KR20200057818A - Energy Diagnosis System And Method For Realization Of Zero Energy Building - Google Patents

Abstract

The present invention relates to an energy diagnosis system for a zero-energy building and a method thereof. More specifically, the energy diagnosis system for a zero-energy building includes: a data collection module collecting building energy data including characteristic data and condition data about an existing building; a data processing module including a data storage part storing the building′s energy data, a data classification part classifying the building′s energy data by item or factor, and a data map formation part forming a data classification map by defining a relation between the classified building condition data and building characteristic data; and an energy diagnosis module deriving an energy consumption type and a production type by combining the data classification map by item or factor. Therefore, the energy diagnosis system is capable of deriving a diagnosis and improvement measures to convert an existing building which is a diagnosis target into a zero-energy building where consumed energy and power generated by new renewable energy in the building are equal or the sum of the values is close to 0.

Description

Energy Diagnosis System And Method For Realization Of Zero Energy Building

The present invention relates to an energy diagnosis system and a method for constructing zero energy, and more specifically, to construct a zero energy building, which is a building in which the energy consumed by the building and the amount of renewable energy generation in the building are the same or the sum of these is close to zero. For this, the present invention relates to an energy diagnosis system and a method for constructing zero energy using deep learning based on big data.

Generally, a zero energy building (ZEB) means a building in which the sum of the energy consumed for the operation of a building and the amount of electricity generated by renewable energy facilities provided in the building becomes zero. In other words, it means a building capable of self-sufficiency of energy, but since there are technical and economic limitations, the zero energy building is defined as the sum of energy consumption and power generation close to zero.

The methods for constructing the zero-energy building as described above are largely referred to as 'passive technology' and 'active technology', and the passive technology is applied through the exterior of the building by applying insulation, double windows, etc. It minimizes the amount of energy leaked to the outside, and active technology means self-solving all energy consumption such as power supply, air conditioning, and cooking by utilizing renewable energy such as geothermal energy and solar power.

This not only contributes to energy savings but also greenhouse gas reduction. As the interest in zero-energy buildings increases significantly worldwide, it not only shows an increasing trend, but also supports various policies and supports for national financial support, technology development, and market preoccupation. Is expanding.

In Korea, we have established a basic plan for this in 2014 and promoted commercialization with the implementation of the 'zero certification system' from 2017 through the building energy efficiency rating certification system.In particular, public buildings are required by 2020, and private buildings by 2025. I am planning to make it mandatory.

To this end, Korean Patent Registration No. 1,571,695 uses a lens or a reflector to amplify a light source to convert solar energy into electrical energy, and a solar heat having a heat medium that exchanges heat with the condensing solar power generator. A hot water hot water supply device and a geothermal heat supply device having a heat collecting device and a first geothermal heat pump device that supplies hot or cold heat energy using a heat source or heat sink of geothermal heat to supply energy for hot water supply. A zero-energy building including a cooling / heating energy supply device that supplies energy for cooling / heating by providing a second geothermal heat pump device that supplies warm or cold heat energy using a heat source or a heat sink. A renewable energy supply system for implementation and a control method thereof are disclosed, but this is related to a means for supplying thermal energy and electric energy to a building, and is limited in application according to detailed conditions and characteristics of the building.

In addition, when additional renewable energy facilities are to be provided in a built building, it is necessary to consider the exact energy consumption and environmental conditions of the building to determine a suitable renewable energy facility means. Therefore, in order to zero-energy an existing building, it is required to develop a system for accurately and quickly diagnosing the type of energy consumption for each main building, and further, to optimize energy consumption through energy diagnosis of the building, and to establish a new and renewable energy facility. The development of an integrated system that proposes a zero-energy building plan, including, is also required.

Korean Registered Patent No. 1,571,695

In order to solve the above-mentioned problem, the present invention classifies the energy consumption and production type of a building built for a zero-energy building in which the energy consumed by the building and the amount of renewable energy generation in the building are equal or the sum of these approaches zero. And to provide an energy diagnosis system and its method for building zero energy that can lead to improvement measures.

In addition, the present invention is to provide an energy diagnosis system and method for constructing zero energy that can derive more detailed and specific building energy diagnosis and improvement methods according to various types of classification through deep learning analysis based on big data.

Other objects of the present invention will be more apparent through the preferred embodiments described below.

According to an aspect of the present invention, a data collection module for collecting building energy data including building condition data and building characteristic data for a key building, a data storage unit for storing the building energy data, and the building energy data for each item Alternatively, a data processing module including a data classification unit for classifying factors and a data map forming unit for forming a data classification map by defining a relationship between each classified building condition data and the building characteristic data, and data classification for each item or factor It is an energy diagnosis system for zero energy construction that includes an energy diagnosis module that derives energy consumption types and production types by combining maps.

According to another aspect of the present invention, the building condition data includes user factor data including the number of occupants, the number of occupants, the frequency of access, the amount of occupant behavior, and the pattern of occupant behavior, and the area and volume of the building, use, temperature and humidity inside the building And spatial and building factor data including illuminance, air quality inside the building, solar radiation, and local and environmental factor data including latitude, longitude, altitude, surrounding environment, atmospheric environment, air quality, floating population, and surrounding traffic. Temperature, humidity, and air pressure in the area where the building is located. It includes weather and seasonal factor data including wind direction, wind speed, sunshine, rainfall, snowfall, and season, and the building characteristic data includes energy facility data including air conditioning, lighting, and air conditioning facilities, and renewable energy facility data and energy. It is characterized by including consumption data and energy production data.

According to another aspect of the present invention, the data collection module is characterized in that at least one of a sensor, BAS (Building Automation System), BEMS (Building Energy Management System), direct input means, information database connected to the Internet network, , The energy diagnosis module is characterized by deriving an energy consumption type and a production type by Deep Learning, HD Data Discrimanative Analysis.

According to another aspect of the present invention, at least one new renewable energy construction method and a building energy efficiency improvement method are derived according to the energy consumption type and production type derived from the energy diagnosis module, and decision making is supported for each of them. It is an energy diagnosis system for zero energy construction that further includes a new and renewable energy construction decision support module and a building energy efficiency improvement decision support module.

According to another aspect of the present invention, a data collection step of collecting building energy data including building condition data and building characteristic data of a key building and a data storage step of storing the collected building energy data in a DB and the stored building The data classification step of classifying energy data into each item or factor, and a data map construction step of defining a relationship between the classified building condition data and the building characteristic data to form a data classification map, and the data classification map for each item or factor. It is an energy diagnosis method for zero energy construction that includes an energy diagnosis step of deriving an energy consumption type and a production type of the main building in combination.

According to another aspect of the present invention, the data collection step is a sensor installed inside or outside a key building, a pre-built BAS (Building Automation System), pre-built BEMS (Building Energy Management System), direct input means, Internet network It characterized in that it is collected by at least one or more means of the information database connected to.

According to another aspect of the invention, the energy diagnosis step (S500);

It is characterized by deriving the energy consumption type and production type of the main building by deep learning over-dense data analysis (Hyperdense, HD Data Discrimanative Analysis), and according to the energy consumption type and production type derived in the energy diagnosis step Characterized in that it further comprises at least one of a renewable energy construction plan and a building energy efficiency improvement plan, and a decision support step for new and renewable energy construction decision support and a decision support step for building energy efficiency improvement for each of them. do.

The energy diagnosis system and method for constructing zero energy according to the present invention are the same as the energy consumed by the building and the amount of new and renewable energy generation in the building or the sum of these approaches zero through the energy-related data of the main building to be diagnosed. It has the advantage of being able to derive diagnosis and improvement measures that can be energy buildings.

In addition, the present invention has the advantage that a large data platform related to building energy can be a big data platform to store and manage and classify each data, and derive more specific and advanced energy diagnosis and improvement methods through deep learning.

1 is a block diagram of an energy diagnosis system for building zero energy according to an embodiment of the present invention.
2 is a conceptual diagram of data processing in data processing module data of an energy diagnosis system for building zero energy according to an embodiment of the present invention.
3 is a conceptual diagram of forming a data classification map of a data processing module of an energy diagnosis system for constructing zero energy according to an embodiment of the present invention.
4 is a detailed configuration diagram of an energy diagnosis system for building zero energy according to an embodiment of the present invention.
5 is a flow chart of an energy diagnosis method for building zero energy according to an embodiment of the present invention.
6 is a detailed flowchart of an energy diagnosis method for constructing zero energy according to an embodiment of the present invention.

The present invention can be applied to a variety of transformations and may have various embodiments, and specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all conversions, equivalents, and substitutes included in the spirit and scope of the present invention. In the description of the present invention, if it is determined that a detailed description of known technologies related to the present invention may obscure the subject matter of the present invention, the detailed description will be omitted.

Terms such as first and second may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from other components.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, terms such as “include” or “have” are intended to indicate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, one or more other features. It should be understood that the existence or addition possibilities of fields or numbers, steps, operations, components, parts or combinations thereof are not excluded in advance. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a configuration diagram of an energy data processing device of a key building according to an embodiment of the present invention, and a detailed configuration of an energy data processing device of a key building according to the present invention will be described below with reference to this.

Data collection module 100 for collecting building energy data 110 including building condition data 111 and building characteristic data 112 for a key building; and a data storage unit for storing the building energy data 110 The data is defined by defining a relationship between the data classification unit 220 for classifying the building energy data 110 and the building energy data 110 for each item or factor, and the classified building condition data 111 and the building characteristic data 112. A data processing module 200 including a data map forming unit 230 forming a classification map; And an energy diagnosis module 300 for deriving an energy consumption type and a production type by combining the data classification map for each item or factor. It is an energy diagnosis system for constructing zero energy.

In this case, the building condition data 111 includes user factor data 111a including the number of occupants, the number of occupants, the frequency of access, the amount of occupant behavior, and the pattern of occupant behavior; and the area and volume of the building, the use, and the temperature inside the building. Spatial and building factor data (111b) including humidity and illuminance, air quality inside the building, and solar radiation; and areas including latitude, longitude, altitude, surrounding environment, atmospheric environment, air quality, floating population, and surrounding traffic And environmental factor data 111c; And temperature, humidity, and air pressure in the area where the building is located. Includes wind direction, wind speed, sunshine, rainfall, snowfall, and seasonal weather and seasonal factor data 111d; and the building characteristic data 112 is energy facility data including air conditioning, lighting, and air conditioning facilities ( 112a); and renewable energy facility data 112b; and energy consumption data 112c; And energy production data 112d.

And, the data collection module 100; is characterized in that the sensor, BAS (Building Automation System), BEMS (Building Energy Management System), direct input means, any one or more of an information database connected to the Internet network.

In more detail, the user factor data may be installed in various types of sensors in the main building to be diagnosed and data sensed through the sensors may be collected. Of course, it is possible to set the data detected by various sensors to be directly transmitted to the big data platform 200 and stored by applying the Internet of Things.

In addition, the area, volume, space, and purpose of building of the target building to be diagnosed among the space and building factor data may be directly input through a mobile device or a PC. At this time, the building factor data may also include building wall information, structure information, and the like.

In addition, the regional and environmental factor data can be connected to various servers or databases, such as the Geographic Information System (GIS) of the National Geographic Information Institute, the Application Program Interface (API) of the Environmental Authority, and the Application Program Interface (API) of the Korea Meteorological Administration through the Internet. Of course, it can be collected, and of course, various known means for data collection may be applied.

In addition, the data processing module 200 is an open non-relational distributed database Apache Hbase for the Hadoop platform, Apache Spark, an open source cluster computing framework, and software systems to collect, store, and manage large-scale heterogeneous data. It can be configured through Apache Nifi, which automates data flow, Apache Hadoop, a freeware Java software framework that supports distributed applications running on large computer clusters that can process large amounts of data. Of course, the platform can be applied.

In addition, the energy diagnosis module 300 is a variety of known techniques that can be used for deep learning (Deep Learning) and machine learning (Machine Learning), such as TensorFlow, keras, Jupyter, Compuda Unified Device Architecture (CUDA), cuDNN, etc. It is applicable.

2 and 3 are a conceptual diagram of a data processing conceptual diagram and a data classification map in the data processing module data of the energy diagnosis system for constructing zero energy according to an embodiment of the present invention, respectively. An example of the operation of the data processing module 200 of the energy diagnosis system is as follows.

The building condition data 111 and the building characteristic data 112 collected by the data collection module 100 are respectively stored in the data storage unit 210. At this time, the data storage unit 210 is preferably formed of two or more server groups connected to each other in order to balance the work load, such as a Hadoop cluster server, or to ensure continuous operation when one server fails.

As described above, the building condition data 111 and the building characteristic data 112 stored in the data storage unit 210 are separately sorted by the data classification unit 220. Thereafter, by the data map forming unit 230, each factor or item of the building condition data 111 and each item of the building characteristic data 112 are synthesized according to a predetermined relationship to form a data map for each factor.

For example, according to weather and seasonal factor data 111d, energy consumption data 112c according to changes in the use of air conditioning and heating equipment and air conditioning facilities may be synthesized to form a data map related to weather and seasonal energy consumption, and simultaneously the weather. And seasonal factor data 111d is synthesized with energy production data 112d according to renewable energy facilities such as solar power generation facilities, solar power generation facilities, geothermal power generation facilities, wind power generation facilities, and data related to energy production according to weather and seasons. A map may be formed, and a large amount of data maps of hundreds of thousands or more may be formed according to a relationship between each data by further including a time variable, and the building condition data 111 except for the building characteristic data 112. Of course, a large number of data maps can be formed through the relationship between).

At this time, the data processing module 200 includes at least one well-known convolutional layer, such as a convolutional neural network (CNN), a convolutional layer, a pooling layer, and fully connected layers. It is preferably composed of a configured neural network.

That is, the convolution layer may be a layer in which each factor or item of the building condition data 111 and the building characteristic data 112 is classified, and the pooling layer is the building It may be a layer formed by synthesis between the condition data 111 and the building characteristic data 112, and the fully connected layer includes the convolution layer and the pooling layer, respectively. It can be configured in a completely combined form.

Accordingly, the data processing module 200 has a structure suitable for data learning, data learning can be performed through reverse transmission, and various artificial neural networks (ANNs) except for the above-described convolutional neural network (CNN). Computing systems may also be applied.

Thereafter, by combining the data classification map formed as described above by the energy diagnosis module 300, the energy consumption type and production type of the main building to be diagnosed are derived. At this time, it is preferable that the energy diagnosis module 300 combines each data classification map through deep learning, HD data discrimanative analysis, as illustrated in FIG. 3. At this time, the data processing module 200 and the energy diagnosis module 300 may be configured in one server or may be configured as respective servers.

4 is a detailed configuration diagram of an energy diagnosis system for building zero energy according to an embodiment of the present invention. Referring to this, the energy diagnosis system for building zero energy according to the present invention is derived from the energy diagnosis module 300. A new and renewable energy construction decision support module 400 that derives at least one of a renewable energy construction plan and a building energy efficiency improvement plan according to the consumed energy consumption type and production type, and supports decision making for each of them; And a building energy efficiency improvement decision support module 500.

In this case, the new and renewable energy construction decision support module 400 and the building energy efficiency improvement decision support module 500 determine the energy consumption type and energy production type respectively derived from the energy diagnosis module 300, one or more, respectively. It may be started through a display means such as a computer or a mobile terminal, and configured to be selectable by a user.

In addition, through the above-mentioned new and renewable energy construction decision support module 400 and building energy efficiency improvement decision support module 500, energy facilities and operations for improving energy efficiency, such as new and renewable energy facilities that are actually applied and operation methods thereof Needless to say, a verification module 600 capable of verifying the present system by collecting the results obtained by applying the method to the main building to be diagnosed may be additionally provided.

In particular, the new and renewable energy construction decision support module 400 and the building energy efficiency improvement decision support module 500 may be configured as one integrated control module to further improve user accessibility, and the new and renewable energy construction intention The decision support module 400 may derive measures to improve its efficiency or reduce costs when a new renewable energy facility is already installed in a key building to be diagnosed.

In addition, the verification module 600 may be configured to perform performance evaluation as well as evaluation thereof and form a verification data map based on the evaluation.

5 is a flowchart of an energy diagnosis method for constructing zero energy according to an embodiment of the present invention. Referring to this, the energy diagnosis method for constructing zero energy according to the present invention will be described below.

Data collection step (S100) for collecting the building energy data 110 including the building condition data 111 and the building characteristic data 113 of the key building and the data for storing the collected building energy data 110 in a DB Defining the relationship between the storage step (S200) and the data classification step (S300) of classifying the stored building energy data 110 for each item or factor, and the classified building condition data 111 and the building characteristic data 112 Zero energy including a data map construction step (S400) of forming a data classification map by combining and an energy diagnosis step (S500) of deriving the energy consumption type and production type of the key building by combining the data classification map for each item or factor It is an energy diagnosis method for construction.

In this case, the building condition data 111 includes user factor data 111a including the number of occupants, the number of occupants, the frequency of access, the amount of occupant behavior, and the pattern of occupant behavior; and the area and volume of the building, the use, and the temperature inside the building. Spatial and building factor data (111b) including humidity and illuminance, air quality inside the building, and solar radiation; and areas including latitude, longitude, altitude, surrounding environment, atmospheric environment, air quality, floating population, and surrounding traffic And environmental factor data 111c; And temperature, humidity, and air pressure in the area where the building is located. Includes wind direction, wind speed, sunshine, rainfall, snowfall, and seasonal weather and seasonal factor data 111d; and the building characteristic data 112 is energy facility data including air conditioning, lighting, and air conditioning facilities ( 112a); and renewable energy facility data 112b; and energy consumption data 112c; And energy production data 112d.

And, the data collection step (S100) is a sensor installed inside or outside the pre-built building, pre-built BAS (Building Automation System), pre-built BEMS (Building Energy Management System), direct input means, information connected to the Internet network It is characterized by collecting by means of at least one or more of the databases.

In addition, in the energy diagnosis step (S500); the energy consumption type and production type of the main building are derived by deep learning, HD Data Discrimanative Analysis, and more specifically, according to the present invention described above. It is the same as the operation concept of the energy diagnosis system for constructing the zero energy described above.

6 is a detailed flowchart of an energy diagnosis method for constructing zero energy according to an embodiment of the present invention. Referring to this, a more detailed description of the energy diagnosis method for constructing zero energy according to the present invention is as follows.

According to the energy consumption type and production type derived in the energy diagnosis step (S500), at least one of a new renewable energy construction method and a building energy efficiency improvement method is derived, and new and renewable energy construction that supports decision-making for each of them. Decision support step (S600); And a building energy efficiency improvement decision support step (S700).

And, the new and renewable energy construction decision support step (S600); And a building energy efficiency improvement decision support step (S700), further comprising a verification step, the new and renewable energy construction decision support step (S600); And a verification step of verifying the result of the energy diagnosis method for constructing zero energy according to the present invention through energy-related data of the building to be diagnosed after the support method determined by the building energy efficiency improvement decision support step (S700) is applied. Of course it can.

The above-described preferred embodiments of the present invention are disclosed for the purpose of illustration, and those skilled in the art will appreciate various modifications, changes, and additions within the spirit and scope of the present invention. The addition should be considered to fall within the scope of the following claims.

100. Data collection module
110. Building Energy Data
111. Building condition data
111a. User factor data
111b. Building factor data
111c. Regional and environmental factors data
111d. Weather and seasonal data
112. Building Characteristics Data
112a. Energy equipment data
112b. Renewable energy facility data
112c. Energy consumption data
112d. Energy production data
200. Data processing module
210. Data storage
220. Data Classification
230. Data Map Formation
300. Energy diagnosis module
400. Renewable energy construction decision support module
500. Building energy efficiency improvement decision support module
600. Verification module
S100. Data collection phase
S200. Data storage steps
S300. Data classification stage
S400. Data Relationship Map Construction Phase
S500. Energy diagnosis stage
S600. New Renewable Energy Construction Decision Support Stage
S700. Building energy efficiency improvement decision support stage

Claims (11)

A data collection module for collecting building energy data including building condition data and building characteristic data for a key building;
Forming a data classification map by defining a relationship between a data storage unit storing the building energy data and a data classification unit classifying the building energy data for each item or factor, and each classified building condition data and the building characteristic data A data processing module including a data map forming unit; And
Energy diagnosis system for building zero energy, including; an energy diagnosis module for deriving an energy consumption type and a production type by combining the data classification map for each item or factor.
According to claim 1,
The building condition data,
User factor data including the number of occupants, the number of occupants, the frequency of access, the amount of occupant behavior, and the pattern of occupant behavior;
Spatial and building factor data including building area and volume, use, temperature and humidity and illumination inside the building, air quality inside the building, and solar radiation;
Regional and environmental factor data, including latitude, longitude, altitude, surrounding environment, air quality, air quality, floating population, and surrounding traffic volume of the building; And
Temperature, humidity, and air pressure in the area where the building is located. Energy diagnosis system for zero energy construction, characterized in that it comprises; weather and seasonal factor data including wind direction, wind speed, sunshine, rainfall, snowfall, season.
The method according to claim 1 or 2,
The building characteristic data,
Energy equipment data including air conditioning equipment, lighting equipment, and air conditioning equipment;
Renewable energy equipment data;
Energy consumption data; And
Energy production system for building zero energy, characterized in that it comprises; energy production data.
According to claim 1,
The data collection module; silver
Energy diagnosis system for zero energy construction, characterized by at least one of a sensor, a building automation system (BAS), a building energy management system (BEMS), a direct input means, and an information database connected to the Internet.
According to claim 1,
The energy diagnosis module; silver
An energy diagnosis system for zero energy construction, characterized by deriving the energy consumption type and production type by Deep Learning, HD Data Discrimanative Analysis.
According to claim 1,
A new and renewable energy construction decision support module that derives at least one new renewable energy construction method and a building energy efficiency improvement method according to the energy consumption type and production type derived from the energy diagnosis module, and supports decision making for each of them. ; And
Building energy efficiency improvement decision support module; Energy diagnosis system for zero energy construction further comprising a.
A data collection step of collecting building energy data including building condition data and building characteristic data of a key building;
A data storage step of storing the collected building energy data in a DB;
A data classification step of classifying the stored building energy data for each item or factor;
A data map construction step of defining a relationship between the classified building condition data and the building characteristic data to form a data classification map; And
And an energy diagnosis step of deriving an energy consumption type and a production type of the key building by combining the item or factor-specific data classification map.
The method of claim 7,
The building condition data,
User factor data including the number of occupants, the number of occupants, the frequency of access, the amount of occupant behavior, and the pattern of occupant behavior;
Spatial and building factor data including building area and volume, use, temperature and humidity and illumination inside the building, air quality inside the building, and solar radiation;
Regional and environmental factor data, including latitude, longitude, altitude, surrounding environment, air quality, air quality, floating population, and surrounding traffic volume of the building; And
Temperature, humidity, and air pressure in the area where the building is located. Includes weather and seasonal factor data including wind direction, wind speed, sunshine, rainfall, snowfall, and season;
The building characteristic data,
Energy equipment data including air conditioning equipment, lighting equipment, and air conditioning equipment;
Renewable energy equipment data;
Energy consumption data; And
Energy production data; energy diagnosis method for building zero energy, characterized in that it comprises a.
The method of claim 7,
The data collection step
Collected by at least one or more of sensors installed inside or outside the existing building, pre-built BAS (Building Automation System), pre-built BEMS (Building Energy Management System), direct input means, and information database connected to the Internet network Energy diagnosis method for building zero energy, characterized in that.
The method of claim 7,
In the energy diagnosis step
An energy diagnosis method for zero energy construction, characterized by deriving the energy consumption type and production type of the main building by deep learning hyper-dense data analysis (Hyperdense, HD Data Discrimanative Analysis).
The method of claim 7,
A new and renewable energy construction decision support step that derives at least one new renewable energy construction method and a building energy efficiency improvement method according to the energy consumption type and production type derived in the energy diagnosis step, and supports decision making for each of them. ; And
Building energy efficiency improvement decision support step; Energy diagnosis method for building zero energy, further comprising a.

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