Building energy optimal transformation strategy generation method and device, equipment and medium

Abstract

The invention relates to the technical field of artificial intelligence, and provides a building energy optimal transformation strategy generation method and device, equipment and a medium. According to the method, a plurality of machine learning models are constructed, and a training sample is constructed according to an urban building energy simulation result to perform integrated training on the plurality of machine learning models to obtain an agent model, so that the agent model can be directly utilized for prediction during subsequent building performance simulation, long-time energy consumption simulation is not required each time, and the processing efficiency is improved; and on the other hand, multi-target optimization processing is performed based on the proxy model and the parallel genetic algorithm plug-in to quickly generate the optimal transformation strategy of the target building, so that the quantization of the transformation result is realized, the method does not depend on empirical meaning, and the generated transformation strategy is more reasonable.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method, device, equipment and medium for generating an optimal building energy transformation strategy. Background Art

[0002] With the continuous advancement of urbanization, the urban heat island effect is becoming more and more serious, and the urban heat island effect has a significant impact on the energy consumption of urban buildings.

[0003] In response to the above problems, implementing effective building energy transformation is crucial to improving urban energy efficiency and reducing carbon emissions.

[0004] However, the existing building energy renovation plans are based only on empiricism, which cannot quantify the renovation effects, and cannot provide specific renovation strategies based on specific cases. At the same time, the existing building energy renovation plans also need to be based on energy consumption simulation as the basis for evaluation, so there is still the problem of being time-consuming. Summary of the invention

[0005] In view of the above, it is necessary to provide a method, device, equipment and medium for generating an optimal building energy transformation strategy, aiming to solve the problem of being unable to efficiently and specifically generate an optimal building energy transformation strategy.

[0006] A method for generating an optimal building energy transformation strategy, the method comprising: Obtain the building data of the target city and build a city model; Acquire typical annual meteorological data of the city and greening data of the target city, and input the city model, the typical annual meteorological data of the city and the greening data into an urban meteorological generator to obtain meteorological data of the target city under the influence of the urban heat island effect; Calling a building energy consumption simulation engine to simulate the building energy consumption of the target city based on the city model, the typical meteorological data of the city in a typical year, or the meteorological data under the influence of the urban heat island effect, to obtain an urban building energy simulation result of the target city; Constructing training samples according to the urban building energy simulation results to perform integrated training on multiple machine learning models to obtain a proxy model; In response to an instruction to generate a renovation strategy for a target building in the target city, acquiring building data of the target building based on the data dimension of the training sample, and calling a parallel genetic algorithm plug-in to generate a plurality of initial renovation strategies for the target building; The building data and the multiple initial renovation strategies are input into the proxy model, and based on the output of the parallel genetic algorithm plug-in and the proxy model, the multiple initial renovation strategies are subjected to multi-objective optimization processing to obtain the optimal renovation strategy for the target building.

[0007] According to a preferred embodiment of the present invention, acquiring the building data of the target city and constructing the city model comprises: Acquire the architectural features, building surrounding features and building thermal features of the specified type of buildings in the target city to construct the city model; Wherein, the building features are used to characterize the geometric properties of the building; Wherein, the building surrounding features are used to characterize the geometric properties of surrounding buildings; Wherein, the building thermal characteristics are used to characterize the thermal properties of the building's maintenance structural materials; Among them, the designated types of buildings include office buildings and commercial buildings.

[0008] According to a preferred embodiment of the present invention, constructing training samples based on the urban building energy simulation results to perform integrated training on multiple machine learning models to obtain a proxy model includes: Generate architectural features, building surrounding features and building thermal features of each building according to the urban building energy simulation results; Obtain the retrofit strategy and total building load for each building; Retrieving a preset number of the machine learning models; Taking the architectural features, building surrounding features and building thermal features of each building, as well as the renovation strategy of each building as inputs, and taking the total building load of each building as a training target, the machine learning model is integratedly trained to obtain the proxy model; During the training process, the Bayesian optimization algorithm is used to optimize the model parameters.

[0009] According to a preferred embodiment of the present invention, after obtaining the proxy model, the method further includes: Performing sensitivity testing, interpretability testing, and uncertainty testing on the proxy model; When the proxy model fails the sensitivity test, and / or the interpretability test, and / or the uncertainty test, performing optimization training on the proxy model; When the proxy model fails the sensitivity test, it is determined that the data volume of the training samples is insufficient, the training samples are supplemented based on the urban building energy simulation results, and the proxy model is optimized and trained based on the supplemented training samples.

[0010] According to a preferred embodiment of the present invention, the calling of a parallel genetic algorithm plug-in to generate a plurality of initial transformation strategies for the target building comprises: Obtaining multiple transformation parameters under the transformation strategy and a sampling range of values ​​of each transformation parameter; Based on the parallel genetic algorithm plug-in, the value of each transformation parameter is sampled within the corresponding sampling range to obtain multiple groups of transformation parameter sets; Each group of transformation parameters is taken as an initial transformation strategy to obtain the multiple initial transformation strategies.

[0011] According to a preferred embodiment of the present invention, the building data and the multiple initial renovation strategies are input into the agent model, and the multiple initial renovation strategies are subjected to multi-objective optimization processing based on the output of the parallel genetic algorithm plug-in and the agent model to obtain the optimal renovation strategy for the target building, including: Construct constraints based on ideal renovation costs; determining a predicted total building load for each initial retrofit strategy based on outputs of the proxy model; Under the constraints, the parallel genetic algorithm plug-in is used to obtain the initial renovation strategy with the lowest predicted total building load as the optimal renovation strategy.

[0012] According to a preferred embodiment of the present invention, the constraint conditions constructed based on the ideal transformation cost include: The constraints are constructed using the following formula: ; Among them, Cost represents the ideal transformation cost, Indicates the transformation strategy adopted i The cost weight of each transformation parameter, It means that when the transformation cost of each transformation parameter in the corresponding sampling range changes linearly under the adopted transformation strategy, i The linear cost index corresponding to the value of the transformation parameter, n is a positive integer; in, ; in, Indicates i The value of the transformation parameter; Indicates i The upper limit of the sampling range of the transformation parameters; Indicates i The lower limit of the sampling range of the transformation parameters; Indicates i The correlation between the values ​​of the transformation parameters and the transformation cost, among which, when When iThe transformation parameters are positively correlated with the transformation cost. i The larger the value of a transformation parameter is, the higher the corresponding transformation cost is. When i The transformation parameters are negatively correlated with the transformation cost. i The smaller the value of a transformation parameter, the higher the corresponding transformation cost.

[0013] A device for generating an optimal building energy transformation strategy, the device comprising: An acquisition unit, used to acquire building data of a target city and construct a city model; An input unit is used to obtain typical annual meteorological data of the city and greening data of the target city, and input the city model, the typical annual meteorological data of the city and the greening data into an urban meteorological generator to obtain meteorological data of the target city under the influence of the urban heat island effect; A simulation unit, configured to call a building energy consumption simulation engine to simulate the building energy consumption of the target city based on the city model, the typical meteorological data of the city in a typical year, or the meteorological data under the influence of the urban heat island effect, to obtain an urban building energy simulation result of the target city; A training unit, used to construct training samples according to the urban building energy simulation results to perform integrated training on multiple machine learning models to obtain a proxy model; A generating unit, configured to, in response to a transformation strategy generating instruction for a target building in the target city, obtain building data of the target building based on the data dimension of the training sample, and call a parallel genetic algorithm plug-in to generate a plurality of initial transformation strategies for the target building; The optimization unit is used to input the building data and the multiple initial transformation strategies into the proxy model, and perform multi-objective optimization processing on the multiple initial transformation strategies based on the output of the parallel genetic algorithm plug-in and the proxy model to obtain the optimal transformation strategy of the target building.

[0014] A computer device, comprising: a memory storing at least one instruction; and A processor executes instructions stored in the memory to implement the method for generating the optimal building energy transformation strategy.

[0015] A computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in a computer device to implement the method for generating an optimal building energy renovation strategy.

[0016] It can be seen from the above technical solutions that, on the one hand, the energy simulation results of urban buildings are obtained based on multi-dimensional data, and training samples are constructed according to the energy simulation results of urban buildings to integrate and train multiple machine learning models to obtain a proxy model. In this way, the proxy model can be directly used for prediction in subsequent building performance simulations, without the need for long-term energy consumption simulation each time, thereby improving processing efficiency; on the other hand, multi-objective optimization processing is performed based on the proxy model and the parallel genetic algorithm plug-in to quickly generate the optimal renovation strategy for the target building, which not only realizes the quantification of the renovation results, but also does not rely on empiricism, making the generated renovation strategy more reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a preferred embodiment of the method for generating the optimal building energy transformation strategy of the present invention; Figure 2 It is a functional module diagram of a preferred embodiment of the device for generating the optimal building energy transformation strategy of the present invention; Figure 3 It is a structural schematic diagram of a computer device of a preferred embodiment of the method for generating the optimal building energy transformation strategy of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 FIG. 1 is a flowchart of a preferred embodiment of the method for generating the optimal building energy transformation strategy of the present invention. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0020] The method for generating the optimal building energy transformation strategy is applied to one or more computer devices, which are devices that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and whose hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0021] The computer device may be a computer or a cloud platform, and the computer device may call rhino and grasshopper devices.

[0022] Among them, Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0023] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0024] The network where the computer device is located includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0025] S10, acquiring building data of the target city to construct a city model.

[0026] In this embodiment, acquiring the building data of the target city and constructing the city model includes: Acquire the architectural features, building surrounding features and building thermal features of the specified type of buildings in the target city to construct the city model; Wherein, the building features are used to characterize the geometric properties of the building; Wherein, the building surrounding features are used to characterize the geometric properties of surrounding buildings; The building thermal characteristics are used to characterize the thermal properties of the building's maintenance structural materials (including wall materials, glass, etc.); Among them, the designated types of buildings include office buildings and commercial buildings.

[0027] For example, the building features may include, but are not limited to, a combination of one or more of the following features: total building area, building volume, building height, window area, shape coefficient, etc.

[0028] The total building area refers to the total area of ​​all floors of the building; The building volume is the total volume of space occupied by the building, including the total volume of all floors; The building height refers to the distance from the highest point of the building to the ground or foundation; The window area mentioned here refers to the total area of ​​all windows of the building; The shape coefficient refers to the ratio of the surface area of ​​a building in contact with the air to its volume.

[0029] For example, the building surrounding features may include, but are not limited to, one or a combination of the following features: the number of surrounding buildings, building density, distance between buildings, sky visibility factor, and perimeter-to-area ratio.

[0030] The number of surrounding buildings mentioned refers to the total number of buildings within the buffer zone; The building density refers to the ratio of the sum of the building base areas within the buffer zone to the area of ​​the buffer zone; The distance between buildings refers to the average distance between buildings in the buffer zone; The sky visibility factor refers to the ratio of the sky area observed on the ground to the total visible area; The perimeter-to-area ratio refers to the ratio of the perimeter of the building's outer contour to the building's floor area.

[0031] For example, the building thermal characteristics may include, but are not limited to, one or a combination of the following characteristics: external wall thermal resistance value, internal wall thermal resistance value, etc.

[0032] The building features and the building surrounding features can be calculated through a GIS (Geographic Information System) platform.

[0033] In the above embodiment, the city model is constructed based on multi-dimensional variables of building characteristics, building surrounding characteristics and building thermal characteristics, so that the established model can more accurately express the properties of urban buildings.

[0034] S11, obtaining typical annual meteorological data of the city and greening data of the target city, and inputting the city model, the typical annual meteorological data of the city and the greening data into an Urban Weather Generator (UWG) to obtain meteorological data of the target city under the influence of the urban heat island effect.

[0035] In this embodiment, the typical meteorological year data of the city may be derived from a meteorological database with high credibility, such as the EnergyPlus typical meteorological year database.

[0036] In this embodiment, the greening data may include greening indicators such as tree coverage rate, grass coverage rate, etc.

[0037] In this embodiment, the city model, the typical annual meteorological data of the city and the greening data are input into the city meteorological generator, so that the meteorological data of the target city under the influence of the urban heat island effect can be quickly generated.

[0038] S12, calling a building energy consumption simulation engine to simulate the building energy consumption of the target city based on the city model, the typical meteorological data of the city in a typical year, or the meteorological data under the influence of the urban heat island effect, to obtain an urban building energy simulation result of the target city.

[0039] For example, the building energy consumption simulation engine may be the EnergyPlus engine. By executing UBEM (Urban Building Energy Modeling), the energy consumption in the following four scenarios can be simulated: energy consumption of office buildings under typical urban meteorological data, energy consumption of commercial buildings under typical urban meteorological data, energy consumption of office buildings under meteorological data under the influence of urban heat island effect, and energy consumption of commercial buildings under meteorological data under the influence of urban heat island effect.

[0040] After obtaining the urban building energy simulation results of the target city, in order to further ensure the accuracy of the model, the building energy consumption report of the target city can also be obtained, and the accuracy of the urban building energy simulation results can be determined by comparing the urban building energy simulation results with the building energy consumption report, thereby further ensuring the availability of the urban building energy simulation results.

[0041] S13, constructing training samples according to the urban building energy simulation results to perform integrated training on multiple machine learning models to obtain a proxy model.

[0042] In this embodiment, the training samples are constructed according to the urban building energy simulation results to perform integrated training on multiple machine learning models to obtain the proxy model, including: Generate architectural features, building surrounding features and building thermal features of each building according to the urban building energy simulation results; Obtain the retrofit strategy and total building load for each building; Retrieving a preset number of the machine learning models; Taking the architectural features, building surrounding features and building thermal features of each building, as well as the renovation strategy of each building as inputs, and taking the total building load of each building as a training target, the machine learning model is integratedly trained to obtain the proxy model; During the training process, the Bayesian optimization algorithm is used to optimize the model parameters.

[0043] The preset number can be configured according to actual accuracy requirements.

[0044] For example, when the preset number is 6, the machine learning model may include 6 integrated learning algorithm models: BaggingClassifier (BR), Extremely randomized trees (ETR), RandomForest Regression (RFR), Gradient Boosting Regression (GBR), Adaptive Boosting (ADB), and Extreme Gradient Boosting (XGB). By integrating the above 6 models, the outputs of the 6 models can be combined to obtain more accurate prediction results.

[0045] This embodiment uses a machine learning model to build a data-driven energy model, uses building characteristics, building surrounding characteristics and building thermal characteristics as model characteristics, and uses the energy consumption in various scenarios obtained through batch simulation of the urban building energy simulation results (such as the energy consumption of office buildings under typical urban meteorological data, the energy consumption of commercial buildings under typical urban meteorological data, the energy consumption of office buildings under meteorological data under the influence of urban heat island effect, and the energy consumption of commercial buildings under meteorological data under the influence of urban heat island effect) as targets to train machine learning models respectively, and integrates the outputs of each model based on an integrated algorithm to make the prediction results of the obtained proxy model more accurate.

[0046] Furthermore, in order to verify the accuracy of the proxy model, the basic model Support Vector Regression (SVR) can be used as a comparison to verify the advantages of the proxy model obtained by ensemble learning.

[0047] In this embodiment, after obtaining the proxy model, the method further includes: Performing sensitivity testing, interpretability testing, and uncertainty testing on the proxy model; When the proxy model fails the sensitivity test, and / or the interpretability test, and / or the uncertainty test, performing optimization training on the proxy model; When the proxy model fails the sensitivity test, it is determined that the data volume of the training samples is insufficient, the training samples are supplemented based on the urban building energy simulation results, and the proxy model is optimized and trained based on the supplemented training samples.

[0048] The sensitivity test refers to whether the amount of data is sufficient. For example, for each set of training, the amount of data is increased by 2% each time to train multiple models. If the model performance tends to be stable, it means that the training data is sufficient.

[0049] The interpretability test refers to testing the contribution of each feature. For example, the SHAP (SHapley Additive exPlanations) method based on game theory can be used to explain the best model for determining the contribution of each feature to the energy performance of the building.

[0050] The uncertainty detection refers to the average output and confidence of the detection model. For example, the average output and confidence of the model can be determined based on the prediction results of the series of models by performing 95% bootstrap sampling on the training data set and training a certain number of models.

[0051] Through the above embodiments, the accuracy of the model can be further verified.

[0052] S14, in response to an instruction to generate a renovation strategy for a target building in the target city, acquiring building data of the target building based on the data dimension of the training sample, and calling a parallel genetic algorithm plug-in to generate a plurality of initial renovation strategies for the target building.

[0053] In this embodiment, the transformation strategy generation instruction may be triggered by relevant staff such as an architectural designer.

[0054] For example, the building characteristics, building surrounding characteristics and building thermal characteristics of the target building can be obtained according to the data dimensions of the training samples.

[0055] In this embodiment, the calling of the parallel genetic algorithm plug-in to generate multiple initial transformation strategies for the target building includes: Obtaining multiple transformation parameters under the transformation strategy and a sampling range of values ​​of each transformation parameter; Based on the parallel genetic algorithm plug-in, the value of each transformation parameter is sampled within the corresponding sampling range to obtain multiple groups of transformation parameter sets; Each group of transformation parameters is taken as an initial transformation strategy to obtain the multiple initial transformation strategies.

[0056] For example, multiple transformation parameters under the transformation strategy and the sampling range of each transformation parameter can be found in the following table: Transformation parameters Sampling range North window-to-wall ratio [0.2-0.8] East window-to-wall ratio [0.2-0.8] South window-to-wall ratio [0.2-0.8] West window-to-wall ratio [0.2-0.8] External wall thermal resistance [0.667-2.80] Roof thermal resistance [0.604-2.350] Floor thermal resistance [0.667-1.42] Glass U value [1.0-5.2] Heat gain coefficient of glass [0.18- 0.52] Furthermore, based on the parallel genetic algorithm plug-in, a north window-to-wall ratio of 0.2, an east window-to-wall ratio of 0.2, a south window-to-wall ratio of 0.2, a west window-to-wall ratio of 0.2, an exterior wall thermal resistance of 0.667, a roof thermal resistance of 0.604, a floor thermal resistance of 0.667, a glass U value of 1, and a glass heat gain coefficient of 0.18 can be extracted from the sampling range of each renovation parameter to form an initial renovation strategy; a north window-to-wall ratio of 0.8, an east window-to-wall ratio of 0.8, a south window-to-wall ratio of 0.8, a west window-to-wall ratio of 0.8, an exterior wall thermal resistance of 2.80, a roof thermal resistance of 2.350, a floor thermal resistance of 1.42, a glass U value of 5.2, and a glass heat gain coefficient of 0.52 can be extracted to form an initial renovation strategy, and so on, ultimately forming multiple initial renovation strategies.

[0057] The parallel genetic algorithm plug-in can be the iGeneS parallel genetic algorithm plug-in developed by Tongji University SD-II Lab, which is used to perform multi-objective optimization tasks. iGeneS is a new generation of genetic algorithm plug-in developed specifically for deploying machine learning proxy models. It implements batch calls of proxy models through matrix operations, which greatly shortens the optimization time compared to traditional optimization algorithm plug-ins.

[0058] Therefore, this embodiment can automatically extract the values ​​of each transformation parameter to form multiple initial transformation strategies through a parallel genetic algorithm without the need for manual experience, and thus is more efficient.

[0059] S15, inputting the building data and the multiple initial renovation strategies into the proxy model, and performing multi-objective optimization processing on the multiple initial renovation strategies based on the parallel genetic algorithm plug-in and the output of the proxy model to obtain the optimal renovation strategy for the target building.

[0060] In this embodiment, the building data and the multiple initial renovation strategies are input into the agent model, and the multiple initial renovation strategies are subjected to multi-objective optimization processing based on the output of the parallel genetic algorithm plug-in and the agent model to obtain the optimal renovation strategy for the target building, including: Construct constraints based on ideal renovation costs; determining a predicted total building load for each initial retrofit strategy based on outputs of the proxy model; Under the constraints, the parallel genetic algorithm plug-in is used to obtain the initial renovation strategy with the lowest predicted total building load as the optimal renovation strategy.

[0061] Specifically, the constraints constructed based on the ideal transformation cost include: The constraints are constructed using the following formula: ; Among them, Cost represents the ideal transformation cost, Indicates the transformation strategy adopted i The cost weight of each transformation parameter, It means that when the transformation cost of each transformation parameter in the corresponding sampling range changes linearly under the adopted transformation strategy, i The linear cost index corresponding to the value of the transformation parameter, n is a positive integer; in, ; in, Indicates i The value of the transformation parameter; Indicates i The upper limit of the sampling range of the transformation parameters; Indicates i The lower limit of the sampling range of the transformation parameters; Indicates i The correlation between the values ​​of the transformation parameters and the transformation cost, among which, when When i The transformation parameters are positively correlated with the transformation cost. i The larger the value of a transformation parameter is, the higher the corresponding transformation cost is. When i The transformation parameters are negatively correlated with the transformation cost. i The smaller the value of a transformation parameter, the higher the corresponding transformation cost.

[0062] Among them, when , it means that the i-th transformation parameter has nothing to do with the transformation cost.

[0063] In the above embodiment, the agent model is optimized with the total building load and renovation cost as the targets. While ensuring a low total building load, it can also avoid the convergence of all renovation parameters to the thermal optimal values, resulting in excessive costs, so that the generated optimal renovation strategy can achieve an ideal balance between the total building load and the renovation cost.

[0064] By using this embodiment, the optimal building energy transformation strategy of a typical city can be obtained, and the building energy transformation plan of similar cities can be studied based on the optimal building energy transformation strategy of the typical city. For example, by studying the optimal building energy transformation strategy of Shenzhen, it is expected to provide a reference for other large cities that also suffer from the heat island effect.

[0065] It can be seen from the above technical solutions that, on the one hand, the energy simulation results of urban buildings are obtained based on multi-dimensional data, and training samples are constructed according to the energy simulation results of urban buildings to integrate and train multiple machine learning models to obtain a proxy model. In this way, the proxy model can be directly used for prediction in subsequent building performance simulations, without the need for long-term energy consumption simulation each time, thereby improving processing efficiency; on the other hand, multi-objective optimization processing is performed based on the proxy model and the parallel genetic algorithm plug-in to quickly generate the optimal renovation strategy for the target building, which not only realizes the quantification of the renovation results, but also does not rely on empiricism, making the generated renovation strategy more reasonable.

[0066] like Figure 2 As shown, it is a functional module diagram of a preferred embodiment of the device for generating the optimal energy transformation strategy for a building of the present invention. The device for generating the optimal energy transformation strategy for a building 11 comprises an acquisition unit 110, an input unit 111, a simulation unit 112, a training unit 113, a generation unit 114, and an optimization unit 115. The module / unit referred to in the present invention refers to a series of computer program segments that can be executed by a processor and can perform fixed functions, which are stored in a memory. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0067] The acquisition unit 110 is used to acquire the building data of the target city to construct a city model; The input unit 111 is used to obtain typical annual urban meteorological data and greening data of the target city, and input the city model, the typical annual urban meteorological data and the greening data into the urban meteorological generator to obtain meteorological data of the target city under the influence of the urban heat island effect; The simulation unit 112 is used to call the building energy consumption simulation engine to simulate the building energy consumption of the target city based on the city model, the typical meteorological data of the city or the meteorological data under the influence of the urban heat island effect, so as to obtain the urban building energy simulation result of the target city; The training unit 113 is used to construct training samples according to the urban building energy simulation results to perform integrated training on multiple machine learning models to obtain a proxy model; The generating unit 114 is used for, in response to the instruction for generating a transformation strategy for a target building in the target city, acquiring the building data of the target building based on the data dimension of the training sample, and calling the parallel genetic algorithm plug-in to generate a plurality of initial transformation strategies for the target building; The optimization unit 115 is used to input the building data and the multiple initial transformation strategies into the agent model, and perform multi-objective optimization processing on the multiple initial transformation strategies based on the output of the parallel genetic algorithm plug-in and the agent model to obtain the optimal transformation strategy for the target building.

[0068] It can be seen from the above technical solutions that, on the one hand, the energy simulation results of urban buildings are obtained based on multi-dimensional data, and training samples are constructed according to the energy simulation results of urban buildings to integrate and train multiple machine learning models to obtain a proxy model. In this way, the proxy model can be directly used for prediction in subsequent building performance simulations, without the need for long-term energy consumption simulation each time, thereby improving processing efficiency; on the other hand, multi-objective optimization processing is performed based on the proxy model and the parallel genetic algorithm plug-in to quickly generate the optimal renovation strategy for the target building, which not only realizes the quantification of the renovation results, but also does not rely on empiricism, making the generated renovation strategy more reasonable.

[0069] like Figure 3 The figure is a schematic diagram of the structure of the computer device of the preferred embodiment of the method for generating the optimal building energy transformation strategy of the present invention.

[0070] The computer device 1 may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a building energy optimal renovation strategy generation program.

[0071] Those skilled in the art will appreciate that the schematic diagram is merely an example of the computer device 1 and does not constitute a limitation on the computer device 1. The computer device 1 may be a bus-type structure or a star-type structure. The computer device 1 may also include more or less other hardware or software than shown in the diagram, or a different arrangement of components. For example, the computer device 1 may also include input and output devices, network access devices, etc.

[0072] It should be noted that the computer device 1 is only an example, and other existing or future electronic products that are suitable for the present invention should also be included in the protection scope of the present invention and included here by reference.

[0073] The memory 12 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 12 may be an internal storage unit of the computer device 1, such as a mobile hard disk of the computer device 1. In other embodiments, the memory 12 may also be an external storage device of the computer device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 1. Further, the memory 12 may also include both an internal storage unit of the computer device 1 and an external storage device. The memory 12 may not only be used to store application software and various types of data installed in the computer device 1, such as the code of the building energy optimal transformation strategy generation program, but also be used to temporarily store data that has been output or is to be output.

[0074] In some embodiments, the processor 13 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 13 is the control core (Control Unit) of the computer device 1, and uses various interfaces and lines to connect various components of the entire computer device 1, and executes various functions and processes data of the computer device 1 by running or executing programs or modules stored in the memory 12 (for example, executing a program for generating a building energy optimal transformation strategy, etc.), and calling data stored in the memory 12.

[0075] The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-mentioned embodiments of the method for generating the optimal building energy transformation strategy, for example Figure 1 Steps shown.

[0076] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to implement the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into an acquisition unit 110, an input unit 111, a simulation unit 112, a training unit 113, a generation unit 114, and an optimization unit 115.

[0077] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, and includes a number of instructions for enabling a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor to execute the part of the method for generating the optimal building energy transformation strategy described in each embodiment of the present invention.

[0078] If the module / unit integrated in the computer device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware devices through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of each of the above-mentioned method embodiments can be implemented.

[0079] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, etc.

[0080] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0081] The blockchain referred to in this invention is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the blockchain underlying platform, platform product service layer, and application service layer.

[0082] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The bus is represented by only one straight line, but it does not mean that there is only one bus or one type of bus. The bus is configured to realize the connection and communication between the memory 12 and at least one processor 13, etc.

[0083] Although not shown, the computer device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 13 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, and power status indicators. The computer device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0084] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used to establish a communication connection between the computer device 1 and other computer devices.

[0085] Optionally, the computer device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the computer device 1 and to display a visual user interface.

[0086] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0087] Figure 3 Only the computer device 1 having components 12-13 is shown, and it can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the computer device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0088] Combination Figure 1 The memory 12 in the computer device 1 stores a plurality of instructions to implement a method for generating an optimal building energy transformation strategy, and the processor 13 can execute the plurality of instructions to implement: Obtain the building data of the target city and build a city model; Acquire typical annual meteorological data of the city and greening data of the target city, and input the city model, the typical annual meteorological data of the city and the greening data into an urban meteorological generator to obtain meteorological data of the target city under the influence of the urban heat island effect; Calling a building energy consumption simulation engine to simulate the building energy consumption of the target city based on the city model, the typical meteorological data of the city in a typical year, or the meteorological data under the influence of the urban heat island effect, to obtain an urban building energy simulation result of the target city; Constructing training samples according to the urban building energy simulation results to perform integrated training on multiple machine learning models to obtain a proxy model; In response to an instruction to generate a renovation strategy for a target building in the target city, acquiring building data of the target building based on the data dimension of the training sample, and calling a parallel genetic algorithm plug-in to generate a plurality of initial renovation strategies for the target building; The building data and the multiple initial renovation strategies are input into the proxy model, and based on the output of the parallel genetic algorithm plug-in and the proxy model, the multiple initial renovation strategies are subjected to multi-objective optimization processing to obtain the optimal renovation strategy for the target building.

[0089] Specifically, the specific implementation method of the processor 13 for the above instructions can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0090] It should be noted that the data involved in this case were all obtained legally.

[0091] 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 illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0092] The present invention can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0093] The modules described as separate components may or may not be physically separated, and the components shown as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0095] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0096] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0097] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the present invention can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for generating an optimal building energy transformation strategy, characterized in that: The method for generating the optimal building energy transformation strategy includes: Obtain the building data of the target city and build a city model; Acquire typical annual meteorological data of the city and greening data of the target city, and input the city model, the typical annual meteorological data of the city and the greening data into an urban meteorological generator to obtain meteorological data of the target city under the influence of the urban heat island effect; Calling a building energy consumption simulation engine to simulate the building energy consumption of the target city based on the city model, the typical meteorological data of the city in a typical year, or the meteorological data under the influence of the urban heat island effect, to obtain an urban building energy simulation result of the target city; Constructing training samples according to the urban building energy simulation results to perform integrated training on multiple machine learning models to obtain a proxy model; In response to an instruction to generate a renovation strategy for a target building in the target city, acquiring building data of the target building based on the data dimension of the training sample, and calling a parallel genetic algorithm plug-in to generate a plurality of initial renovation strategies for the target building; The building data and the multiple initial renovation strategies are input into the proxy model, and based on the output of the parallel genetic algorithm plug-in and the proxy model, the multiple initial renovation strategies are subjected to multi-objective optimization processing to obtain the optimal renovation strategy for the target building.

2. The method for generating the optimal building energy transformation strategy according to claim 1, characterized in that: The step of acquiring the building data of the target city and constructing the city model comprises: Acquire the architectural features, building surrounding features and building thermal features of the specified type of buildings in the target city to construct the city model; Wherein, the building features are used to characterize the geometric properties of the building; Wherein, the building surrounding features are used to characterize the geometric properties of surrounding buildings; Wherein, the building thermal characteristics are used to characterize the thermal properties of the building's maintenance structural materials; Among them, the designated types of buildings include office buildings and commercial buildings.

3. The method for generating the optimal building energy transformation strategy according to claim 2, characterized in that: The training samples are constructed according to the urban building energy simulation results to perform integrated training on multiple machine learning models to obtain the proxy model, including: Generate architectural features, building surrounding features and building thermal features of each building according to the urban building energy simulation results; Obtain the retrofit strategy and total building load for each building; Retrieving a preset number of the machine learning models; Taking the building characteristics, building surrounding characteristics and building thermal characteristics of each building as inputs and the total building load of each building as a training target, the machine learning model is integratedly trained to obtain the proxy model; During the training process, the Bayesian optimization algorithm is used to optimize the model parameters.

4. The method for generating the optimal building energy transformation strategy according to claim 3, characterized in that: After obtaining the proxy model, the method further includes: Performing sensitivity testing, interpretability testing, and uncertainty testing on the proxy model; When the proxy model fails the sensitivity test, and / or the interpretability test, and / or the uncertainty test, performing optimization training on the proxy model; When the proxy model fails the sensitivity test, it is determined that the data volume of the training samples is insufficient, the training samples are supplemented based on the urban building energy simulation results, and the proxy model is optimized and trained based on the supplemented training samples.

5. The method for generating the optimal building energy transformation strategy according to claim 3, characterized in that: The calling of the parallel genetic algorithm plug-in to generate multiple initial transformation strategies for the target building includes: Obtaining multiple transformation parameters under the transformation strategy and a sampling range of values ​​of each transformation parameter; Based on the parallel genetic algorithm plug-in, the value of each transformation parameter is sampled within the corresponding sampling range to obtain multiple groups of transformation parameter sets; Each group of transformation parameters is taken as an initial transformation strategy to obtain the multiple initial transformation strategies.

6. The method for generating the optimal building energy transformation strategy according to claim 5, characterized in that: The step of inputting the building data and the multiple initial renovation strategies into the proxy model, and performing multi-objective optimization processing on the multiple initial renovation strategies based on the output of the parallel genetic algorithm plug-in and the proxy model to obtain the optimal renovation strategy for the target building includes: Construct constraints based on ideal renovation costs; determining a predicted total building load for each initial retrofit strategy based on outputs of the proxy model; Under the constraints, the parallel genetic algorithm plug-in is used to obtain the initial renovation strategy with the lowest predicted total building load as the optimal renovation strategy.

7. The method for generating the optimal building energy transformation strategy according to claim 6, characterized in that: The constraints constructed based on the ideal transformation cost include: The constraints are constructed using the following formula: ； Among them, Cost represents the ideal transformation cost, Indicates the transformation strategy adopted i The cost weight of each transformation parameter, It means that when the transformation cost of each transformation parameter in the corresponding sampling range changes linearly under the adopted transformation strategy, i The linear cost index corresponding to the value of the transformation parameter, n is a positive integer; in, ; in, Indicates i The value of the transformation parameter; Indicates i The upper limit of the sampling range of the transformation parameters; Indicates i The lower limit of the sampling range of the transformation parameters; Indicates i The correlation between the values ​​of the transformation parameters and the transformation cost, among which, when When i The transformation parameters are positively correlated with the transformation cost. i The larger the value of a transformation parameter is, the higher the corresponding transformation cost is. When i The transformation parameters are negatively correlated with the transformation cost. i The smaller the value of a transformation parameter, the higher the corresponding transformation cost.

8. A device for generating an optimal building energy transformation strategy, characterized in that: The building energy optimal transformation strategy generating device comprises: An acquisition unit, used to acquire building data of a target city and construct a city model; An input unit, used to obtain typical annual meteorological data of the city and greening data of the target city, and input the city model, the typical annual meteorological data of the city and the greening data into an urban meteorological generator to obtain meteorological data of the target city under the influence of the urban heat island effect; A simulation unit, used for calling a building energy consumption simulation engine to simulate the building energy consumption of the target city based on the city model, the typical meteorological data of the city in a typical year, or the meteorological data under the influence of the urban heat island effect, to obtain an urban building energy simulation result of the target city; A training unit, used to construct training samples according to the urban building energy simulation results to perform integrated training on multiple machine learning models to obtain a proxy model; A generating unit, configured to, in response to a transformation strategy generating instruction for a target building in the target city, obtain building data of the target building based on the data dimension of the training sample, and call a parallel genetic algorithm plug-in to generate a plurality of initial transformation strategies for the target building; The optimization unit is used to input the building data and the multiple initial transformation strategies into the proxy model, and perform multi-objective optimization processing on the multiple initial transformation strategies based on the output of the parallel genetic algorithm plug-in and the proxy model to obtain the optimal transformation strategy of the target building.

9. A computer device, characterized in that: The computer device comprises: a memory storing at least one instruction; and A processor executes instructions stored in the memory to implement the method for generating the optimal building energy transformation strategy as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in a computer device to implement the method for generating an optimal building energy renovation strategy as described in any one of claims 1 to 7.

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