Smart green ESG platform for implementing carbon neutrality of building on basis of artificial intelligence, IoT and big data, and method for implementing carbon neutrality of building

Abstract

The present invention relates to a smart green ESG platform for implementing carbon neutrality of a building on the basis of artificial intelligence and big data, and a method for implementing carbon neutrality of a building, the smart green ESG platform comprising: a data collection unit for collecting operation data from at least one building operation system and collecting meteorological data of an area in which the building is located; an AI energy optimization unit which analyzes the collected building operation data and the meteorological data so as to analyze energy usage and carbon emission amount in the building, and thus extract energy saving factors, and which reflects renewable energy production factors so as to generate an energy management optimization policy in accordance with energy and greenhouse gas reduction regulation standards; an AI air quality optimization unit which analyzes the collected building operation data and meteorological data so as to predict the air quality in the building, and which controls an air conditioning facility of the building according to air quality prediction information; and an ESG performance evaluation unit for calculating a carbon emission amount through life cycle assessment for the building to which an energy optimization policy and an air quality optimization policy are applied, and evaluating ESG performance.

Description

A smart green ESG platform and method for achieving carbon neutrality in buildings based on artificial intelligence, IoT, and big data.

[0001] The present invention relates to an artificial intelligence and big data-based smart green ESG platform for implementing carbon neutrality in a building, and a method for implementing carbon neutrality in a building, which collects basic building operation data in real time from building operation systems such as a building energy management system (BEMS), an indoor air quality monitoring system, an air conditioning system, various sensors installed in a building, an IoT system, and CCTV, and collects information on outdoor influencing factors such as weather information, analyzes and predicts the building's energy consumption, indoor air quality status, and occupancy status, extracts energy saving elements and renewable energy utilization elements for realizing low-energy / zero-energy / green remodeling buildings with the goal of carbon neutrality, optimizes energy efficiency, and generates / applies an optimized operation policy for an air conditioning system to maintain and manage healthy and pleasant indoor air quality by applying WELL certification, LEED certification, and GresB certification standards, and performs an LCA evaluation of the building, and then calculates and provides ESG performance indicators for the building.

[0002] As climate change causes unusual signs and damage worldwide, responding to the climate crisis is no longer a distant future, but a pressing issue directly linked to survival. Recognizing the severity of the climate crisis, countries around the world are aligning their overall societal policies with carbon neutrality. Carbon neutrality refers to a state in which greenhouse gases are absorbed and removed from the atmosphere, effectively reducing emissions to zero (net-zero). The international community aims to achieve carbon neutrality by 2050.

[0003] To achieve this, policies and legal standards for achieving carbon neutrality, especially in the most critical construction sector, are being established, and much research and effort is being conducted. The Ministry of Land, Infrastructure and Transport is institutionalizing energy reduction measures, including indoor air quality management such as heating, cooling, and ventilation, and the Ministry of Trade, Industry and Energy is promoting national standardization of the Building Energy Management System (BEMS) for systematic and optimal management of building energy consumption through data.

[0004] In addition, as public interest in indoor air quality management has increased recently due to fine dust and COVID-19, the Ministry of Science and ICT is carrying out a national project aimed at controlling indoor air quality as well as reducing energy consumption through a management chain that includes indoor air quality measurement, monitoring, analysis, control, and evaluation.

[0005] However, existing BEMS systems only provide information on current energy usage and do not function as an integrated platform for future energy savings and usage predictions, making it difficult to implement high-performance zero-energy buildings (ZEBs).

[0006] In addition, with the growing interest in ESG, as well as energy efficiency issues, there is a growing need for air conditioning management systems and healthy buildings that can monitor and control indoor air quality according to LEED's building materials and raw materials, indoor air quality items, and WELL certification standards.

[0007] The present invention aims to solve the above problems by providing a smart green ESG platform and a method for implementing carbon neutrality of a building by collecting information on energy consumption and indoor air quality of a building in conjunction with a building operation system such as a building energy management system (BEMS), an indoor air quality monitoring system, an air conditioning management system, various sensors installed in a building, an IoT system, and CCTV, thereby deriving energy saving factors for realizing a low-energy or zero-energy building, optimizing energy use through energy analysis and usage prediction using artificial intelligence, and optimizing the operation of an air conditioning system for maintaining and managing healthy and comfortable indoor air quality by applying the WELL certification standard.

[0008] According to one embodiment of the present invention, a smart green ESG platform for implementing carbon neutrality in a building based on artificial intelligence and big data may include: a data collection unit that collects operation data from at least one building operation system and collects meteorological data of an area where a building is located; an AI energy optimization unit that analyzes the collected building operation data and the meteorological data to analyze energy consumption and carbon emissions within the building, thereby extracting energy-saving factors and reflecting renewable energy production factors to generate an energy management optimization policy in accordance with energy and greenhouse gas reduction laws and regulations; an AI air quality optimization unit that analyzes the collected building operation data and meteorological data to predict air quality within the building and controls the building's air conditioning equipment based on the air quality prediction information; and an ESG performance evaluation unit that calculates carbon emissions through a life cycle assessment for a building to which an energy optimization policy and an air quality optimization policy have been applied, and evaluates ESG performance.

[0009] In addition, the AI ​​energy optimization unit includes an energy analysis unit that extracts energy load elements within a building from the building's BIM (Building Information Modeling) data and the building operation data, and calculates energy usage and carbon emissions for the energy load elements; and the energy analysis unit analyzes the building's BIM (Building Information Modeling) data, air conditioning facility operation history, weather data, energy usage of the energy load elements, and carbon emissions of the energy load elements in a time series manner, and applies a deep learning-based energy usage prediction model that predicts energy usage and carbon emissions for a specific point in time and / or a specific period, thereby analyzing building energy.

[0010] In addition, the AI ​​energy optimization unit may further include an energy saving unit that analyzes and extracts energy reduction elements corresponding to each energy load element, calculates a carbon emission reduction amount when applying the energy reduction elements, and calculates a carbon emission reduction amount when applying facilities such as renewable energy production facilities and waste separation and discharge management facilities applicable to buildings.

[0011] In addition, the AI ​​energy optimization unit may further include an energy optimization unit that applies the energy reduction elements extracted for the building, the renewable energy production facility, and the waste separation and discharge management facility to the BIM data of the building, and applies the BIM data to the deep learning-based energy usage prediction model to perform a simulation to predict energy usage and carbon emissions for the entire building.

[0012] In addition, the AI ​​air quality optimization unit includes a CCTV AI analysis unit that analyzes CCTV video data collected in real time from the building operation system to detect occupant information and energy waste factors; the occupant information includes the number of occupants and the amount of clothing worn, and the energy waste factors may include the window / door open / close status and lighting status.

[0013] In addition, the AI ​​air quality optimization unit may further include an air quality optimization unit that uses a deep learning-based indoor air quality prediction model trained by applying at least one or more influence factor data among indoor air quality measurement information collected in real time, building structure information, building facility information, occupant information and weather data extracted by analyzing the CCTV video data, to predict indoor air quality for a specific space after a certain period of time, and controls ventilation and heating and cooling facilities according to the predicted indoor air quality.

[0014] In addition, the air quality optimization unit is characterized by controlling lighting, ventilation, and heating and cooling facilities by checking occupant information and energy waste factors extracted through analysis of CCTV video data, and predicting the amount of carbon emission reduction that can be achieved through facility control.

[0015] In addition, the indoor air quality measurement information is a measurement value measured by a monitoring sensor installed in an indoor space for at least one indoor air quality standard item among temperature / humidity, fine dust, pollutants, volatile organic compounds, inorganic gases, and radon, and the indoor air quality standard item is characterized in that it is included in at least one certification standard among the Green Building Certification System (LEED) and the Healthy Building Certification (WELL) standards.

[0016] In addition, the AI ​​air quality optimization unit may further include an air quality improvement unit that analyzes the indoor air quality measurement information to extract building materials that satisfy the allowable standards set for indoor air quality criteria, and calculates the amount of carbon emissions reduced when replacing with the building materials.

[0017] In addition, the ESG performance evaluation department manages raw material LCA data on carbon emissions required in the production, transportation, disposal, and recycling stages of building materials used in building construction, and calculates the carbon emissions of the entire building by applying the raw material LCA data.

[0018] In addition, the building operation system may include a building energy management system that monitors energy usage in real time through sensors and measuring equipment installed in the building's energy-using facilities; an indoor air quality monitoring system that measures and monitors air quality in real time within the building through sensors installed indoors; an air conditioning system that monitors / controls cooling / heating, ventilation, and air conditioning facilities; and a CCTV system that receives and stores real-time images from various sensors, IoT systems, and CCTV cameras installed in the building.

[0019] Meanwhile, a method for implementing carbon neutrality of a building based on artificial intelligence and big data according to one embodiment of the present invention may include the steps of collecting operational data from at least one building operation system and collecting meteorological data of an area where a building is located in a smart green ESG platform; analyzing the collected building operation data and meteorological data to analyze energy consumption and carbon emissions within the building, extracting energy-saving factors, and reflecting renewable energy production factors to generate an energy management optimization policy in accordance with energy and greenhouse gas reduction laws and regulations; analyzing the collected building operation data and meteorological data to predict air quality within the building, and controlling the building's air conditioning equipment based on the air quality prediction information; and calculating carbon emissions through a life cycle assessment for a building to which an energy optimization policy and an air quality optimization policy have been applied, and evaluating ESG performance.

[0020] It can manage energy and indoor air quality based on LEED, WELL, ESG, and GresB certifications by linking with various building operation systems installed within the building.

[0021] In addition, it can effectively carry out key tasks of the carbon neutrality roadmap for the building sector, such as establishing a building data base, achieving zero energy in new buildings, green remodeling of existing buildings, and managing building energy demand.

[0022] In addition, by applying energy reduction plans and usage prediction technologies throughout the entire process from environmental planning, which is the pre-design stage for achieving carbon neutrality, to design, construction, operation, and disposal, it is possible to implement high-performance zero-energy buildings (ZEBs), green remodeling, net-zero buildings, and smart cities.

[0023] Additionally, by applying LEED's EPD (Environmentally Friendly Designated Building Materials) and WELL certification, it is possible to achieve healthy and comfortable indoor air quality.

[0024] Figure 1 is an overall relationship diagram of a smart green ESG platform for implementing carbon neutrality in an artificial intelligence and big data-based building according to one embodiment of the present invention.

[0025] FIG. 2 is a block diagram of the functions of a smart green ESG platform for implementing carbon neutrality in an artificial intelligence and big data-based building according to one embodiment of the present invention.

[0026] FIG. 3 is a diagram illustrating the hardware structure of a smart green ESG platform for implementing carbon neutrality in an artificial intelligence and big data-based building according to one embodiment of the present invention.

[0027] Figure 4 is a flowchart of a method for implementing carbon neutrality in a building based on artificial intelligence and big data according to one embodiment of the present invention.

[0028] Hereinafter, specific embodiments of the present invention will be described in detail with reference to the drawings. However, the spirit of the present invention is not limited to the presented embodiments, and those skilled in the art who understand the spirit of the present invention will be able to easily propose other inventions that are retrograde or other embodiments included within the scope of the spirit of the present invention by adding, modifying, or deleting other components within the scope of the same spirit. However, this will also be considered to be included within the scope of the spirit of the present invention.

[0029] In addition, the terms described below are terms established in consideration of their functions in the present invention, and may vary depending on the inventor's intention or custom, so their definitions should be based on the contents throughout this specification, and if it is determined that a specific description of a known structure or function related to the present invention in this specification may obscure the gist of the present invention, a detailed description thereof will be omitted.

[0030]

[0031] Below, with reference to the drawings, we describe a smart green ESG platform for implementing carbon neutrality in buildings based on artificial intelligence and big data, and a method for implementing carbon neutrality in buildings.

[0032]

[0033] Figure 1 is an overall relationship diagram of a smart green ESG platform (100) for implementing carbon neutrality in an artificial intelligence and big data-based building according to one embodiment of the present invention.

[0034] Referring to FIG. 1, a smart green ESG platform (100) can be connected to at least one building operation system (200), a weather system (300), and a user terminal (400) via a network and communicate with each other.

[0035] The network referred to in the present invention may be a core network integrated with a wired public network, a wireless mobile communication network, or a mobile Internet, or may mean a global open computer network structure that provides various services existing in the TCP / IP protocol and its upper layer, such as HTTP (Hyper Text Transfer Protocol), HTTPS (Hyper Text Transfer Protocol Secure), Telnet, FTP (File Transfer Protocol), etc., and is not limited to these examples, but comprehensively means a data communication network that can transmit and receive data in various forms.

[0036]

[0037] The smart green ESG platform (100) collects basic building operation data from building operation systems such as a building energy management system (BEMS), an indoor air quality monitoring system, an air conditioning system, and various sensors, IoT systems, and CCTVs installed in the building, collects information on outdoor influence factors such as weather information, analyzes the building's energy usage, indoor air quality, and occupancy status, and applies artificial intelligence technology to extract energy saving elements and renewable energy utilization elements for realizing low-energy / zero-energy buildings to optimize energy efficiency, and applies artificial intelligence technology to create / apply an optimized operation policy for the air conditioning system to maintain and manage healthy and comfortable indoor air quality by applying WELL certification and LEED certification standards, and performs an LCA evaluation of the building and then calculates and provides ESG performance indicators for the building.

[0038]

[0039] The building operation system (200) includes a building energy management system (210) that monitors energy usage in real time through sensors and measuring equipment installed in energy-using facilities of the building, an indoor air quality monitoring system (220) that measures and monitors air quality in the building in real time through sensors installed indoors, an air conditioning system (230) that monitors / controls cooling / heating, ventilation and air conditioning facilities, and a CCTV system (240) that receives and stores real-time images from various sensors, IoT systems, and CCTV cameras installed in the building.

[0040] The building operation system (200) may include a data server capable of storing collected data, or may be connected to a big data server in a cloud environment to store the collected data. Furthermore, the collected data may be provided to the Smart Green ESG platform (100) via a standardized linkage interface or a linkage interface agreed upon in advance with the Smart Green ESG platform (100).

[0041] The building energy management system (210) can collect and monitor energy usage for various energy consumption elements of the building through various IoT measuring devices or smart meters installed within the building.

[0042] An indoor air quality monitoring system (220) can measure the air quality of an indoor space of a building, collect the data, and monitor it through an IoT-based air quality measurement sensor (IAQ IoT sensor) installed within the building.

[0043] Here, the IAQ IoT sensor can be composed of sensors that can measure indoor air quality monitoring items based on WELL and LEED certifications, such as fine dust, carbon dioxide, temperature, humidity, total volatile organic compounds, carbon monoxide, and formaldehyde.

[0044] The air conditioning system (230) is a system for controlling ventilation, air conditioning, and heating / cooling installed in a building, and can control not only the air conditioning equipment within the building but also the opening / closing devices of windows and doors to control indoor temperature / humidity, purify fine dust and harmful substances, ventilate, and control wind direction.

[0045] In addition, the air conditioning system (230) can provide a linkage interface so that the smart green ESG platform (100) can control the air conditioning equipment within the building, and can receive control commands from the outside by adding a linkage server such as a gateway to control the actual equipment within the building.

[0046] The CCTV system (240) controls CCTV cameras installed for security and surveillance within a building, and can collect images from the CCTV cameras in real time to perform replay, storage, and data analysis.

[0047] Here, the CCTV system (240) may include an NVR (Network Video Recorder) for storing video data, and the stored video may be analyzed in advance so that the analysis data and video data may be provided together.

[0048] For example, in a CCTV system (240), images collected from a CCTV camera are first analyzed to detect changes in occupancy status and window / door open / close status, and image data in which changes are detected can be provided to a smart green ESG platform (100).

[0049] In addition, the collected images can be transmitted to the smart green ESG platform (100) as is without separate analysis work, and processed to perform image analysis work on the smart green ESG platform (100).

[0050] Not limited to this, the video can be analyzed and provided according to various conditions as needed.

[0051]

[0052] The Korea Meteorological Administration system (300) is a data server or big data server that provides real-time weather data, forecast data, past weather history data, and weather statistical data by region, and allows an external server or system to collect weather data for a specific region and time through an Open API or data linkage module.

[0053]

[0054] The user terminal (400) can request energy performance evaluation of a building, setting of energy performance and certification standards for the building, and energy optimization of the building that meets the above standards using the smart green ESG platform (100), and can monitor energy usage, energy efficiency, energy usage prediction, carbon emissions, carbon emission reduction, and ESG evaluation information for the building.

[0055] In addition, the monitoring and control status of the indoor air quality of the building can be checked, and the operating standards of the air conditioning system (230) can be set.

[0056] Here, the terminal should be interpreted as including various communication means such as desktops, tablets, laptops, smartphones, wearable smart devices, etc., and can execute various functions provided by the smart green ESG platform (100) through web-based or separate software / applications, etc.

[0057]

[0058] Figure 2 is a block diagram of the functions of a smart green ESG platform (100) for implementing carbon neutrality in an artificial intelligence and big data-based building according to one embodiment of the present invention.

[0059] Referring to FIG. 2, the smart green ESG platform (100) may be equipped with a data collection unit (110), an AI energy optimization unit (120), an AI air quality optimization unit (130), an ESG performance evaluation unit (140), a facility control unit (150), and a monitoring unit (160).

[0060] The data collection unit (110) collects building operation data including building facility information, energy usage, building space structure, indoor air quality measurement information, and CCTV video information from at least one building operation system (200).

[0061] The building operation system (200) may include a building energy management system (BEMS, 210), an indoor air quality monitoring system (220), an air conditioning system (230), and a CCTV system (240), and may collect data through standardized specifications or predefined linkage interfaces with the above systems.

[0062] The data collection unit (110) can collect data in real time or periodically depending on the type of building operation data, and the collected data can be stored in a database or a separate big data server.

[0063] In addition, the data collection unit (110) is constructed with multiple collection processes or multiple data collection servers and can independently / parallelly collect data from multiple systems included in the building operation system (200).

[0064] However, without being limited to this, the implementation form of the data collection unit (110) can be configured in various ways.

[0065] Meanwhile, the data collection unit (110) can collect weather data from the weather station system (300) to collect data on external environmental influence factors.

[0066] For example, using the Open API provided by the Korea Meteorological Administration system (300), real-time weather data, hourly forecast data, past weather history data, and weather statistics data for each area where a building is located can be collected, and weather data such as temperature, humidity, and wind speed / direction that affect the energy load of a building can be applied to energy usage analysis and energy usage prediction in conjunction with building operation data.

[0067] Meanwhile, the collected building operation data and weather data can be preprocessed for data analysis and stored in the form of a pre-determined data set.

[0068] The data set format determined at this time can correspond to the database schema.

[0069]

[0070] The AI ​​energy optimization unit (120) analyzes the collected building operation data and the weather data, analyzes the energy usage and carbon emissions within the building, extracts energy saving factors, and reflects renewable energy production factors to generate an energy management optimization policy in accordance with energy and greenhouse gas reduction laws and regulations.

[0071] The AI ​​energy optimization unit (120) includes an energy analysis unit (121), an energy saving unit (122), and an energy optimization unit (123).

[0072] The energy analysis unit (121) extracts energy load elements within the building from the building's BIM (Building Information Modeling) data and building operation data, and calculates energy usage and carbon emissions for the energy load elements.

[0073] Here, BIM (Building Information Modeling) is a technology that creates one or more accurate virtual models of a building digitally, and is a standardized format that contains all the data necessary to support design, construction, and procurement activities required for design, construction, and operation throughout the life cycle of a building in the construction industry.

[0074] For example, BIM data may include information such as building structure, height, area, materials used (walls, windows), design structure of each space, and information on air conditioning equipment (type, efficiency, operating settings).

[0075] The energy analysis unit (121) extracts energy load factors such as cooling, heating, hot water, ventilation, and lighting through BIM data of a specific building and collected building operation data, calculates energy usage and carbon emissions, establishes basic building energy data, and can generate statistical data on hourly, daily, monthly, and yearly energy usage / carbon emissions.

[0076] Additionally, energy performance and energy efficiency ratings of buildings can be calculated through analysis of basic energy data.

[0077] In addition, a deep learning-based energy usage prediction model that predicts energy usage and carbon emissions at a specific point in time and / or for a specific period of time through time series analysis by applying BIM (Building Information Modeling) data for a specific building, air conditioning facility operation history, weather data, and the above energy basic data as learning data can be created and applied to building energy prediction.

[0078]

[0079] The energy saving unit (122) extracts energy reduction elements corresponding to each energy load element for cooling, heating, hot water supply, ventilation, and lighting, calculates the amount of carbon emission reduction when the energy reduction elements are applied, and extracts renewable energy production facilities and waste separation and discharge management facilities applicable to the building, and calculates the amount of carbon emission reduction when the facilities are applied.

[0080] Here, the energy saving unit (122) determines whether the energy performance of the building calculated by the energy analysis unit (121) satisfies the energy-related building certification standards set by energy-related laws or goals, and extracts energy reduction factors if the standards are not met.

[0081] More specifically, the energy usage of each energy load element can be checked using the energy basic data and statistical data analyzed by the energy analysis unit (121), and if the energy usage is higher than the standard usage, the cause of the energy load element can be analyzed.

[0082] For example, if the energy used for heating and cooling is high, BIM data can be used to check performance information about windows installed in the building, such as the shading coefficient, solar heat gain coefficient, window frame correlation coefficient, and airtightness, and recommend high-performance windows.

[0083] At this time, energy saving information can be provided when applying high-performance windows.

[0084] As another example, if the energy used for heating and cooling is high, energy performance information for heating and cooling equipment can be checked and high-efficiency boilers, appliances, etc. can be recommended.

[0085] Meanwhile, the energy saving department (122) can review and recommend renewable energy facilities applicable within the building to increase the energy self-sufficiency rate by producing energy within the building itself.

[0086] Renewable energy facilities may include facilities that produce energy using solar energy, solar heat, wind energy, geothermal energy, and water heat.

[0087] Additionally, it can be recommended for application to waste separation collection management facilities.

[0088] For example, by automatically separating recyclables collected in a building using AI vision technology and applying equipment that can identify the total capacity of recyclables, the carbon emissions reduction for the separated recyclables can be calculated and reflected when calculating the overall carbon emissions reduction for the building.

[0089] The smart green ESG platform (100) proposes high-efficiency energy reduction elements such as energy reduction elements, renewable energy production facilities, and waste separation and collection facilities, and manages the high-efficiency energy reduction elements by item to calculate the amount of carbon emission reduction that is saved when applying the proposed energy reduction elements, manages information on various high-efficiency building materials and high-efficiency facilities, and can manage them in response to energy load elements.

[0090]

[0091] The energy optimization unit (123) applies the energy reduction elements, renewable energy production facilities, and waste separation and discharge management facilities extracted for the building to the BIM data of the building, and applies the BIM data to the deep learning-based energy usage prediction model to perform a simulation to predict energy usage and carbon emissions for the entire building.

[0092] The energy optimization unit (123) can provide a plurality of high-efficiency energy reduction elements and carbon emission reduction amounts, including energy reduction elements, renewable energy production facilities, and waste separation and discharge management facilities suggested by the energy saving unit (122), to the user terminal (400).

[0093] Users (building owners, managers, designers, etc.) can check the high-efficiency energy reduction factors provided to the user terminal (400), select desired items, and request energy performance simulation for the entire building.

[0094] The energy optimization unit (123) can apply high-efficiency energy reduction factors selected by the user terminal (400) to BIM data at the request of the user terminal (400), repeatedly perform dynamic simulation for energy performance analysis, predict the energy performance of the entire building, and provide the results to the user terminal (400).

[0095] At this time, a combination of high-efficiency energy reduction elements can be recommended to the user terminal (400) so that the energy usage and carbon emissions for the predicted building satisfy the energy and greenhouse gas reduction laws and regulations.

[0096]

[0097] The AI ​​air quality optimization unit (130) analyzes collected building operation data and weather data to predict air quality within the building and controls the building's air conditioning equipment based on the air quality prediction information.

[0098] The AI ​​air quality optimization unit (130) includes a CCTV AI analysis unit (131), an air quality improvement unit (132), and an air quality optimization unit (133) to perform the above function.

[0099] The CCTV AI analysis unit (131) analyzes CCTV video data collected in real time from the building operation system (200) to detect occupant information and energy waste factors.

[0100] The collected CCTV video data may be video data in which changes in the occupant status and window / door open / close status are detected through primary video analysis in the CCTV system (240).

[0101] If the first image analysis process is not performed in the CCTV system (240), the first image analysis process can be performed in the CCTV AI analysis unit (131).

[0102] The CCTV AI analysis unit (131) can apply artificial intelligence-based image analysis technology to video data that detects changes in the occupant status and window / door open / close status, analyze the number of occupants and the type of clothing worn by the occupants, and determine the amount of clothing worn.

[0103] Here, the amount of clothing is expressed as the insulation of the clothing being worn and can be expressed in units called clo. Clo, the unit of clothing condition, can mean the thermal resistance value from the skin surface to the clothing surface in a state of clothing in which the amount of heat dissipation from the human body surface is in balance with the amount of activity of 1 meter in a room with an air temperature of 21℃, a relative humidity of 50%, and an air velocity of 0.5 m / s or less.

[0104] The number of occupants and the amount of clothing can be used as indicators related to indoor comfort. By determining the number of occupants per indoor area and analyzing the amount of clothing of occupants, the appropriate temperature and humidity for maintaining indoor comfort can be calculated, and based on this, the air conditioning system (230) can be controlled to automatically maintain comfortable indoor air.

[0105] In addition, by analyzing CCTV video data, the open / close status of windows / doors and lighting status (ON / OFF) in indoor spaces can be identified, and combined with occupant information, windows, doors, and lighting equipment can be controlled, or the operation of a notification system (230) can be controlled.

[0106] Meanwhile, the CCTV AI analysis unit (131) can extract patterns of changes in the presence or absence of occupants, number of occupants, and amount of clothing over time by re-analyzing information on changes in the number of occupants, amount of clothing, and window / door opening / closing status extracted through analysis of CCTV video data.

[0107] For example, the change pattern of the number of occupants can be used to determine commuting times, lunch times, meeting times, etc., and accordingly, the operation plan of the air conditioning system (230) can be set.

[0108] As another example, by analyzing the number of occupants and the time patterns in which windows and doors are open for a certain amount of time, it may be possible to predict when ventilation is needed or during what time period.

[0109]

[0110] The air quality improvement unit (132) analyzes the collected indoor air quality measurement information, extracts building materials that satisfy the allowable standards set for indoor air quality criteria, and calculates the amount of carbon emissions reduced when replacing with the building materials.

[0111] At this time, the BIM data of the building is analyzed to extract information on the amount of hazardous substances emitted from indoor building materials such as wallpaper, paint, and construction-related finishing materials used during building construction, whether they are eco-friendly materials, and the level of eco-friendly materials, and alternative building materials are searched based on this information.

[0112] For example, the air quality improvement unit (132) can first analyze indoor air quality measurement information included in the collected building operation data to evaluate indoor air quality for each space and the entire space.

[0113] At this time, the indoor air quality measurement information included in the building operation data is data collected by the indoor air quality monitoring system (220) through the IAQ IoT sensor installed in the building, and may include at least one measurement item among fine dust, pollutants, volatile organic compounds, inorganic gases, and radon contained in the indoor air, as well as temperature / humidity.

[0114] The air quality improvement unit (132) analyzes the indoor air quality measurement information and determines whether each measured item value satisfies the LEED certification and / or WELL certification standards.

[0115] If the LEED and WELL certification standards are not met, a list of currently constructed or scheduled interior building materials is extracted from the BIM data, and EDP (Environmental Product Declaration) materials that can be applied as replacements to buildings that meet the LEED and WELL certification standards are extracted.

[0116] To recommend alternative materials, the Air Quality Improvement Department (132) can manage information such as a list of EDP materials and carbon emissions, eco-friendly level grade, energy efficiency, and construction method for each EDP material.

[0117] Additionally, material information can be collected periodically from manufacturers to manage the EDP material list.

[0118] Once a list of alternative EDP materials is extracted, it can be applied to BIM data, a simulation of the overall energy performance of the building can be performed, and prediction information on reduced energy usage, energy costs, and carbon emissions can be produced and provided to the user terminal (400).

[0119] In addition, BIM data applied with EDP materials extracted as alternative materials can be applied to an indoor air quality prediction model to predict indoor air quality, the degree of improvement in indoor air quality can be evaluated based on the prediction results, and the evaluation results can be provided to a user terminal (400).

[0120]

[0121] The air quality optimization unit (133) uses a deep learning-based indoor air quality prediction model trained by applying at least one influence factor data among indoor air quality measurement information collected in real time, building structure information, building facility information, occupant information and weather data extracted by analyzing the CCTV image data, to predict indoor air quality for a specific space after a certain period of time, and controls ventilation and heating and cooling facilities according to the predicted indoor air quality.

[0122] Here, indoor air quality measurement information may include indoor temperature / humidity, fine dust, pollutants, volatile organic compounds (VOCs), inorganic gases, and radon.

[0123] In addition, the presence or absence of occupants, number of occupants, amount of clothing worn by occupants, and open / closed status of windows / doors, extracted through analysis of CCTV video data, can be applied as one of the influencing factors for predicting indoor air quality.

[0124] Additionally, weather information may include external temperature, humidity, wind speed, and weather conditions (rain, snow, cloudy, clear).

[0125] The air quality optimization unit (133) applies collected building operation data and weather information as learning data to create a deep learning-based indoor air quality prediction model, and can predict indoor air quality at a specific point in time for a specific space by applying the prediction model.

[0126] Here, a specific point in time may be an hour unit as an operating unit for effectively controlling the air conditioning system (230).

[0127] For example, by checking the area of ​​a specific office space, the number of occupants, the state of clothing, and the current indoor temperature / humidity, indoor carbon dioxide, fine dust, VOC, etc., the comfort of the indoor air one hour later can be predicted, and the air conditioning system (230) can be automatically controlled to satisfy the preset indoor comfort level.

[0128] At this time, an operation plan can be applied to reduce energy consumption by continuously and stepwise controlling the air conditioning system (230) so that the difference between the current building air quality and the target indoor air quality is not large.

[0129] For example, the air conditioning system (230) can be operated by considering occupancy information such as work hours, lunch hours, and end-of-work hours.

[0130] The indoor temperature / humidity can be predicted by reflecting the weather information during commuting hours, and the operation method such as the start time of the air conditioning system (230), temperature / humidity control stage, and cooling / heating intensity can be calculated and applied to reach the appropriate temperature / humidity while minimizing energy usage.

[0131] Additionally, lighting, ventilation, and heating and cooling equipment can be controlled by identifying occupant information and energy waste factors extracted through analysis of CCTV video data.

[0132] For example, by checking whether there are people in the office, if there are no people in the office, the operating range of the air conditioning system (230) can be reduced or stopped, and the lighting can be turned ON / OFF depending on the number of people in the office, and if a window or door is open in the space where the air conditioning system (230) is operated, this can be detected and the window or door can be closed or a notification can be sent to a monitoring or control system within the building.

[0133] Meanwhile, the air quality optimization unit (133) can calculate the expected carbon emission reduction amount after controlling the lighting, ventilation, and heating / cooling facilities as described above according to the predicted indoor air quality and energy waste factors, and provide this to the user terminal (400).

[0134]

[0135] The ESG Performance Evaluation Department (140) calculates carbon emissions through a life cycle assessment of a building, calculates the amount of carbon emissions reduced after applying energy optimization and air quality optimization, and evaluates ESG performance.

[0136] The ESG performance evaluation department (140) can define evaluation environmental impact factors and evaluation periods corresponding to the life cycle stages of a building (construction, use, maintenance, and demolition) for the entire building life cycle evaluation.

[0137] Additionally, the building operation data collection items and period can be determined accordingly.

[0138] In addition, the AI ​​energy optimization unit (120) and the AI ​​air quality optimization unit (130) can perform a life cycle assessment (LCA) of a building by analyzing data collected through a building operation system (200) including a building energy management system (210), an indoor air quality monitoring system (220), an air conditioning system (230), and a CCTV system (240) and calculating the carbon emissions of the building for each evaluation factor calculated.

[0139] Here, ESG evaluation indicators may include greenhouse gas emissions, energy usage, renewable energy usage ratio, waste emissions, water usage, air pollutant emissions, use of eco-friendly certified products, indoor fine dust concentration, and indoor air pollutant generation.

[0140]

[0141] The facility control unit (150) remotely controls air conditioning facilities, windows / doors, lighting, and devices installed in the building.

[0142] The facility control unit (150) can be linked with an air conditioning system (230), a building automation system, and a control system for remote control of the facility of the building, and can receive control instructions from at least one of the AI ​​energy optimization unit (120) and the AI ​​air quality optimization unit (130).

[0143] In addition, the equipment can be controlled by receiving control instructions from the equipment control interface provided to the user terminal (400).

[0144]

[0145] The monitoring unit (160) provides building energy usage, energy performance / efficiency analysis information, energy usage statistics information, carbon emission information for energy load factors, energy usage prediction information, indoor air quality analysis information, indoor air quality statistics information, indoor air quality prediction information, and ESG evaluation information to the user terminal (400).

[0146]

[0147] FIG. 4 is a diagram showing the hardware structure of a smart green ESG platform (100) for implementing carbon neutrality in an artificial intelligence and big data-based building according to one embodiment of the present invention.

[0148] Referring to FIG. 4, the hardware structure of the smart green ESG platform (100) is configured to include a central processing unit (1000), memory (2000), user interface (3000), database interface (4000), network interface (5000), web server (6000), etc.

[0149] The user interface (3000) provides input and output interfaces to the user by using a graphical user interface (GUI).

[0150] The database interface (4000) provides an interface between the database and the hardware structure.

[0151] The network interface (5000) provides network connectivity between devices owned by the user.

[0152] The web server (6000) provides a means for users to access the hardware structure via a network. Most users can access the smart green ESG platform (100) remotely by accessing the web server.

[0153] Each step of the above-described configuration or method may be implemented as computer-readable code on a computer-readable recording medium or transmitted via a transmission medium. The computer-readable recording medium is a data storage device capable of storing data that can be read by a computer system.

[0154] Examples of computer-readable recording media include, but are not limited to, databases, ROMs, RAMs, CD-ROMs, DVDs, magnetic tapes, floppy disks, and optical data storage devices. Transmission media may include carrier waves transmitted over the Internet or various types of communication channels. Furthermore, computer-readable recording media may be distributed through a network-connected computer system, such that computer-readable code is stored and executed in a distributed manner.

[0155] In addition, at least one component applied to the present invention may include or be implemented by a processor, such as a central processing unit (CPU) or microprocessor, which performs each function, and two or more of the components may be combined into a single component that may perform all operations or functions of the two or more combined components. In addition, a part of at least one component applied to the present invention may be performed by another component among these components. In addition, communication between the components may be performed via a bus (not shown).

[0156]

[0157] Figure 4 is a flowchart of a method for implementing carbon neutrality in buildings based on artificial intelligence and big data.

[0158] Referring to Fig. 4, the method for implementing carbon neutrality of a building is performed on a smart green ESG platform (100).

[0159] First, the smart green ESG platform (100) collects building operation data including building facility information, energy usage, building space structure, indoor air quality measurement information, and CCTV video information from the building operation system (200) through the data collection unit (110) and meteorological data of the area where the building is located from the Korea Meteorological Administration system (S410).

[0160] The collected items include the building's ESG evaluation items and basic data required for ESG evaluation, and can be collected through multiple collection processes by selecting the necessary data in real time or periodically.

[0161] Collected data can be stored on a big data server in a cloud environment, and data analysis tasks can be performed periodically.

[0162] The collected data can be stored and managed by region, building, and date / time.

[0163] Additionally, meteorological data can be collected from the weather system (300) to collect data on external environmental influence factors.

[0164] Collected weather data can be stored and linked by date / time with information collected from the building.

[0165] Next, the smart green ESG platform (100) analyzes the building operation data and the weather data collected through the AI ​​energy optimization unit (120), analyzes the energy usage and carbon emissions within the building, extracts energy saving factors, and reflects new and renewable energy production factors to generate an energy management optimization policy in accordance with energy and greenhouse gas reduction laws and regulations (S420).

[0166] Step S420 analyzes the collected building operation data to calculate energy consumption for each energy load element of the building and calculate carbon emissions. This determines whether the building satisfies energy-related building certification standards established by energy and greenhouse gas reduction laws or targets. If the standards are not met, high-efficiency energy reduction elements such as energy reduction elements, renewable energy production facilities, and waste separation and collection facilities can be proposed. Furthermore, energy reduction elements, renewable energy production facilities, and waste separation and discharge management facilities extracted as optimization policies for the building are applied to the building BIM data for simulation, and the energy consumption, carbon emissions, and carbon emission reduction compared to the previous building can be calculated for the entire building.

[0167] Thereafter, the smart green ESG platform (100) analyzes building operation data and weather data collected through the AI ​​air quality optimization unit (130), predicts air quality within the building, and performs a step (S430) of controlling the building's air conditioning equipment according to the air quality prediction information.

[0168] In step S430, indoor air quality measurement information collected in real time, building structure information, building facility information, CCTV video data, occupant information and weather data within the building space are analyzed to predict indoor air quality, and the air conditioning system (230), lighting, and window / door opening / closing can be automatically controlled based on the predicted information. At this time, the energy efficiency and energy waste factors of the relevant facility can be determined and the facility can be controlled.

[0169] Finally, the smart green ESG platform (100) calculates carbon emissions through a life cycle assessment of the building through the ESG performance evaluation department (140), calculates the amount of carbon emissions reduced after applying energy optimization and air quality optimization, and performs a step (S440) of evaluating ESG performance.

[0170]

[0171] Although the configuration and features of the present invention have been described above based on embodiments according to the present invention, the present invention is not limited thereto, and it is obvious to those skilled in the art that various changes or modifications can be made within the spirit and scope of the present invention, and therefore, it is made clear that such changes or modifications fall within the scope of the appended patent claims.

[0172] It can manage energy and indoor air quality based on LEED, WELL, ESG, and GresB certifications by linking with various building operation systems installed within the building.

[0173] In addition, it can effectively carry out key tasks of the carbon neutrality roadmap for the building sector, such as establishing a building data base, achieving zero energy in new buildings, green remodeling of existing buildings, and managing building energy demand.

[0174] In addition, by applying energy reduction plans and usage prediction technologies throughout the entire process from environmental planning, which is the pre-design stage for achieving carbon neutrality, to design, construction, operation, and disposal, it is possible to implement high-performance zero-energy buildings (ZEBs), green remodeling, net-zero buildings, and smart cities.

[0175] Additionally, by applying LEED's EPD (Environmentally Friendly Designated Building Materials) and WELL certification, it is possible to achieve healthy and comfortable indoor air quality.

Claims

1. A data collection unit that collects operational data from at least one building operation system and meteorological data of the area where the building is located; An AI energy optimization department that analyzes the collected building operation data and the above-mentioned weather data, analyzes the energy usage and carbon emissions within the building, extracts energy saving factors, and reflects renewable energy production factors to generate an energy management optimization policy in accordance with energy and greenhouse gas reduction laws and regulations; An AI air quality optimization unit that analyzes collected building operation data and weather data to predict air quality within a building and controls the building's air conditioning equipment based on the air quality prediction information; and Equipped with an ESG performance evaluation department that calculates carbon emissions and evaluates ESG performance through life cycle assessment for buildings that have applied energy optimization policies and air quality optimization policies; Smart Green ESG Platform.

2. In paragraph 1, The above AI energy optimization unit, An energy analysis unit that extracts energy load elements within a building from the building's BIM (Building Information Modeling) data and the building's operation data, and calculates energy usage and carbon emissions for the energy load elements; The above energy analysis unit, A building energy analysis method characterized by applying a deep learning-based energy usage prediction model that predicts energy usage and carbon emissions at a specific point in time and / or for a specific period by analyzing the building's BIM (Building Information Modeling) data, air conditioning facility operation history, weather data, energy usage of the energy load element, and carbon emissions of the energy load element in a time series. Smart Green ESG Platform.

3. In paragraph 2, The above AI energy optimization unit, An energy saving unit that analyzes and extracts energy reduction elements corresponding to each energy load element, calculates the amount of carbon emission reduction when applying the energy reduction elements, and calculates the amount of carbon emission reduction when applying renewable energy production facilities and waste separation and discharge management facilities applicable to buildings; further comprising; Smart Green ESG Platform.

4. In paragraph 3, The above AI energy optimization unit, An energy optimization unit further comprising: an energy optimization unit that applies the extracted energy reduction elements for the building, the renewable energy production facility, and the waste separation and discharge management facility to BIM data of the building, and applies the BIM data to the deep learning-based energy usage prediction model to perform a simulation to predict energy usage and carbon emissions for the entire building; Smart Green ESG Platform.

5. In paragraph 1, The above AI air quality optimization unit, Includes a CCTV AI analysis unit that analyzes CCTV video data collected in real time from the building operation system to detect occupant information and energy waste factors; The above occupant information includes the number of occupants and the amount of clothing worn, and the energy waste factors include the open / close status of windows / doors and the lighting status. Smart Green ESG Platform.

6. In paragraph 5, The above AI air quality optimization unit, An air quality optimization unit further includes an air quality optimization unit that predicts indoor air quality for a specific space after a certain period of time and controls ventilation and heating and cooling facilities according to the predicted indoor air quality by applying at least one influence factor data among indoor air quality measurement information collected in real time, building structure information, building facility information, occupant information and weather data within a building space extracted by analyzing the CCTV video data, using a deep learning-based indoor air quality prediction model trained. Smart Green ESG Platform.

7. In paragraph 6, The above air quality optimization unit, It is characterized by extracting occupant information and energy waste factors through analysis of CCTV video data, controlling lighting, ventilation, and heating / cooling facilities, and predicting the amount of carbon emissions reduction that can be achieved through facility control. Smart Green ESG Platform.

8. In paragraph 6, The above indoor air quality measurement information is: It is a measurement value measured by a monitoring sensor installed in an indoor space for at least one indoor air quality standard item among temperature / humidity, fine dust, pollutants, volatile organic compounds, inorganic gases, and radon. The above indoor air quality standards are: Characterized by being included in at least one of the certification standards of the Leadership in Energy and Environmental Design (LEED) and Healthy Building Certification (WELL) standards. Smart Green ESG Platform.

9. In paragraph 8, The above AI air quality optimization unit, An air quality improvement unit further comprising: analyzing the indoor air quality measurement information to extract building materials that satisfy the allowable standards set for indoor air quality criteria, and calculating the amount of carbon emissions reduction when replacing with the building materials; Smart Green ESG Platform.

10. In paragraph 1, The above ESG performance evaluation department, It is characterized by managing raw material LCA data on carbon emissions required in the production, transportation, disposal, and recycling stages of building materials used in building construction, and applying the raw material LCA data when calculating the carbon emissions of the entire building. Smart Green ESG Platform.

11. In paragraph 1, The above building operation system is, A building energy management system that monitors energy usage in real time through sensors and measuring equipment installed in the building's energy-using facilities; An indoor air quality monitoring system that measures and monitors the air quality inside a building in real time through sensors installed indoors; Air conditioning system that monitors / controls heating / cooling, ventilation and air conditioning equipment; and CCTV system that receives and stores real-time images from various sensors, IoT systems, and CCTV cameras installed in the building; Smart Green ESG Platform.

12. In the smart green ESG platform, a step of collecting operational data from at least one building operation system and collecting meteorological data of the area where the building is located; A step of analyzing collected building operation data and meteorological data, analyzing energy usage and carbon emissions within the building, extracting energy saving factors, and reflecting renewable energy production factors to create an energy management optimization policy in accordance with energy and greenhouse gas reduction laws and regulations; A step of analyzing collected building operation data and weather data to predict air quality within the building and control the building's air conditioning equipment based on the air quality prediction information; and A step for calculating carbon emissions and evaluating ESG performance through life cycle assessment for buildings that have applied energy optimization policies and air quality optimization policies; including; How to make buildings carbon neutral.

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