KR102498340B1 - Apparatus and method for determining an operation schedule of heat pumps based on building energy management system - Google Patents

Abstract

The present disclosure relates to a heat pump scheduling apparatus for determining an operation schedule of heat pumps installed in a parallel in a building based on a building energy management system (BEMS). According to the present disclosure, an operation method of the heat pump scheduling apparatus, which can increase efficiency of building energy management, comprises the steps of: receiving driving information from the BEMS; categorizing the heat pumps into at least one heat pump group based on the driving information and determining a control method related to driving order of the at least one heat pump group; determining a driving time of the at least one heat pump group based on the driving information; and generating a driving schedule of the heat pumps based on the control method and the driving time.

Description

Apparatus and method for determining the operation schedule of heat pumps based on BEMS

The present disclosure generally relates to a BEMS-based heat pump control system, and more particularly, to an apparatus and method for determining an operation schedule of heat pumps by controlling the heat pumps based on operation information transmitted from the BEMS. will be.

A building energy management system (BEMS) directs a system that manages building facilities such as electricity, air conditioning, and crime prevention and disaster prevention by utilizing IT technology in order to efficiently manage energy used in a building. Since the efficiency of energy consumption in a building is determined by the proper management of the building, BEMS is currently widely used for energy saving design, efficient energy use, and management.

A heat pump refers to a cooling/heating device that transfers a low-temperature heat source to a high temperature or transfers a high-temperature heat source to a low-temperature temperature by using heat or condensation heat of a refrigerant. That is, the heat pump, as one of the devices for controlling energy inside a building in a building air conditioning system, performs a function of managing energy of a building by operating as a cooling/heating load. In general, a building is installed by connecting a plurality of parallel heat pumps in parallel, and energy is managed through a cooling/heating cycle of the heat pumps.

Conventionally, an air conditioning system in a building is operated in a manner in which heat pumps are controlled using rules based on a manager's experience. That is, the manager predicts and operates the necessary cooling and heating load according to rules determined based on experience and how to derive result values in response to given inputs. However, when the heat pump is operated based on experience, there is a problem in that energy is produced more than necessary or excessively due to a difference between an actual required cooling and heating load and a predicted cooling and heating load. Accordingly, there is a demand for technology development for accurately predicting and controlling required energy by improving the operation method of the heat pump.

Based on the above discussion, the present disclosure provides an apparatus and method for determining an operation schedule of heat pumps based on operation information transmitted from a BEMS.

In addition, the present disclosure provides an apparatus and method for classifying heat pumps into heat pump groups based on operation information transmitted from BEMS, and determining an operation control method related to an operation sequence of the heat pump groups and a power load section. do.

In addition, the present disclosure provides an apparatus and method for determining an operation method of a heat pump for energy saving using an artificial neural network based on operation information transmitted from a BEMS.

In addition, the present disclosure provides an apparatus and method for determining an operation time of a heat pump group based on operation information transmitted from a BEMS.

In addition, the present disclosure provides an apparatus and method for calculating energy savings and costs by determining a heat pump schedule based on operation information transmitted from the BEMS.

According to various embodiments of the present disclosure, a method of operating a heat pump scheduling apparatus for determining an operation schedule of heat pumps installed in parallel in a building based on a BEMS includes receiving operation information from the BEMS, the operation classifying the heat pumps into at least one heat pump group based on information and determining a control method related to an operation sequence of the at least one heat pump group; based on the operation information, the at least one heat pump group Determining an operating time of the heat pump, and generating an operating schedule of the heat pumps based on the control method and the operating time.

According to another embodiment, the step of classifying the heat pumps into at least one heat pump group and determining a control method related to the operation sequence of the at least one heat pump group may include determining a plurality of temperature sections, determining at least one first heat pump having a temperature within a first temperature range; determining at least one second heat pump having an outlet water temperature within the same temperature range among the at least one first heat pump; and determining the at least one second heat pump as a heat pump group.

According to another embodiment, the determining of the control method may include determining a COP corresponding to each of the at least one heat pump group, and determining an operation priority of the at least one heat pump group based on the COP. determining a power load section according to the amount of heat produced by the heat pump in each of the at least one heat pump group based on the operation priority; and determining the at least one heat pump group based on the power load section. and determining, as a control method, a sequential operation method in order of operation priority.

According to another embodiment, the determining of the COP corresponding to each of the at least one heat pump group may include a heat pump having a pump rated heat capacity and a pump heat output ratio greater than or equal to a first threshold value in each of the at least one heat pump group. and determining a median of COP values of heat pumps equal to or greater than the first threshold value.

According to another embodiment, the determining of the operating priorities of the at least one heat pump group includes determining the operating priorities in order of groups having higher COPs.

According to another embodiment, the operation information includes on-time and off-time of the heat pumps, inlet and outlet temperatures of the heat pumps, and power consumption of the heat pumps.

According to another embodiment, the determining of the operating time may include determining the cooling and heating loads produced by the heat pumps using an artificial neural network, and using a nonlinear regression in the heat pumps, the heat storage tank. Determining a correlation between the heat capacity stored in the heat pump and the COP of the heat pumps, and determining an operation time of the at least one heat pump by using the correlation with the cooling/heating load.

According to another embodiment, the artificial neural network is a neural network that has learned a correlation between the cooling and heating loads produced by the heat pumps at a second time after the first time from the outside air information of the building and the amount of heat supplied at the first time. includes

According to another embodiment, the operation information includes outside air information of the building, supply temperature and return temperature of a coil inside an air handling unit (AHU) of the building, and inlet and outlet temperatures of the heat pumps. do.

According to another embodiment, the determining of the operating hours includes determining late-night operating hours and daytime operating hours of the heat pumps according to the cooling/heating load and the power cost table using the correlation.

According to another embodiment, the operation information includes inlet and outlet temperatures of the heat pumps, heat used in the AHU, and power consumption of the heat pumps.

According to another embodiment, the generating of the operation schedule of the heat pumps includes determining the amount of heat stored in the heat storage tank based on a late night operating time and a daytime operating time of the heat pumps.

According to an embodiment of the present disclosure, a heat pump scheduling apparatus for determining an operation schedule of heat pumps installed in parallel in a building based on a BEMS includes a driving information receiving unit receiving driving information from the BEMS, the driving information a control method determining unit for each group that classifies the heat pumps into at least one heat pump group based on and determines a control method related to an operation sequence of the at least one heat pump group; and an operating time determination unit configured to determine operating hours of the heat pump group of the group, and a scheduling generator configured to generate operating schedules of the heat pumps based on the control method and the operating time.

According to another embodiment, the control method determination unit for each group determines a COP corresponding to each of the at least one heat pump group, and determines an operation priority of the at least one heat pump group based on the COP, Based on the operation priority order, a power load section according to the amount of heat produced by the heat pump is determined in each of the at least one heat pump group, and based on the power load section, the at least one heat pump group is selected in the order of operation priority. The sequential operation method is determined as the control method.

According to another embodiment, the operation time determining unit determines the cooling and heating loads produced by the heat pumps using an artificial neural network, and determines the heat capacity stored in the heat storage tank and the heat pump in the heat pumps using linear regression. A correlation of the COP with respect to the heat pump is determined, and an operating time of the at least one heat pump is determined using the correlation with the cooling/heating load.

According to another embodiment, the artificial neural network is a neural network that has learned a correlation between the cooling and heating loads produced by the heat pumps at a second time after the first time from the outside air information of the building and the amount of heat supplied at the first time. includes

Each of the various aspects and features of the invention are defined in the appended claims. Combinations of features of the dependent claims may be combined with features of the independent claims as appropriate, not just those explicitly set forth in the claims.

In addition, one or more selected features of any one embodiment described in this disclosure may be combined with one or more selected features of any other embodiment described in this disclosure, and alternatives of such features The combination of the present disclosure at least partially alleviates one or more technical problems discussed in the present disclosure, or at least partially alleviates the technical problems discernable by a person skilled in the art from the present disclosure, and further features of the embodiments ( A particular combination or permutation so formed of embodiment features is possible, provided that it is not understood by a person skilled in the art to be incompatible.

In any described example implementation, two or more physically separate components may alternatively be integrated into a single component, where such integration is possible, and a single component so formed If the same function is performed by , the integration is possible. Conversely, a single component in any embodiment described in this disclosure may alternatively be implemented as two or more separate components that achieve the same function, where appropriate.

It is an object of certain embodiments of the present invention to address, mitigate, or eliminate, at least in part, at least one of the problems and/or disadvantages associated with the prior art. Certain embodiments aim to provide at least one of the advantages described below.

An apparatus and method according to various embodiments of the present disclosure may increase the efficiency of building energy management by determining an operation schedule of heat pumps based on BEMS.

Also, the apparatus and method according to various embodiments of the present disclosure determine an efficient cooling/heating operation mode for each heat pump group by determining a control method of the heat pumps based on the BEMS.

In addition, the apparatus and method according to various embodiments of the present disclosure determine the operation schedule of the heat pumps using an artificial neural network, thereby predicting the amount of heat for cooling and heating at a future point in time.

In addition, an apparatus and method according to various embodiments of the present disclosure may determine the storage capacity of a heat storage tank by determining an operation schedule of heat pumps using an artificial neural network.

Also, an apparatus and method according to various embodiments of the present disclosure may reduce energy costs of a building by determining an operation schedule of heat pumps using an artificial neural network.

Effects obtainable in the present disclosure are not limited to the effects mentioned above, and other effects not mentioned may be clearly understood by those skilled in the art from the description below. will be.

1 illustrates a system for determining a heat pump operation schedule according to various embodiments of the present disclosure.
2 illustrates a configuration of a heat pump scheduling device in a heat pump operation schedule determination system according to various embodiments of the present disclosure.
3 is a schematic diagram illustrating an operating method of a heat pump scheduling device in a heat pump operation schedule determination system according to various embodiments of the present disclosure.
4 illustrates an example of a graph related to a method of determining a control method for each heat pump group and a table related to a result of the determination in the system for determining a heat pump operation schedule according to various embodiments of the present disclosure.
5 is a flowchart illustrating an operating method of a heat pump scheduling device in a heat pump operation schedule determination system according to various embodiments of the present disclosure.
FIG. 6 is a flowchart illustrating a method for determining a control method for each group by a heat pump scheduling device in the heat pump operation schedule determination system according to various embodiments of the present disclosure.
FIG. 7 is a flowchart illustrating a method for determining an operating time by a heat pump scheduling apparatus in a heat pump operation schedule determination system according to various embodiments of the present disclosure.

Terms used in the present disclosure are only used to describe a specific embodiment, and may not be intended to limit the scope of other embodiments. Singular expressions may include plural expressions unless the context clearly dictates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by one of ordinary skill in the art described in this disclosure. Among the terms used in the present disclosure, terms defined in general dictionaries may be interpreted as having the same or similar meanings as those in the context of the related art, and unless explicitly defined in the present disclosure, ideal or excessively formal meanings. not be interpreted as In some cases, even terms defined in the present disclosure cannot be interpreted to exclude embodiments of the present disclosure.

In various embodiments of the present disclosure described below, a hardware access method is described as an example. However, since various embodiments of the present disclosure include technology using both hardware and software, various embodiments of the present disclosure do not exclude software-based access methods.

Hereinafter, the present disclosure relates to an apparatus and method for determining a heat pump operation schedule based on a building energy management system (BEMS). Specifically, the present disclosure describes a method for controlling the operation of heat pump groups in a heat pump operation schedule determination system and a technique for increasing building energy efficiency by determining an operation time.

Hereinafter, various embodiments will be described in detail so that those skilled in the art can easily implement the present disclosure with reference to the accompanying drawings. However, since the technical spirit of the present disclosure may be implemented in various forms, it is not limited to the embodiments described herein. In describing the embodiments disclosed in this specification, if it is determined that a detailed description of a related known technology may obscure the gist of the technical idea of the present disclosure, a detailed description of the known technology will be omitted. The same or similar components are assigned the same reference numerals, and duplicate descriptions thereof will be omitted.

In this specification, when an element is described as being “connected” to another element, this includes not only the case of being “directly connected” but also the case of being “indirectly connected” with another element intervening therebetween. When an element "includes" another element, this means that it may further include another element without excluding another element in addition to the other element unless otherwise stated.

Some embodiments may be described as functional block structures and various processing steps. Some or all of these functional blocks may be implemented with any number of hardware and/or software components that perform a particular function. For example, functional blocks of the present disclosure may be implemented by one or more microprocessors or circuit configurations for a predetermined function. The functional blocks of this disclosure may be implemented in a variety of programming or scripting languages. The functional blocks of this disclosure may be implemented as an algorithm running on one or more processors. The functions performed by the function blocks of the present disclosure may be performed by a plurality of function blocks, or the functions performed by the plurality of function blocks in the present disclosure may be performed by one function block. In addition, the present disclosure may employ prior art for electronic environment setting, signal processing, and/or data processing.

In addition, in the present disclosure, the expression of more than or less than is used to determine whether a specific condition is satisfied or fulfilled, but this is only a description to express an example and excludes more or less description. It's not about doing it. Conditions described as 'above' may be replaced with 'exceeds', conditions described as 'below' may be replaced with 'below', and conditions described as 'above and below' may be replaced with 'above and below'.

'...' is used below. wealth', '… A term such as 'group' refers to a unit that processes at least one function or operation, and may be implemented as hardware, software, or a combination of hardware and software. The heat pump scheduling device may include at least one processor, and the at least one processor may control an operation of the heat pump scheduling device.

1 illustrates a heat pump operation schedule determination system 100 according to various embodiments of the present disclosure.

Referring to FIG. 1 , a heat pump operation scheduling determination system 100 includes a building 110 , a BEMS 120 , and a heat pump scheduling device 130 . Here, the components included in the heat pump operation scheduling determination system 100 are according to an embodiment, and all the blocks shown in FIG. 1 are not essential components, and are included in the heat pump operation scheduling determination system in another embodiment. Some blocks may be added, changed or deleted.

Building 110 refers to a building structure equipped with an energy heating and cooling system. According to an embodiment of the present disclosure, the building 110 is connected to the BEMS 120 to transmit and receive energy usage information to and from each other. In addition, the building 110 may include a heat pump for cooling and heating the inside of the building. According to an embodiment of the present disclosure, the building 110 may be designed with a structure in which a plurality of heat pumps are installed in parallel, and selective cooling and heating of the inside of the building may be performed by operating the heat pumps connected in parallel. 1 illustrates a case where the building 110 is one building structure, the building 110 is not limited thereto and may be a building group including a plurality of buildings.

BEMS (120) indicates a system that manages building facilities such as electricity, air conditioning, crime prevention and disaster prevention. The BEMS 120 may instruct a management management system for enabling efficient energy use in the building 110 and efficiently maintaining and managing a pleasant and functional work environment. The BEMS 120 identifies information related to total energy such as electricity, water, and gas for energy management of the entire building 110, and optimizes performance of systems such as electrical or mechanical facilities according to the performance of consumed energy. ) can be controlled in real time. According to an embodiment of the present disclosure, the BEMS 120 may generate operation information for cooling and heating of the heat pump based on a building state. Also, the BEMS 120 may store the generated operation information in a database or transmit it to the heat pump scheduling device 130 . The BEMS 120 may control the operation of a heat pump installed in a building in association with the heat pump scheduling device 130 .

The heat pump scheduling device 130 determines an operation schedule of the heat pump based on the BEMS 120 . The heat pump scheduling device 130 may receive operation information on building conditions from the BEMS 120 and determine a control method and operation time of the heat pumps based on the operation information. Also, the heat pump scheduling device 130 may generate a heat pump operation schedule according to the determined control method and operation time, and check energy saving according to the heat pump operation schedule. Details of the operation of the heat pump scheduling device 130 are described in FIG. 2 .

FIG. 2 illustrates a configuration 200 of a heat pump scheduling device 130 in a heat pump operation schedule determination system according to various embodiments of the present disclosure.

Referring to FIG. 2 , the heat pump scheduling device 130 indicates a device for determining an operation plan of heat pumps connected in parallel inside a building. The heat pump scheduling device 130 may receive operation information related to building conditions of the heat pumps from the BEMS, and determine an operation method of the heat pumps based on the operation information.

According to an embodiment of the present disclosure, the heat pump scheduling device 130 may classify a plurality of heat pumps into at least one heat pump group according to a predetermined condition. In addition, the heat pump scheduling device 130 may determine a control method of the heat pump group by setting operation priorities and power load sections for each operation in units of at least one heat pump group. According to an embodiment of the present disclosure, the heat pump scheduling device 130 may generate a machine learning model using an artificial neural network based on operation information. The heat pump scheduling device 130 may determine the daytime operation time and the late-night operation time of the heat pump by using a machine learning model. The heat pump scheduling device 130 may determine an operating schedule for saving energy in the heat pump cooling and heating system using a control method and operating time for each heat pump group, and may calculate an expected energy saving amount and cost according to the operating schedule. there is. The heat pump scheduling device 130 may include an operation information reception unit 131 , a control method determination unit 132 for each group, an operation time determination unit 133 , and a scheduling generation unit 134 .

The driving information receiving unit 131 performs a function of receiving driving information from the BEMS. The operation information receiving unit 131 may receive operation information including cooling and heating operation data of the heat pump from the BEMS. According to an embodiment of the present disclosure, the operation information may include information on at least one of on time and off time of the heat pumps, inlet and outlet temperatures of the heat pumps, and power consumption of the heat pumps. According to an embodiment of the present disclosure, the operation information may further include information on at least one of outside air information of the building, supply temperature of a coil inside an air handling unit (AHU) of the building, and water return temperature.

The control method determining unit 132 for each group classifies heat pumps into heat pump groups based on operation information and determines a control method for each heat pump in each group. The control method determination unit 132 for each group may classify the heat pumps into at least one heat pump group based on the operation information and determine a control method related to an operation sequence of the at least one heat pump group.

According to an embodiment of the present disclosure, the control method determining unit 132 for each group determines operation priorities in the order of highest efficiency for each heat pump group based on a coefficient of performance (COP) indicating the efficiency of the heat pump. can Thereafter, the control method determination unit 132 for each group may determine a power load section according to the amount of heat produced in each heat pump group in consideration of the operation priority. Thereafter, the control method determination unit 132 for each group may determine a control method related to the operation order of the heat pump groups based on the operation priority and the power load period.

The operating time determining unit 133 determines the operating time of the heat pump. The operation time determination unit 133 may determine the operation time of each of the at least one heat pump group based on the operation information.

According to an embodiment of the present disclosure, the operating time determination unit 133 may predict the amount of heat required for cooling and heating produced by the heat pumps. The driving time determination unit 133 may determine a cooling/heating load demand at a future point in time through a machine learning model using an artificial neural network. Also, the operating time determining unit 133 may determine an operating time for minimizing an operating cost of the heat pump. The operating time determination unit 133 determines the correlation between the heat capacity stored in the heat storage tank and the COP of the heat pump by a nonlinear regression method, and uses the determined correlation to determine the ratio of the heat pumps according to the cooling/heating load and the power cost table. You can decide between late-night driving hours and daytime driving hours.

The scheduling generating unit 134 performs a function of determining operation schedules of heat pumps. The scheduling generating unit 134 may determine scheduling so that the heat pumps operate at the determined operation time using the determined control method for each heat pump group. According to an embodiment of the present disclosure, in the process of determining the schedule, the scheduling generator 134 may determine the heat capacity stored in the heat storage tank using the correlation and the cooling/heating load, and may determine the schedule for each heat pump group. Accordingly, the heat pump scheduling device 130 may reduce energy use and cost of a target building through a simulation model.

Most buildings control heating and cooling using heat pumps installed in parallel. In general, in buildings, heat pumps are installed in parallel, and the heat pumps are controlled by using a method in which on/off operation is controlled according to a return water temperature. According to the prior art, buildings are operated based on rules based on the operator's experience, but the rule-based management method often undesirably wastes heating and cooling energy more than necessary. According to the present disclosure, the heat pump scheduling apparatus 130 controls a heat pump having high efficiency to be preferentially operated through a model for estimating a COP of the heat pump based on data stored in a BEMS. In addition, the heat pump scheduling device 130 predicts the amount of heating and cooling the next day with high accuracy through machine learning, so that low-cost late-night power is used and the heat storage tank is used to efficiently perform cooling and heating.

FIG. 3 illustrates a schematic diagram 300 of an operating method of a heat pump scheduling device 130 in a heat pump operation schedule determination system according to various embodiments of the present disclosure.

In a first step, the heat pump scheduling device 130 receives operation information (310). The heat pump scheduling device 130 receives operation information from the BEMS. The heat pump scheduling device 130 may receive operation information including cooling and heating operation data of the heat pump from the BEMS. Heating and cooling operation information of the heat pumps from the BEMS is the on-time and off-time of the heat pumps, the inlet and outlet temperatures of the heat pumps, the power consumption of the heat pumps, the outside air information of the building, and the air handling unit (AHU) internal information. At least one of driving information may be included. According to an embodiment of the present disclosure, the operation information inside the AHU may include an internal coil supply temperature and water return temperature, and the outside air information of the building may include at least one of building external temperature, humidity, enthalpy information, and solar radiation information. can include

When the flow rate exists in the heat pump system, the operation information may further include flow rate information of the heat pump system. Here, the amount of heat for cooling and heating used in the heat pump may be determined based on the first law of thermodynamics, and may be determined assuming a constant flow amount when flow rate information is not stored through the BEMS.

In addition, when the flow rate exists in the AHU, the driving information inside the AHU may further include flow rate information of an internal coil of the AHU. Here, the amount of heat used in the AHU may be determined based on the first law of thermodynamics.

In a second step, the heat pump scheduling device 130 determines a control method for each heat pump group (320). The heat pump scheduling device 130 classifies the heat pumps into at least one heat pump group based on the operation information, determines operation priorities of the at least one heat pump group (321), and power load section for each heat pump group. can be determined (323). Here, the operation information may include on and off times of the heat pumps, inlet and outlet temperatures of the heat pumps, and power consumption of the heat pumps.

The heat pump scheduling device 130 may classify a plurality of heat pumps connected in parallel into at least one heat pump group. The heat pump scheduling device 130 may determine, as one group, pumps having similar data values of inlet temperature and outlet temperature of the heat pumps. According to an embodiment of the present disclosure, the heat pump scheduling device 130 determines a plurality of temperature ranges, determines at least one first heat pump whose water temperature is included in the first temperature range, and determines at least one first heat pump. Among one heat pump, at least one second heat pump having an outlet temperature within the same temperature range may be determined, and at least one second heat pump may be determined as one heat pump group.

According to an embodiment of the present disclosure, the heat pump scheduling apparatus 130 may determine an operation priority in the classified heat pump groups by using COPs of heat pumps. The COP of the heat pump is information indicating the efficiency of the heat pump, and the higher the COP, the better the efficiency of the heat pump. The COP of the heat pump may be determined as shown in Equation 1.

Referring to Equation 1, COP is the efficiency of the heat pump, Q is the heat produced by the heat pump, and E is the power used by the heat pump. The heat pump scheduling apparatus 130 may determine COPs in each heat pump group and prioritize operations in the order of groups having higher COPs. According to an embodiment of the present disclosure, the COP of each heat pump group may be determined based on a part load ratio (PLR). PLR is a value indicating the amount of heat produced by the pump against the rated heat amount of the pump, and indicates the value obtained by dividing the rated heat amount of the pump by the amount of heat produced by the pump. The heat pump scheduling apparatus 130 may determine a median COP value of heat pumps having a PLR equal to or greater than the threshold value as the COP of each heat pump group. The heat pump scheduling device 130 may determine operation priority in the order of heat pump groups having the highest COP.

According to an embodiment of the present disclosure, the heat pump scheduling device 130 may determine a power load section based on the amount of heat produced by the heat pumps in the classified heat pump groups. The heat pump scheduling apparatus 130 may determine a power load section that the heat pump can handle based on the maximum COP values of heat pumps having a PLR equal to or greater than a threshold value.

The heat pump scheduling device 130 may control operation of the heat pump based on operation priorities and power load intervals. That is, the heat pump scheduling device 130 may control the first heat pump group to operate based on the operation priority, and control the sequentially ranked heat pump groups to additionally operate as the power load section is determined. there is.

In a third step, the heat pump scheduling device 130 determines an operating time (340). The heat pump scheduling device 130 predicts the heating and cooling load based on the operation information (341), determines the correlation between the heat capacity stored in the heat storage tank and the COP (343), and uses the predicted heating and cooling load and the correlation to heat the heat. Operation time for each pump group may be determined (345).

The heat pump scheduling device 130 predicts a cooling/heating load at a future time point based on an artificial neural network. The heat pump scheduling device 130 may predict the amount of heat required for cooling and heating produced by the heat pumps based on the operation information. Here, the operation information may include outside air information of the building, supply temperature and return temperature of the coil inside the AHU, and inlet and outlet temperatures of the heat pumps. The heat pump scheduling device 130 may determine the cooling/heating load demand at a future point in time through a machine learning model using an artificial neural network. The artificial neural network can receive outside air information and heat supply information, and output the cooling and heating load required for the next day. According to an embodiment of the present disclosure, the artificial neural network includes a neural network that has learned a correlation between air conditioning and heating loads produced by heat pumps at a second time after the first time, from outside air information of a building and heat supply at a first time. can do. The input variables of the machine learning model generated by the artificial neural network may include outside air information and the amount of heat supplied, and the output variables may include the heating and cooling load generated by the heat pump. Here, the supply heat amount indicates a heat amount value calculated by applying the first law of thermodynamics to the coil supply temperature and return temperature inside the AHU. In addition, the cooling and heating load indicates a load value calculated by applying the first law of thermodynamics to the inlet and outlet temperatures of the heat pump. The heat pump scheduling device 130 may consider heating and cooling loads when determining an operation control method of a heat pump group.

The heat pump scheduling device 130 determines a correlation between the heat capacity stored in the heat storage tank and the COP of the entire heat pump by using a linear regression method. The heat pump scheduling device 130 may determine a correlation between heat capacity and COP based on operation information received from the BEMS. Here, the operation information may include inlet and outlet temperatures of the heat pumps, heat used in the AHU, and power consumption of the heat pumps. The heat pump scheduling device 130 estimates COP of late-night driving using a heat storage tank. As the heat capacity stored in the heat storage tank increases, the energy efficiency used by the heat source decreases. The heat pump scheduling device 130 determines the heat capacity stored in the heat storage tank based on the difference between the amount of heat generated by the heat pump and the amount of heat used in the building. Then, the heat pump scheduling device 130 may determine a correlation between the heat capacity stored in the heat storage tank and the overall COP of the heat pump by using a linear regression method.

The heat pump scheduling device 130 may determine an operating time for each heat pump group. The heat pump scheduling apparatus 130 may determine late-night operation time and daytime operation time of the heat pumps according to the heating load and power cost table by using the correlation. Here, the power cost table may include a table indicating a predetermined power unit price per time. That is, the heat pump scheduling apparatus may search the late-night operation time and the daytime operation time in order to reduce the operating cost of the heat pumps. Accordingly, the heat pump scheduling device 130 may determine an operating time of each of the at least one heat pump group based on the operating information.

In a fourth step, the heat pump scheduling device 130 generates an operation schedule of the heat pumps (350). The heat pump scheduling apparatus may generate an operation schedule of the heat pumps based on a control method and operation time. The heat pump scheduling device 130 may determine the amount of heat stored in the heat storage tank to minimize cost by searching for late-night operation time and daytime operation time. The heat pump scheduling apparatus 130 may perform scheduling so that the heat pump operates at the determined operation time using the determined control method for each heat pump group.

FIG. 4 is an example 400 of a graph 410 of a method of determining a control method for each heat pump group and a table 460 of the determination result in the system for determining a heat pump operation schedule according to various embodiments of the present disclosure. ) is shown.

Graph 410 illustrates the COP of each group of heat pumps. The horizontal axis of the graph 410 indicates part load ratio (PLR), and the vertical axis indicates COP.

The heat pump scheduling apparatus 130 may determine a median COP value of heat pumps having a PLR equal to or greater than the threshold value as the COP of each heat pump group, and determine operational priorities in the order of the heat pump group having the highest COP. Referring to FIG. 4 , the relationship between the PLR and the COP in the first heat pump group (A), the second heat pump group (B), and the third heat pump group (C) is shown. Since the efficiency of pump production improves as the amount of heat produced by the pump relative to the rated heat amount of the pump increases, the COP increases as the PLR increases, as shown in graph 410. Here, the heat pump scheduling device 130 may determine operation priorities in the order of the highest COP of the heat pump group. Referring to the graph 410, the operation priority may be determined in the order of the first heat pump group (A), the second heat pump group (B), and the third heat pump group (C).

In addition, the heat pump scheduling apparatus 130 may determine a power load section that the heat pump can handle based on the maximum value of the COP of the heat pumps having a PLR equal to or greater than the threshold value. That is, the heat pump scheduling device 130 may determine power load sections according to operation priorities. Referring to the table 460 regarding the determination result, operation priorities may be determined in the order of A, B, and C, and a power load section may be determined based on the maximum COP value. Accordingly, in the heat pump groups, the first heat pump group A operates in the first power load section, and the first heat pump group A and the second heat pump group B operate in the second power load section. In the third power load section, the first heat pump group (A), the second heat pump group (B), and the third heat pump group (C) may operate.

5 is a flowchart 500 of an operating method of a heat pump scheduling device in a heat pump operation schedule determination system according to various embodiments of the present disclosure. 5 illustrates an operating method of a heat pump scheduling device for determining an operation schedule of heat pumps installed in parallel in a building based on BEMS.

Referring to FIG. 5 , in step 501, the heat pump scheduling device 130 receives operation information from the BEMS. The heat pump scheduling device 130 may receive operation information including cooling and heating operation data of the heat pump from the BEMS. According to an embodiment of the present disclosure, the operation information may include information on at least one of on time and off time of the heat pumps, inlet and outlet temperatures of the heat pumps, and power consumption of the heat pumps. According to an embodiment of the present disclosure, the operation information may further include information on at least one of outside air information of the building, supply temperature of a coil inside the air conditioner of the building, and water return temperature.

In step 503, the heat pump scheduling device 130 classifies the heat pumps into at least one heat pump group based on the operation information, and determines a control method related to an operation sequence of the at least one heat pump group. The heat pump scheduling apparatus 130 may classify heat pumps into heat pump groups based on operation information and determine a control method for each heat pump. According to an embodiment of the present disclosure, the heat pump scheduling apparatus 130 may determine operation priorities for each heat pump group in order of highest efficiency based on the COP. Thereafter, the heat pump scheduling device 130 may determine a power load section according to the amount of heat produced in each heat pump group in consideration of the operation priority. The heat pump scheduling apparatus 130 may determine a control method related to an operation sequence of heat pump groups based on operation priorities and power load intervals.

In step 505, the heat pump scheduling device 130 determines the operation time of at least one heat pump group based on the operation information. The heat pump scheduling device 130 may determine a cooling/heating load demand at a future point in time based on a machine learning model using an artificial neural network. Also, the heat pump scheduling device 130 may determine an operating time for minimizing an operating cost of the heat pump. The heat pump scheduling device 130 determines the correlation between the heat capacity stored in the heat storage tank and the COP of the heat pump based on a linear regression method, and uses the determined correlation to operate the heat pumps at night according to the cooling/heating load and power cost table. You can decide the hours and weekly driving hours.

In step 507, the heat pump scheduling device 130 generates an operation schedule of the heat pumps based on the control method and operation time. The heat pump scheduling apparatus 130 may determine scheduling to operate the heat pumps based on the control method for each heat pump group and the determined operation time. According to an embodiment of the present disclosure, in the process of determining the schedule, the heat pump scheduling device 130 may determine the heat capacity stored in the heat storage tank by using the correlation and the cooling/heating load, and determine the schedule for each heat pump group.

FIG. 6 is a flowchart 600 of a method for determining a control method for each group by the heat pump scheduling apparatus 130 in the heat pump operation schedule determination system according to various embodiments of the present disclosure.

Referring to FIG. 6 , in step 601, the heat pump scheduling device 130 determines a COP corresponding to each of at least one heat pump group. The heat pump scheduling device 130 classifies heat pumps into at least one heat pump group. The heat pump scheduling device 130 may determine heat pumps having an inlet temperature and an outlet temperature within the same range as one heat pump group. According to an embodiment of the present disclosure, the heat pump scheduling device 130 determines a plurality of temperature ranges, determines at least one first heat pump whose water temperature is included in the first temperature range, and determines at least one first heat pump. Among the heat pumps, at least one second heat pump having an outlet temperature within the same temperature range may be determined, and the at least one second heat pump may be determined as a heat pump group.

The heat pump scheduling device 130 may determine a COP corresponding to at least one heat pump group. According to an embodiment of the present disclosure, the heat pump scheduling device 130 determines heat pumps having a pump rated heat capacity to a pump rated heat capacity equal to or greater than a first threshold value in each of the at least one heat pump group, and The median value of the COP values of the heat pumps can be determined.

In step 603, the heat pump scheduling device 130 determines the operation priority of at least one heat pump group based on the COP. The heat pump scheduling device 130 may determine an operation priority using COP values of the heat pump group. According to an embodiment of the present disclosure, the heat pump scheduling apparatus 130 may determine operation priority in the order of groups having higher COPs.

In step 605, the heat pump scheduling device 130 determines a power load section according to the amount of heat produced by the heat pump in each of the at least one heat pump group based on the operation priority. The heat pump scheduling device 130 may determine a power load section using the COP in each heat pump group. The heat pump scheduling apparatus 130 may determine a power load section that the heat pump can handle based on the maximum COP values of heat pumps having a PLR equal to or greater than a threshold value.

In step 607, the heat pump scheduling device 130 determines, in a control manner, how to sequentially operate at least one heat pump group in order of operation priority based on the power load interval. According to an embodiment of the present disclosure, the heat pump scheduling apparatus 130 may determine a method of controlling a heat pump group to correspond to a power load section and an operation priority as a control method.

FIG. 7 is a flowchart 700 of a method for determining an operating time by the heat pump scheduling device 130 in the heat pump operation schedule determination system according to various embodiments of the present disclosure.

Referring to FIG. 7 , in step 701, the heat pump scheduling device 130 uses an artificial neural network to determine cooling and heating loads generated by the heat pumps. The heat pump scheduling device 130 may generate a machine learning model through an artificial neural network and predict the heating and cooling load of the next day by using the machine learning model. According to an embodiment of the present disclosure, the artificial neural network includes a neural network that has learned a correlation between air conditioning and heating loads produced by heat pumps at a second time after the first time, from outside air information of a building and heat supply at a first time. can do.

In step 703, the heat pump scheduling device 130 determines a correlation between the heat capacity stored in the heat storage tank and the COP of the heat pumps in the heat pumps by using linear regression. The heat pump scheduling apparatus 130 may generate a model for estimating a relationship between stored heat capacity and COP in late-night driving based on driving information. The heat pump scheduling apparatus 130 may determine a correlation between the heat capacity stored in the heat storage tank and the COP of the heat pumps by using a relationship estimation model.

In step 705, the heat pump scheduling device 130 determines the operating time of at least one heat pump by using a correlation with the cooling/heating load. The heat pump scheduling device 130 may calculate the amount of heat stored in the heat storage tank using the correlation and generate a heat pump operation schedule using the determined heat pump group control method. According to an embodiment of the present disclosure, the heat pump scheduling apparatus 130 may determine late-night operation hours and daytime operation hours of the heat pumps according to the cooling/heating load and power cost tables by using the correlation. The heat pump scheduling device 130 may determine the amount of heat stored in the heat storage tank based on the late-night operation time and the daytime operation time of the heat pumps. Accordingly, the heat pump scheduling device 130 may select an optimal amount of heat stored in the heat storage tank and perform heat pump scheduling for each group in order to minimize operating costs.

Methods according to the embodiments described in the claims or specification of the present disclosure may be implemented in the form of hardware, software, or a combination of hardware and software.

When implemented in software, a computer readable storage medium storing one or more programs (software modules) may be provided. One or more programs stored in a computer-readable storage medium are configured for execution by one or more processors in an electronic device. The one or more programs include instructions that cause the electronic device to execute methods according to embodiments described in the claims or specification of the present disclosure.

Such programs (software modules, software) may include random access memory, non-volatile memory including flash memory, read only memory (ROM), and electrically erasable programmable ROM. (electrically erasable programmable read only memory (EEPROM), magnetic disc storage device, compact disc-ROM (CD-ROM), digital versatile discs (DVDs), or other It can be stored on optical storage devices, magnetic cassettes. Alternatively, it may be stored in a memory composed of a combination of some or all of these. In addition, each configuration memory may be included in multiple numbers.

In addition, the program is provided through a communication network such as the Internet, an intranet, a local area network (LAN), a wide area network (WAN), or a storage area network (SAN), or a communication network consisting of a combination thereof. It can be stored on an attachable storage device that can be accessed. Such a storage device may be connected to a device performing an embodiment of the present disclosure through an external port. In addition, a separate storage device on a communication network may be connected to a device performing an embodiment of the present disclosure.

In the specific embodiments of the present disclosure described above, components included in the disclosure are expressed in singular or plural numbers according to the specific embodiments presented. However, the singular or plural expressions are selected appropriately for the presented situation for convenience of explanation, and the present disclosure is not limited to singular or plural components, and even components expressed in plural are composed of the singular number or singular. Even the expressed components may be composed of a plurality.

Meanwhile, in the detailed description of the present disclosure, specific embodiments have been described, but various modifications are possible without departing from the scope of the present disclosure. Therefore, the scope of the present disclosure should not be limited to the described embodiments and should not be defined by the scope of the claims described below as well as those equivalent to the scope of these claims.

110 Building 120 BEMS
130 Heat pump scheduling device 131 Operation information receiver
132 Control method determination unit for each group 133 Operation time determination unit
134 Scheduling generator

Claims (16)

A method of operating a heat pump scheduling device for determining an operation schedule of heat pumps installed in parallel in a building based on a building energy management system (BEMS),
Receiving driving information from the BEMS;
classifying the heat pumps into at least one heat pump group based on the operation information, and determining a control method related to an operation sequence of the at least one heat pump group;
determining an operation time of the at least one heat pump group based on the operation information; and
Generating an operation schedule of the heat pumps based on the control method and the operation time;
The step of classifying the heat pumps into at least one heat pump group and determining a control method related to the operation sequence of the at least one heat pump group,
determining a plurality of temperature intervals;
determining at least one first heat pump having an inlet temperature within a first temperature range;
determining at least one second heat pump having an outlet temperature within the same temperature range among the at least one first heat pump; and
and determining the at least one second heat pump as a heat pump group. delete The method of claim 1,
The step of determining the control method is,
determining a coefficient of performance (COP) corresponding to each of the at least one heat pump group;
determining an operation priority of the at least one heat pump group based on the COP;
determining a power load section according to the amount of heat produced by the heat pump in each of the at least one heat pump group based on the operation priority; and
and determining in a control manner how the at least one heat pump group sequentially operates in the order of operation priority based on the power load period.
The method of claim 3,
Determining the COP corresponding to each of the at least one heat pump group,
determining heat pumps in each of the at least one heat pump group, the heat pumps having a pump rated heat output ratio equal to or greater than a first threshold value; and
and determining a median value of COP values of heat pumps equal to or greater than the first threshold value.
The method of claim 3,
The step of determining the operation priority of the at least one heat pump group includes determining the operation priority in the order of groups having the highest COP.
The method of claim 1,
The operation information includes on-time and off-time of the heat pumps, inlet and outlet temperatures of the heat pumps, and power consumption of the heat pumps.
The method of claim 1,
The step of determining the driving time,
determining cooling and heating loads produced by the heat pumps using an artificial neural network;
determining a correlation between a heat capacity stored in a heat storage tank and a COP of the heat pumps in the heat pumps using nonlinear regression; and
and determining an operation time of the at least one heat pump using the cooling/heating load and the correlation.
The method of claim 7,
The artificial neural network,
A method of operating a heat pump scheduling device including a neural network that learns a correlation between cooling and heating loads produced by the heat pumps at a second time after the first time from the outside air information of the building and the amount of heat supplied at the first time.
The method of claim 8,
The operation information is the operation of the heat pump scheduling device including the outside air information of the building, the supply temperature and return temperature of the coil inside the air handling unit (AHU) of the building, and the inlet and outlet temperatures of the heat pumps. method.
The method of claim 7,
The step of determining the driving time,
and determining a late-night operation time and a daytime operation time of the heat pumps according to the cooling/heating load and power cost table by using the correlation.
The method of claim 10,
The operation information includes inlet and outlet temperatures of the heat pumps, amount of heat used in the AHU, and power consumption of the heat pumps.
The method of claim 7,
The generating of the operation schedule of the heat pumps includes determining a heat capacity stored in the heat storage tank based on a late-night operation time and a daytime operation time of the heat pumps.
In a heat pump scheduling device for determining an operation schedule of heat pumps installed in parallel in a building based on a building energy management system (BEMS),
a driving information receiving unit receiving driving information from the BEMS;
a control method determination unit for each group that classifies the heat pumps into at least one heat pump group based on the operation information and determines a control method related to an operation sequence of the at least one heat pump group;
an operating time determining unit configured to determine an operating time of the at least one heat pump group based on the operating information; and
A scheduling generating unit generating an operating schedule of the heat pumps based on the control method and the operating time;
The control method determining unit for each group,
determining a coefficient of performance (COP) corresponding to each of the at least one heat pump group;
determining an operation priority of the at least one heat pump group based on the COP;
Determine a power load section according to the amount of heat produced by the heat pump in each of the at least one heat pump group based on the operation priority;
The heat pump scheduling apparatus of claim 1 , wherein a method of controlling sequentially operating the at least one heat pump group in the order of operation priority based on the power load interval is determined. delete The method of claim 13,
The driving time determination unit,
Determining the heating and cooling loads produced by the heat pumps using an artificial neural network;
In the heat pumps using nonlinear regression, determining a correlation between the heat capacity stored in the heat storage tank and the COP for the heat pumps;
The heat pump scheduling device for determining an operating time of the at least one heat pump using the cooling/heating load and the correlation.
The method of claim 15
The artificial neural network is a heat pump scheduling device including a neural network that has learned a correlation between cooling and heating loads produced by the heat pumps at a second time after the first time, from information on outside air of the building and heat supply at a first time. .

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