Building energy saving benefit diagnosis analysis system and method based on data portrait matching

Abstract

The present application relates to the technical field of building energy-saving benefit analysis, and proposes a building energy-saving benefit diagnosis analysis method based on data portrait matching, comprising: obtaining benefit analysis support data of a target building; screening a building to be diagnosed from the target building based on the benefit analysis support data; classifying the building to be diagnosed by energy consumption categories to obtain energy consumption categories including air conditioning categories, lighting categories, elevator categories and power categories; calculating air conditioning energy consumption by using enclosure structure parameters and cold source system configuration information in the benefit analysis support data, calculating lighting energy consumption by using area of each functional area and lighting fixture parameters, calculating elevator energy consumption by using elevator energy consumption parameters, and calculating power energy consumption by using total power consumption of a power system, total water consumption of the building and water pump configuration parameters; and evaluating energy-saving benefits of the building to be diagnosed based on the four types of energy consumption. The present application can improve the efficiency and accuracy of building energy-saving benefit diagnosis analysis.

Description

Technical Field

[0001] This invention relates to a building energy efficiency benefit diagnosis and analysis system and method based on data profile matching, belonging to the field of building energy efficiency benefit analysis technology. Background Technology

[0002] Driven by my country's "dual carbon" goals, the demand for building energy efficiency assessments has surged. Numerous energy-saving renovation demonstration projects have been launched across the country, accumulating rich energy consumption data and renovation experience. Currently, integrated energy businesses are promoting the Energy Performance Contracting (EPC) model, particularly for energy-saving renovations of older buildings, with a large number of potential clients.

[0003] Currently, the diagnosis of building energy efficiency benefits mainly relies on collecting building energy consumption data through on-site surveys and energy audits. This data is then analyzed and calculated manually, based on experience comparisons, or using general energy consumption models to assess the building's energy-saving potential and the benefits of renovations. However, this approach often directly compares measured energy consumption data with industry-standard or fixed templates when assessing building energy efficiency, ignoring the different business characteristics of various buildings, climate differences, and the building's own system configuration constraints and energy consumption behavior patterns. This results in assessments that are out of touch with the actual operating conditions of the building. Summary of the Invention

[0004] This invention provides a building energy efficiency benefit diagnosis and analysis system and method based on data profile matching, the main purpose of which is to improve the efficiency and accuracy of building energy efficiency benefit diagnosis and analysis.

[0005] To achieve the above objectives, the present invention provides a building energy efficiency benefit diagnosis and analysis system based on data profile matching, comprising: The profile filtering module is used to respond to building energy efficiency diagnosis. It obtains the building energy consumption data, building overview information, system parameters and operation pattern information of the target building input by the user, obtains benefit analysis support data, and uses the profile matching method to filter out the buildings to be diagnosed from the target buildings based on the benefit analysis support data. The energy consumption classification module is used to classify the building to be diagnosed into energy consumption categories to obtain energy consumption categories. The energy consumption system categories include air conditioning category, lighting category, elevator category and power category. The air conditioning energy consumption calculation module is used to calculate the energy consumption of the air conditioning category by utilizing the building envelope parameters and cold source system configuration information in the benefit analysis support data, and obtain the air conditioning energy consumption. The lighting category calculation module is used to calculate the energy consumption of the lighting category by utilizing the area of ​​each functional area, the type and power of lighting fixtures in the benefit analysis support data, and obtain the lighting energy consumption. The elevator energy consumption calculation module is used to calculate the energy consumption of the elevator category using the elevator energy consumption parameters in the benefit analysis support data, and obtain the elevator energy consumption. The power energy consumption calculation module is used to calculate the energy consumption of the power category using the total power consumption of the power system, the total water consumption of the building, and the water pump configuration parameters in the benefit analysis support data, and obtain the power energy consumption. The energy efficiency assessment module is used to assess the energy efficiency of the building to be diagnosed based on the energy consumption of the air conditioning, lighting, elevator, and power systems.

[0006] Optionally, based on the air conditioning energy consumption, lighting energy consumption, elevator energy consumption, and power energy consumption, the energy-saving benefits of the building to be diagnosed are evaluated, including: The system uses a profile matching method to match industry standard energy consumption that is compatible with the building type, building area, and usage scenario to be diagnosed. Based on the industry standard energy consumption, assess the energy consumption deviation of the air conditioning energy consumption, the lighting energy consumption, the elevator energy consumption, and the power energy consumption; Based on the profiling data of the building to be diagnosed, an energy-saving renovation analysis is performed on the building to obtain its energy-saving potential; Based on the building's energy-saving potential and energy consumption deviation, the energy-saving benefits of the building to be diagnosed are analyzed.

[0007] Optionally, the step of performing energy-saving renovation analysis on the building to be diagnosed based on the building profile data to obtain the building's energy-saving potential includes: Using the image data of the building to be diagnosed, four-dimensional parameters of the building to be diagnosed are generated; The four-dimensional parameters are matched with the industry-optimal template corresponding to the building to be diagnosed to determine the energy-saving defects of the building. Based on the aforementioned energy-saving deficiencies, the optimizable dimensions of the building to be diagnosed are determined; Based on the aforementioned optimizable dimensions, an energy-saving renovation analysis is performed on the building to be diagnosed to obtain its energy-saving potential.

[0008] Optionally, the four-dimensional parameters include building envelope, equipment configuration, operation and management, and usage scenarios; Optionally, the step of using profile matching methods to filter out buildings to be diagnosed from target buildings based on benefit analysis support data includes: Based on the benefit analysis support data, a comprehensive energy consumption data profile of the target building is constructed. The comprehensive energy consumption data profile includes at least the total energy consumption characteristics, the proportion of energy consumption by item, and the carbon emission intensity characteristics. The comprehensive energy consumption data profile is matched with the industry standard profile library to determine the deviation of the target building's energy consumption level. The comprehensive energy consumption data profile is matched with a preset energy consumption profile database of similar buildings to determine the energy consumption ranking range of the target building among similar buildings. Buildings to be diagnosed are selected from the target buildings based on the deviation of the energy consumption level and the energy consumption ranking range.

[0009] Optionally, using the building envelope parameters and cooling system configuration information in the benefit analysis support data, the energy consumption of the air conditioning category is calculated to obtain the air conditioning energy consumption, including: The building envelope parameters of the target building are parsed from the benefit analysis support data, and the cooling source system configuration information of the building to be diagnosed is extracted. The building envelope parameters include the heat transfer coefficient of the exterior wall, the shading coefficient of the exterior window and the window-to-wall ratio. The cooling source system configuration information includes at least the chiller rated COP, the water pump head and the cooling tower air volume. The building envelope parameters and the cold source system configuration information are processed into feature vectors to obtain the air conditioning system feature vectors. The feature vector of the air conditioning system is matched with the standard simulated building data generated by DeST simulation software to determine the baseline air conditioning load for the air conditioning category; The benchmark air conditioning load is corrected by a coefficient using the actual air conditioning operating time of the target building and the efficiency of the cold source system, and the energy consumption of the air conditioning category is calculated using the corrected benchmark air conditioning load to obtain the air conditioning energy consumption.

[0010] Optionally, using the area of ​​each functional area, type of lighting fixture, and power in the benefit analysis support data, the energy consumption of the lighting category is calculated to obtain the lighting energy consumption, including: The building area, lighting fixture type, single lamp power, and number of lighting fixtures for each functional area are extracted from the benefit analysis support data. Calculate the total installed lighting power for each functional area based on the single lamp power and the number of lighting fixtures; Based on the total installed lighting power and the area of ​​each functional area, calculate the lighting power density of each functional area; Based on the lighting power density, the energy consumption of the lighting category in the building to be diagnosed is calculated to obtain the lighting energy consumption.

[0011] Optionally, the benefit analysis supports the calculation of elevator energy consumption parameters in the data to obtain the energy consumption of the elevator category, including: Elevator energy consumption parameters are extracted from the benefit analysis support data. These parameters include elevator installed power, number of elevators, rated load, and passenger flow statistics. The passenger flow statistics include average daily passenger flow and peak hour passenger flow. Based on the elevator's installed power and rated load, calculate the unit load power of the elevator in the building to be diagnosed. Based on the average daily passenger flow and the peak hour passenger flow, calculate the passenger flow fluctuation coefficient of the elevators in the building to be diagnosed; Based on the unit load power and the passenger flow fluctuation coefficient, the energy consumption of the elevator category is calculated to obtain the elevator energy consumption.

[0012] Optionally, the energy consumption of the power category can be calculated using the total power system electricity consumption, total building water consumption, and pump configuration parameters in the benefit analysis support data, to obtain the power energy consumption, including: Extract the total water consumption of the building to be diagnosed, the total power consumption of the power system, and the water pump configuration parameters from the benefit analysis support data. The total power consumption of the power system includes the power consumption of the water supply pump and the power consumption of other power equipment. The water supply demand flow rate of the building to be diagnosed is determined based on the total water consumption of the building, and the theoretical shaft power of the water supply pump in the building is calculated based on the water supply demand flow rate. The theoretical power consumption of the water pump is calculated based on the theoretical shaft power of the water pump and the water pump configuration parameters. Based on the theoretical power consumption of the water supply pump and the power consumption of other power equipment, the power consumption of the building to be diagnosed is determined.

[0013] To achieve the above objectives, the present invention provides a building energy efficiency benefit diagnosis and analysis method based on data profile matching, comprising: In response to building energy efficiency diagnosis, the system obtains the building energy consumption data, building overview information, system parameters and operation pattern information of the target building input by the user, obtains benefit analysis support data, and uses a profile matching method to filter out the buildings to be diagnosed from the target buildings based on the benefit analysis support data. The building to be diagnosed is classified into energy consumption categories to obtain energy consumption categories. The energy consumption system categories include air conditioning, lighting, elevator, and power categories. Using the building envelope parameters and cold source system configuration information in the benefit analysis support data, the energy consumption of the air conditioning category is calculated to obtain the air conditioning energy consumption; Using the area of ​​each functional area, type of lighting fixture, and power in the benefit analysis support data, the energy consumption of the lighting category is calculated to obtain the lighting energy consumption; Using the elevator energy consumption parameters in the benefit analysis support data, the energy consumption of the elevator category is calculated to obtain the elevator energy consumption. The energy consumption of the power category is calculated using the total power consumption of the power system, the total water consumption of the building, and the water pump configuration parameters in the benefit analysis support data, thus obtaining the power energy consumption. The energy efficiency of the building to be diagnosed is evaluated based on the energy consumption of the air conditioning, lighting, elevator, and power systems.

[0014] Compared to the problems described in the background technology, this method firstly acquires building energy consumption data, building overview information, system parameters, and operational patterns through user input. This transforms the traditionally required on-site surveys of building physical models into structured benefit analysis support data, overcoming the problems of traditional methods relying on on-site surveys, high data collection costs, and long cycles. Secondly, it uses a profile matching method to quickly filter out buildings to be diagnosed from target buildings and classifies the selected buildings into four independent energy consumption categories: air conditioning, lighting, elevators, and power. This solves the efficiency bottleneck of traditional methods that comprehensively diagnose all buildings and cannot quickly focus on high-potential targets. Thirdly, it employs differentiated energy consumption calculation methods for different energy consumption categories—the air conditioning category uses building envelope parameters and cold source configuration information for calculation through load database matching; the lighting category uses functional area area and luminaire parameters for calculation through power density benchmarking; and so on. Elevator categories are calculated using linear regression based on installed power and passenger flow, while power categories are calculated using demand-driven back-calculation based on total electricity consumption and water consumption. This forms a subsystem, multi-path, and configurable energy consumption accounting system, avoiding the low accuracy and poor applicability issues of traditional methods that use a unified model to estimate the energy consumption of each system. Subsequently, based on the above four energy consumption categories, an overall energy-saving benefit assessment of the building is conducted, realizing a complete analysis link from "data input—profile matching—energy consumption classification—subsystem accounting—comprehensive assessment," overcoming the limitation of existing assessment tools that can only provide overall indicators and cannot locate the source of energy-saving potential. Finally, through the structured design of the multi-level assessment system, a progressive diagnostic mechanism of "rapid screening—precise positioning—quantitative calculation" is formed, ensuring from the source that the output energy-saving benefit assessment results can meet the needs of users at different stages in terms of accuracy and operability, effectively improving the efficiency and decision-making reliability of the early stage of building energy-saving renovation. Therefore, this invention can improve the efficiency and accuracy of building energy-saving benefit diagnosis and analysis. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of a building energy efficiency diagnosis and analysis system based on data profile matching provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a building energy efficiency diagnosis and analysis method based on data profile matching, provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a computer device for a building energy efficiency diagnosis and analysis method based on data profile matching, as provided in an embodiment of the present invention.

[0016] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0019] In practice, the server-side equipment deployed in a data profile matching-based building energy efficiency benefit diagnosis and analysis system may consist of one or more devices. This system can be implemented as a business instance, a virtual machine, or hardware devices. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node, providing data profile matching-based building energy efficiency benefit diagnosis and analysis services to various users. Alternatively, it can be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various users. Or, it can be implemented as a server composed of numerous identical or different types of hardware devices, with one or more devices configured to provide data profile matching-based building energy efficiency benefit diagnosis and analysis services to various users.

[0020] In terms of implementation, the building energy efficiency diagnostic and analysis system based on data profile matching and the user terminal are mutually adaptable. That is, if the building energy efficiency diagnostic and analysis system based on data profile matching is implemented as an application installed on a cloud service platform, then the user terminal is a client that establishes a communication connection with the application; or if the building energy efficiency diagnostic and analysis system based on data profile matching is implemented as a website, then the user terminal is implemented as a webpage; or if the building energy efficiency diagnostic and analysis system based on data profile matching is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0021] Reference Figure 1The diagram shown is a functional block diagram of a building energy efficiency diagnosis and analysis system based on data profile matching provided in an embodiment of the present invention.

[0022] The building energy efficiency diagnostic and analysis system 100 based on data profile matching described in this invention can be set up in a cloud server. In terms of implementation, it can be used as one or more service devices, as an application installed in the cloud, or developed as a website. Depending on the functions implemented, the building energy efficiency diagnostic and analysis system 100 based on data profile matching includes a profile screening module 101, an energy consumption classification module 102, an air conditioning energy consumption calculation module 103, a lighting category calculation module 104, an elevator energy consumption calculation module 105, a power energy consumption calculation module 106, and an energy efficiency assessment module 107.

[0023] In this embodiment of the invention, the building energy efficiency benefit diagnosis and analysis based on data profile matching can be implemented independently and called by other modules. This "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the building energy efficiency benefit diagnosis and analysis system based on data profile matching provided in this embodiment of the invention, the applicable scope of the data profile matching-based building energy efficiency benefit diagnosis and analysis architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion. This allows for quick and flexible expansion of the data profile matching-based building energy efficiency benefit diagnosis and analysis system. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0024] The following describes, with reference to specific embodiments, each component of the building energy efficiency diagnosis and analysis system based on data profile matching, as well as its specific workflow.

[0025] The profile screening module 101 is used to respond to the building energy efficiency diagnosis, obtain the building energy consumption data, building overview information, system parameters and operation pattern information of the target building input by the user, obtain benefit analysis support data, and use the profile matching method to screen out the buildings to be diagnosed from the target buildings based on the benefit analysis support data.

[0026] In response to building energy efficiency diagnosis, this invention obtains the target building's energy consumption data, building overview information, system parameters, and operating rules information input by the user. The resulting benefit analysis support data can transform the original data such as the target building's actual energy consumption, structural configuration, and operating rules into standardized benefit analysis support data that the system can recognize, thereby ensuring that subsequent diagnoses are based on the actual condition of the user's building.

[0027] The building energy efficiency diagnosis refers to a comprehensive assessment of the target building, including energy consumption benchmarking, potential assessment, and energy saving calculation for renovation. The building energy consumption data refers to the total annual / monthly electricity, natural gas, and heat consumption of the building, as well as the individual electricity consumption data for optional systems such as air conditioning and lighting. The building overview information describes the basic physical and usage attributes of the target building, including its climate zone, building type, total building area, building envelope parameters (heat transfer coefficients of walls, windows, roof, etc.), and indoor thermal disturbance parameters. The system parameters refer to the configuration parameters of the building's air conditioning, lighting, elevators, and power systems. The operational pattern information refers to the building and equipment usage duration and start-up / shutdown patterns, such as daily air conditioning operating time, lighting operating hours, and peak / valley elevator passenger flow.

[0028] Optionally, the benefit analysis support data can be obtained through the data receiving layer in the data analysis platform configured for the building energy efficiency benefit diagnosis, wherein the data analysis platform can be built through a cloud platform, and the data receiving layer obtains data through manual input by the user.

[0029] Furthermore, in this embodiment of the invention, by using a profile matching method based on benefit analysis support data, buildings to be diagnosed can be quickly eliminated from the target buildings, removing those that clearly meet the standards or have no value for renovation, and only selecting buildings that have the necessity for energy-saving diagnosis, thereby avoiding invalid analysis.

[0030] As an embodiment of the present invention, the step of using a profile matching method based on benefit analysis support data to filter out buildings to be diagnosed from target buildings includes: Based on the benefit analysis support data, a comprehensive energy consumption data profile of the target building is constructed. The comprehensive energy consumption data profile includes at least the total energy consumption characteristics, the proportion of energy consumption by item, and the carbon emission intensity characteristics. The comprehensive energy consumption data profile is matched with the industry standard profile library to determine the deviation of the target building's energy consumption level. The comprehensive energy consumption data profile is matched with a preset energy consumption profile database of similar buildings to determine the energy consumption ranking range of the target building among similar buildings. Buildings to be diagnosed are selected from the target buildings based on the deviation of the energy consumption level and the energy consumption ranking range.

[0031] The total energy consumption characteristic refers to the sum of all types of energy consumed by the target building within a specific statistical period, covering all energy consumption such as electricity, natural gas, and heat, and is used to characterize the overall energy consumption scale and burden of the building. The sub-item energy consumption ratio characteristic refers to the proportion of energy consumption of each functional system in the total energy consumption, mainly including air conditioning, lighting, elevators, and power systems. The carbon emission intensity characteristic refers to the amount of carbon dioxide emissions generated by the target building per unit building area. The energy consumption level deviation refers to the degree of quantitative deviation between the comprehensive energy consumption data profile of the target building and the benchmark value set by the current national or local mandatory energy consumption standards (such as the "Energy Consumption Standard for Civil Buildings").

[0032] Optionally, the annual energy consumption data (electricity, natural gas, heat, etc.) input by the user can be standardized first. Then, the standardized data can be used to calculate the total energy consumption per unit area of ​​the target building, the proportion of energy consumption for major sub-items such as air conditioning and lighting, and the converted carbon emission intensity. A comprehensive energy consumption profile of the target building can then be constructed. The total energy consumption per unit area of ​​the target building can then be compared with national standards (such as GB / T). The standard or guiding values ​​for the corresponding climate zone and building type in 51161) are compared, and the relative deviation between the two is calculated, i.e., the deviation of energy consumption level. If the deviation of energy consumption level exceeds 15%, it is judged as a significant deviation. Based on the climate zone, building type and building area range of the target building, a sample set matching the conditions is selected from the energy consumption profile library of similar buildings, as well as the actual operation data accumulated from historical projects. Then, the energy consumption index per unit area of ​​the target building is sorted in the sample set to determine its ranking range. When the energy consumption ranking of the target building is in the top 30% of the sample set, it is considered to have good energy efficiency, and when it is in the bottom 30%, it is considered to have poor energy efficiency. The sample set is obtained from the building load simulation results pre-established by DeST simulation software. If the deviation of the energy consumption level of the target building exceeds 15%, or its energy consumption ranking falls into the bottom 30% range of similar buildings, it is marked as a "building to be diagnosed".

[0033] It should be explained that when judging a building to be diagnosed, meeting either of the two criteria will trigger the screening. This is to ensure that neither high-energy-consuming projects with obvious energy-saving potential are overlooked, nor cases that, although ranked reasonably well, deviate significantly from industry benchmarks are ignored. The final screening results are presented to the user in list form for subsequent refined diagnosis.

[0034] The energy consumption classification module 102 is used to classify the building to be diagnosed into energy consumption categories to obtain energy consumption categories. The energy consumption system categories include air conditioning category, lighting category, elevator category and power category.

[0035] This invention categorizes the building to be diagnosed into different energy consumption categories, such as air conditioning, lighting, elevators, and power, to break down the total energy consumption of the building. This clarifies the proportion of each system in the overall energy consumption, thereby clarifying the structural distribution of energy consumption within the building and providing a direct basis for identifying which specific system has abnormal energy consumption.

[0036] The energy consumption system categories include air conditioning, lighting, elevators, and power systems.

[0037] The air conditioning energy consumption calculation module 103 is used to calculate the energy consumption of the air conditioning category by utilizing the building envelope parameters and cold source system configuration information in the benefit analysis support data, and obtain the air conditioning energy consumption.

[0038] This invention utilizes the building envelope parameters and cold source system configuration information in the benefit analysis support data to calculate the energy consumption of the air conditioning category. The resulting air conditioning energy consumption can be calculated using the building envelope parameters and cold source configuration information of the building to be diagnosed, to obtain the theoretical energy consumption value that the air conditioning system should have under the current physical conditions, thus obtaining an air conditioning energy consumption benchmark that truly fits the actual situation of the building.

[0039] The building envelope parameters refer to the thermal insulation performance of building walls, doors and windows, and the window-to-wall ratio. The cold source system configuration information refers to the equipment selection and performance parameters of the cold source side of the air conditioning system, including at least the rated cooling capacity of the chiller unit, the rated coefficient of performance (COP), the rated power and head of the water pump, and the rated air volume of the cooling tower.

[0040] As an embodiment of the present invention, the step of calculating the energy consumption of the air conditioning category using the building envelope parameters and cold source system configuration information in the benefit analysis support data to obtain the air conditioning energy consumption includes: The building envelope parameters of the target building are parsed from the benefit analysis support data, and the cooling source system configuration information of the building to be diagnosed is extracted. The building envelope parameters include the heat transfer coefficient of the exterior wall, the shading coefficient of the exterior window and the window-to-wall ratio. The cooling source system configuration information includes at least the chiller rated COP, the water pump head and the cooling tower air volume. The building envelope parameters and the cold source system configuration information are processed into feature vectors to obtain the air conditioning system feature vectors. The feature vector of the air conditioning system is matched with the standard simulated building data generated by DeST simulation software to determine the baseline air conditioning load for the air conditioning category; The benchmark air conditioning load is corrected by a coefficient using the actual air conditioning operating time of the target building and the efficiency of the cold source system, and the energy consumption of the air conditioning category is calculated using the corrected benchmark air conditioning load to obtain the air conditioning energy consumption.

[0041] The DeST simulation software refers to a professional simulation software for building environment and energy consumption. It can generate standard air conditioning load and energy consumption sample data for different types of buildings in batches based on building structural parameters, equipment configuration and meteorological conditions. The benchmark air conditioning load refers to the benchmark value of the standard air conditioning load of the same type of building with similar characteristics to the target building, which is obtained by simulation matching through DeST software under standard meteorological conditions, rated equipment conditions and normal operating mode. It is used as the basic reference for building air conditioning energy consumption accounting.

[0042] Optionally, the system can first retrieve three key building envelope parameters—exterior wall heat transfer coefficient, exterior window shading coefficient, and window-to-wall ratio for each orientation—from the user-input building overview table. Simultaneously, it can retrieve user-output data on chiller rated COP, pump head, and cooling tower airflow. The building envelope parameters and the cooling source system configuration information are then combined in a fixed order into a multi-dimensional numerical vector, such as [exterior wall heat transfer coefficient, exterior window shading coefficient, window-to-wall ratio, chiller COP, pump head, cooling tower airflow]. These parameters are then normalized to unify their dimensions to the 0-1 range. Finally, the feature vector of the target building is compared with the feature vectors of thousands of standard simulated buildings in the pre-established DeST simulation database. Euclidean distance or cosine similarity is calculated for each building, and the three simulated buildings with the smallest distance or highest similarity are selected as neighbors. The weighted average of the simulated air conditioning load values ​​of these three neighbors is then taken as the benchmark air conditioning load of the target building. Based on the benchmark air conditioning load obtained by matching, combined with the statistical data of the actual start-up and shutdown and running time of the target building throughout the year, as well as the deviation between the actual operating efficiency and the rated efficiency of the cold source system equipment, the benchmark air conditioning load is dynamically corrected by coefficients to eliminate the difference between the standard simulated operating conditions and the actual operating conditions of the building. Based on the corrected accurate air conditioning benchmark load, combined with the air conditioning system energy efficiency accounting logic, iterative calculations are performed to finally obtain the air conditioning energy consumption.

[0043] The lighting category calculation module 104 is used to calculate the energy consumption of the lighting category by utilizing the area of ​​each functional area, the type and power of lighting fixtures in the benefit analysis support data, and obtain the lighting energy consumption.

[0044] This invention utilizes the area of ​​each functional area, lighting fixture type, and power in the benefit analysis support data to calculate the energy consumption of the lighting category. The lighting energy consumption can be obtained by using the area of ​​each functional area of ​​the building and the lighting fixture configuration data to calculate the actual or theoretical energy consumption value of the lighting system under the current conditions, thereby quantifying the energy consumption level of the lighting link to help users judge whether the energy consumption of the system is reasonable.

[0045] As an embodiment of the present invention, the step of calculating the energy consumption of the lighting category by utilizing the area of ​​each functional area, the type and power of lighting fixtures in the benefit analysis support data to obtain the lighting energy consumption includes: The building area, lighting fixture type, single lamp power, and number of lighting fixtures for each functional area are extracted from the benefit analysis support data. Calculate the total installed lighting power for each functional area based on the single lamp power and the number of lighting fixtures; Based on the total installed lighting power and the area of ​​each functional area, calculate the lighting power density of each functional area; Based on the lighting power density, the energy consumption of the lighting category in the building to be diagnosed is calculated to obtain the lighting energy consumption.

[0046] The total installed lighting power refers to the total installed power obtained by summing the rated power of all installed lighting fixtures in a single functional area. It is used to characterize the overall installed load scale of lighting equipment in the corresponding functional area and is the basic load parameter for calculating the energy consumption of zoned lighting. The lighting power density refers to the lighting load index per unit area obtained by converting the total installed lighting power of the functional area based on the building area of ​​the functional area. It is used to quantify the density of lighting layout in different functional areas.

[0047] Optionally, basic data such as building area, type of lighting fixtures, rated power of a single lamp, and number of lamps for each functional area of ​​the building to be diagnosed can be read in batches from the pre-stored benefit analysis support data. Combined with the rated power of a single lamp in each functional area and the total number of corresponding lamps, the total installed lighting power for each functional area can be calculated by power accumulation calculation.

[0048] Furthermore, as another embodiment of the present invention, the lighting power density can be calculated using the following formula:

[0049] in, For lighting power density, Indicates the total installed power of lighting. This indicates the area of ​​each functional zone.

[0050] Furthermore, as another embodiment of the present invention, the lighting power density can be calculated using the following formula:

[0051] in, Indicates lighting energy consumption. For lighting power density, Indicates the area of ​​each functional zone. This indicates the number of operating hours per year.

[0052] The elevator energy consumption calculation module 105 is used to calculate the energy consumption of the elevator category using the elevator energy consumption parameters in the benefit analysis support data, and obtain the elevator energy consumption.

[0053] This invention utilizes elevator energy consumption parameters from the benefit analysis support data to calculate the energy consumption of the elevator category. The resulting elevator energy consumption can be correlated with installed power and passenger flow to quantify the reasonable energy consumption benchmark of the elevator system under existing model configuration and usage frequency. This allows for the determination of whether the elevator system has high energy consumption due to unreasonable control strategies or low equipment energy efficiency.

[0054] The elevator energy consumption parameters refer to parameters that affect the specific energy consumption during elevator operation, such as elevator installed power, number of elevators, rated load, and passenger flow.

[0055] As an embodiment of the present invention, the energy consumption of the elevator category is calculated using the elevator energy consumption parameters in the benefit analysis support data to obtain the elevator energy consumption, including: Elevator energy consumption parameters are extracted from the benefit analysis support data. These parameters include elevator installed power, number of elevators, rated load, and passenger flow statistics. The passenger flow statistics include average daily passenger flow and peak hour passenger flow. Based on the elevator's installed power and rated load, calculate the unit load power of the elevator in the building to be diagnosed. Based on the average daily passenger flow and the peak hour passenger flow, calculate the passenger flow fluctuation coefficient of the elevators in the building to be diagnosed; Based on the unit load power and the passenger flow fluctuation coefficient, the energy consumption of the elevator category is calculated to obtain the elevator energy consumption.

[0056] The passenger flow fluctuation coefficient refers to a dimensionless correction coefficient calculated based on the average daily passenger flow of the building elevator and combined with the peak change of passenger flow during peak hours. It is used to quantify the fluctuation difference of passenger flow load at different times of the day.

[0057] Optionally, all elevator energy consumption parameters of the building to be diagnosed can be extracted from the benefit analysis support data, specifically including elevator installed power, number of elevators, elevator rated load, and passenger flow statistics. The unit load power is calculated as follows: Unit load power = Total elevator installed power / Elevator rated load. The average hourly passenger flow can be obtained by dividing the average daily passenger flow by the daily effective operating hours. Then, the peak hour passenger flow can be divided by the average hourly passenger flow to obtain the fluctuation coefficient. Taking 12 hours of daily effective operation as an example, if the average daily passenger flow is 2400 people, the average hourly passenger flow is 200 people, and the peak hour passenger flow is 500 people, then the fluctuation coefficient is 2.5. If the peak passenger flow is less than the average hourly flow, then the fluctuation coefficient is 1.0.

[0058] Furthermore, as another embodiment of the present invention, the elevator energy consumption can be calculated using the following formula:

[0059] in, Indicates elevator energy consumption. This indicates the elevator's unit load capacity. Indicates the total installed capacity of the elevator. This indicates the number of days the elevator operates per year. Indicates the daily operating hours of the elevator. This represents the passenger flow fluctuation coefficient.

[0060] The power energy consumption calculation module 106 is used to calculate the energy consumption of the power category using the total power consumption of the power system, the total water consumption of the building, and the water pump configuration parameters in the benefit analysis support data, and obtain the power energy consumption.

[0061] This invention calculates the energy consumption of the power category by utilizing the total power consumption of the power system, the total water consumption of the building, and the water pump configuration parameters in the benefit analysis support data. The obtained power energy consumption can be linked to the energy demand of the power system. By using the water consumption to infer the reasonable operating energy consumption of the water pump, the energy consumption deviation caused by improper water pump matching or low operating efficiency can be eliminated, thereby obtaining a more realistic benchmark value for the energy consumption of the power system.

[0062] As an embodiment of the present invention, the step of calculating the energy consumption of the power category using the total power system electricity consumption, total building water consumption, and pump configuration parameters in the benefit analysis support data to obtain the power energy consumption includes: Extract the total water consumption of the building to be diagnosed, the total power consumption of the power system, and the water pump configuration parameters from the benefit analysis support data. The total power consumption of the power system includes the power consumption of the water supply pump and the power consumption of other power equipment. The water supply demand flow rate of the building to be diagnosed is determined based on the total water consumption of the building, and the theoretical shaft power of the water supply pump in the building is calculated based on the water supply demand flow rate. The theoretical power consumption of the water pump is calculated based on the theoretical shaft power of the water pump and the water pump configuration parameters. Based on the theoretical power consumption of the water supply pump and the power consumption of other power equipment, the power consumption of the building to be diagnosed is determined.

[0063] The theoretical shaft power refers to the basic power load required for the water pump to complete the water delivery operation, and the water pump configuration parameters refer to the inherent equipment parameters that match the operating performance of the building water supply pump, including key indicators such as the pump's rated efficiency, design head, pipeline loss coefficient, and energy efficiency attenuation coefficient.

[0064] Optionally, the total water consumption, total power consumption of the power system, and all pump configuration parameters for the building to be diagnosed can be uniformly extracted from the benefit analysis support data. The total power consumption of the power system is specifically divided into the power consumption of the water supply pumps and the power consumption of other power equipment in the building; the water supply demand flow rate is the total water consumption of the building. The power energy consumption of the building to be diagnosed can be obtained by adding the theoretical power consumption of the water supply pumps and the power consumption of the other power equipment.

[0065] Furthermore, as another embodiment of the present invention, the theoretical shaft power can be calculated using the following formula:

[0066] in, Represents theoretical shaft power. This indicates the density of water. Represents gravitational acceleration. This indicates the required flow rate for water supply.

[0067] Furthermore, as another embodiment of the present invention, the theoretical power consumption of the water pump can be calculated using the following formula:

[0068] in, This indicates the theoretical power consumption of the water pump. Represents theoretical shaft power. Indicates the effective operating time of the water pump. Indicates the rated efficiency of the water pump. This represents a correction factor used to correct for the decline in pump performance.

[0069] The energy-saving benefit assessment module 107 is used to assess the energy-saving benefits of the building to be diagnosed based on the air conditioning energy consumption, the lighting energy consumption, the elevator energy consumption, and the power energy consumption.

[0070] As an embodiment of the present invention, the energy-saving benefits of the building to be diagnosed are evaluated based on the air conditioning energy consumption, the lighting energy consumption, the elevator energy consumption, and the power energy consumption, including: The system uses a profile matching method to match industry standard energy consumption that is compatible with the building type, building area, and usage scenario to be diagnosed. Based on the industry standard energy consumption, assess the energy consumption deviation of the air conditioning energy consumption, the lighting energy consumption, the elevator energy consumption, and the power energy consumption; Based on the profiling data of the building to be diagnosed, an energy-saving renovation analysis is performed on the building to obtain its energy-saving potential; Based on the building's energy-saving potential and energy consumption deviation, the energy-saving benefits of the building to be diagnosed are analyzed.

[0071] Optionally, the system can read the building's business type, total building area, and typical usage scenarios, and then call a pre-established industry standard energy consumption database, which can be pre-built from the DeST simulation database. Based on the industry standard energy consumption, the deviation of the energy consumption of air conditioning, lighting, elevators, and power components from the standard values ​​is calculated, and the exceedance of each system is quantified by constructing an energy consumption deviation rate matrix. Based on the building's energy-saving potential and the energy consumption deviation, the potential energy savings are converted into economic benefit indicators. Combining the local energy unit price and the estimated renovation cost, a comprehensive energy-saving benefit assessment report, including the static investment payback period, is finally output.

[0072] Furthermore, as a preferred embodiment of the present invention, the step of performing energy-saving renovation analysis on the building to be diagnosed based on the portrait data of the building to be diagnosed to obtain the building's energy-saving potential includes: Using the image data of the building to be diagnosed, four-dimensional parameters of the building to be diagnosed are generated; The four-dimensional parameters are matched with the industry-optimal template corresponding to the building to be diagnosed to determine the energy-saving defects of the building. Based on the aforementioned energy-saving deficiencies, the optimizable dimensions of the building to be diagnosed are determined; Based on the aforementioned optimizable dimensions, an energy-saving renovation analysis is performed on the building to be diagnosed to obtain its energy-saving potential.

[0073] The four-dimensional parameters refer to four core feature parameters extracted from the original building profile data, specifically covering building envelope parameters, equipment configuration parameters, operation and control parameters, and usage scenario parameters. The energy-saving defect refers to the situation where the difference between the score of a certain dimension of the building to be diagnosed and the corresponding dimension score of the industry's best template in the same industry type and climate zone exceeds a preset threshold, indicating that there is significant room for energy saving optimization in that dimension. The building energy-saving potential refers to the total energy consumption reduction space that can be achieved by each energy-consuming system after optimizing and rectifying various energy-saving defects in the building. It is a comprehensive indicator for quantifying the benefits of building energy-saving renovation and assessing the space for building energy-saving improvement.

[0074] Optionally, complete multi-dimensional profile data of the building to be diagnosed is retrieved. Through data classification, aggregation, and feature extraction techniques, scattered building profile information is organized and integrated. Then, it is categorized and sorted according to four dimensions: building envelope, equipment configuration, operation and management, and usage scenarios, generating four-dimensional parameters. Based on the building's business type, building scale, and usage attributes, the corresponding industry-optimal energy-saving parameter template is retrieved. The generated four-dimensional building parameters are compared and matched with the industry-optimal template parameters dimension by dimension. By comparing parameter differences, deviations from industry-optimal standards in each dimension of the building are screened, accurately identifying various energy-saving deficiencies in energy configuration and operation management. For each energy-saving defect identified through screening, a targeted analysis is conducted based on the energy consumption characteristics and operating patterns of each building system. According to the energy-saving optimization logic corresponding to different defects, appropriate optimization and rectification methods are matched accordingly. The specific dimensions and directions for optimization of each energy-consuming system are clarified. For example, energy saving in the air conditioning system can be achieved by thickening the external wall insulation or replacing windows with energy-efficient ones; energy saving in the lighting system can be achieved by replacing lamps or optimizing control strategies to reduce lighting energy consumption; energy saving in the elevator system can be achieved by optimizing variable frequency control strategies or replacing equipment to reduce elevator energy consumption; energy saving in the power system can be achieved by using an "experienced benchmark value + water consumption back-calculation correction" method to eliminate ineffective energy consumption caused by pipeline leakage or pressure redundancy. For each optimizable dimension, corresponding renovation measures are selected from the energy-saving renovation measure library, and the corresponding energy-saving calculation algorithm is used to calculate the expected energy savings after implementing each measure. The sum of the expected energy savings of each dimension yields the building's energy-saving potential.

[0075] like Figure 2 The diagram shown is a flowchart illustrating a building energy efficiency diagnostic analysis method based on data profile matching according to an embodiment of the present invention. In this embodiment, the building energy efficiency diagnostic analysis method based on data profile matching includes: In response to building energy efficiency diagnosis, the system obtains the building energy consumption data, building overview information, system parameters and operation pattern information of the target building input by the user, obtains benefit analysis support data, and uses a profile matching method to filter out the buildings to be diagnosed from the target buildings based on the benefit analysis support data. The building to be diagnosed is classified into energy consumption categories to obtain energy consumption categories. The energy consumption system categories include air conditioning, lighting, elevator, and power categories. Using the building envelope parameters and cold source system configuration information in the benefit analysis support data, the energy consumption of the air conditioning category is calculated to obtain the air conditioning energy consumption; Using the area of ​​each functional area, type of lighting fixture, and power in the benefit analysis support data, the energy consumption of the lighting category is calculated to obtain the lighting energy consumption; Using the elevator energy consumption parameters in the benefit analysis support data, the energy consumption of the elevator category is calculated to obtain the elevator energy consumption. The energy consumption of the power category is calculated using the total power consumption of the power system, the total water consumption of the building, and the water pump configuration parameters in the benefit analysis support data, thus obtaining the power energy consumption. The energy efficiency of the building to be diagnosed is evaluated based on the energy consumption of the air conditioning, lighting, elevator, and power systems.

[0076] In one embodiment, a computer device is provided, which may be a server or a client, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of the data profile matching-based building energy efficiency benefit diagnostic analysis method on the server or client side.

[0077] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: In response to building energy efficiency diagnosis, the system obtains the building energy consumption data, building overview information, system parameters and operation pattern information of the target building input by the user, obtains benefit analysis support data, and uses a profile matching method to filter out the buildings to be diagnosed from the target buildings based on the benefit analysis support data. The building to be diagnosed is classified into energy consumption categories to obtain energy consumption categories. The energy consumption system categories include air conditioning, lighting, elevator, and power categories. Using the building envelope parameters and cold source system configuration information in the benefit analysis support data, the energy consumption of the air conditioning category is calculated to obtain the air conditioning energy consumption; Using the area of ​​each functional area, type of lighting fixture, and power in the benefit analysis support data, the energy consumption of the lighting category is calculated to obtain the lighting energy consumption; Using the elevator energy consumption parameters in the benefit analysis support data, the energy consumption of the elevator category is calculated to obtain the elevator energy consumption. The energy consumption of the power category is calculated using the total power consumption of the power system, the total water consumption of the building, and the water pump configuration parameters in the benefit analysis support data, thus obtaining the power energy consumption. The energy efficiency of the building to be diagnosed is evaluated based on the energy consumption of the air conditioning, lighting, elevator, and power systems.

[0078] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: In response to building energy efficiency diagnosis, the system obtains the building energy consumption data, building overview information, system parameters and operation pattern information of the target building input by the user, obtains benefit analysis support data, and uses a profile matching method to filter out the buildings to be diagnosed from the target buildings based on the benefit analysis support data. The building to be diagnosed is classified into energy consumption categories to obtain energy consumption categories. The energy consumption system categories include air conditioning, lighting, elevator, and power categories. Using the building envelope parameters and cold source system configuration information in the benefit analysis support data, the energy consumption of the air conditioning category is calculated to obtain the air conditioning energy consumption; Using the area of ​​each functional area, type of lighting fixture, and power in the benefit analysis support data, the energy consumption of the lighting category is calculated to obtain the lighting energy consumption; Using the elevator energy consumption parameters in the benefit analysis support data, the energy consumption of the elevator category is calculated to obtain the elevator energy consumption. The energy consumption of the power category is calculated using the total power consumption of the power system, the total water consumption of the building, and the water pump configuration parameters in the benefit analysis support data, thus obtaining the power energy consumption. The energy efficiency of the building to be diagnosed is evaluated based on the energy consumption of the air conditioning, lighting, elevator, and power systems.

[0079] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0080] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0081] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0082] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0083] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A building energy efficiency benefit diagnosis and analysis system based on data profile matching, characterized in that: The system includes: a profile screening module, an energy consumption classification module, an air conditioning energy consumption calculation module, a lighting category calculation module, an elevator energy consumption calculation module, a power energy consumption calculation module, and an energy-saving benefit assessment module; The profile filtering module is used to respond to building energy efficiency diagnosis, obtain the building energy consumption data, building overview information, system parameters and operation pattern information of the target building input by the user, obtain benefit analysis support data, and use the profile matching method to filter out the buildings to be diagnosed from the target buildings based on the benefit analysis support data. The energy consumption classification module is used to classify the building to be diagnosed into energy consumption categories to obtain energy consumption categories. The energy consumption system categories include air conditioning category, lighting category, elevator category and power category. The air conditioning energy consumption calculation module is used to calculate the energy consumption of the air conditioning category by utilizing the building envelope parameters and cold source system configuration information in the benefit analysis support data, and obtain the air conditioning energy consumption. The lighting category calculation module is used to calculate the energy consumption of the lighting category by utilizing the area of ​​each functional area, the type and power of lighting fixtures in the benefit analysis support data, and obtain the lighting energy consumption. The elevator energy consumption calculation module is used to calculate the energy consumption of the elevator category using the elevator energy consumption parameters in the benefit analysis support data, and obtain the elevator energy consumption. The power energy consumption calculation module is used to calculate the energy consumption of the power category using the total power consumption of the power system, the total water consumption of the building, and the water pump configuration parameters in the benefit analysis support data, and obtain the power energy consumption. The energy-saving benefit assessment module is used to assess the energy-saving benefits of the building to be diagnosed based on the air conditioning energy consumption, lighting energy consumption, elevator energy consumption, and power energy consumption.

2. The building energy efficiency benefit diagnosis and analysis method based on data profile matching as described in claim 1, characterized in that, Based on the air conditioning energy consumption, lighting energy consumption, elevator energy consumption, and power energy consumption, the energy efficiency of the building to be diagnosed is evaluated, including: The system uses a profile matching method to match industry standard energy consumption that is compatible with the building type, building area, and usage scenario to be diagnosed. Based on the industry standard energy consumption, assess the energy consumption deviation of the air conditioning energy consumption, the lighting energy consumption, the elevator energy consumption, and the power energy consumption; Based on the profiling data of the building to be diagnosed, an energy-saving renovation analysis is performed on the building to obtain its energy-saving potential; Based on the building's energy-saving potential and energy consumption deviation, the energy-saving benefits of the building to be diagnosed are analyzed.

3. The building energy efficiency benefit diagnosis and analysis method based on data profile matching as described in claim 2, characterized in that, The energy-saving renovation analysis of the building to be diagnosed, based on the building profile data, yields the building's energy-saving potential, including: Using the image data of the building to be diagnosed, four-dimensional parameters of the building to be diagnosed are generated; The four-dimensional parameters are matched with the industry-optimal template corresponding to the building to be diagnosed to determine the energy-saving defects of the building. Based on the aforementioned energy-saving deficiencies, the optimizable dimensions of the building to be diagnosed are determined; Based on the aforementioned optimizable dimensions, an energy-saving renovation analysis is performed on the building to be diagnosed to obtain its energy-saving potential.

4. The building energy efficiency benefit diagnosis and analysis method based on data profile matching as described in claim 3, characterized in that, The four-dimensional parameters include building envelope, equipment configuration, operation and management, and usage scenarios.

5. The building energy efficiency benefit diagnosis and analysis method based on data profile matching as described in claim 1, characterized in that, The benefit analysis-based support data utilizes a profile matching method to filter out buildings to be diagnosed from the target buildings, including: Based on the benefit analysis support data, a comprehensive energy consumption data profile of the target building is constructed. The comprehensive energy consumption data profile includes at least the total energy consumption characteristics, the proportion of energy consumption by item, and the carbon emission intensity characteristics. The comprehensive energy consumption data profile is matched with the industry standard profile library to determine the deviation of the target building's energy consumption level. The comprehensive energy consumption data profile is matched with a preset energy consumption profile database of similar buildings to determine the energy consumption ranking range of the target building among similar buildings. Buildings to be diagnosed are selected from the target buildings based on the deviation of the energy consumption level and the energy consumption ranking range.

6. The building energy efficiency benefit diagnosis and analysis method based on data profile matching as described in claim 1, characterized in that, Using the building envelope parameters and cooling system configuration information in the benefit analysis support data, the energy consumption of the air conditioning category is calculated to obtain the air conditioning energy consumption, including: The building envelope parameters of the target building are parsed from the benefit analysis support data, and the cooling source system configuration information of the building to be diagnosed is extracted. The building envelope parameters include the heat transfer coefficient of the exterior wall, the shading coefficient of the exterior window and the window-to-wall ratio. The cooling source system configuration information includes at least the chiller rated COP, the water pump head and the cooling tower air volume. The building envelope parameters and the cold source system configuration information are processed into feature vectors to obtain the air conditioning system feature vectors. The feature vector of the air conditioning system is matched with the standard simulated building data generated by DeST simulation software to determine the baseline air conditioning load for the air conditioning category; The benchmark air conditioning load is corrected by a coefficient using the actual air conditioning operating time of the target building and the efficiency of the cold source system, and the energy consumption of the air conditioning category is calculated using the corrected benchmark air conditioning load to obtain the air conditioning energy consumption.

7. The building energy efficiency benefit diagnosis and analysis method based on data profile matching as described in claim 1, characterized in that, Using the area of ​​each functional zone, type of lighting fixture, and power in the benefit analysis support data, the energy consumption of the lighting category is calculated to obtain the lighting energy consumption, including: The building area, lighting fixture type, single lamp power, and number of lighting fixtures for each functional area are extracted from the benefit analysis support data. Calculate the total installed lighting power for each functional area based on the single lamp power and the number of lighting fixtures; Based on the total installed lighting power and the area of ​​each functional area, calculate the lighting power density of each functional area; Based on the lighting power density, the energy consumption of the lighting category in the building to be diagnosed is calculated to obtain the lighting energy consumption.

8. The building energy efficiency benefit diagnosis and analysis method based on data profile matching as described in claim 1, characterized in that, The benefit analysis supports the calculation of elevator energy consumption parameters in the data to determine the energy consumption of the elevator category, including: Elevator energy consumption parameters are extracted from the benefit analysis support data. These parameters include elevator installed power, number of elevators, rated load, and passenger flow statistics. The passenger flow statistics include average daily passenger flow and peak hour passenger flow. Based on the elevator's installed power and rated load, calculate the unit load power of the elevator in the building to be diagnosed. Based on the average daily passenger flow and the peak hour passenger flow, calculate the passenger flow fluctuation coefficient of the elevators in the building to be diagnosed; Based on the unit load power and the passenger flow fluctuation coefficient, the energy consumption of the elevator category is calculated to obtain the elevator energy consumption.

9. The building energy efficiency benefit diagnosis and analysis method based on data profile matching as described in claim 1, characterized in that, Using the total power system electricity consumption, total building water consumption, and pump configuration parameters in the benefit analysis support data, the energy consumption of the power category is calculated to obtain the power energy consumption, including: Extract the total water consumption of the building to be diagnosed, the total power consumption of the power system, and the water pump configuration parameters from the benefit analysis support data. The total power consumption of the power system includes the power consumption of the water supply pump and the power consumption of other power equipment. The water supply demand flow rate of the building to be diagnosed is determined based on the total water consumption of the building, and the theoretical shaft power of the water supply pump in the building is calculated based on the water supply demand flow rate. The theoretical power consumption of the water pump is calculated based on the theoretical shaft power of the water pump and the water pump configuration parameters. Based on the theoretical power consumption of the water supply pump and the power consumption of other power equipment, the power consumption of the building to be diagnosed is determined.

10. A method for diagnosing and analyzing building energy efficiency benefits based on data profile matching, characterized in that: The method includes: In response to building energy efficiency diagnosis, the system obtains the building energy consumption data, building overview information, system parameters and operation pattern information of the target building input by the user, obtains benefit analysis support data, and uses a profile matching method to filter out the buildings to be diagnosed from the target buildings based on the benefit analysis support data. The building to be diagnosed is classified into energy consumption categories to obtain energy consumption categories. The energy consumption system categories include air conditioning, lighting, elevator, and power categories. Using the building envelope parameters and cold source system configuration information in the benefit analysis support data, the energy consumption of the air conditioning category is calculated to obtain the air conditioning energy consumption; Using the area of ​​each functional area, type of lighting fixture, and power in the benefit analysis support data, the energy consumption of the lighting category is calculated to obtain the lighting energy consumption; Using the elevator energy consumption parameters in the benefit analysis support data, the energy consumption of the elevator category is calculated to obtain the elevator energy consumption. The energy consumption of the power category is calculated using the total power consumption of the power system, the total water consumption of the building, and the water pump configuration parameters in the benefit analysis support data, thus obtaining the power energy consumption. The energy efficiency of the building to be diagnosed is evaluated based on the energy consumption of the air conditioning, lighting, elevator, and power systems.

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