Building prototype generation method based on city big data

Abstract

The invention discloses a building prototype generation method based on urban big data. The method comprises the following steps: step 1, calculating each characteristic parameter of a residential land and each characteristic parameter of a residential building; 2, deleting redundant characteristic parameters based on the correlation between the characteristic parameters of the residential land and the residential building; determining clustering characteristic parameters of the residential land and the residential building for clustering on the basis of correlation between the reserved characteristic parameters of the residential land and the residential building and the energy consumption predicted value of the residential building; step 3, performing two-stage clustering based on the determined residential land clustering center and residential building clustering center to obtain an optimal residential building clustering result focused by the optimal residential land clustering center so as to generate a building prototype set; 4, respectively calculating building energy demand indexes based on the building prototype set; and when the comprehensive value of the building energy demand index is smaller than a set threshold, repeating the step 3 to regenerate the building prototype set. And the building prototype meets urban building energy demand prediction.

Description

Technical Field

[0001] The invention belongs to the technical field of urban building model construction, relates to the operation and management stage of urban buildings, and is suitable for generating and constructing urban-scale building prototype models. Background Art

[0002] In recent years, as urban building forms have become increasingly diversified, the differences between buildings have brought great challenges to the construction of urban-scale building models and energy demand analysis. This highlights the key role of urban-scale building prototype construction, which can accurately capture the energy consumption characteristics of different building types and support the evaluation and analysis of the overall energy performance of urban building complexes. In addition, standardized urban building prototypes also provide a scientific basis for urban planning and energy-saving policies, and help optimize the design and implementation of energy-saving strategies. However, there is still a lack of a complete urban-scale model system, as well as objective calculation methods and standards to effectively guide the analysis and prototype construction of building energy demand.

[0003] In the prior art, the automatic generation method of urban building complex morphology based on artificial intelligence and the generation method of building complex based on building vector contours both generate urban residential building complex models based on the vector contours and height morphology of the buildings, and the selected building feature parameters focus on geometric morphology and building topological structure. The building prototypes generated by this method fail to fully consider the non-geometric topological factors in urban planning (such as green space ratio, nesting density, land morphology and other important indicators in the field of urban planning), which have a significant impact on building demand analysis. Therefore, ignoring these indicators may lead to inaccurate assessment of building complex energy consumption, limiting the application of the model in actual urban scale modeling. In addition, the comprehensive regional building complex load prediction method and the regional building complex dynamic evolution load prediction method both realize energy demand analysis by constructing representative building complexes. Although they can optimize and reduce the modeling workload to a certain extent, their classification basis is often relatively general and rough. These technologies fail to widely cover the diversity of building morphology at the urban scale, especially when facing complex and diverse urban environments, they lack pertinence and adaptability. This limitation makes the existing models insufficient in dealing with large-scale urban building energy consumption analysis, and it is difficult to provide accurate energy evaluation results. Therefore, it is urgent to develop a model construction method that can take into account the architectural diversity at the scale of urban architectural big data and the impact of urban planning indicators on architectural form, so as to improve the construction accuracy and energy consumption representativeness of architectural prototypes at the urban scale. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a building prototype generation method based on urban big data, which combines urban planning indicators with building form parameters to construct a building prototype model that is representative of energy consumption. This model can be used to accurately and effectively build an urban building energy demand prediction model.

[0005] The present invention adopts the following technical solution.

[0006] The present invention proposes a method for generating a building prototype based on urban big data, comprising:

[0007] Step 1, obtaining the number and area of ​​each residential land, and obtaining the building base area, number of floors and height of each residential building belonging to each residential land, so as to calculate various characteristic parameters of the residential land and various characteristic parameters of the residential building; wherein the characteristic parameters of the residential land include: volume ratio, building density, building height limit, land compactness index, and high-rise proportion, and the characteristic parameters of the residential building include: building shape coefficient;

[0008] Step 2, based on the correlation between each characteristic parameter of the residential land and each characteristic parameter of the residential building, the redundant characteristic parameters are deleted; after the deletion process, based on the correlation between each characteristic parameter of the retained residential land and the predicted energy consumption value of the residential building, the clustering characteristic parameters of the residential land used for clustering are determined, and at the same time, based on the correlation between each characteristic parameter of the retained residential building and the predicted energy consumption value of the residential building, the clustering characteristic parameters of the residential building used for clustering are determined;

[0009] Step 3, respectively determine the cluster center of residential land and the cluster center of residential buildings based on the evaluation indicators of residential land and residential buildings; based on the determined cluster center of residential land and the cluster center of residential buildings, use the cluster characteristic parameters of residential land and residential buildings to perform two-stage clustering to obtain the optimal clustering result of residential buildings focused on by the optimal cluster center of residential land; generate a building prototype set based on the optimal clustering result of residential buildings focused on by the optimal cluster center of residential land;

[0010] Step 4, based on the building prototype set, calculate the building energy demand indicators separately, including: the peak load density of cooling and heating demand throughout the year, and the total intensity of cooling and heating load demand throughout the year; when the comprehensive value of the building energy demand indicators is less than the set threshold, repeat step 3 to regenerate the building prototype set; when the comprehensive value of the building energy demand indicators is not less than the set threshold, output the building prototype set.

[0011] Preferably, the floor area ratio satisfies the following relationship:

[0012]

[0013] In the formula, FAR i is the plot ratio of the i-th residential land, BBA i,j is the building base area of ​​the jth residential building belonging to the i-th residential land, Storeys i,j is the number of floors of the jth residential building belonging to the i-th residential land, LAAi is the area of ​​the i-th residential land, m i is the set of residential buildings belonging to the i-th residential land;

[0014] The building density satisfies the following relationship:

[0015]

[0016] In the formula, BCR i is the building density of the i-th residential land;

[0017] The building height limit meets the following relationship:

[0018] MAH i =max(BUH i,j ) (3)

[0019] Where, MAH i is the building height limit for the i-th residential land, BUH i,j is the height of the jth residential building belonging to the i-th residential land;

[0020] The land compactness index satisfies the following relationship:

[0021]

[0022] In the formula, NCI i is the compactness index of the ith residential land, LAP i is the perimeter of the ith residential land, LAA i is the area of ​​the ith residential land;

[0023] The proportion of high-rise buildings satisfies the following relationship:

[0024]

[0025] In the formula, HRP i is the proportion of high-rise buildings in the i-th residential land;

[0026] The building shape coefficient satisfies the following relationship:

[0027]

[0028] Where, Building surface area i,j It is the surface area of ​​the jth residential building belonging to the i-th residential land that is in contact with the outdoor atmosphere.

[0029] Preferably, step 2 comprises:

[0030] When the Spearman correlation coefficient between the characteristic parameters of the residential land and the characteristic parameters of the residential building is less than the set first limit value, delete the characteristic parameters of the corresponding residential land and the characteristic parameters of the residential building; otherwise, retain the characteristic parameters of the corresponding residential land and the characteristic parameters of the residential building;

[0031] When the Spearman correlation coefficient between the retained characteristic parameters of the residential land and the energy consumption prediction values of each residential building belonging to the residential land is not less than the set second limit value, determine the retained characteristic parameters of the residential land as the clustering characteristic parameters; otherwise, the retained characteristic parameters of the residential land are the standby characteristic parameters;

[0032] When the Spearman correlation coefficient between the retained characteristic parameters of the residential building and the energy consumption prediction value of the residential building is not less than the set second limit value, determine the retained characteristic parameters of the residential building as the clustering characteristic parameters; otherwise, the characteristic parameters of the building land are the standby characteristic parameters.

[0033] Preferably, the first limit value is 0.5.

[0034] Preferably, the second limit value is 0.7.

[0035] Preferably, step 3 includes:

[0036] Step 3.1, determine the residential land clustering center according to the various evaluation indicators of the residential land, and determine the residential building clustering center according to the various evaluation indicators of the residential building;

[0037] Step 3.2, extract n 1 residential land clustering centers as the first cluster centers of the residential land, satisfying 3 ≤ n 1 < N, where N is the number of residential land clustering centers; use the K-medoids clustering method to cluster the clustering characteristic parameters of the residential building to the first cluster centers of the residential land, and use the Bayesian information criterion scoring method to score the clustering results to obtain the first score corresponding to the first cluster centers of the residential land; randomly extract residential land clustering centers different from each first cluster center of the residential land, use the K-medoids clustering method to cluster the clustering characteristic parameters of the residential buildings that have been clustered to the first cluster centers of the residential land to the extracted residential land clustering centers, and use the Bayesian information criterion scoring method to score the clustering results to obtain the second scores corresponding to each first cluster center of the residential land; when the second score is greater than or equal to the first score, retain the extracted residential land clustering centers; otherwise, when the second score is less than the first score, delete the extracted residential land clustering centers;

[0038] After repeating the above operations, obtain the optimal residential land clustering center and the residential buildings focused by the optimal residential land clustering center;

[0039] Step 3.3, using the K-medoids clustering method, cluster the residential buildings focused on the optimal residential land cluster center to the residential building cluster center, and use the Bayesian information criterion scoring method to score the clustering results. When the score is greater than the set threshold, the optimal residential building clustering result focused on the optimal residential land cluster center is obtained;

[0040] Step 3.4, generating a set of building prototypes based on the optimal residential building clustering results focused on the optimal residential land clustering center.

[0041] Preferably, the evaluation indicators of residential land include but are not limited to: land area, plot ratio, building density, building height limit, green space ratio, land compactness index, unit density, and high-rise ratio; the evaluation indicators of residential buildings include but are not limited to: projected area, building height, and building shape coefficient;

[0042] The number N of residential land cluster centers is consistent with the number of evaluation indicators of residential land; the number M of residential building cluster centers is consistent with the number of evaluation indicators of residential buildings.

[0043] Preferably, in step 3.2, the residential land evaluation index of the optimal residential land cluster center is used as the key index for evaluating residential land and energy consumption prediction.

[0044] Preferably, the score of the clustering result scored by the Bayesian Information Criterion scoring method satisfies the following relationship:

[0045]

[0046] In the formula, is the maximum log-likelihood function of the i-th group of clustering feature parameters, is the clustering feature parameter x of the i-th group i Belongs to cluster C i The maximum likelihood estimate of the clustering characteristic parameter x of the i-th group i Including clustering characteristic parameters of residential land and residential buildings; n i is the i-th cluster C i is the number of samples in , and p is the number of features of clustering.

[0047] Preferably, in step 3.3, the score of the clustering result scored by the Bayesian Information Criterion scoring method is processed into a range of [0, 1], and the threshold value is set to be no less than 0.62.

[0048] Preferably, the building prototype set includes K building prototypes. For the kth building prototype, k=1, 2, ..., K, the building energy demand index satisfies the following relationship:

[0049]

[0050]

[0051] In the formula, is the total intensity of the annual cooling and heating load demand of the k-th building prototype, is the peak load density of cooling and heating demand of the k-th building prototype throughout the year, h is the hour, It is the hourly energy consumption intensity of the building.

[0052] Preferably, the normalized value of the weighted sum of the total intensity of the cooling and heating load demand of the k-th building prototype throughout the year and the peak load density of the cooling and heating demand throughout the year is used as the comprehensive value of the building energy demand index; and the threshold is set to 0.88.

[0053] The present invention is also a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0054] The present invention is also a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0055] The beneficial effect of the present invention is that, compared with the prior art, the method proposed by the present invention combines the two-stage clustering method based on urban big data with the building performance simulation automation program, and proposes a building prototype generation method for the urban scale. Compared with the prior art, the method proposed by the present invention can be oriented to the construction of the building group magnitude model and energy demand analysis at the urban scale, making full use of the advantages of clustering algorithms in data mining and feature extraction, significantly reducing the computational cost of building energy demand simulation at the urban scale, improving the scientificity and accuracy of system evaluation, and more objectively, comprehensively and efficiently evaluating the building energy demand characteristics at the urban scale. The present invention obtains a residential building prototype set through a two-stage clustering method of plot-building. Compared with the prior art, the data-driven clustering algorithm introduced by the present invention can realize completely objective feature analysis and has good scalability. It can be applied to building groups of different regional scales, ensuring that the calculation results truly reflect the differences between the energy properties of different residential buildings, and realizing the extraction of building energy demand characteristics at the urban scale at an acceptable calculation cost. This method can more efficiently evaluate the demand for urban energy and realize the quantification and indexation of urban building energy consumption indicators.

[0056] Through the method proposed in the present invention, building prototypes can be constructed more accurately, thereby providing a scientific basis for urban-scale energy demand forecasting and helping to achieve the optimization and sustainable development of urban energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The present invention provides a flow chart of a method for generating a building prototype based on urban big data. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0059] The present invention provides a big data-based building prototype generation method suitable for urban scale, which is aimed at building energy demand analysis. Compared with the prior art, the method proposed in the present invention not only takes into account the land use characteristics, but also takes into account the impact of non-geometric topological factors inside and outside the building on the building energy consumption, and can be applied to energy consumption demand prediction at all stages from planning and design to building operation.

[0060] like Figure 1 As shown, including:

[0061] Step 1, obtain the number and area of ​​each residential land, obtain the building base area, number of floors and height of each residential building belonging to each residential land, so as to calculate various characteristic parameters of the residential land and various characteristic parameters of the residential buildings; wherein the characteristic parameters of the residential land include: plot ratio, building density, building height limit, land compactness index, and high-rise proportion, and the characteristic parameters of the residential buildings include: building shape coefficient.

[0062] Taking into account that residential quarters are the most common land units in built-up residential communities, the land and building clustering method proposed in the present invention refers to a two-stage clustering method from residential land (in units of plots) to residential buildings. This method can be applied to the generation of prototypes of urban building complexes of different scales, and comprehensively considers the influence of building geometric parameters and non-geometric parameters on energy consumption. Therefore, the clustering parameter group of land and buildings is first determined.

[0063] Compared with the prior art, the present invention makes a more systematic combination of the calculation of the existing residential land characteristic parameters, accurately describes the coupling relationship between land characteristics and building forms, and the calculation methods of some characteristic parameters are shown in the following formulas:

[0064] The floor area ratio satisfies the following relationship:

[0065]

[0066] In the formula, FAR i is the plot ratio of the i-th residential land, BBA i,jis the building base area of ​​the jth residential building belonging to the i-th residential land, Storeys i,j is the number of floors of the jth residential building belonging to the i-th residential land, LAA i is the area of ​​the i-th residential land, m i is the set of residential buildings belonging to the i-th residential land.

[0067] The building density satisfies the following relationship:

[0068]

[0069] In the formula, BCR i is the building density of the i-th residential land.

[0070] The building height limit meets the following relationship:

[0071] MAH i =max(BUH i,j ) (3)

[0072] Where, MAH i is the building height limit for the i-th residential land, BUH i,j is the height of the j-th residential building belonging to the i-th residential land.

[0073] The land compactness index satisfies the following relationship:

[0074]

[0075] In the formula, NCI i is the compactness index of the ith residential land, LAP i is the perimeter of the ith residential land, LAA i is the area of ​​the i-th residential land.

[0076] The proportion of high-rise buildings satisfies the following relationship:

[0077]

[0078] In the formula, HRP i is the proportion of high-rise buildings in the i-th residential land.

[0079] The building shape coefficient satisfies the following relationship:

[0080]

[0081] Where, Building surface area i,j It is the surface area of ​​the jth residential building belonging to the i-th residential land that is in contact with the outdoor atmosphere.

[0082] Step 2, based on the correlation between each characteristic parameter of residential land and each characteristic parameter of residential buildings, delete redundant characteristic parameters; after the deletion process, based on the correlation between each characteristic parameter of the retained residential land and the predicted energy consumption value of the residential building, determine the clustering characteristic parameters of the residential land used for clustering, and at the same time, based on the correlation between each characteristic parameter of the retained residential building and the predicted energy consumption value of the residential building, determine the clustering characteristic parameters of the residential buildings used for clustering.

[0083] The Spearman correlation coefficient is a typical indicator for evaluating the correlation between continuous or ordered variables. It does not make special requirements on the distribution characteristics of the original variables, has a wide range of applications, and can be applied even to discrete variables. Therefore, considering that the Spearman correlation coefficient has a stronger data adaptability, the present invention introduces the Spearman correlation coefficient to analyze the strength of the correlation between each characteristic parameter and between the characteristic parameter and the energy consumption prediction value. This method avoids overfitting by eliminating redundant variables. Among them, the numerical range of the Spearman correlation coefficient of any two parameters is [-1,1], and its absolute value is less than 0.3, indicating that the correlation is negligible; the absolute value is not less than 0.3 and less than 0.5, indicating low correlation; the absolute value is not less than 0.5 and less than 0.7, indicating moderate correlation; the absolute value is not less than 0.7 and less than 0.9, indicating high correlation; the absolute value is not less than 0.9 and less than 1, indicating extremely high correlation. It is worth noting that the present invention uses the Spearman correlation coefficient to calculate the correlation between two parameters, which is a non-restrictive and preferred choice. Those skilled in the art can select different correlation coefficients and calculation methods according to the characteristics of the parameters to be analyzed for correlation.

[0084] Before clustering urban residential building data, it is necessary to conduct correlation analysis and screening of characteristic parameters, including:

[0085] When the Spearman correlation coefficient between the characteristic parameters of residential land and the characteristic parameters of residential buildings is less than the set first limit, the corresponding characteristic parameters of residential land and the characteristic parameters of residential buildings are deleted; otherwise, the corresponding characteristic parameters of residential land and the characteristic parameters of residential buildings are retained; wherein the first limit is 0.5;

[0086] When the Spearman correlation coefficient between the characteristic parameter of the reserved residential land and the predicted energy consumption value of each residential building belonging to the residential land is not less than the set second limit value, the characteristic parameter of the reserved residential land is determined to be the cluster characteristic parameter; otherwise, the characteristic parameter of the reserved residential land is the spare characteristic parameter; wherein the second limit value is 0.7;

[0087] When the Spearman correlation coefficient between the characteristic parameters of the retained residential buildings and the predicted energy consumption value of the residential buildings is not less than the set second limit value, the characteristic parameters of the retained residential buildings are determined to be cluster characteristic parameters; otherwise, the characteristic parameters of the retained building land are determined to be standby characteristic parameters; wherein the second limit value is 0.7;

[0088] Through the above deletion processing, the influence of redundant parameters on the credibility of cluster analysis results is eliminated, and only parameters closely related to building energy consumption prediction are extracted and used as cluster feature parameters for subsequent cluster analysis and modeling. At the same time, parameters that are generally correlated with building energy consumption prediction but are still related are retained as backup feature parameters. When there are problems such as a small number of cluster feature parameters, the backup feature parameters can be used as approximate cluster feature parameters through data processing methods, thereby improving the reliability of cluster analysis results.

[0089] Step 3, respectively determine the residential land cluster center and the residential building cluster center based on the evaluation indicators of residential land and residential buildings; based on the determined residential land cluster center and residential building cluster center, use the clustering characteristic parameters of residential land and residential buildings to perform two-stage clustering to obtain the optimal residential building clustering result focused on by the optimal residential land cluster center; generate a building prototype set based on the optimal residential building clustering result focused on by the optimal residential land cluster center.

[0090] Compared with the prior art, the present invention proposes a two-stage X-medoids clustering method, which combines the Bayesian information criterion scoring method with the K-medoids clustering method, wherein the Bayesian information criterion scoring method evaluates and scores each cluster obtained by the K-medoids clustering method in each stage, and determines whether the clustering result is optimal based on the scoring result, thereby achieving completely objective acquisition of the best clustering result.

[0091] Among them, the Bayesian information criterion BIC satisfies the following relationship:

[0092]

[0093] In the formula, is the maximum log-likelihood function of the i-th group of clustering feature parameters, is the clustering feature parameter x of the i-th group i Belongs to cluster C i The maximum likelihood estimate of the clustering characteristic parameter x of the i-th group i Including clustering characteristic parameters of residential land and residential buildings; n i is the i-th cluster C i is the number of samples in , and p is the number of features of clustering.

[0094] Specifically, step 3 includes:

[0095] Step 3.1: Determine the clustering centers of residential land according to various evaluation indicators of residential land, and determine the clustering centers of residential buildings according to various evaluation indicators of residential buildings.

[0096] Specifically, the various evaluation indicators of residential land include but are not limited to: land area, plot ratio, building density, building height limit, green space rate, land compactness index, unit density, and high-rise proportion; the various evaluation indicators of residential buildings include but are not limited to: projected area, building height, and building shape coefficient.

[0097] The number N of the clustering centers of residential land is consistent with the number of the evaluation indicators of residential land; the number M of the clustering centers of residential buildings is consistent with the number of the evaluation indicators of residential buildings.

[0098] Step 3.2: Extract n 1 clustering centers of residential land as the first cluster centers of residential land, satisfying 3 ≤ n 1 < N. Use the K-medoids clustering method to cluster the clustering characteristic parameters of residential buildings for the first cluster centers of residential land, and use the Bayesian information criterion scoring method to score the clustering results to obtain the first score S 1 corresponding to the first cluster centers of residential land; Arbitrarily extract clustering centers of residential land different from the first cluster centers of residential land, use the K-medoids clustering method to cluster the clustering characteristic parameters of residential buildings that have been clustered in the first cluster centers of residential land for the extracted clustering centers of residential land, and use the Bayesian information criterion scoring method to score the clustering results to obtain the second score S 2 corresponding to the first cluster centers of residential land; When the second score is greater than or equal to the first score, retain the extracted clustering centers of residential land; otherwise, when the second score is less than the first score, delete the extracted clustering centers of residential land.

[0099] After repeating the above operations, the optimal clustering centers of residential land and the residential buildings focused by the optimal clustering centers of residential land are obtained.

[0100] Through the above operations, the division and determination of the optimal clusters of residential land are realized, and the optimization of the evaluation indicators of residential land is also realized. The evaluation indicators of residential land corresponding to the optimal clustering centers of residential land can be used as key indicators for evaluating residential land and energy consumption prediction.

[0101] Step 3.3, using the K-medoids clustering method, cluster the residential buildings focused on the optimal residential land cluster center into residential building cluster centers, and use the Bayesian information criterion scoring method to score the clustering results. When the score is greater than the set threshold, the optimal residential building clustering result focused on the optimal residential land cluster center is obtained.

[0102] In the embodiment, the score of the clustering result scored by the Bayesian Information Criterion scoring method is processed into a range of [0, 1], and the threshold value is set to be no less than 0.62.

[0103] The method proposed in the present invention uses the X-medoids clustering method to objectively divide the sample set into several typical clusters in the first stage of the plot feature clustering process. Residential buildings are considered to be the accompanying attributes of each individual plot. Although they are not selected as clustering features in this stage, they still need to be classified into each plot cluster. Afterwards, the second stage clustering process focuses on the residential building level. At the same time, Manhattan distance is selected as the measurement indicator for both stage clustering. Compared with the current clustering methods and energy consumption prediction models for the geometric topological characteristics of building complexes, the two-stage clustering method adopted by the present invention not only takes into account the land use characteristics, but also takes into account the building form, and introduces non-geometric parameters as clustering features, giving a systematic, complete, and quantifiable parameter description, which provides a more scientific and accurate reference scheme for energy consumption prediction at the scale of building complexes.

[0104] Step 3.4, generating a set of building prototypes based on the optimal residential building clustering results focused on the optimal residential land clustering center.

[0105] Step 4, based on the building prototype set, calculate the building energy demand indicators separately, including: the peak load density of cooling and heating demand throughout the year, and the total intensity of cooling and heating load demand throughout the year; when the comprehensive value of the building energy demand indicators is less than the set threshold, repeat step 3 to regenerate the building prototype set; when the comprehensive value of the building energy demand indicators is not less than the set threshold, output the building prototype set.

[0106] The building prototype set includes K building prototypes. For the kth building prototype, k = 1, 2, ..., K, its energy demand is calculated by energy demand simulation, that is, 8760 hourly cooling and heating loads. In general, building energy system designers only focus on the annual peak load and the annual total demand. Compared with the time series hourly cooling and heating load data, the cooling and heating demand per unit building area can eliminate the interference of the volume ratio on the energy demand index of the plot. Therefore, the present invention selects the annual cooling and heating demand peak load density (PLI) and the annual cooling and heating load demand total intensity (EUI) as the performance evaluation indicators of the cooling and heating energy demand of residential buildings, wherein the annual cooling and heating demand peak load density (PLI) is used to describe the peak load characteristics of the corresponding building prototype, and the annual cooling and heating load demand total intensity (EUI) is used to describe the load total demand characteristics of the corresponding building prototype. The introduction of the above indicators eliminates the influence of time series data, solves the problem of difficult comparison of energy demand data between plots, and takes into account the real-time load characteristics of the building and the annual cumulative load demand of the building, providing a quantifiable evaluation scheme for the energy demand prediction of the building complex scale.

[0107] The building energy demand index satisfies the following relationship:

[0108]

[0109] In the formula, is the total intensity of the annual cooling and heating load demand of the k-th building prototype, is the peak load density of cooling and heating demand of the k-th building prototype throughout the year, h is the hour, It is the hourly energy consumption intensity of the building.

[0110] The normalized value of the weighted sum of the total intensity of the cooling and heating load demand of the k-th building prototype throughout the year and the peak load density of the cooling and heating demand throughout the year is used as the comprehensive value of the building energy demand index; the threshold is set to 0.88.

[0111] The present invention provides important theoretical and technical support for the energy demand analysis of urban buildings during the operation and maintenance stage. The method of the present invention can provide an evaluation basis for the energy demand of urban-scale buildings, which is convenient for comparing schemes and making optimal decisions. In the present invention, a building prototype generation method based on urban big data is proposed, and the most suitable number of building prototypes is determined by using a two-stage clustering analysis method based on plots and buildings. Furthermore, according to the classification of building clusters and the building energy demand simulation method, the building performance of the building prototype set is simulated to obtain its energy demand characteristics. This method significantly reduces the computational cost of predicting and evaluating the energy demand of urban buildings.

[0112] By evaluating the energy demand characteristics of urban residential buildings, the present invention can provide a solid theoretical basis and technical support for the design and optimization of building energy systems, and help make more scientific optimization decisions for system design and operation, thereby achieving accurate configuration of energy system capacity. This not only helps to improve the reliability and economy of system operation, but also has important significance for achieving energy conservation and emission reduction goals.

[0113] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0114] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0115] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0116] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for generating building prototypes based on urban big data, characterized in that: include: Step 1, obtaining the number and area of ​​each residential land, and obtaining the building base area, number of floors and height of each residential building belonging to each residential land, so as to calculate various characteristic parameters of the residential land and various characteristic parameters of the residential building; wherein the characteristic parameters of the residential land include: volume ratio, building density, building height limit, land compactness index, and high-rise proportion, and the characteristic parameters of the residential building include: building shape coefficient; Step 2, based on the correlation between each characteristic parameter of the residential land and each characteristic parameter of the residential building, the redundant characteristic parameters are deleted; after the deletion process, based on the correlation between each characteristic parameter of the retained residential land and the predicted energy consumption value of the residential building, the clustering characteristic parameters of the residential land used for clustering are determined, and at the same time, based on the correlation between each characteristic parameter of the retained residential building and the predicted energy consumption value of the residential building, the clustering characteristic parameters of the residential building used for clustering are determined; Step 3, respectively determine the cluster center of residential land and the cluster center of residential buildings based on the evaluation indicators of residential land and residential buildings; based on the determined cluster center of residential land and the cluster center of residential buildings, use the cluster characteristic parameters of residential land and residential buildings to perform two-stage clustering to obtain the optimal clustering result of residential buildings focused on by the optimal cluster center of residential land; generate a building prototype set based on the optimal clustering result of residential buildings focused on by the optimal cluster center of residential land; Step 4, based on the building prototype set, calculate the building energy demand indicators separately, including: the peak load density of cooling and heating demand throughout the year, and the total intensity of cooling and heating load demand throughout the year; when the comprehensive value of the building energy demand indicators is less than the set threshold, repeat step 3 to regenerate the building prototype set; when the comprehensive value of the building energy demand indicators is not less than the set threshold, output the building prototype set.

2. The method for generating building prototypes based on urban big data according to claim 1, characterized in that: The floor area ratio satisfies the following relationship: In the formula, FAR i is the plot ratio of the i-th residential land, BBA i,j is the building base area of ​​the jth residential building belonging to the i-th residential land, Storeys i,j is the number of floors of the jth residential building belonging to the i-th residential land, LAA i is the area of ​​the i-th residential land, m i is the set of residential buildings belonging to the i-th residential land; The building density satisfies the following relationship: In the formula, BCR i is the building density of the i-th residential land; The building height limit meets the following relationship: MAH i =max(BUH i,j ) (3) Where, MAH i is the building height limit for the i-th residential land, BUH i,j is the height of the jth residential building belonging to the i-th residential land; The land compactness index satisfies the following relationship: In the formula, NCI i is the compactness index of the ith residential land, ALP i is the perimeter of the ith residential land; The proportion of high-rise buildings satisfies the following relationship: In the formula, HRP i is the proportion of high-rise buildings in the i-th residential land; The building shape coefficient satisfies the following relationship: Where, Building surface area i,j It is the surface area of ​​the jth residential building belonging to the i-th residential land that is in contact with the outdoor atmosphere.

3. The method for generating building prototypes based on urban big data according to claim 1 is characterized in that: Step 2 includes: When the Spearman correlation coefficient between the characteristic parameters of residential land and the characteristic parameters of residential buildings is less than the set first limit, the corresponding characteristic parameters of residential land and the characteristic parameters of residential buildings are deleted; otherwise, the corresponding characteristic parameters of residential land and the characteristic parameters of residential buildings are retained; When the Spearman correlation coefficient between the characteristic parameter of the reserved residential land and the predicted energy consumption value of each residential building belonging to the residential land is not less than the set second limit value, the characteristic parameter of the reserved residential land is determined to be the clustering characteristic parameter; otherwise, the characteristic parameter of the reserved residential land is the spare characteristic parameter; When the Spearman correlation coefficient between the characteristic parameters of the retained residential buildings and the predicted energy consumption values of the residential buildings is not less than the set second limit value, determine the characteristic parameters of the retained residential buildings as the clustering characteristic parameters; otherwise, the characteristic parameters of the retained building land are the alternative characteristic parameters.

4. A method for generating building prototypes based on urban big data according to claim 3, characterized in that The first limit value is 0.

5.

5. A method for generating building prototypes based on urban big data according to claim 3, characterized in that The second limit value is 0.

7.

6. A method for generating building prototypes based on urban big data according to claim 1, characterized in that Step 3 includes: Step 3.1, determine the clustering centers of residential land according to various evaluation indicators of residential land, and determine the clustering centers of residential buildings according to various evaluation indicators of residential buildings; Step 3.2, extract n1 clustering centers of residential land as the first cluster centers of residential land, where 3 ≤ n1 < N, and n is the number of clustering centers of residential land; use the K-medoids clustering method to cluster the clustering characteristic parameters of residential buildings with respect to the first cluster centers of residential land, and use the Bayesian information criterion scoring method to score the clustering results to obtain the first score corresponding to the first cluster centers of residential land; arbitrarily extract clustering centers of residential land different from each first cluster center of residential land, use the K-medoids clustering method to cluster the clustering characteristic parameters of the residential buildings that have been clustered in the first cluster centers of residential land with respect to the extracted clustering centers of residential land, and use the Bayesian information criterion scoring method to score the clustering results to obtain the second score corresponding to each first cluster center of residential land; when the second score is greater than or equal to the first score, retain the extracted clustering centers of residential land; otherwise, when the second score is less than the first score, delete the extracted clustering centers of residential land; After repeating the above operations, obtain the optimal clustering centers of residential land and the residential buildings focused by the optimal clustering centers of residential land; Step 3.3, use the K-medoids clustering method to cluster the residential buildings focused by the optimal clustering centers of residential land with respect to the clustering centers of residential buildings, and use the Bayesian information criterion scoring method to score the clustering results. When the score is greater than the set threshold value, obtain the optimal clustering result of the residential buildings focused by the optimal clustering centers of residential land; Step 3.4, generate a building prototype set based on the optimal clustering result of the residential buildings focused by the optimal clustering centers of residential land.

7. A method for generating building prototypes based on urban big data according to claim 6, characterized in that The various evaluation indicators of residential land include but are not limited to: land area, plot ratio, building density, building height limit, green space rate, land compactness index, unit density, and high-rise proportion; the various evaluation indicators of residential buildings include but are not limited to: projected area, building height, and building shape coefficient; The number N of clustering centers of residential land is consistent with the number of evaluation indicators of residential land; The number M of residential building cluster centers is consistent with the number of evaluation indicators of residential buildings.

8. The method for generating building prototypes based on urban big data according to claim 7 is characterized in that: In step 3.2, the residential land evaluation index of the optimal residential land cluster center is used as the key indicator for evaluating residential land and energy consumption prediction.

9. The method for generating building prototypes based on urban big data according to claim 6, characterized in that: The Bayesian information criterion scoring method scores the clustering results in the following relationship: In the formula, is the maximum log-likelihood function of the i-th group of clustering feature parameters, is the clustering feature parameter x of the i-th group i Belongs to cluster C i The maximum likelihood estimate of the clustering characteristic parameter x of the i-th group i Including clustering characteristic parameters of residential land and residential buildings; n i For cluster I i is the number of samples in , and p is the number of features of clustering.

10. The method for generating building prototypes based on urban big data according to claim 9, characterized in that: In step 3.3, the scores of the clustering results scored by the Bayesian Information Criterion scoring method are processed into the range of [0,1], and the threshold value is set to be no less than 0.

62.

11. The method for generating building prototypes based on urban big data according to claim 1, characterized in that: The building prototype set includes K building prototypes. For the kth building prototype, k = 1, 2, ..., K, the building energy demand index satisfies the following relationship: In the formula, is the total intensity of the annual cooling and heating load demand of the k-th building prototype, is the peak load density of cooling and heating demand of the k-th building prototype throughout the year, h is the hour, It is the hourly energy consumption intensity of the building.

12. The method for generating building prototypes based on urban big data according to claim 11, characterized in that: The normalized value of the weighted sum of the total intensity of the cooling and heating load demand of the k-th building prototype throughout the year and the peak load density of the cooling and heating demand throughout the year is used as the comprehensive value of the building energy demand index; the threshold is set to 0.

88.

13. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 12.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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