Green building intelligent evaluation method and system

Abstract

The invention discloses an intelligent evaluation method and system for a green building, and relates to the technical field of building evaluation, and the method comprises the steps: inputting obtained building data into a preset energy consumption prediction model, so as to obtain a building energy consumption value; comparing the building energy consumption value with a set energy consumption threshold value to obtain an evaluation label; inputting the building energy consumption value into a preset green building rating model based on the evaluation label to obtain a green building grade; the energy consumption prediction model is constructed to predict the energy consumption value of a certain building, and then the energy consumption value and the energy consumption threshold value are compared and analyzed, so that whether the building meets the basic requirement for evaluating the green building or not is preferentially judged, on the basis of existing model evaluation, invalid analysis of the model is reduced, and the evaluation efficiency is improved. Therefore, the evaluation efficiency of the model on a plurality of buildings is optimized, the accuracy and reliability of rating are effectively improved, and meanwhile, the complexity of historical data analysis is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of building assessment, and in particular to a green building intelligent assessment method and system. Background Art

[0002] Green buildings enable people to enjoy a healthier and more sustainable lifestyle by optimizing resource utilization, reducing energy consumption and minimizing environmental impact. They not only improve the comfort of living and working environments, but also play a positive role in addressing climate change and resource shortages. The evaluation standards for green buildings are receiving increasing attention. Green buildings not only require compliance with environmental protection principles during the design and construction stages, but also require efficient use of energy during operation. Therefore, accurately evaluating whether a building's energy consumption meets green building standards can greatly improve the construction efficiency of green buildings.

[0003] At present, most green building assessment methods often rely on manual data collection and historical data analysis, which not only leads to low assessment efficiency, but also may affect the reliability of assessment results due to inaccurate data. In addition, when dealing with complex building energy consumption data, existing assessment models are often unable to effectively distinguish between buildings that meet green building standards and those that do not, resulting in erroneous assessment results and waste of resources, and further leading to the situation where buildings are not green buildings after construction is completed. Furthermore, in the process of model assessment, when conducting green assessments on multiple groups of buildings, there is a phenomenon of low assessment efficiency and high computing power consumption. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a green building intelligent evaluation method and system.

[0005] A green building intelligent evaluation method, the method comprising:

[0006] Input the acquired building data into a preset energy consumption prediction model to obtain the building energy consumption value;

[0007] Compare the building energy consumption value with the established energy consumption value threshold to obtain an assessment label;

[0008] The building energy consumption value is input into a preset green building rating model based on the assessment label to obtain the green building grade.

[0009] Furthermore, the building data includes building drawing information and affiliated organization information.

[0010] Furthermore, the generation logic of the energy consumption prediction model is:

[0011] P1, obtaining multiple sets of energy consumption prediction historical data, and dividing the energy consumption prediction historical data into a first training set and a first test set, wherein the energy consumption historical data includes building data and its corresponding predicted building energy consumption value;

[0012] P2, constructing a first regression network, taking the building data in the first training set as input data of the first regression network, taking the corresponding building energy consumption values ​​in the first training set as output data, training the first regression network, and generating an initial first machine learning network;

[0013] P3, using the first test set to verify the initial first machine learning model, and outputting the initial first machine learning network that is less than or equal to a preset first test error threshold as a pre-built energy consumption prediction model.

[0014] Furthermore, the logic for obtaining the building energy consumption value is:

[0015] Collect n groups of historical energy consumption data associated with building data, the historical energy consumption data includes building area, building daily power consumption, building ventilation efficiency and thermal conductivity of building external materials, set standard values ​​of building daily power consumption, building ventilation efficiency and thermal conductivity of building external materials, substitute the historical energy consumption data into a preset building energy consumption value calculation model to generate a building energy consumption value, the logical formula of the building energy consumption value calculation model is: Where n>1 and n is an integer, E is the building energy consumption value, A i is the building area of ​​group i, K i is the daily electricity consumption of the building in group i, M k is the standard value of daily electricity consumption of buildings, G i is the building ventilation efficiency of group i, M g is the standard value of building ventilation efficiency, W i is the thermal conductivity of the building exterior material of group i, M w It is the standard value of thermal conductivity of building exterior materials.

[0016] Furthermore, the evaluation labels include green building labels and non-green building labels.

[0017] Furthermore, the building energy consumption value is compared with the established energy consumption value threshold to obtain the logic of the evaluation label: if the building energy consumption value is greater than the established energy consumption value threshold, a non-green building label is generated; if the building energy consumption value is less than or equal to the established energy consumption value threshold, a green building label is generated.

[0018] Furthermore, the logic of inputting the building energy consumption value into the preset green building rating model based on the evaluation tag is:

[0019] If a non-green building label is generated, the building energy consumption value of the building will not be input into the preset green building rating model. If a green building label is generated, the building energy consumption value of the building will be input into the preset green building rating model.

[0020] Furthermore, the generation logic of the green building rating model is:

[0021] Q1, obtaining historical building rating data, and dividing the historical building rating data into a second training set and a second test set, wherein the historical building rating data includes building energy consumption values ​​and their corresponding predicted green building grades;

[0022] Q2, construct a second regression network, use the building energy consumption values ​​in the second training set as input data of the second regression network, use the corresponding green building grades in the second training set as output data, train the second regression network, and generate an initial second machine learning network;

[0023] Q3. Use the second test set to verify the initial second machine learning model, and output the initial second machine learning network that is less than or equal to a preset second test error threshold as a pre-built green building rating model.

[0024] Furthermore, the logic for obtaining the green building grade is:

[0025] Obtain historical data of X groups of building energy consumption values, and perform green rating on the buildings based on the numerical values ​​of the historical data of building energy consumption values. Suppose the number of rating gradients is y, y≥3 and y is an integer.

[0026] A green building intelligent assessment system, used to implement any green building intelligent assessment method, the system comprising:

[0027] Consumption prediction module: used to input the acquired building data into the preset energy consumption prediction model to obtain the building energy consumption value;

[0028] The first evaluation module is used to compare the building energy consumption value with the established energy consumption value threshold to obtain an evaluation label;

[0029] The second assessment module: inputs the building energy consumption value into a preset green building rating model based on the assessment label to obtain the green building grade.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present invention proposes a green building intelligent evaluation method, comprising: inputting the acquired building data into a preset energy consumption prediction model to obtain the building energy consumption value; comparing the building energy consumption value with a predetermined energy consumption value threshold to obtain an evaluation label; inputting the building energy consumption value into a preset green building rating model based on the evaluation label to obtain the green building grade; the present invention predicts the energy consumption value of a certain building through the construction of an energy consumption prediction model, and then compares and analyzes the energy consumption value with the energy consumption threshold, so as to give priority to determining whether the building has the basic requirements for evaluating green buildings, and then reduces the invalid analysis of the model on the basis of the existing model evaluation, thereby optimizing the model's evaluation efficiency for multiple buildings; the present invention further inputs the building's energy consumption value into the green building rating model based on the evaluation label, so as to obtain the building's green grade; to ensure that only buildings that meet the green building standards can be included in the rating, effectively improving the accuracy and reliability of the rating, and at the same time, by optimizing the green building rating model, reducing the complexity of historical data analysis, and enhancing the systematicness and scientificity of the building green evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0033] Figure 1 A flow chart of a green building intelligent assessment method provided in Example 1 of the present invention;

[0034] Figure 2 This is a module diagram of a green building intelligent assessment system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Example 1

[0037] See also Figure 1 As shown, this embodiment discloses a green building intelligent assessment method, the method comprising:

[0038] S110, inputting the acquired building data into a preset energy consumption prediction model to obtain a building energy consumption value;

[0039] Specifically, building data includes building drawing information and affiliated organization information;

[0040] It is important to understand that: the architectural drawing information includes but is not limited to floor plans, elevations and sections; the architectural drawing information is obtained by collecting digital files of architectural designs (such as CAD or BIM models), and automatically extracting information such as floor plans, elevations and sections of the building using the API or image recognition technology of related software; the ancillary structure information is manually determined through the developer's house layout diagram to distinguish the ancillary structures, including but not limited to the area occupied by internal appliances, window areas and furniture areas.

[0041] Specifically, the generation logic of the energy consumption prediction model is:

[0042] P1, obtaining multiple sets of energy consumption prediction historical data, and dividing the energy consumption prediction historical data into a first training set and a first test set, wherein the energy consumption historical data includes building data and its corresponding predicted building energy consumption value;

[0043] Specifically, the logic for obtaining the building energy consumption value is:

[0044] Collect n groups of historical energy consumption data associated with building data, the historical energy consumption data includes building area, building daily power consumption, building ventilation efficiency and thermal conductivity of building external materials, set standard values ​​of building daily power consumption, building ventilation efficiency and thermal conductivity of building external materials, substitute the historical energy consumption data into a preset building energy consumption value calculation model to generate a building energy consumption value, the logical formula of the building energy consumption value calculation model is: ; Where n>1 and n is an integer, E is the building energy consumption value, is the building area of ​​the i-th group, is the daily electricity consumption of the building in group i, is the standard value of daily electricity consumption of buildings, is the building ventilation efficiency of group i, is the standard value of building ventilation efficiency, is the thermal conductivity of the building exterior materials of group i, is the standard value of thermal conductivity of building exterior materials;

[0045] It is important to understand that the greater the thermal conductivity, the lower the ventilation efficiency, which means that the indoor power consumption in summer is greater, and the energy consumption of the building will increase;

[0046] P2, constructing a first regression network, taking the building data in the first training set as input data of the first regression network, taking the corresponding building energy consumption values ​​in the first training set as output data, training the first regression network, and generating an initial first machine learning network;

[0047] P3, using the first test set to verify the initial first machine learning model, and outputting the initial first machine learning network that is less than or equal to a preset first test error threshold as a pre-built energy consumption prediction model.

[0048] S120, comparing the building energy consumption value with a predetermined energy consumption value threshold to obtain an evaluation label;

[0049] Specifically, the evaluation labels include green building labels and non-green building labels;

[0050] Specifically, the building energy consumption value is compared with the established energy consumption value threshold to obtain the logic of the evaluation label:

[0051] If the building energy consumption value is greater than the established energy consumption value threshold, a non-green building label is generated;

[0052] If the building energy consumption value is less than or equal to the established energy consumption value threshold, a green building label is generated.

[0053] It should be noted that: the green building label indicates that the predicted building energy consumption value meets the green building standards, and the non-green building label indicates that the predicted building energy consumption value does not meet the green building standards;

[0054] S130, inputting the building energy consumption value into a preset green building rating model based on the evaluation label to obtain a green building grade;

[0055] Specifically, the logic of inputting the building energy consumption value into the preset green building rating model based on the evaluation tag is:

[0056] If a non-green building label is generated, the building energy consumption value of the building will not be input into the preset green building rating model. If a green building label is generated, the building energy consumption value of the building will be input into the preset green building rating model.

[0057] It should be noted that when the green building grade is finally obtained, if no green building grade is generated for a certain building, it means that the building is not eligible for green building rating.

[0058] Specifically, the generation logic of the green building rating model is:

[0059] Q1, obtaining historical building rating data, and dividing the historical building rating data into a second training set and a second test set, wherein the historical building rating data includes building energy consumption values ​​and their corresponding predicted green building grades;

[0060] Specifically, the logic for obtaining the green building grade is as follows:

[0061] Obtain historical data of X groups of building energy consumption values, and perform green rating on the buildings based on the values ​​of the historical data of building energy consumption values. Let the number of rating gradients be y, y≥3 and y is an integer;

[0062] It should be noted that the smaller the historical data of the building energy consumption value, the more energy-saving and environmentally friendly the green building is, and the higher the green level.

[0063] Q2, construct a second regression network, use the building energy consumption values ​​in the second training set as input data of the second regression network, use the corresponding green building grades in the second training set as output data, train the second regression network, and generate an initial second machine learning network;

[0064] Q3. Use the second test set to verify the initial second machine learning model, and output the initial second machine learning network that is less than or equal to a preset second test error threshold as a pre-built green building rating model.

[0065] Example 2

[0066] See also Figure 2 As shown, based on the same inventive concept, this embodiment discloses a green building intelligent evaluation system, the system comprising:

[0067] The consumption prediction module S210 is used to input the acquired building data into a preset energy consumption prediction model to obtain the building energy consumption value;

[0068] Among them, building data includes building drawing information and affiliated organization information;

[0069] The generation logic of the energy consumption prediction model is as follows:

[0070] P1, obtaining multiple sets of energy consumption prediction historical data, and dividing the energy consumption prediction historical data into a first training set and a first test set, wherein the energy consumption historical data includes building data and its corresponding predicted building energy consumption value;

[0071] Among them, the logic for obtaining the building energy consumption value is:

[0072] Collect n groups of historical energy consumption data associated with building data, the historical energy consumption data includes building area, building daily power consumption, building ventilation efficiency and thermal conductivity of building external materials, set standard values ​​of building daily power consumption, building ventilation efficiency and thermal conductivity of building external materials, substitute the historical energy consumption data into a preset building energy consumption value calculation model to generate a building energy consumption value, the logical formula of the building energy consumption value calculation model is: ; Where n>1 and n is an integer, E is the building energy consumption value, is the building area of ​​the i-th group, is the daily electricity consumption of the building in group i, is the standard value of daily electricity consumption of buildings, is the building ventilation efficiency of group i, is the standard value of building ventilation efficiency, is the thermal conductivity of the building exterior materials of group i, is the standard value of thermal conductivity of building exterior materials;

[0073] P2, constructing a first regression network, taking the building data in the first training set as input data of the first regression network, taking the corresponding building energy consumption values ​​in the first training set as output data, training the first regression network, and generating an initial first machine learning network;

[0074] P3, using the first test set to verify the initial first machine learning model, and outputting the initial first machine learning network that is less than or equal to a preset first test error threshold as a pre-built energy consumption prediction model.

[0075] The first evaluation module S220 is used to compare the building energy consumption value with a predetermined energy consumption value threshold to obtain an evaluation label;

[0076] Wherein, the evaluation labels include green building labels and non-green building labels;

[0077] Among them, the logic of comparing the building energy consumption value with the established energy consumption value threshold to obtain the evaluation label is:

[0078] If the building energy consumption value is greater than the established energy consumption value threshold, a non-green building label is generated;

[0079] If the building energy consumption value is less than or equal to the established energy consumption value threshold, a green building label is generated.

[0080] The second evaluation module S230: inputs the building energy consumption value into a preset green building rating model based on the evaluation tag to obtain a green building grade;

[0081] The logic of inputting the building energy consumption value into the preset green building rating model based on the evaluation tag is as follows:

[0082] If a non-green building label is generated, the building energy consumption value of the building will not be input into the preset green building rating model. If a green building label is generated, the building energy consumption value of the building will be input into the preset green building rating model.

[0083] The generation logic of the green building rating model is as follows:

[0084] Q1, obtaining historical building rating data, and dividing the historical building rating data into a second training set and a second test set, wherein the historical building rating data includes building energy consumption values ​​and their corresponding predicted green building grades;

[0085] Among them, the logic for obtaining the green building grade is:

[0086] Get the historical data of X groups of building energy consumption values, and rate the buildings based on the values ​​of the historical data of building energy consumption values. Let the number of rating gradients be y, y≥3 and y is an integer.

[0087] Q2, construct a second regression network, use the building energy consumption values ​​in the second training set as input data of the second regression network, use the corresponding green building grades in the second training set as output data, train the second regression network, and generate an initial second machine learning network;

[0088] Q3: Use the second test set to verify the initial second machine learning model, and output the initial second machine learning network that is less than or equal to the preset second test error threshold as the pre-built green building rating model.

[0089] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired network or a wireless network. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD) or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0090] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0091] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0092] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a green building intelligent assessment method and system. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0093] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0095] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0096] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A green building intelligent evaluation method, characterized in that: The method comprises: Input the acquired building data into a preset energy consumption prediction model to obtain the building energy consumption value; Compare the building energy consumption value with the established energy consumption value threshold to obtain an assessment label; The building energy consumption value is input into a preset green building rating model based on the assessment label to obtain the green building grade.

2. A green building intelligent evaluation method according to claim 1, characterized in that: The building data includes building drawing information and affiliated organization information.

3. A green building intelligent assessment method according to claim 2, characterized in that: The generation logic of the energy consumption prediction model is: P1, obtaining multiple sets of energy consumption prediction historical data, and dividing the energy consumption prediction historical data into a first training set and a first test set, wherein the energy consumption historical data includes building data and its corresponding predicted building energy consumption value; P2, constructing a first regression network, taking the building data in the first training set as input data of the first regression network, taking the corresponding building energy consumption values ​​in the first training set as output data, training the first regression network, and generating an initial first machine learning network; P3, using the first test set to verify the initial first machine learning model, and outputting the initial first machine learning network that is less than or equal to a preset first test error threshold as a pre-built energy consumption prediction model.

4. A green building intelligent assessment method according to claim 3, characterized in that: The logic for obtaining building energy consumption values ​​is: Collect n groups of historical energy consumption data associated with building data, the historical energy consumption data includes building area, building daily power consumption, building ventilation efficiency and thermal conductivity of building external materials, set standard values ​​of building daily power consumption, building ventilation efficiency and thermal conductivity of building external materials, substitute the historical energy consumption data into a preset building energy consumption value calculation model to generate a building energy consumption value, the logical formula of the building energy consumption value calculation model is: ; Where n>1 and n is an integer, E is the building energy consumption value, is the building area of ​​the i-th group, is the daily electricity consumption of the building in group i, is the standard value of daily electricity consumption of buildings, is the building ventilation efficiency of group i, is the standard value of building ventilation efficiency, is the thermal conductivity of the building exterior materials of group i, It is the standard value of thermal conductivity of building exterior materials.

5. A green building intelligent assessment method according to claim 4, characterized in that: The evaluation labels include green building labels and non-green building labels.

6. A green building intelligent assessment method according to claim 5, characterized in that: The logic of comparing the building energy consumption value with the established energy consumption value threshold to obtain the evaluation label is as follows: if the building energy consumption value is greater than the established energy consumption value threshold, a non-green building label is generated; If the building energy consumption value is less than or equal to the established energy consumption value threshold, a green building label is generated.

7. A green building intelligent assessment method according to claim 6, characterized in that: The logic of inputting the building energy consumption value into the preset green building rating model based on the evaluation tag is: If a non-green building label is generated, the building energy consumption value of the building will not be input into the preset green building rating model. If a green building label is generated, the building energy consumption value of the building will be input into the preset green building rating model.

8. A green building intelligent assessment method according to claim 7, characterized in that: The generation logic of the green building rating model is: Q1, obtaining historical building rating data, and dividing the historical building rating data into a second training set and a second test set, wherein the historical building rating data includes building energy consumption values ​​and their corresponding predicted green building grades; Q2, construct a second regression network, use the building energy consumption values ​​in the second training set as input data of the second regression network, use the corresponding green building grades in the second training set as output data, train the second regression network, and generate an initial second machine learning network; Q3. Use the second test set to verify the initial second machine learning model, and output the initial second machine learning network that is less than or equal to a preset second test error threshold as a pre-built green building rating model.

9. A green building intelligent assessment method according to claim 8, characterized in that: The logic for obtaining green building grades is as follows: Obtain historical data of X groups of building energy consumption values, and perform green rating on the buildings based on the numerical values ​​of the historical data of building energy consumption values. Suppose the number of rating gradients is y, y≥3 and y is an integer.

10. A green building intelligent assessment system, used to execute any one of the green building intelligent assessment methods of claims 1-9, characterized in that: The system comprises: Consumption prediction module: used to input the acquired building data into the preset energy consumption prediction model to obtain the building energy consumption value; The first evaluation module is used to compare the building energy consumption value with the established energy consumption value threshold to obtain an evaluation label; The second assessment module: inputs the building energy consumption value into a preset green building rating model based on the assessment label to obtain the green building grade.

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