KR20180108275A - Method of energy consumption prediction of builds using for energy consumption pattern - Google Patents

Abstract

The present invention relates to a method for predicting energy consumption of a building by using an energy consumption pattern. The method for predicting energy consumption comprises predicting energy consumption of a building through an energy consumption pattern and energy bench marking according to a correlation between occupancy information, construction information, weather information and geographical information collected for predicting energy consumption from the building.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method of predicting energy consumption of a building,

More particularly, the present invention relates to a method for estimating energy consumption to be used in a building through energy benchmarking based on various information collected in a building.

Various energy saving technologies are being researched and developed as a measure against energy saving, which is an international issue. In particular, buildings (home / commercial / public), which accounts for more than 20% of total energy use, utilize building energy management systems and building automation systems to perform energy diagnosis and analysis, do.

However, the analytical methods used in building energy management systems and building automation systems have been focused on ways to improve data management efficiency with minimal impact on the reliability of results. To do this, Sampling process. As a result, the data becomes less reliable and hence less accurate, due to compression or sampling information.

Therefore, there is a need for a method for more energy saving without lowering the reliability of data through compression or sampling of data.

The present invention utilizes a big data platform capable of collecting and processing information on building envelopes and facilities such as buildings, machines, and electricity related to the buildings with large amounts of data, thereby analyzing energy consumption patterns of buildings and estimating energy consumption of buildings And provides a method for predicting energy consumption to support energy benchmarking.

The present invention utilizes meteorological information and geographical information centered on the location of a building as well as information collected through a building energy management system (BEMS) or a building automation system (BAS) And a method of predicting energy consumption that generates a more accurate energy consumption pattern through factor analysis.

The energy processing apparatus according to an exemplary embodiment of the present invention includes collecting redundant information, architectural information, weather information, and geographical information for predicting energy consumption of a building; Determining an energy consumption pattern of the building in consideration of association between the speaker information, the building information, the weather information, and the geographical information; And estimating an energy consumption amount of the building using a variation amount between the room information, the building information, the weather information, and the geographical information based on the energy consumption pattern.

According to an embodiment of the present invention, the determining step may include analyzing a change amount with respect to remaining information that changes according to a change amount of at least one of the loud room information, the building information, the weather information, and the geographical information; Determining a linkage relation between the room information, the building information, the weather information, and the geographical information according to the result of the analysis; And generating a consumption pattern of energy used in the building corresponding to the generated association.

According to an exemplary embodiment of the present invention, the generating step may include: changing a numerical value represented by at least one of the room information, the building information, the weather information, and the geographical information based on the association; And generating a consumption pattern of the energy by patterning variations of the room information, the building information, the weather information, and the geographical information that vary according to the changed numerical value.

According to an exemplary embodiment, the generating step may estimate the energy consumption of the building from the louver schedule included in the current weather and redundancy information included in the weather information based on the energy consumption pattern.

According to one embodiment of the present invention, the energy consumption prediction method analyzes the energy consumption pattern of the building and provides a benchmarking service for predicting the energy consumption of the building, thereby reducing the energy Management methods can be flexibly provided to the building operator.

According to one embodiment of the present invention, the energy consumption prediction method is based on information collected through a building energy management system or a building automation system, weather information and geographical information, By creating an energy consumption pattern through analysis, you can help to find the key elements of building energy savings.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a diagram illustrating an overall configuration for predicting energy consumption of a building according to an exemplary embodiment of the present invention. FIG.
2 is a view for explaining the detailed operation of the energy processing apparatus according to one embodiment.
3 is a flowchart illustrating a method of predicting energy consumption of an energy processing apparatus according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a diagram illustrating an overall configuration for predicting energy consumption of a building according to an exemplary embodiment of the present invention. FIG.

Referring to FIG. 1, the energy processing apparatus 101 can generate a consumption pattern of energy used in the building 102, and predict an energy consumption amount of the building 102 based on a consumption pattern of the generated energy. To this end, the energy processing apparatus 101 may collect the room information, the building information, the weather information, and the geographical information for predicting the energy consumption of the building 102.

The energy processing apparatus 101 may collect the redundant and building information to check whether the facilities and facilities installed in the building 102 perform their functions. For example, the energy processing apparatus 101 is a building energy management system (BEMS) that efficiently utilizes energy used in a building by utilizing IT technology in the building, or a building energy management system (BEMS) And Building Automation System (BAS) that controls heating, cooling, and electricity facilities.

The energy processing apparatus 101 can collect weather information that observes changes in the state and trend of natural phenomena occurring in the convection in the area where the building 102 is located. For example, the energy processing apparatus 101 can collect meteorological information about the weather or the weather as a phenomenon occurring in the atmosphere such as wind, rain, clouds, snow, etc. in an area where the building 102 is located from the weather server 103 .

The energy processing apparatus 101 may collect geographical information indicating the geographical position of the building 102 and the density of adjacent buildings with respect to the geographical position. In one example, the energy processing apparatus 101 may collect geographical information from the geographical server 104 that represents the natural and human phenomena occurring on the land surface according to the geographical position of the building 102 from a geographical viewpoint.

Here, the energy processing apparatus 101 can collect real-time information, building information, weather information, and geographical information in real time, and the information thus collected in real time can be managed in the form of a big data platform supporting distributed processing of data . In other words, the energy processing apparatus 101 can collect the room information, the building information, the weather information, and the geographical information based on the big data platform capable of collecting, storing and analyzing a large amount of information in real time.

Thereafter, the energy processing apparatus 101 analyzes the association of each item of the room information, the building information, the weather information, and the geographical information, and forms an association relation among the items of the information having a high degree of influence according to the analysis result. Specifically, the energy processing apparatus 101 can identify the collected items according to the attribute of each information, and analyze the association between the items identified according to the respective information. In other words, the energy processing apparatus 101 can confirm each item related to the attribute in correspondence with the attribute represented by the collected information. For example, the loudspeaker information indicates properties related to the internal environment of the building, and may include items such as a building's main space or zone internal temperature, internal humidity, and the like. In another example, the architectural information represents the attributes of the building envelope, the facility, and may include items for each of the architectural and mechanical electrical aspects of the building.

Then, the energy processing apparatus 101 can analyze the correlation of each item included in each information. In detail, the energy processing apparatus 101 can analyze a correlation between numerical values represented by items included in different information that changes in correspondence to numerical values represented by items included in each information. For example, the energy processing apparatus 101 analyzes the association between 1) whether or not the building information is heated, or 2) the waiting state represented by the weather information, which responds to the variation of the numerical value of the room temperature among the room information, It is possible to analyze the association between different information that necessarily changes as one of the information changes based on various information collected by the energy processing apparatus 101. [

As a result, the energy processing apparatus 101 can analyze the association, which is a rule that occurs between the information according to the attribute of the collected information, that is, the property (characteristic) represented by the information.

Specifically, the energy processing apparatus 101 can generate the energy consumption pattern of the building based on the association relationship between the items of the collected information. Specifically, the energy processing apparatus 101 uses the building environment information, the building operation information, and the energy usage information continuously collected through the building energy management system (BEMS), the building automation system (BAS) You can generate energy usage for a building. Then, the energy processing apparatus 101 can determine the energy consumption of the building as the result data, and determine the building environment information and the building operation information as the cause data.

Thereafter, the energy processing apparatus 101 can analyze the causal relationship between the determined result data and the cause data. The energy processing apparatus 101 can record and learn energy usage information according to specific building environment conditions and building operation conditions. The present invention can generate the energy consumption pattern information of the building based on the energy usage data for each specific condition.

The energy processing apparatus 101 can analyze the factors that use the energy in the building in consideration of the association derived between the respective items of information. Specifically, when the energy consumption pattern information is inversely estimated, the energy processing apparatus 101 finds environment and building operating conditions of the building that can consume the same level of energy as the energy currently consumed in the building . Thus, the energy processing apparatus 101 can quickly find the energy consumption factor of the building.

In other words, when the room temperature is assumed to be increased, the heating can be turned ON, which confirms that the external temperature due to the atmospheric change is low. That is, as the outside temperature is low, energy is consumed through the heating device in the building, so that the energy processing device 101 can confirm that the heating device is operated because the energy in the building is used.

Then, the energy processing apparatus 101 can generate a plurality of energy consumption patterns by analyzing various factors through such an energy consumption situation. Then, the energy processing apparatus 101 can perform energy benchmarking for predicting energy consumption in the same environment depending on each energy consumption situation. In other words, the energy processing apparatus 101 can confirm the measured energy consumption in the past in the same environment. Further, the energy processing apparatus 101 can change only the information of the specific condition in the same environment and derive the degree of change of the energy consumption amount according to the changed information. Then, the energy processing apparatus 101 can predict the future energy consumption of the building from the current weather and the livelihood schedule of the building by performing comparison analysis according to the degree of change of the derived energy consumption amount.

As a result, the present invention can generate a more accurate energy consumption pattern by analyzing the factors of lighting and energy consumption among the room information, the building information, the weather information, and the geographical information. The present invention also provides an energy benchmarking service for analyzing building energy consumption patterns based on a big data platform that utilizes information about buildings and machinery such as building, machinery, and electricity, and estimating building energy consumption. .

2 is a view for explaining the detailed operation of the energy processing apparatus according to one embodiment.

Referring to FIG. 2, the energy processing apparatus 201 estimates energy consumption so as to more efficiently reduce building energy by providing benchmarking for predicting building energy consumption patterns and building energy consumption, It is possible to provide a way to flexibly provide this to the operator.

For this, the energy processing apparatus 201 may include an information collecting unit 202, a consumption pattern determining unit 203, and an energy consumption predicting unit 204.

The information collecting unit 202 may collect the room information 205, the building information 206, the weather information 207, and the geographical information 208 for predicting the energy consumption of the building. At this time, although the room information 205, the building information 206, the weather information 207 and the geographical information 208 are not shown in FIG. 2, a separate data collection device for collecting information related to the building Can be collected. The information collection unit 202 can receive the redundancy information 205, the construction information 206, the weather information 207, and the geographical information 208 from the data collection device.

In addition, the room information 205 may include information on the internal environment formed by the energy used in the building. In other words, the loudspeaker information may include occupant information residing in the building, a space within the building in which the occupant lives, or an internal temperature in a main space of the building, an internal humidity, and the like.

The building information (206) can collect the room information and the building information to check whether the facilities and facilities installed in the building perform their functions. In other words, the building information 206 may include building basic information of a building, facility operation information, energy consumption information, and the like. For example, the architectural information 206 may include information about building envelopes and facilities such as walls, roofs, floors, windows and doors, air conditioning, heating, fans, pumps, have.

The weather information 207 may include information on observations of changes in the state and trend of natural phenomena occurring in the convection region where the building is located. For example, the weather information 207 is a phenomenon occurring in the atmosphere such as wind, rain, clouds, snow, rainfall, radiation amount, temperature, and humidity in the area where the building 102 is located from the weather server 103, .

The geographical information 208 may include information such as the geographical position of the building and the density of adjacent buildings depending on the geographical position. For example, the geographical information 208 may include information such as latitude, longitude, altitude, and building density of the natural and human phenomena occurring on the land according to the geographical position of the building from the geographical server.

Here, the information collecting unit 202 may collect data based on an Internet-based wired or wireless network environment, and may be able to perform real-time distributed processing on the collected information 205, 206, 207, and 208.

The consumption pattern determining unit 203 can determine the energy consumption pattern of the building in consideration of the correlation between the room information 205, the building information 206, the weather information 207, and the geographical information 208. [

Specifically, the consumption pattern determining unit 203 determines the consumption pattern determining unit 203 as the remaining information that varies according to the amount of change for at least one of the re-compartment information 205, the building information 206, the weather information 207 and the geographical information 208 Can be analyzed. Here, the consumption pattern determining unit 203 can express the amount of change for each piece of information by digitizing it.

The consumption pattern determining unit 203 can determine the association between the occupancy information 205, the building information 206, the weather information 207, and the geographical information 208 based on the numerically expressed change amount . For example, the consumption pattern determining unit 203 can express the degree of association between items of two information by numerically calculating the amount of change of different information according to the amount of change of each information item.

The consumption pattern determining unit 203 can generate a consumption pattern of energy used in the building corresponding to the generated association. In detail, the consumption pattern determining unit 203 can change the numerical value represented by at least one of the room information 205, the building information 206, the weather information 207, and the geographical information 208 based on the association relationship have. The consumption pattern determining unit 203 patterns the variation amounts of the room information 205, the building information 206, the weather information 207, and the geographical information 208, which change according to the changed numerical values, Can be generated.

In other words, the consumption pattern determining unit 203 determines the state of the lean room information 205, the building information 206, the weather information 207, and the geographical information 208, which are each cause data, based on the association results of the derived information Energy consumption patterns can be generated by analyzing energy consumption data at the same time unit with the result data according to information.

The energy consumption predicting unit 204 can predict the energy consumption amount of the building by using the amount of change between the room information, the building information, the weather information, and the geographical information based on the energy consumption pattern. The energy consumption predicting unit 204 can determine the past energy consumption amount in the same environment based on the energy consumption pattern or change only the item of information corresponding to a specific condition such as the outside temperature temperature in the same environment, have. Then, the energy consumption prediction unit 204 can compare and analyze the energy consumption efficiency according to the change in the derived energy consumption amount. In addition, this approach is referred to as energy benchmarking.

The energy benchmarking according to the present invention refers to energy consumption pattern, compares and analyzes the energy consumption amount according to the amount of change in the surrounding environment information, and outputs the analysis result to a screen for the building operator to confirm.

Then, the energy consumption predicting unit 204 can predict the energy consumption amount of the building from the livelihood schedule included in the current weather and redundancy information included in the weather information, based on the energy consumption pattern. Here, the livelihood schedule can refer to the energy use plan to be used according to the energy consumption pattern, which can be determined by past energy consumption or the building operator.

Therefore, the energy consumption prediction unit 204 can predict future energy consumption through the external environment condition and the facility operation plan.

As a result, the present invention is based on the data of the building energy management system (BEMS), which is obliged to emphasize the necessity of building energy saving, and all the data collected from unprocessed pure buildings based on the weather information and geographical information disclosed by the government And the energy consumption pattern can be determined through factor analysis. The present invention provides a flexible energy management method for the building operator by comparing the energy consumption of the building according to the energy consumption pattern and the past energy consumption of the building and further estimating the future energy consumption, You can give.

3 is a flowchart illustrating a method of predicting energy consumption of an energy processing apparatus according to an embodiment of the present invention.

In step 301, the energy processing apparatus can collect the loudspeaker information, the building information, the weather information, and the geographical information for predicting the energy consumption of the building.

In step 302, the energy processing device may determine the energy consumption pattern of the building, taking into account the association between loud room information, building information, weather information, and geographical information.

The energy processing apparatus can analyze a change amount with respect to remaining information that changes according to a variation amount of at least one of the occupancy information, the building information, the weather information, and the geographical information. The energy processing unit can determine the association between the occupancy information, the building information, the weather information and the geographical information according to the analysis result.

The energy processing device may generate a consumption pattern of energy used in the building corresponding to the generated association. At this time, the energy processing apparatus can change the numerical value represented by at least one of the room information, the building information, the weather information, and the geographical information on the basis of the association. The energy processing apparatus can generate the energy consumption pattern by patterning the amount of change of the room information, the building information, the weather information, and the geographical information which change according to the changed numerical value.

In step 303, the energy processing apparatus can estimate the energy consumption amount of the building by using the amount of change between the redundancy information, the building information, the weather information, and the geographical information based on the energy consumption pattern. Here, the energy processing apparatus can predict the energy consumption of the building from the residence schedule included in the current weather and redundancy information included in the weather information based on the energy consumption pattern.

Meanwhile, the method according to the present invention may be embodied as a program that can be executed by a computer, and may be embodied as various recording media such as a magnetic storage medium, an optical reading medium, and a digital storage medium.

Implementations of the various techniques described herein may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or combinations thereof. Implementations may be implemented in a computer program product, such as an information carrier, e.g., a machine readable storage device, such as a computer readable storage medium, for example, for processing by a data processing apparatus, Apparatus (computer readable medium) or as a computer program tangibly embodied in a propagation signal. A computer program, such as the computer program (s) described above, may be written in any form of programming language, including compiled or interpreted languages, and may be stored as a stand-alone program or in a module, component, subroutine, As other units suitable for use in the present invention. A computer program may be deployed to be processed on one computer or multiple computers at one site or distributed across multiple sites and interconnected by a communications network.

Processors suitable for processing a computer program include, by way of example, both general purpose and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The elements of a computer may include at least one processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer may include one or more mass storage devices for storing data, such as magnetic, magneto-optical disks, or optical disks, or may receive data from them, transmit data to them, . ≪ / RTI > Information carriers suitable for embodying computer program instructions and data include, for example, semiconductor memory devices, for example, magnetic media such as hard disks, floppy disks and magnetic tape, compact disk read only memory A magneto-optical medium such as a floppy disk, an optical disk such as a DVD (Digital Video Disk), a ROM (Read Only Memory), a RAM , Random Access Memory), a flash memory, an EPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), and the like. The processor and memory may be supplemented or included by special purpose logic circuitry.

In addition, the computer-readable medium can be any available media that can be accessed by a computer, and can include both computer storage media and transmission media.

While the specification contains a number of specific implementation details, it should be understood that they are not to be construed as limitations on the scope of any invention or claim, but rather on the description of features that may be specific to a particular embodiment of a particular invention Should be understood. Certain features described herein in the context of separate embodiments may be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented in multiple embodiments, either individually or in any suitable subcombination. Further, although the features may operate in a particular combination and may be initially described as so claimed, one or more features from the claimed combination may in some cases be excluded from the combination, Or a variant of a subcombination.

Likewise, although the operations are depicted in the drawings in a particular order, it should be understood that such operations must be performed in that particular order or sequential order shown to achieve the desired result, or that all illustrated operations should be performed. In certain cases, multitasking and parallel processing may be advantageous. Also, the separation of the various device components of the above-described embodiments should not be understood as requiring such separation in all embodiments, and the described program components and devices will generally be integrated together into a single software product or packaged into multiple software products It should be understood.

It should be noted that the embodiments of the present invention disclosed in the present specification and drawings are only illustrative of specific examples for the purpose of understanding and are not intended to limit the scope of the present invention. It will be apparent to those skilled in the art that other modifications based on the technical idea of the present invention are possible in addition to the embodiments disclosed herein.

101: Energy processing device
102: Building
103: weather server
104: Geography Server

Claims (1)

Collecting redundant information, architectural information, weather information and geographical information for predicting the energy consumption of the building;
Determining an energy consumption pattern of the building in consideration of association between the speaker information, the building information, the weather information, and the geographical information; And
Estimating an energy consumption amount of the building by using a variation amount between the room information, the building information, the weather information, and the geographical information based on the energy consumption pattern
/ RTI >

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