the present invention pertains will be able to 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 limiting. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as being distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the claims to be described later rather than the detailed description, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being 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 generator
420: algorithm transmission unit
600: apartment house household
601: air conditioner
602: lighting
610: temperature sensor
620: moving body detection sensor
630: occupancy detection unit
640: power consumption monitoring unit
650: temperature control unit
660: lighting control unit
670: standby power cut-off
680: ventilation control
690: Security Department
700: demand response participant
710: output

Claims (13)

In the apartment house automatic temperature control system that controls the temperature of each household equipped with an AI switch in the apartment house,
An energy management server for managing energy in apartment houses;
AI switches installed in each household; And
Including a controller installed for each of the households,
The energy management server
A data collection unit for collecting at least one piece of information collected from the AI switch; And
Including an AI switch control unit for controlling the AI switch to adjust the temperature of each generation by using control information generated from a machine learning algorithm based on the at least one information,
The AI switch is
An environmental sensor including a temperature sensor measuring the indoor temperature of the household; And
And a temperature controller for controlling the temperature of the corresponding household based on the control information,
The AI switch is connected to the controller of the corresponding household,
The 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,
The AI switch communicates through serial communication including a polling method or an event-driven protocol,
The controller receives a control command or status inquiry of the wall pad, and receives a status value from the AI switch or controls the AI switch based on the received control command or status inquiry, automatic temperature control of an apartment house system.
The method of claim 1,
The at least one 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 Automatic temperature control system.
The method of claim 2,
The energy management server may include a user pattern learning unit that learns a generation pattern for each generation based on the at least one piece of information; And
Temperature control schedule generation unit that generates a temperature control schedule based on the generation pattern
It will further include, apartment housing automatic temperature control system.
The method of claim 3,
The AI switch control unit controls the AI switch based on the temperature control schedule.
The method of claim 1,
The apartment housing automatic temperature control system further comprises a cloud server,
The energy management server
A data transmission unit for transmitting the collected at least one piece of 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 piece of information received from the energy management server; And
A machine learning algorithm transmission unit for transmitting the machine learning algorithm generated and updated for each generation to the energy management server
Including, multi-family thermostat system.
The method of claim 1,
Further comprising a lower AI switch of the AI switch,
The lower AI switch,
An occupancy detector that detects whether the user is occupied within the household.
Including that, apartment housing automatic temperature control system.
The method of claim 6,
The lower AI switch,
A lighting controller configured to control lighting of the corresponding household based on the control information or the user's control;
A standby power cut-off unit configured to periodically collect instantaneous power for each outlet in the corresponding household to monitor standby power and cut off the standby power;
An output unit that outputs at least one of the temperature of the household, the power consumption, and the lighting state; And
Ventilation control unit for controlling the ventilation device in the household
It will further include, apartment housing automatic temperature control system.
The method of claim 7,
The standby power cut-off unit 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 less than or equal to the power value corresponding to the standby power for a preset time, the current An apartment house automatic temperature control system that judges instantaneous power as standby power.
The method of claim 6,
The occupancy detection unit is to detect whether the occupancy of the user by tracking the body of the user, apartment apartment automatic temperature control system.
The method of claim 7,
The ventilation control unit will operate the ventilation device when the concentration of CO ₂ collected from the CO ₂ sensor exceeds a preset value, the automatic temperature control system for a apartment house.
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 preset value, apartment housing automatic temperature control system.
The method of claim 1,
The AI switch control unit adjusts the temperature of each household based on information on one of a plurality of modes received from the user terminal,
The plurality of modes include at least one of a power saving mode, a normal mode, and a comfortable mode.
In the apartment house automatic temperature control system that controls the temperature of each household equipped with an AI switch in the apartment house,
Cloud server;
AI switches installed in each household; And
Including a controller installed for each of the households,
The cloud server
Collecting data for collecting at least one piece of information collected from the AI switch,
Controlling the AI switch to adjust the temperature of each generation by using control information generated from a machine learning algorithm based on the at least one information,
The AI switch is
An environmental sensor including a temperature sensor measuring the indoor temperature of the household; And
And a temperature controller for controlling the temperature of the corresponding household based on the control information,
The AI switch is connected to the controller of the corresponding household,
The 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,
The AI switch communicates through serial communication including a polling method or an event-driven protocol,
The controller receives a control command or status inquiry of the wall pad, and receives a status value from the AI switch or controls the AI switch based on the received control command or status inquiry, automatic temperature control of an apartment house system.

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