KR20180138463A - Cloud server and method for controlling cooling system of target building based on artificial intelligence, and apparatus for controlling cooling system - Google Patents

Abstract

A cloud server for controlling a cooling system of a target building includes: a receiving unit for receiving actual situation data on a plurality of cooling equipment included in the cooling system from a cooling system control unit installed in the target building; a derivation unit for generating a plurality of control signal sets for controlling the cooling system, performing a rewarding operation on each of the plurality of control signal sets based on the actual situation data based on reinforcement learning, and deriving one of the plurality of control signal sets based on the rewarding operation; and a transmitting unit for transmitting the derived control signal set to the cooling system control unit. The derivation unit provides rewards to each of the plurality of control signal sets based on the reference energy consumption amount corresponding to the actual situation data and the total energy consumption amount corresponding to each of the plurality of control signal sets, and the cooling system is operated to control the plurality of cooling equipment based on the transmitted control signal set.

Description

Technical Field [0001] The present invention relates to a cloud server and method for optimally controlling a cooling system of a target building on the basis of AI, a cooling system control device, and a cooling system control device.

The present invention relates to a cloud server and method for optimally controlling a cooling system of an object building based on AI (Artificial Intelligence), and a cooling system control device.

The building energy management system is an integrated system that integrates construction technology, ICT, and energy technology to collect energy information in buildings and analyze data to improve energy efficiency.

Recent buildings are becoming more fragmented and complicated due to technological developments. As a result, the individual facilities are controlled individually, and management is sometimes omitted in this process. In some cases, management is performed in duplicate and ineffective management is performed.

In addition, the existing building energy management system operates optimally in off-line, making it difficult to control facilities in real time.

Korean Patent Laid-Open Publication No. 2014-0141923 (published Dec. 11, 2014)

SUMMARY OF THE INVENTION The present invention has been made to solve the above problems of the prior art, and it is an object of the present invention to provide a cloud server and method for controlling a cooling system of a target building that optimally controls a cooling system, a cooling tower, , And a cooling system control device. The present invention is to provide a cloud server and method for controlling an air-conditioning system of an object to be optimally operated in real-time online based on a cloud, rather than offline operation based on a building automation system (BAS). A cloud server and method for controlling a cooling system of a target building applicable to a new space, and a cooling system control device by providing learning data for model and algorithm design using virtual data based on building energy simulation technology . It is to be understood, however, that the technical scope of the present invention is not limited to the above-described technical problems, and other technical problems may exist.

According to an aspect of the present invention, there is provided a cloud server for controlling a cooling system of a target building, the system comprising: a plurality of cooling units included in the cooling system, A control unit for generating a plurality of control signal sets for controlling the cooling system based on the actual situation data and for each of the plurality of control signal sets based on the reinforcement learning, A deriving unit for deriving one of the plurality of control signal sets based on the rewarding operation and a transmitting unit for transmitting the derived control signal set to the cooling system control apparatus, The reference energy consumption amount corresponding to the actual situation data and the plurality of controls Wherein each of the plurality of cooling systems is operated by controlling the plurality of cooling systems based on the transmitted control signal set, wherein the cooling systems are each operative to reward each of the plurality of control signal sets based on the total energy consumption corresponding to each of the signal sets, A cloud server can be provided.

Another embodiment of the present invention is directed to a cooling system comprising a collection unit for collecting actual situation data information for a plurality of cooling equipment included in a cooling system, a transmission unit for transmitting the actual situation data to the cloud server, Comprising: a receiver for receiving a set of control signals derived from the plurality of control signal sets based on the actual situation data; and a controller for controlling the plurality of air conditioners based on the received control signal set, Wherein the plurality of control signal sets are derived from the plurality of control signal sets on which a rewarding operation has been performed based on the actual situation data based on reinforcement learning, and the plurality of control signal sets include a reference energy consumption amount corresponding to the actual situation data, Based on the total energy consumption corresponding to each of the signal sets, And a reward is given from the server to the cooling system control device.

Yet another embodiment of the present invention is a method for controlling a cooling system comprising the steps of receiving actual situation data from a cooling system control device installed in a target building for a plurality of cooling equipment included in the cooling system, Performing a rewarding operation on each of the plurality of control signal sets based on the actual situation data and the total energy consumption amount corresponding to each of the plurality of control signal sets based on reinforcement learning, Deriving one of the plurality of control signal sets based on the operation, and transmitting the derived control signal set to the cooling system control device.

The above-described task solution is merely exemplary 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 the detailed description of the invention.

According to any one of the above-described objects of the present invention, a cloud server and method for controlling a cooling system of a target building that optimally controls a system that has been individually controlled, such as a refrigerator, a cooling tower, A cooling system control device can be provided. It is possible to provide a cloud server and method for controlling an air-conditioning system of an object to be optimally operated in real-time online based on a cloud, rather than an off-line operation focused on a building automation system (BAS). A cloud server and method for controlling a cooling system of a target building applicable to a new space, and a cooling system control device can be provided by acquiring learning data for designing models and algorithms by utilizing virtual data based on building energy simulation technology have.

1 is a configuration diagram of a cooling control system according to an embodiment of the present invention.
2 is a configuration diagram of a cooling system control apparatus according to an embodiment of the present invention.
3 is a flowchart of a method for controlling a cooling system of a target building in cooperation with a cloud server in a cooling system control device 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.
FIGS. 5A and 5B are exemplary diagrams showing an interest rate betting relation with respect to energy consumption of each cooling apparatus according to an embodiment of the present invention. FIG.
6A and 6B are exemplary diagrams illustrating a process of deriving one of a plurality of control signal sets in accordance with an embodiment of the present invention.
7 is a flowchart illustrating a method of controlling a cooling system of a target building in a cloud server according to an exemplary embodiment of the present invention.
8 is a flowchart illustrating a process of controlling a cooling system of a target building in a cooling control system according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

Throughout the specification, when a part is referred to as being "connected" to another part, it includes not only "directly connected" but also "electrically connected" with another part in between . Also, when an element is referred to as "comprising ", it means that it can include other elements as well, without departing from the other elements unless specifically stated otherwise.

In this specification, the term " part " includes a unit realized by hardware, a unit realized by software, and a unit realized by using both. Further, one unit may be implemented using two or more hardware, or two or more units may be implemented by one hardware. On the other hand, 'to' is not limited to software or hardware, 'to' may be configured to be an addressable storage medium, and may be configured to play one or more processors. Thus, by way of example, 'parts' may refer to components such as software components, object-oriented software components, class components and task components, and processes, functions, , Subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. The functions provided in the components and components may be further combined with a smaller number of components and components or further components and components. In addition, the components and components may be implemented to play back one or more CPUs in a device or a secure multimedia card.

1 is a configuration diagram of a cooling control system according to an embodiment of the present invention. 1, the cooling control system 1 may include a cooling system control device 110, a cloud server 120, and a cooling system 130. The cooling system control device 110, the cloud server 120, and the cooling system 130 illustratively show the components that can be controlled by the cooling control system 1. [

Each component of the cooling control system 1 of Fig. 1 is generally connected via a network. For example, as shown in FIG. 1, the cooling system control apparatus 110 may be connected to the cloud server 120 or the cooling system 130 at the same time or at intervals.

The network refers to a connection structure in which information can be exchanged between each node such as terminals and servers. The network includes a local area network (LAN), a wide area network (WAN), a wide area network (WWW) Wide Web, wired / wireless data communication network, telephone network, wired / wireless television communication network, and the like. Examples of wireless data communication networks include 3G, 4G, 5G, 3GPP, LTE, WIMAX, Wi-Fi, Bluetooth, infrared, Communications, Visible Light Communication (VLC), LiFi, and the like.

The cooling system control unit 110 may control the cooling system 130 installed in the target building 100 in cooperation with the cloud server 120. Specifically, the cooling system control device 110 may collect actual situation data for a plurality of cooling devices included in the cooling system 130, and may transmit the collected actual situation data to the cloud server 120.

The cooling system control device 110 receives a set of control signals derived based on actual situation data among a plurality of control signal sets generated by the cloud server 120, Can be controlled. At this time, the cooling system control device 110 may control the plurality of cooling equipments by comparing the warm comfort feeling of the target building 100 with the target expected dissatisfaction degree according to the control signal set.

For example, when a plurality of cooling equipments includes the cooling tower 131 and the refrigerator 132, the cooling system control device 110 acquires the cooling water from the cooling tower 131 to the refrigerator 132 according to the control signal set It is possible to control the cooling tower 131 and the freezer 132 in consideration of the interest rate betal relationship between the energy consumption amount of the cooling tower 131 and the energy consumption amount of the freezer 132 generated in relation to the temperature. For example, when a plurality of cooling equipments include the cold water pump 133 and the air conditioner 134, the cooling system control device 110 sets the cold water outflow rate The cold water pump 133 and the air conditioner 134 are controlled in consideration of the energy consumption amount of the cold water pump 133 generated in relation to the supply air temperature provided by the air conditioner 134 according to the air conditioner 134 and the rate- Can be controlled.

The cooling system control device 110 collects the actual situation data newly generated from the plurality of cooling devices controlled based on the control signal set received from the cloud server 120 and transmits the newly generated actual situation data to the cloud server 120 ).

The cloud server 120 can derive an optimized control combination for the cooling system 130 installed in the target building 100 in conjunction with the cooling system control device 110. [ Specifically, the cloud server 120 may receive actual situation data for a plurality of cooling devices included in the cooling system 130 from the cooling system control device 110 installed in the target building 100.

The cloud server 120 generates a plurality of sets of control signals for controlling the cooling system 130 and performs a rewarding operation on each of the plurality of control signal sets based on the actual situation data based on the reinforcement learning, One of a plurality of control signal sets can be derived based on the rewound operation. The control signal set includes an on / off control value of each cooling equipment, a cold water outflow temperature to be provided through control for each cooling equipment, a cold water outflow rate, an air supply temperature, an air supply air volume, Values. ≪ / RTI > At this time, the cloud server 120 can reward each of the plurality of control signal sets based on the reference energy consumption amount corresponding to the actual situation data and the total energy consumption amount corresponding to each of the plurality of control signal sets.

Specifically, the cloud server 120 assigns a reward to each of a plurality of control signal sets according to the degree that the reference energy consumption amount exceeds the total energy consumption consumed by the plurality of cooling equipment controlled by the control signal set, It is possible to derive one of a plurality of control signal sets to which a high reward is given. At this time, the cloud server 120 may award a reward to each of the plurality of control signal sets in consideration of a trade-off relationship with respect to the energy consumption amount of each cooling equipment.

For example, when a plurality of cooling equipments include the cooling tower 131 and the refrigerator 132, the cloud server 120 may control the temperature of the cooling tower 131, which is generated in relation to the temperature of the cooling water entering the refrigerator 132 from the cooling tower 131, The relationship between the energy consumption of the refrigerator 131 and the energy consumption of the refrigerator 132 can be considered.

For example, in a case where a plurality of cooling equipments include the cold water pump 133 and the air conditioner 134, the cloud server 120 may control the supply of air supplied by the air conditioner according to the flow rate of cold water supplied through the cold water pump 133 The relationship between the energy consumption of the cold water pump 133 and the energy consumption of the air conditioner 134 can be considered.

The cloud server 120 derives the expected dissatisfaction degree based on the estimated average warmth sensation derived based on the actual situation data and derives the warmth sensation for the occupant in the target building 100 using the derived expected dissatisfaction degree, It is possible to give a reward to each of a plurality of control signal sets in consideration of warm comfort. At this time, the cloud server 120 compares the target anticipated dissatisfaction with the warm feeling of comfort to the target building 100, and gives rewards to each of the plurality of control signal sets.

The cloud server 120 may collect virtual data through a simulation model for the cooling system 130. [ The cloud server 120 may supplement the actual situation data using virtual data and perform a rewinding operation on each of the plurality of control signal sets based on the supplemented actual situation data.

The cloud server 120 may send the derived control signal set to the cooling system control device 110. In addition, the cloud server 120 can receive the newly created actual situation data from a plurality of cooling devices controlled based on the control signal set transmitted from the cooling system control device 110. [

The cooling system 130 may be a system installed in the target building 100 to provide a cooling state in the target building 100. Specifically, the cooling system 130 may transmit actual situation data information for a plurality of cooling devices included in the cooling system 130 to the cooling system control device 110. The plurality of cooling equipments included in the cooling system 130 includes a cooling tower 131 for supplying cooling water required for producing cold water of the freezer 132, a freezer 132 for producing cold water, a cold water pump 133 for circulating the cold water An air conditioner 134 for supplying low temperature air to the room using the produced cold water, a cooling water pump 135 for circulating the cooling water, and the like.

 The cooling system 130 may be controlled by the cooling system control device 110 according to a set of control signals derived based on actual situation data information. For example, a plurality of cooling equipment contained in the cooling system 130 may be controlled according to the derived control signal set, respectively.

When the cooling system 130 is controlled according to the control signal set by the cooling system control device 110, it may transmit the newly generated actual situation data to the cooling system control device 110.

2 is a configuration diagram of a cooling system control apparatus according to an embodiment of the present invention. 2, the cooling system control apparatus 110 may include a collecting unit 210, a transmitting unit 220, a receiving unit 230, and a control unit 240.

The collection unit 210 may collect actual situation data for a plurality of cooling devices included in the cooling system 130. Also, the collecting unit 210 may collect actual situation data newly generated from a plurality of cooling apparatuses based on the received control signal set.

The transmitting unit 220 may transmit the actual situation data to the cloud server 120. In addition, when the collecting unit 210 collects the actual situation data newly generated, the transmitting unit 220 may transmit the newly generated actual situation data to the cloud server 120. [

The receiving unit 230 may receive the control signal set derived based on the actual situation data among the plurality of sets of control signals generated by the cloud server 120. [ Here, the control signal set may be derived from a plurality of control signal sets on which a rewarding operation is performed based on actual situation data based on reinforcement learning.

The control unit 240 can control a plurality of cooling equipments based on the received control signal set.

For example, when a plurality of cooling equipments include the cooling tower 131 and the freezer 132, the control unit 240 may control the temperature of the cooling water entering the freezer 132 from the cooling tower 131 The cooling tower 131 and the freezer 132 can be controlled in consideration of the energy consumption amount of the cooling tower 131 generated by the cooling tower 131 and the energy consumption rate of the freezer 132. [

For example, when a plurality of cooling equipments include the cold water pump 133 and the air conditioner 134, the control unit 240 controls the air conditioner 134 according to the set cold water flow rate through the cold water pump 133, It is possible to control the cold water pump 133 and the air conditioner 134 in consideration of the rate-of-return relationship between the energy consumption amount of the cold water pump 133 and the energy consumption amount of the air conditioner 134, have.

The control unit 240 can control a plurality of cooling equipments by comparing the warm feeling of comfort with the target building 100 according to a predetermined target dissatisfaction degree according to the control signal set. The control signal set is derived from a plurality of control signal sets that are respectively given rewards in consideration of the warm comfort to occupants in the target building 100. The warm comfort sense is obtained by calculating the estimated average warming sensation derived based on the actual situation data Based on the expected dissatisfaction level.

3 is a flowchart of a method for controlling a cooling system of a target building in cooperation with a cloud server in a cooling system control device according to an embodiment of the present invention. The method of controlling the cooling system 130 of the target building 100 in conjunction with the cloud server 120 performed by the cooling control system 1 shown in FIG. 3 is the same as that of the embodiment shown in FIGS. 1 and 2 And then processes it in a time-series manner. 1 and 2, the air conditioning system 130 of the target building 100 is interlocked with the cloud server 120 performed by the cooling system control apparatus 110 according to the embodiment shown in FIGS. 1 and 2, Is also applied.

In step S310, the cooling system control apparatus 110 may collect actual situation data information for a plurality of cooling apparatuses included in the cooling system 130. [

In step S320, the cooling system control device 110 may transmit the actual situation data to the cloud server 120. [

In step S330, the cooling system control device 110 can receive the control signal set derived based on the actual situation data among the plurality of control signal sets generated by the cloud server 120. [

In step S340, the cooling system control device 110 can control a plurality of cooling devices based on the received control signal set.

In the above description, steps S310 to S340 may be further divided into further steps or combined into fewer steps, according to an embodiment of the present invention. In addition, some of the steps may be omitted as 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. 4, the cloud server 120 may include a receiving unit 410, an output unit 420, a transmitting unit 430, and a simulation unit 440.

The receiving unit 410 can receive actual situation data for a plurality of cooling equipment included in the cooling system from the cooling system control unit 110 installed in the target building 100. [ The receiving unit 410 can also receive newly generated actual situation data from a plurality of cooling devices controlled based on the control signal set transmitted from the cooling system control device. The plurality of cooling equipments may include, for example, a refrigerator, a cooling tower, a cold water pump, a cooling water pump, an air conditioner, and the like.

The derivation unit 420 may derive an optimal cooling system control combination by comprehensively considering the trade-off relationship among a plurality of cooling equipment included in the cooling system 130. [ For example, the derivation unit 420 may derive a control combination of individual devices in the cooling system that satisfies the cooling load (Qcool) of the target space and minimizes the energy consumption (Etotal) of the entire cooling system.

For example, the derivation unit 420 may generate a plurality of control signal sets for controlling the cooling system 130, and may perform a rewarding operation on each of the plurality of control signal sets based on real- Can be performed.

For example, the derivation unit 420 may include a load allocation algorithm / chiller logarithmic optimal control algorithm that maximizes the COP (Coefficient of Performance) of the refrigerator based on the cooling load prediction, Optimum control algorithm for outlet temperature, Cooling tower fan operation optimal cooling algorithm to minimize total energy consumption of cooling tower based on predicted wetting temperature of outside air, Cooling system based on outside air temperature and cooling load, Cooling system optimal control algorithm to minimize total energy consumption Based re-learning operation using the reinforcement learning algorithm.

The cooling load ( _Qcool ) can be expressed by the following equation (1).

Here, Q _cool the cooling load (W) and, T _rchw is a cold water return temperature (℃) the temperature of the cold water entering the freezer, T _schw is (℃) temperature of cold water outlet temperature of the water leaving the chiller, V _schw the refrigerator (Kg / s), and C _p is the specific heat of water (J / kg ° C).

The energy consumption of the entire cooling system can be expressed by the following equation (2).

Where E _total is the energy consumption of the entire cooling system (kWh), E _chiller is the energy consumption of the entire refrigerator (kWh), E _tower is the energy consumption of the entire cooling _tower (kWh), and E _pumpchw is the total cooling water circulation pump (KWh), E _pumpcw is the energy consumption of the entire cooling water circulation pump (kWh), and E _ahu is the energy consumption of the entire air conditioner (kWh).

The control signal set includes an on / off control value of each cooling device, a cold water outflow temperature to be provided through control for each cooling device, a cold water outflow rate, an air supply temperature, an air supply air volume, . ≪ / RTI > A set of control signals can be given, for example, +1 if the total energy consumption is below the total reference consumption, -1 if it is lower, +1 if the expected dissatisfaction of the target space does not exceed 10% have.

Specifically, the control signal set of the entire cooling system can be expressed by the following equation (3).

Where C _total is the control signal set for the entire cooling system, P _chiller is the individual on / off control value of the refrigerator, P _tower is the on / off control value of the cooling _tower , , T _setchw is cold heading set point control value (℃), V _setchw the cold water outflow pipe set flow rate control value (kg / s) and, T _setsa air conditioner and the air supply set point control value (℃), V _setsa air conditioner supply air Indicates the set air flow control value (kg / s).

The derivation unit 420 may supplement the actual situation data using the virtual data and perform a rewinding operation on each of the plurality of control signal sets based on the supplemented actual situation data.

The derivation unit 420 may reward each of the plurality of control signal sets based on the reference energy consumption amount corresponding to the actual situation data and the total energy consumption amount corresponding to each of the plurality of control signal sets. At this time, the derivation unit 420 may give a reward to each of the plurality of control signal sets in consideration of a trade-off relationship with respect to the energy consumption amount of each cooling apparatus. The process of considering the interest rate betting relation to the energy consumption amount of each cooling equipment will be described in detail with reference to FIGS. 5A and 5B.

FIGS. 5A and 5B are exemplary diagrams showing an interest rate betting relation with respect to energy consumption of each cooling apparatus according to an embodiment of the present invention. FIG.

FIG. 5A is an exemplary diagram illustrating the relationship between the energy consumption of the cooling tower and the energy consumption of the freezer according to an embodiment of the present invention. FIG. When the plurality of cooling devices include the cooling tower 131 and the refrigerator 132, the derivation unit 420 calculates the temperature of the cooling tower 131, which is generated in relation to the temperature of the cooling water 510 entering the refrigerator 132 from the cooling tower 131 131 and the energy consumption 520 of the refrigerator 132 can be considered.

Referring to FIG. 5A, it can be seen that as the temperature of the cooling water decreases, the energy consumption 530 of the cooling tower increases and the energy consumption 540 of the refrigerator decreases.

FIG. 5B is an exemplary diagram illustrating the relationship between the energy consumption of the cold water pump and the energy consumption of the air conditioner according to an embodiment of the present invention. When the plurality of cooling apparatuses include the cold water pump 133 and the air conditioner 134, the lead-out portion 420 may be provided by the air conditioner 134 according to the cold water outflow rate 550 provided through the cold water pump 133 It is possible to consider the interest rate betting relationship between the cold water pump 133 and the energy consumption amount 560 of the air conditioner 134 that occurs in relation to the air supply temperature.

Referring to FIG. 5B, it can be seen that as the flow rate of the cold water increases, the energy consumption 570 of the cold water pump increases but the energy consumption 580 of the air conditioner decreases.

Referring again to FIG. 4, the derivation unit 420 derives the expected dissatisfaction degree based on the estimated average warming sensation derived based on the actual situation data, and calculates the expected dissatisfaction degree using the derived expected dissatisfaction degree, It is possible to derive a warm and pleasant feeling and to give a reward to each of a plurality of control signal sets with further consideration of warm comfort.

Predicted Meqn Vote (PMV) means predicting the mean value of the vote for the warmth measure of many people exposed to the same environment. For example, the thermal sensation scale is hot, +3, warm (+2), slightly warm (+1), neutral (0), slightly cool (-1) , Cool (-2), cold (-3), and so on.

The average thermal sensation indexes are metabolic rate, thermal resistance, air temperature, mean radiant temperature, relative air velocity, partial water vapor pressure, , Which may be statistically related to the physiological response of the human body heat control system, which automatically adjusts the skin temperature and releases the sweat to maintain the thermal equilibrium, and the warming sensation signals collected from many people.

Predicted Percentage of Dissatisfied (PPD) predicts the percentage of people who feel thermally uncomfortable among many people. For example, among the seven levels of warmth, hot, +3, warm , Cold (-2), cold (-2), cold (-3), and so on.

For those who predicted the number of people who were thermally insecure among many people through an indicator of expected dissatisfaction and who excluded those who were predicted to be thermally unpleasant among many people, they were found to be thermally neutral, slightly bland warm, and slightly cool.

Acceptable thermal environments for comfort is defined as a psychological state that expresses satisfaction with a thermal environment, for example, a warm comfort sense may correspond to indexing / quantifying the expected dissatisfaction according to a predetermined policy .

The derivation unit 420 may compare the target expected dissatisfaction with the warmth feeling of comfort to the target building 100 and give a reward to each of the plurality of control signal sets. For example, +1 if the energy consumption is lower than the total reference consumption amount, -1 if the energy consumption amount is lower than -1, 1 if the energy consumption amount does not exceed 10%, and -1 if the energy consumption amount is lower than the total reference consumption amount.

The expected dissatisfaction rate can be changed by adjusting the weight between the energy consumption reduction and the thermal comfort sense. For example, depending on the purpose of operation of the reinforcement learning, the purpose of operation of the space, etc., the reward is less than 10% and the reward is set to -1 when the expected dissatisfaction is less than 10% The weight of the heat comfort sense can be adjusted by changing the reward to +1 when the reward is over 20% and the reward to -1.

The derivation unit 420 may derive one of a plurality of control signal sets based on the rewound operation. For example, the derivation unit 420 may assign a reward to each of a plurality of control signal sets according to the degree that the reference energy consumption amount exceeds the total energy consumption consumed by a plurality of cooling equipment controlled by the control signal set, It is possible to derive one of the plurality of sets of control signals to which a high reward is given. A process of deriving one of a plurality of control signal sets based on the rewound operation will be described in detail with reference to FIGS. 6A and 6B.

6A and 6B are exemplary diagrams illustrating a process of deriving one of a plurality of control signal sets in accordance with an embodiment of the present invention.

6A is an exemplary diagram illustrating a plurality of control signal sets in accordance with one embodiment of the present invention. Referring to FIG. 6A, a plurality of control signal sets 600 are weighted according to the energy consumption 610 for each cooling equipment, and are rewarded. The highest one can be derived.

For example, a 'control signal set B' 620 may be derived with a control combination that minimizes the energy consumption of the entire cooling system by taking into account the rate betting relationship between each cooling equipment among the plurality of control signal sets 600 have.

6B is an exemplary diagram illustrating a control combination of control signal sets derived to minimize energy consumption in accordance with an embodiment of the present invention. 6B, the 'control signal set B' 620 includes' refrigerator 1: on, refrigerator 2: off, cooling tower 1: on, cooling tower 2: on, A set temperature for outgoing water: 7 캜, a set flow rate for cold water outflow: 70 kg / s, an air conditioner supply set temperature: 16 캜, and an air conditioner set air flow rate: 1,000 kg / s.

4, the transmission unit 430 may transmit the derived control signal set to the cooling system control unit 110. [

The simulation unit 440 may collect virtual data through the simulation model for the cooling system 130. [ That is, the present invention can complement the actual situation data using the virtual data collected through the simulation unit 440, and perform the rewinding operation on each of the plurality of control signal sets based on the actual situation data supplemented .

In the present invention, optimum control can be enabled by effectively adapting to a change in the environment of the target building 100 (change of use purpose of the target space, change of operation schedule, etc.) through a rewarding operation based on the reinforcement learning algorithm. In addition, it has an advantage that it is effective to apply the algorithm developed for a specific building relative to other machine learning algorithms to another building and operate it.

7 is a flowchart of a method of controlling a cooling system of a target building in a cooling control system according to an embodiment of the present invention. The method of controlling the cooling system 130 of the target building 100 performed by the cloud server 120 shown in Fig. 7 includes steps that are processed in a time-series manner according to the embodiment shown in Figs. 1 to 6B do. Accordingly, the present invention is also applicable to a method of controlling the cooling system 130 of the target building 100 performed by the cloud server 120 according to the embodiment shown in FIGS.

In step S710, the cloud server 120 may receive actual situation data for a plurality of cooling devices included in the cooling system from the cooling system control device 110 installed in the target building 100. [

In step S720, the cloud server 120 may generate a plurality of control signal sets for controlling the cooling system 130. [

In step S730, the cloud server 120 may perform a rewarding operation on each of the plurality of control signal sets based on the actual situation data and the total energy consumption amount corresponding to each of the plurality of control signal sets, based on the reinforcement learning.

In step S740, the cloud server 120 may derive one of the plurality of control signal sets based on the rewarding operation.

In step S750, the cloud server 120 may transmit the derived control signal set to the cooling system control device 110. [

In the above description, steps S710 to S750 may be further divided into further steps, or combined in fewer steps, according to an embodiment of the present invention. In addition, some of the steps may be omitted as necessary, and the order between the steps may be switched.

8 is a flowchart illustrating a process of controlling a cooling system of a target building in a cooling control system according to an embodiment of the present invention. Referring to FIG. 8, the cooling control system 1 may include a cooling system control device 110, a cloud server 120, and a cooling system 130.

The cooling system 130 may transmit actual situation data information for each cooling apparatus to the cooling system control apparatus 110 (S801).

The cooling system control device 110 collects actual situation data for a plurality of cooling equipments (S802), and transmits the collected actual condition data to the cloud server 120 (S803).

The cloud server 120 may generate a plurality of sets of control signals for controlling the cooling system (S804) and perform a rewarding operation on each of the plurality of control signal sets based on the actual situation data on the basis of the reinforcement learning (S805).

The cloud server 120 derives one of a plurality of sets of control signals based on the rewarding operation (S806) and transmits the derived control signal set to the cooling system control device 110 (S807).

The cooling system control device 110 stores a control signal set (S808), and can control a plurality of cooling devices included in the cooling system 130 based on the control signal set (S809).

In the above description, steps S801 to S809 may be further divided into further steps or combined into fewer steps, according to an embodiment of the present invention. In addition, some of the steps may be omitted as necessary, and the order between the steps may be switched.

One embodiment of the present invention may also be embodied in the form of a recording medium including instructions executable by a computer, such as program modules, being 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, the computer readable medium may include both 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.

While the methods and systems of the present invention have been described in connection with specific embodiments, some or all of those elements or operations may be implemented using a computer system having a general purpose hardware architecture.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

100: Target building
110: cooling system control device
120: Cloud server
130: Cooling system
131: Cooling tower
132: refrigerator
133: cold water pump
134: air conditioner
135: Coolant pump
210:
220:
230: Receiver
240:
410:
420:
430:
440: Simulation section

Claims (19)

A cloud server for controlling a cooling system of a target building,
A receiving unit for receiving actual situation data on a plurality of cooling equipment included in the cooling system from a cooling system control unit installed in the target building;
Generating a plurality of control signal sets for controlling the cooling system, performing a rewarding operation on each of the plurality of control signal sets based on the actual situation data on the basis of reinforcement learning, A derivation unit for deriving one of the plurality of control signal sets; And
And a transmission unit for transmitting the derived control signal set to the cooling system control device,
Wherein the derivation unit applies a reward to each of the plurality of control signal sets based on the reference energy consumption amount corresponding to the actual situation data and the total energy consumption amount corresponding to each of the plurality of control signal sets,
Wherein the cooling system is operated by controlling the plurality of cooling equipment based on the transmitted control signal set.
The method according to claim 1,
Further comprising a simulation unit for collecting virtual data through a simulation model for the cooling system,
Wherein the derivation unit complements the actual situation data using the virtual data and performs a rewiring operation on each of the plurality of control signal sets based on the actual condition data supplemented.
The method according to claim 1,
Wherein the derivation unit applies a reward to each of the plurality of control signal sets in accordance with the degree that the reference energy consumption amount exceeds the total energy consumption consumed by a plurality of cooling apparatuses controlled by the control signal set, Wherein one of the plurality of control signal sets derives a higher one of the rewards.
The method of claim 3,
Wherein the derivation unit gives a reward to each of the plurality of control signal sets in consideration of a trade-off relationship between energy consumption of the respective cooling apparatuses.
5. The method of claim 4,
Wherein the plurality of cooling devices include a cooling tower and a refrigerator,
Wherein the derivation unit considers an interest rate betting relationship between an energy consumption amount of the cooling tower and an energy consumption amount of the refrigerator, which occurs in relation to an input temperature of cooling water entering the refrigerator from the cooling tower.
5. The method of claim 4,
Wherein the plurality of cooling devices include a cold water pump and an air conditioner,
Wherein the derivation unit considers an interest rate betting relationship between an energy consumption amount of the cold water pump and an energy consumption amount of the air conditioner generated in relation to an air supply temperature provided by the air conditioner according to a cold water outflow rate provided through the cold water pump, server.
5. The method of claim 4,
Wherein the derivation unit derives a predicted degree of unsatisfactory degree based on the predicted average feeling of warmth derived based on the actual situation data, derives a warmth sense of comfort for the occupant in the target building using the derived expected degree of dissatisfaction, Wherein each of the plurality of control signal sets gives a reward to each of the plurality of control signal sets.
8. The method of claim 7,
Wherein the derivation unit compares the target expected dissatisfaction set for the target building with the warm comfort sense to give a reward to each of the plurality of control signal sets.
The method according to claim 1,
Wherein the set of control signals comprises a combination of control values for each of the plurality of cooling devices.
10. The method of claim 9,
Wherein the plurality of cooling equipments includes a refrigerator, a cooling tower, a cold water pump, a cooling water pump, and an air conditioner,
Wherein the control signal set includes at least one of an on / off control value of each of the cooling devices, a cold water outflow temperature to be provided through control for each cooling device, a cold water outflow rate, an air supply temperature and an air supply airflow, server.
The method according to claim 1,
Wherein the receiving unit receives newly generated actual situation data from the plurality of cooling devices controlled based on the transmitted control signal set from the cooling system control device.
An apparatus for controlling a cooling system of a target building in cooperation with a cloud server,
A collection unit for collecting actual situation data information on a plurality of cooling equipment included in the cooling system;
A transmitting unit for transmitting the actual situation data to the cloud server;
A receiver for receiving a control signal set derived from the actual situation data among a plurality of control signal sets generated by the cloud server; And
And a control unit for controlling the plurality of cooling devices based on the received control signal set
Lt; / RTI >
Wherein the control signal set is derived from the plurality of control signal sets on which a rewarding operation has been performed based on the actual situation data based on reinforcement learning and wherein the plurality of control signal sets includes a reference energy consumption amount And a reward is given from the cloud server based on the total energy consumption amount corresponding to each of the plurality of control signal sets.
13. The method of claim 12,
Wherein the plurality of control signal sets are rewarded in consideration of a trade-off relationship with energy consumption of each of the cooling devices.
14. The method of claim 13,
Wherein the plurality of cooling devices include a cooling tower and a refrigerator,
The control unit controls the cooling tower and the refrigerator in consideration of an interest rate betal relationship between the energy consumption amount of the cooling tower and the energy consumption amount of the refrigerator, which is generated in relation to the temperature of the cooling water entering from the cooling tower to the refrigerator, To the cooling system control device.
14. The method of claim 13,
Wherein the plurality of cooling devices include a cold water pump and an air conditioner,
Wherein the control unit sets the rate-of-return relation between the energy consumption amount of the cold water pump and the energy consumption amount of the air conditioner generated in relation to the supply air temperature provided by the air conditioner according to the cold water outflow rate provided through the cold water pump And controls the cold water pump and the air conditioner.
14. The method of claim 13,
Wherein the plurality of control signal sets are respectively given rewards in consideration of a warmth sense of comfort for occupants in the target building,
Wherein the warm comfort sense is derived using an expected dissatisfaction degree derived based on the estimated average warm feeling derived based on the actual situation data.
17. The method of claim 16,
Wherein the control unit controls the plurality of cooling apparatuses by comparing the warm comfort sense with a predetermined target dissatisfaction degree with respect to the target building according to the control signal set.
13. The method of claim 12,
Wherein the collecting unit collects newly generated actual situation data from the plurality of cooling devices controlled based on the received control signal set,
Wherein the transmitting unit transmits the newly created actual situation data to the cloud server.
A method for controlling a cooling system of a target building,
Receiving actual situation data for a plurality of cooling devices included in the cooling system from a cooling system control device installed in the target building;
Generating a plurality of control signal sets for controlling the cooling system;
Performing a rewarding operation on each of the plurality of control signal sets based on the actual situation data and the total energy consumption amount corresponding to each of the plurality of control signal sets based on reinforcement learning;
Deriving one of the plurality of control signal sets based on the reordering operation; And
Transmitting the derived control signal set to the control device
Wherein the cooling system control method comprises the steps of:

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