Building energy consumption simulation method, system and device based on big data and BIM

Abstract

The application is suitable for new generation information technology industry and digital economy, and particularly relates to a building energy consumption simulation method, system and equipment based on big data and BIM. The building energy consumption simulation method based on big data and BIM comprises the following steps: obtaining a building energy consumption dataset based on big data; analyzing the building energy consumption dataset to obtain energy consumption fluctuation; generating building design parameters to be tested based on BIM; analyzing the building design parameters to be tested and the building energy consumption dataset to obtain analogous building energy consumption; and simulating and analyzing the analogous building energy consumption and the energy consumption fluctuation to obtain building energy consumption information to be tested. The method deeply analyzes building energy consumption in multiple dimensions, overcomes the limitations of traditional evaluation methods, provides regional and overall energy consumption evaluation perspectives, combines with fine modeling of BIM technology, realizes multi-dimensional and dynamic energy consumption analysis, and thus improves the accuracy and reliability of building energy consumption prediction.

Description

Technical Field

[0001] This application belongs to the fields of new-generation information technology industry and digital economy technology, and in particular relates to a building energy consumption simulation method, system and equipment based on big data and BIM. Background Technology

[0002] With increasing global focus on energy efficiency and environmental protection, the management and optimization of building energy consumption has become a crucial issue in the construction industry. Buildings consume a significant amount of energy throughout their lifecycle for purposes such as heating, cooling, lighting, and other electrical equipment. According to the International Energy Agency (IEA), buildings account for approximately 36% of global energy consumption. Therefore, accurately predicting building energy consumption is of great importance for achieving energy conservation and emission reduction goals.

[0003] However, traditional building energy consumption assessment methods often rely on static theoretical models and fail to fully consider dynamic factors in actual operation. Furthermore, traditional energy consumption assessments are usually based on data from a single building, lacking a regional and holistic perspective. In addition, fluctuations in energy prices have a significant impact on building operating costs. Therefore, traditional building energy consumption assessment methods have certain limitations, resulting in insufficient accuracy and flexibility. Summary of the Invention

[0004] This application provides a building energy consumption simulation method, system, and device based on big data and BIM, which can solve the problems of insufficient accuracy and flexibility of traditional building energy consumption assessment methods.

[0005] In a first aspect, embodiments of this application provide a building energy consumption simulation method based on big data and BIM, including:

[0006] Based on big data, a building energy consumption dataset is obtained; wherein, the building energy consumption dataset is used to reflect the types and energy consumption data of other buildings in the area where the building under test is located;

[0007] The energy consumption data set is analyzed to obtain energy consumption fluctuations; wherein, the energy consumption fluctuations are used to reflect the degree of fluctuation in energy prices.

[0008] Based on BIM, design parameters for the building to be measured are generated; wherein, the design parameters for the building to be measured include geometry, material properties and system configuration;

[0009] The design parameters of the building to be tested are analyzed with the building energy consumption dataset to obtain the analog building energy consumption; wherein, the analog building energy consumption is used to reflect the energy consumption of buildings with the same design parameters as the building to be tested in the building energy consumption dataset;

[0010] According to the analog building energy consumption and the energy consumption fluctuation, simulation analysis is performed to obtain the to-be-measured building energy consumption information, wherein the to-be-measured building energy consumption information is used to reflect the energy consumption situation of the to-be-measured building.

[0011] The technical solutions described above in the embodiments of the present application have at least the following technical effects:

[0012] The building energy consumption simulation method based on big data and BIM provided in the present application obtains a building energy consumption dataset reflecting the types and energy consumption data of other buildings in the region where the to-be-measured building is located based on big data, then performs analysis according to the building energy consumption dataset to obtain an energy consumption fluctuation reflecting the fluctuation degree of the energy price, generates to-be-measured building design parameters including geometric shapes, material properties and system configurations based on BIM, then performs analysis on the to-be-measured building design parameters and the building energy consumption dataset to obtain an analog building energy consumption reflecting the energy consumption situation of a building similar to the to-be-measured building, and finally performs simulation analysis on the analog building energy consumption and the energy consumption fluctuation to obtain to-be-measured building energy consumption information reflecting the energy consumption situation of the to-be-measured building. This method provides a regional and overall energy consumption evaluation perspective by analyzing the data of other buildings in the region, which helps to identify commonalities and differences, and in combination with the three-dimensional visualization and data correlation provided by BIM, the building energy consumption can be analyzed in multiple dimensions, and the fluctuation of the energy price is considered to simulate the building energy consumption. This method not only overcomes the limitations of traditional evaluation methods, but also provides strong support for building energy efficiency management and optimization, and promotes the process of green building and sustainable development. By providing a regional and overall energy consumption evaluation perspective and combining the refined modeling of BIM technology, multi-dimensional and dynamic energy consumption analysis is achieved, thereby improving the accuracy and reliability of building energy consumption prediction.

[0013] In a possible implementation form of the first aspect, the analysis according to the building energy consumption dataset to obtain the energy consumption fluctuation comprises:

[0014] According to the building energy consumption dataset, analysis is performed to obtain a building energy consumption fluctuation group;

[0015] According to the building energy consumption fluctuation group, analysis is performed to obtain the energy consumption fluctuation;

[0016] The analysis according to the building energy consumption dataset to obtain the building energy consumption fluctuation group comprises:

[0017] According to the building energy consumption dataset, a first building energy consumption fluctuation group and a second building energy consumption fluctuation group are obtained through analysis; the first building energy consumption fluctuation group comprises energy consumption data of all buildings in the building energy consumption dataset within a preset analysis interval, and the second building energy consumption fluctuation group comprises energy consumption data of buildings with a design parameter similarity greater than a preset similarity to the design parameter of the building to be measured within the preset analysis interval.

[0018] In a possible implementation manner of the first aspect, the analysis according to the building energy consumption dataset to obtain the energy consumption fluctuation comprises:

[0019] According to the first building energy consumption fluctuation group, a first energy consumption fluctuation is obtained through analysis; the first energy consumption fluctuation is used to reflect energy consumption fluctuation conditions of all buildings in the first building energy consumption fluctuation group;

[0020] According to the second building energy consumption fluctuation group, a second energy consumption fluctuation is obtained through analysis; the second energy consumption fluctuation is used to reflect energy consumption fluctuation conditions of buildings in the second building energy consumption fluctuation group;

[0021] According to the first energy consumption fluctuation and the second energy consumption fluctuation, an energy consumption fluctuation is obtained through analysis.

[0022] In a possible implementation manner of the first aspect, the analysis according to the first energy consumption fluctuation and the second energy consumption fluctuation to obtain the energy consumption fluctuation comprises:

[0023] According to the first energy consumption fluctuation, a first current period fluctuation trend value and a first same-period fluctuation trend value are obtained through analysis; the first current period fluctuation trend value is used to reflect an energy consumption fluctuation degree of all buildings within a preset analysis period, and the first same-period fluctuation trend value is used to reflect an energy consumption fluctuation degree of all buildings within a previous preset analysis period;

[0024] According to the second energy consumption fluctuation, a second current period fluctuation trend value and a second same-period fluctuation trend value are obtained through analysis; the second current period fluctuation trend value is used to reflect an energy consumption fluctuation degree of buildings in the second building energy consumption fluctuation group within a preset analysis period, and the second same-period fluctuation trend value is used to reflect an energy consumption fluctuation degree of buildings in the second building energy consumption fluctuation group within a previous preset analysis period;

[0025] According to the first current period fluctuation trend value, the first same-period fluctuation trend value, the second current period fluctuation trend value, and the second same-period fluctuation trend value, an energy consumption fluctuation is obtained through analysis.

[0026] In a possible implementation manner of the first aspect, the analyzing according to the first current period fluctuation trend value, the first same-period fluctuation trend value, the second current period fluctuation trend value and the second same-period fluctuation trend value to obtain energy consumption fluctuation comprises:

[0027] performing mean value calculation according to the first current period fluctuation trend value and the second current period fluctuation trend value to obtain a first fluctuation index;

[0028] performing difference calculation according to the first same-period fluctuation trend value and the second same-period fluctuation trend value to obtain a second fluctuation index;

[0029] performing weighted calculation according to the first fluctuation index and the second fluctuation index to obtain energy consumption fluctuation.

[0030] In a possible implementation manner of the first aspect, the analyzing the to-be-tested building design parameter and the building energy consumption dataset to obtain analogy building energy consumption comprises:

[0031] performing feature matching on the to-be-tested building design parameter and the building energy consumption dataset to obtain geometric shape energy consumption, material attribute energy consumption and system configuration energy consumption; wherein the geometric shape energy consumption is used to indicate average energy consumption of a building in the building energy consumption dataset that has the same geometric shape as the to-be-tested building design parameter, the material attribute energy consumption is used to indicate average energy consumption of a building in the building energy consumption dataset that has the same material attribute as the to-be-tested building design parameter, and the system configuration energy consumption is used to indicate average energy consumption of a building in the building energy consumption dataset that has the same system configuration as the to-be-tested building design parameter;

[0032] performing analysis according to the geometric shape energy consumption, the material attribute energy consumption and the system configuration energy consumption to obtain analogy building energy consumption.

[0033] In a possible implementation manner of the first aspect, the analyzing according to the geometric shape energy consumption, the material attribute energy consumption and the system configuration energy consumption to obtain analogy building energy consumption comprises:

[0034] performing analysis according to the material attribute energy consumption to obtain a material influence coefficient; wherein the material influence coefficient is used to reflect a degree of influence of material attribute on energy consumption;

[0035] performing analysis according to the geometric shape energy consumption and the system configuration energy consumption to obtain similar building energy consumption; wherein the similar building energy consumption is used to indicate a mean value of the geometric shape energy consumption and the system configuration energy consumption;

[0036] performing weighting on the similar building energy consumption based on the material influence coefficient to obtain analogy building energy consumption.

[0037] In one possible implementation of the first aspect, the step of performing simulation analysis based on the analog building energy consumption and the energy consumption fluctuation to obtain the energy consumption information of the building to be measured includes:

[0038] The energy consumption fluctuations are analyzed to obtain correction values; wherein, the correction values ​​reflect the degree of impact of the energy consumption fluctuations on the energy consumption of the analog building.

[0039] The energy consumption of the analog building is corrected based on the correction value to obtain the energy consumption information of the building to be measured.

[0040] Secondly, embodiments of this application corresponding to any of the methods described in the first aspect provide a building energy consumption simulation system, which may include:

[0041] The acquisition module is used to acquire a building energy consumption dataset based on big data; wherein, the building energy consumption dataset is used to reflect the types and energy consumption data of other buildings in the area where the building under test is located;

[0042] The first analysis module is used to analyze the building energy consumption dataset to obtain energy consumption fluctuations; wherein, the energy consumption fluctuations are used to reflect the degree of fluctuation in energy prices.

[0043] The generation module is used to generate design parameters for a building under test based on BIM; wherein the design parameters for a building under test include geometry, material properties, and system configuration.

[0044] The second analysis module is used to analyze the design parameters of the building to be tested and the building energy consumption dataset to obtain the analog building energy consumption; wherein, the analog building energy consumption is used to reflect the energy consumption of buildings in the building energy consumption dataset that have the same design parameters as the building to be tested.

[0045] The third analysis module is used to perform simulation analysis based on the energy consumption of the analog building and the energy consumption fluctuation to obtain the energy consumption information of the building under test; wherein, the energy consumption information of the building under test is used to reflect the energy consumption of the building under test.

[0046] Thirdly, embodiments of this application provide a building energy consumption simulation device based on big data and BIM, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any one of the first aspects above.

[0047] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any one of the first aspects above.

[0048] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a building energy consumption simulation device based on big data and BIM, causes the building energy consumption simulation device based on big data and BIM to perform the building energy consumption simulation method based on big data and BIM according to any one of the first aspect.

[0049] It can be understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description in the first aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0051] Figure 1 is a flowchart of a building energy consumption simulation method based on big data and BIM provided by an embodiment of the present application;

[0052] Figure 2 is a flowchart of a building energy consumption simulation method based on big data and BIM provided by an embodiment of the present application;

[0053] Figure 3 is a structural diagram of a building energy consumption simulation system based on big data and BIM provided by an embodiment of the present application;

[0054] Figure 4 is a structural diagram of a building energy consumption simulation device based on big data and BIM provided by an embodiment of the present application. DETAILED DESCRIPTION

[0055] In the following description, specific details are set forth in order to provide a thorough understanding of the embodiments of the present application. However, persons skilled in the art should understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary details.

[0056] It should be understood that when used in the specification and the appended claims of the present application, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0057] It should also be understood that the term “and / or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items, and that the term “at least one of’ denotes one, or a combination of two or more items.

[0058] As used in the description of the application and the appended claims, the term “if’ can be interpreted to mean “when” or “upon” or “in response to determining” or “in response to detecting” depending on the context. Similarly, the phrase “if it is determined” or “if a described condition or event occurs” can be interpreted to mean “upon determining,” or “in response to determining” or “upon detecting,” or “in response to detecting” the described condition or event, depending on the context.

[0059] In addition, in the description and the appended claims of the application, the terms “first”, “second”, “third”, etc. are used only to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0060] Reference in the specification to “one embodiment” or “some embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase “in one embodiment” or “in some embodiments” in various places in the specification are not necessarily all referring to the same embodiment, although it can. The terms “comprising,” “including,” “having” and their variations, as used in the specification and claims, mean “including but not limited to,” unless otherwise noted. The terms “coupled” and “connected,” as used in the specification, mean to be directly or indirectly connected, and not necessarily mechanically.

[0061] With the increasing global concern for energy efficiency and environmental protection, the management and optimization of building energy consumption have become an important issue in the construction industry. Buildings consume a large amount of energy during their life cycle, for heating, cooling, lighting, and other electrical equipment. According to the International Energy Agency (IEA), buildings account for about 36% of global energy consumption. Therefore, accurate prediction of building energy consumption is of great significance to achieve the goal of energy saving and emission reduction.

[0062] However, traditional building energy consumption evaluation methods often rely on static theoretical models, failing to fully consider dynamic factors in actual operation. In addition, traditional energy consumption evaluation is usually based on data of a single building, lacking a regional and overall perspective, and in addition, energy price fluctuations also have an important impact on building operating costs, thus leading to certain limitations of traditional building energy consumption evaluation methods, resulting in insufficient accuracy and flexibility.

[0063] To solve the above problems, the embodiment of the present application provides a building energy consumption simulation method, system and equipment based on big data and BIM. In the method, the building energy consumption dataset reflecting the types and energy consumption data of other buildings in the region where the to-be-tested building is located is obtained based on big data; then the energy consumption fluctuation reflecting the fluctuation degree of the energy price is obtained by analyzing the building energy consumption dataset; then the to-be-tested building design parameters including geometric shape, material attribute and system configuration are generated based on BIM; then the to-be-tested building design parameters are analyzed with the building energy consumption dataset to obtain the analogy building energy consumption reflecting the energy consumption of the building similar to the to-be-tested building; the to-be-tested building energy consumption information reflecting the energy consumption of the to-be-tested building is obtained by simulating and analyzing the analogy building energy consumption and the energy consumption fluctuation. The method provides a regional and overall energy consumption evaluation perspective by analyzing the data of other buildings in the region, which helps to identify commonalities and differences, and combined with the three-dimensional visualization and data correlation provided by BIM, the building energy consumption can be analyzed in multiple dimensions, and the fluctuation of the energy price is considered to simulate the building energy consumption, which not only overcomes the limitations of the traditional evaluation method, but also provides strong support for the energy efficiency management and optimization of buildings, and promotes the process of green building and sustainable development. By providing a regional and overall energy consumption evaluation perspective, combined with the refined modeling of BIM technology, multi-dimensional and dynamic energy consumption analysis is realized, thereby improving the accuracy and reliability of building energy consumption prediction.

[0064] The building energy consumption simulation method based on big data and BIM provided by the embodiment of the present application can be applied to a building energy consumption simulation device based on big data and BIM, at this time the building energy consumption simulation device based on big data and BIM is the execution subject of the building energy consumption simulation method based on big data and BIM provided by the embodiment of the present application, and the embodiment of the present application does not make any limitation on the specific type of the building energy consumption simulation device based on big data and BIM.

[0065] For example, the building energy consumption simulation device based on big data and BIM can be a terminal device such as a mobile phone, a tablet computer, a wearable device, an augmented reality (AR) / virtual reality (VR) device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), a desktop computer, a smart large screen, a smart television, and the like, a handheld device with a wireless communication function, a computing device, or other processing devices connected to a wireless modem, an Internet of Things terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a television set top box (STB), customer premise equipment (CPE), and / or other devices for communicating over a wireless system, and a next-generation communication system, for example, a mobile terminal in a 5G network or a mobile terminal in a future evolved public land mobile network (PLMN), and the like.

[0066] In order to better understand the building energy consumption simulation method based on big data and BIM provided by the embodiments of the present application, the specific implementation process of the building energy consumption simulation method based on big data and BIM provided by the embodiments of the present application is exemplarily introduced as follows.

[0067] Figure 1 And Figure 2 A schematic flowchart of the building energy consumption simulation method based on big data and BIM provided by the embodiments of the present application is shown, and the building energy consumption simulation method based on big data and BIM includes:

[0068] S100, based on big data, obtaining a building energy consumption dataset; wherein the building energy consumption dataset is used to reflect the types and energy consumption data of other buildings in the area where the building to be measured is located.

[0069] It can be understood that the region refers to the geographical range (for example, South China, North China, etc.) or the administrative region (for example, the province where the building is located, the city where the building is located, etc.) where the building to be measured is located. The setting range of the region is pre-set, which can be manually input by human, or can be obtained in the building energy consumption database, etc., but not limited to this. The building energy consumption database refers to a database containing the range of the region, the types of buildings collected in the region and the corresponding energy consumption data. These data can be obtained by laboratory experiments, field measurements and monitoring, and past experience, etc. After obtaining, the collected data is sorted, classified and archived, useful information and rules are extracted, and related data is saved to the database to form the building energy consumption database. For example, the types and energy consumption data of other buildings in the region where the building to be measured is located can be obtained by questionnaire survey, on-site measurement, or by network crawler technology to capture building energy consumption information on public websites. The obtained information is integrated to form the building energy consumption dataset.

[0070] In S200, the building energy consumption dataset is analyzed to obtain the energy consumption fluctuation; wherein the energy consumption fluctuation is used to reflect the fluctuation degree of the energy price.

[0071] It can be understood that the price of energy refers to the price of electric energy. This is the main form of energy consumption of building energy, and its price fluctuation has a significant impact on the overall energy consumption cost of the building. When the price of electric energy decreases, the energy consumption fluctuation is negative, and when the price of electric energy rises, the energy consumption fluctuation is positive. Illustratively, the building energy consumption dataset can be classified to obtain the energy consumption of buildings similar to the building to be measured, and then the periodic changes of the energy consumption trends of all buildings and the energy consumption of buildings similar to the building to be measured are comprehensively measured to obtain the energy consumption fluctuation; or the building energy consumption dataset can be input into a learning model, and the learning model outputs the corresponding energy consumption fluctuation, etc., but not limited to this. The learning model is trained by multiple sets of training data, and each set of training data includes a building energy consumption dataset and an energy consumption fluctuation, that is, the energy consumption data of multiple buildings in continuous time and the corresponding energy consumption fluctuation are used as training samples to train the learning model (such as neural network, decision tree, etc.).

[0072] In a possible implementation, in step S200, the building energy consumption dataset is analyzed to obtain the energy consumption fluctuation, including:

[0073] In S210, the building energy consumption dataset is analyzed to obtain a building energy consumption fluctuation group.

[0074] It can be understood that the building energy consumption fluctuation group refers to a set of all or part of buildings in the building energy consumption data set. For example, by setting a specified time or interval, all building energy consumption data in the building energy consumption data set in the recent period or specified time and the energy consumption data of buildings similar or identical to the design parameters of the building to be tested in the recent period or specified time can be extracted as two different fluctuation groups in the building energy consumption fluctuation group; or all buildings in the building energy consumption data set can be divided into a high energy consumption group and a low energy consumption group in the building energy consumption fluctuation group according to the energy consumption of the buildings, etc., but not limited thereto.

[0075] In one possible implementation, in step S210, the building energy consumption fluctuation group is obtained by analyzing the building energy consumption data set, including:

[0076] S211, the first building energy consumption fluctuation group and the second building energy consumption fluctuation group of the building energy consumption fluctuation group are obtained by analyzing the building energy consumption data set; wherein the first building energy consumption fluctuation group includes the energy consumption data of all buildings in the building energy consumption data set in the preset analysis interval, and the second building energy consumption fluctuation group includes the energy consumption data of buildings with a similarity greater than a preset similarity to the design parameters of the building to be tested in the preset analysis interval.

[0077] It can be understood that the preset analysis interval is a time interval set in advance, which can be manually input by human, or obtained from the building energy consumption database, etc., but not limited thereto. For example, the preset analysis interval can be 2 years, 3 years, etc., but not limited thereto. The similarity refers to the comprehensive similarity of the geometric shape, material property and system configuration of the building design parameters, which can be obtained by calculating the similarity of the geometric shape, the similarity of the material property and the similarity of the system configuration, and then calculating the comprehensive similarity by weight or mean value, etc., but not limited thereto. The preset similarity is a similarity value set in advance, which can be manually input by human, or obtained from the building energy consumption database, etc., but not limited thereto.

[0078] In this way, without considering the design parameters of the building, the energy consumption characteristics and fluctuation of the overall building in the region can be reflected by the first building energy consumption fluctuation group, and the overall trend of the electricity energy price can be captured; and by the second building energy consumption fluctuation group with high similarity to the design parameters of the building to be tested, the specific use of electricity energy in the building type of the building to be tested can be focused on, not only the inherent energy consumption mode of the building type is reflected, but also the specific behavior habits of the building user are revealed to a certain extent, thereby providing strong support for accurate energy consumption prediction and personalized energy efficiency management.

[0079] S220, the energy consumption fluctuation is obtained by analyzing the building energy consumption fluctuation group.

[0080] Exemplarily, the energy consumption fluctuation can be obtained by analyzing the energy consumption fluctuation degree of the first building energy consumption fluctuation group and the second building energy consumption fluctuation group respectively, and then comprehensively analyzing the two energy consumption fluctuation degrees. Alternatively, the building energy consumption fluctuation group can be input into a learning model, and the learning model outputs the corresponding energy consumption fluctuation, and the like, but is not limited thereto.

[0081] In a possible implementation, in step S220, the energy consumption fluctuation is obtained by analyzing the building energy consumption fluctuation group, including:

[0082] S221, the first energy consumption fluctuation is obtained by analyzing the first building energy consumption fluctuation group; wherein the first building energy consumption fluctuation reflects the energy consumption fluctuation of all buildings in the first building energy consumption fluctuation group.

[0083] Exemplarily, the mean value of the energy consumption fluctuation of each building in the first building energy consumption fluctuation group can be calculated, that is, the set of each mean value in the preset analysis interval is taken as the first energy consumption fluctuation. Alternatively, the energy consumption fluctuation of a certain representative building in the first building energy consumption fluctuation group can be taken as the first energy consumption fluctuation, and the like, but is not limited thereto.

[0084] S222, the second energy consumption fluctuation is obtained by analyzing the second building energy consumption fluctuation group; wherein the second building energy consumption fluctuation reflects the energy consumption fluctuation of the buildings in the second building energy consumption fluctuation group.

[0085] It can be understood that the fluctuation of the average value of the energy consumption of all buildings in the second building energy consumption fluctuation group in the preset analysis interval can be taken as the second energy consumption fluctuation.

[0086] S223, the energy consumption fluctuation is obtained by analyzing the first energy consumption fluctuation and the second energy consumption fluctuation.

[0087] Exemplarily, the first energy consumption fluctuation and the second energy consumption fluctuation can be analyzed respectively to obtain the energy consumption fluctuation degree in the current analysis period in the preset analysis interval and the energy consumption fluctuation degree in the last analysis period in the preset analysis interval, and then the energy consumption fluctuation is obtained by comprehensively analyzing. Alternatively, the first energy consumption fluctuation and the second energy consumption fluctuation can be input into a learning model, and the learning model outputs the corresponding energy consumption fluctuation, and the like, but is not limited thereto.

[0088] In this way, the first energy consumption fluctuation reflects the electric energy consumption and the overall trend of all buildings in the region, and the second energy consumption fluctuation focuses on the energy consumption characteristics of the specific type of building similar to the building to be measured. By comparing and comprehensively analyzing the two fluctuation conditions, the energy consumption change of the building to be measured can be more comprehensively and accurately predicted.

[0089] In a possible implementation, in step S223, the analysis according to the first energy consumption fluctuation and the second energy consumption fluctuation obtains the energy consumption fluctuation, including:

[0090] S2231, the analysis according to the first energy consumption fluctuation obtains a first current period fluctuation trend value and a first same-period fluctuation trend value; wherein the first current period fluctuation trend value is used to reflect the energy consumption fluctuation degree of all buildings in a current preset analysis period, and the first same-period fluctuation trend value is used to reflect the energy consumption fluctuation degree of all buildings in a previous preset analysis period.

[0091] It can be understood that the preset analysis period is a time period set in advance, which can be manually input by human, obtained in a building energy consumption database, etc., but is not limited thereto. Exemplarily, if the preset analysis interval is 3 years and the preset analysis period is 3 months, assuming that the current month is June, the first current period is April to June of the current year, the first same-period is April to June of the previous year, the first current period fluctuation trend value is the energy consumption fluctuation degree of April to June of the current year, and the first same-period fluctuation trend value is the energy consumption fluctuation degree of April to June of the previous year; assuming that the current month is May, the first current period is March to May of the current year, the first same-period is May to June of the previous year, and so on. The calculation of the first current period fluctuation trend value and the first same-period fluctuation trend value can be quantitatively calculated by constructing a fluctuation rate index, for example, by an exponential function , respectively calculating the first current period fluctuation trend value and the first same-period fluctuation trend value; wherein r i is the relative change rate at each time point, r j is the average relative change rate, and n is the number of buildings; the first energy consumption fluctuation, the preset analysis interval, and the preset analysis period can also be input into a learning model, and the learning model outputs the first current period fluctuation trend value and the first same-period fluctuation trend value, etc., but is not limited thereto.

[0092] S2232, the analysis according to the second energy consumption fluctuation obtains a second current period fluctuation trend value and a second same-period fluctuation trend value; wherein the second current period fluctuation trend value is used to reflect the energy consumption fluctuation degree of the buildings in the second building energy consumption fluctuation group in the current preset analysis period, and the second same-period fluctuation trend value is used to reflect the energy consumption fluctuation degree of the buildings in the second building energy consumption fluctuation group in the previous preset analysis period.

[0093] It can be understood that the second current period fluctuation trend value and the second same-period fluctuation trend value can be obtained in a similar manner to the first current period fluctuation trend value and the first same-period fluctuation trend value obtained in step S2231, and details are not repeated here.

[0094] S2233, analyze according to the first current period fluctuation trend value, the first same period fluctuation trend value, the second current period fluctuation trend value and the second same period fluctuation trend value, and obtain the energy consumption fluctuation.

[0095] Exemplarily, the mean value calculation can be performed on the first current period fluctuation trend value and the second current period fluctuation trend value, then the analysis calculation is performed according to the first same period fluctuation trend value and the second same period fluctuation trend value, and then the energy consumption fluctuation is obtained by weighted summation; or the first current period fluctuation trend value, the first same period fluctuation trend value, the second current period fluctuation trend value and the second same period fluctuation trend value can be input into a learning model, and the learning model outputs the corresponding energy consumption fluctuation, and the like, but not limited thereto.

[0096] In this way, by systematically analyzing the first current period fluctuation trend value, the first same period fluctuation trend value, the second current period fluctuation trend value and the second same period fluctuation trend value, the consumption of the building electric energy in the region and the overall trend can be comprehensively reflected, and the specific use of the specific type of building similar to the building to be measured is more focused. This method significantly improves the accuracy and reliability of energy consumption prediction, and provides a scientific basis for building operation cost control and energy saving and emission reduction. By combining the fluctuation trends of the current period and the same period, not only short-term fluctuations can be captured, but also long-term change patterns can be revealed, improving the timeliness and forward-looking of the analysis results.

[0097] In one possible implementation, in step S2233, the energy consumption fluctuation is obtained by analyzing the first current period fluctuation trend value, the first same period fluctuation trend value, the second current period fluctuation trend value and the second same period fluctuation trend value, including:

[0098] S22331, mean value calculation is performed according to the first current period fluctuation trend value and the second current period fluctuation trend value, and the first fluctuation index is obtained.

[0099] It can be understood that the first fluctuation index = (the first current period fluctuation trend value + the second current period fluctuation trend value) ÷ 2.

[0100] S22332, difference calculation is performed according to the first same period fluctuation trend value and the second same period fluctuation trend value, and the second fluctuation index is obtained.

[0101] It can be understood that the second fluctuation index = the second same period fluctuation trend value - the first same period fluctuation trend value.

[0102] S22333, weighted calculation is performed according to the first fluctuation index and the second fluctuation index, and the energy consumption fluctuation is obtained.

[0103] It can be understood that the weight of the first fluctuation index and the weight of the second fluctuation index can be manually inputted by human, or can be obtained in the building energy consumption database, etc., but are not limited thereto.

[0104] In this way, the first fluctuation index reflects the average energy consumption fluctuation degree between all buildings of the same type as the to-be-tested building in the current period, which helps to identify the comprehensive energy consumption characteristics and fluctuation of different building types in the current period; the second fluctuation index reflects the energy consumption fluctuation difference between all buildings and the specific type of building in the last period, which provides historical background support for the energy consumption fluctuation changes of different building types in the past period; by combining the fluctuation trends of the current period and the same period, the change mode of building energy consumption can be more comprehensively captured, not only improving the accuracy and reliability of energy consumption prediction, but also providing strong support for building energy efficiency management and optimization, which significantly enhances the scientificity and flexibility of decision-making.

[0105] S300, generating a to-be-tested building design parameter based on BIM; wherein the to-be-tested building design parameter includes geometry, material attribute and system configuration.

[0106] It can be understood that BIM refers to a data modeling technology used for building design, construction and management, which not only creates a three-dimensional geometric model of the building, but also integrates various information related to the building, such as structure, material, equipment, cost and schedule. Geometry refers to the physical form and spatial layout of the to-be-tested building, which can specifically include the shape of the building, internal structure, room layout, floor height, etc. Material attribute refers to various materials used in the to-be-tested building and corresponding physical and chemical properties, such as wall materials reflecting thermal insulation performance, glass materials reflecting light transmittance. System configuration refers to the settings and operating parameters of various mechanical and electrical systems and intelligent systems in the to-be-tested building, such as heating, ventilation and air conditioning systems, water supply and drainage systems, electrical lighting systems, building automation systems, etc.

[0107] S400, analyzing the to-be-tested building design parameter and the building energy consumption dataset to obtain a comparative building energy consumption; wherein the comparative building energy consumption is used to reflect the energy consumption of the building in the building energy consumption dataset which has the same design parameter as the to-be-tested building.

[0108] It can be understood that by matching the to-be-tested building design parameters with the building energy consumption dataset, the analogous building energy consumption can be obtained. Exemplarily, the geometry, material properties and system configuration of the to-be-tested building can be matched with the design parameters of all buildings in the building energy consumption dataset respectively, to obtain a set of buildings in the building energy consumption dataset that have the same geometry as the to-be-tested building, a set of buildings in the building energy consumption dataset that have the same material properties as the to-be-tested building, and a set of buildings in the building energy consumption dataset that have the same system configuration as the to-be-tested building respectively, and then the energy consumption data of the buildings in each set is analyzed comprehensively to obtain the analogous building energy consumption; or the to-be-tested building design parameters can be input into the learning model, and the learning model outputs the corresponding analogous building energy consumption, etc., but not limited thereto.

[0109] In a possible implementation, in step S400, the to-be-tested building design parameters are analyzed with the building energy consumption dataset to obtain the analogous building energy consumption, including:

[0110] S410, the to-be-tested building design parameters are feature-matched with the building energy consumption dataset to obtain the geometry energy consumption, the material property energy consumption and the system configuration energy consumption; wherein the geometry energy consumption is used to indicate the average energy consumption of the buildings in the building energy consumption dataset that have the same geometry as the to-be-tested building design parameters, the material property energy consumption is used to indicate the average energy consumption of the buildings in the building energy consumption dataset that have the same material properties as the to-be-tested building design parameters, and the system configuration energy consumption is used to indicate the average energy consumption of the buildings in the building energy consumption dataset that have the same system configuration as the to-be-tested building design parameters.

[0111] It can be understood that after the geometry of the to-be-tested building design parameters is feature-matched with the building energy consumption dataset, a set of buildings in the building energy consumption dataset that have the same geometry as the to-be-tested building design parameters is obtained, and then the energy consumption data of each building in the set is summed up to calculate the average, which is the geometry energy consumption. After the material properties of the to-be-tested building design parameters are feature-matched with the building energy consumption dataset, a set of buildings in the building energy consumption dataset that have the same material properties as the to-be-tested building design parameters is obtained, and then the energy consumption data of each building in the set is summed up to calculate the average, which is the material property energy consumption. After the system configuration of the to-be-tested building design parameters is feature-matched with the building energy consumption dataset, a set of buildings in the building energy consumption dataset that have the same system configuration as the to-be-tested building design parameters is obtained, and then the energy consumption data of each building in the set is summed up to calculate the average, which is the system configuration energy consumption.

[0112] The way of judging whether the geometric shape of the building in the building energy consumption data set is the same as the design parameter of the building to be measured can be comprehensively judged by the area, height and room layout reflected in the geometric shape, that is, the area similarity, height similarity and room layout similarity are calculated respectively, and the similarity of the geometric shape is obtained by averaging or weighted summation according to the area similarity, height similarity and room layout similarity. If the similarity of the geometric shape is greater than or equal to the preset geometric shape similarity, it is determined that the building and the design parameter of the building to be measured have the same geometric shape. If the similarity of the geometric shape is less than the preset geometric shape similarity, it is determined that the building and the design parameter of the building to be measured have different geometric shapes. The way of judging whether the material attribute and the system configuration of all buildings in the building energy consumption data set are the same as the design parameter of the building to be measured is the same, and will not be repeated here.

[0113] In step S420, the geometric shape energy consumption, the material attribute energy consumption and the system configuration energy consumption are analyzed to obtain the analogy building energy consumption.

[0114] For example, the geometric shape energy consumption and the system configuration energy consumption can be analyzed to obtain a building energy consumption, the material attribute energy consumption can be analyzed to obtain a weight, and the building energy consumption can be weighted based on the weight to obtain the analogy building energy consumption. The geometric shape energy consumption, the material attribute energy consumption and the system configuration energy consumption can also be calculated by averaging or weighted summation to obtain the analogy building energy consumption, and the like, but are not limited thereto.

[0115] In this way, by matching the design parameters of the building to be measured with the building energy consumption data set, the average energy consumption of the geometric shape, the material attribute and the system configuration is calculated, and the analogy building energy consumption is obtained based on the energy consumption values. This method not only precisely considers the specific influence of different factors on energy consumption, simplifies the analysis process and improves the calculation efficiency, but also enhances the flexibility and adaptability, and can be dynamically adjusted to adapt to new data and technology.

[0116] In one possible implementation, in step S420, the geometric shape energy consumption, the material attribute energy consumption and the system configuration energy consumption are analyzed to obtain the analogy building energy consumption, including:

[0117] In step S421, the material attribute energy consumption is analyzed to obtain a material influence coefficient. The material influence coefficient is used to reflect the degree of influence of the material attribute on the energy consumption.

[0118] It can be understood that different material attribute energy consumptions correspond to a material influence coefficient. The material attribute energy consumption can be matched with the material attribute energy consumption interval in the building energy consumption database to obtain the material influence coefficient corresponding to the material attribute energy consumption interval; or the material attribute energy consumption can be input into a learning model, and the learning model outputs the corresponding material influence coefficient, etc., but not limited thereto.

[0119] S422, analyzing the geometric shape energy consumption and the system configuration energy consumption to obtain a similar building energy consumption; wherein, the similar building energy consumption is used to indicate the mean value of the geometric shape energy consumption and the system configuration energy consumption.

[0120] It can be understood that the similar building energy consumption = (geometric shape energy consumption + system configuration energy consumption) ÷ 2.

[0121] S423, weighting the similar building energy consumption based on the material influence coefficient to obtain a comparative building energy consumption.

[0122] It can be understood that the comparative building energy consumption = material influence coefficient x similar building energy consumption.

[0123] In this way, the material properties (such as the thermal conductivity, light transmittance, etc. of the wall, roof, window and other materials) have a direct and important influence on the building energy consumption. The difference in thermal performance of different materials can lead to significantly different energy consumption results of the same building under the same use conditions. By separately analyzing the material attribute energy consumption and calculating the material influence coefficient, the specific influence of the material properties on the energy consumption can be more finely reflected. This method ensures that the prediction model can capture the subtle changes brought by material selection, improves the accuracy and scientificity of the prediction; the geometric shape and system configuration provide a reference for the basic energy consumption level, and the material properties further refine the energy consumption influence, so that the final comparative building energy consumption is closer to the actual situation. This method not only improves the prediction accuracy, but also enhances the reliability and practicability of the model.

[0124] S500, simulating and analyzing the comparative building energy consumption and the energy consumption fluctuation to obtain building energy consumption information of the to-be-tested building; wherein, the building energy consumption information of the to-be-tested building is used to reflect the energy consumption situation of the to-be-tested building.

[0125] Exemplarily, the influence degree of the fluctuation degree of the current electric energy price on the energy consumption calculation can be obtained through the energy consumption fluctuation analysis, and then the comparative building energy consumption is combined for analysis to obtain the building energy consumption information of the to-be-tested building; or the comparative building energy consumption and the energy consumption fluctuation can be input into a learning model, and the learning model outputs the corresponding building energy consumption information of the to-be-tested building.

[0126] In one possible implementation, in step S500, simulating and analyzing the comparative building energy consumption and the energy consumption fluctuation to obtain building energy consumption information of the to-be-tested building, comprising:

[0127] S510, analyzing according to the energy consumption fluctuation to obtain a correction value; wherein the correction value reflects the influence degree of the energy consumption fluctuation on the analog building energy consumption.

[0128] It can be understood that different energy consumption fluctuations correspond to a correction value. The energy consumption fluctuation can be matched with different energy consumption fluctuation intervals in the building energy consumption database to obtain the correction value corresponding to the energy consumption fluctuation interval matched with the energy consumption fluctuation.

[0129] S520, correcting the analog building energy consumption based on the correction value to obtain the to-be-measured building energy consumption information.

[0130] It can be understood that the to-be-measured building energy consumption information = correction value + analog building energy consumption.

[0131] In this way, by analyzing the energy consumption fluctuation and calculating the correction value, the method can dynamically reflect the energy consumption change in actual operation, improve the real-time performance and accuracy of the prediction result; secondly, the analog building energy consumption is corrected based on the correction value, which ensures that the to-be-measured building energy consumption information not only considers the influence of static design parameters, but also comprehensively reflects the energy consumption characteristics under actual operation conditions.

[0132] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0133] Corresponding to the building energy consumption simulation method based on big data and BIM described in the above embodiment, the embodiment of the present application also provides a building energy consumption simulation system based on big data and BIM. Each module of the system can realize each step of the building energy consumption simulation method based on big data and BIM. Figure 3 The structure block diagram of the building energy consumption simulation system based on big data and BIM provided by the embodiment of the present application is shown, and only the part related to the embodiment of the present application is shown for the convenience of description.

[0134] Referring to Figure 3 The building energy consumption simulation system based on big data and BIM includes:

[0135] The acquisition module is configured to acquire a building energy consumption dataset based on big data; wherein the building energy consumption dataset is used to reflect the types and energy consumption data of other buildings in the region where the to-be-measured building is located.

[0136] The first analysis module is configured to analyze according to the building energy consumption dataset to obtain an energy consumption fluctuation; wherein the energy consumption fluctuation is used to reflect the fluctuation degree of the energy price.

[0137] The generating module is configured to generate a to-be-tested building design parameter based on the BIM, wherein the to-be-tested building design parameter comprises a geometric shape, a material attribute, and a system configuration.

[0138] The second analyzing module is configured to analyze the to-be-tested building design parameter and the building energy consumption dataset to obtain an analogous building energy consumption, wherein the analogous building energy consumption is used to reflect an energy consumption situation of a building in the building energy consumption dataset that has the same design parameter as the to-be-tested building.

[0139] The third analyzing module is configured to perform simulation analysis on the analogous building energy consumption and the energy consumption fluctuation to obtain to-be-tested building energy consumption information, wherein the to-be-tested building energy consumption information is used to reflect an energy consumption situation of the to-be-tested building.

[0140] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of the present application, and the specific functions and technical effects brought by them can be referred to the method embodiments part, which will not be repeated here.

[0141] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above modules is taken as an example for illustration, and in actual application, the above functions can be completed by different modules according to needs, that is, the internal structure of the system is divided into different modules to complete all or part of the functions described above. Each module in the embodiment can be integrated in one processing unit, or each module can be physically independent, or two or more modules can be integrated in one unit. The above integrated unit can be realized in the form of hardware or in the form of software functional unit. In addition, the specific name of each module is only for easy distinction, and does not limit the protection scope of the present application. The specific working process of the modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0142] The present application also provides a building energy consumption simulation device based on big data and BIM. Figure 4 The structure schematic diagram of the building energy consumption simulation device 6 based on big data and BIM provided by an embodiment of the present application is shown in FIG. 1. Figure 4 As shown in the figure, the building energy consumption simulation device 6 based on big data and BIM of this embodiment comprises at least one processor 60 (only one is shown in the figure), at least one memory 61 (only one is shown in the figure), and a communication interface 62. Figure 4 Figure 4 ​The computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60, when the processor 60 executes the computer program 62, causes the big data and BIM based building energy consumption simulation device 6 to implement the steps in any of the above respective big data and BIM based building energy consumption simulation method embodiments, or causes the big data and BIM based building energy consumption simulation device 6 to implement the functions of the respective modules in the above system embodiments.

[0143] For example, the computer program 62 can be divided into one or more modules / units stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 62 in the big data and BIM based building energy consumption simulation device 6.

[0144] The big data and BIM based building energy consumption simulation device 6 can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The big data and BIM based building energy consumption simulation device can include, but is not limited to, the processor 60, the memory 61. Those skilled in the art can understand that Figure 4 The big data and BIM based building energy consumption simulation device 6 is only an example and does not constitute a limitation on the big data and BIM based building energy consumption simulation device 6, and can include more or fewer components than those shown, or combine some components, or different components, for example, it can also include an input / output device, a network access device, a bus, etc.

[0145] The processor 60 can be a central processing unit (CPU), and the processor 60 can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0146] The memory 61 may, in some embodiments, be an internal storage unit of the big data and BIM-based building energy consumption simulation device 6, such as a hard disk or a memory of the big data and BIM-based building energy consumption simulation device 6. The memory 61 may, in other embodiments, also be an external storage device of the big data and BIM-based building energy consumption simulation device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the big data and BIM-based building energy consumption simulation device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the big data and BIM-based building energy consumption simulation device 6. The memory 61 is used to store an operating system, an application program, a boot loader, data, and other programs, etc., such as program codes of the computer program, etc. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0147] The embodiments of the present application further provide a computer readable storage medium storing a computer program, and the computer program is executed by a processor to implement the steps in any of the method embodiments described above.

[0148] The embodiments of the present application provide a computer program product, and when the computer program product is run on a big data and BIM-based building energy consumption simulation device, the big data and BIM-based building energy consumption simulation device implements the steps in any of the method embodiments described above.

[0149] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application can implement all or part of the processes in the above-mentioned embodiment methods through a computer program to instruct the relevant hardware to complete, and the computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the building energy consumption simulation device based on big data and BIM, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk and the like.

[0150] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0151] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be realized by electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0152] In the embodiments provided in the present application, it should be understood that the disclosed building energy consumption simulation device and system based on big data and BIM can be implemented in other ways. For example, the above-described building energy consumption simulation system embodiment based on big data and BIM is only illustrative, for example, the division of the modules is only a logical functional division, and actual implementation can have another division method, for example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed can be indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0153] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed to multiple network elements. Part or all of the modules can be selected as needed to achieve the purpose of the embodiments.

[0154] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A building energy consumption simulation method based on big data and BIM, characterized in that, include: Based on big data, a building energy consumption dataset is obtained; wherein, the building energy consumption dataset is used to reflect the types and energy consumption data of other buildings in the area where the building under test is located; The energy consumption data set is analyzed to obtain energy consumption fluctuations; wherein, the energy consumption fluctuations are used to reflect the degree of fluctuation in energy prices. Based on BIM, design parameters for the building to be measured are generated; wherein, the design parameters for the building to be measured include geometry, material properties and system configuration; The design parameters of the building to be tested are analyzed with the building energy consumption dataset to obtain the analog building energy consumption; wherein, the analog building energy consumption is used to reflect the energy consumption of buildings with the same design parameters as the building to be tested in the building energy consumption dataset; Based on the analog building energy consumption and the energy consumption fluctuation, simulation analysis is performed to obtain the energy consumption information of the building under test; wherein, the energy consumption information of the building under test is used to reflect the energy consumption of the building under test; The analysis based on the building energy consumption dataset to obtain energy consumption fluctuations includes: Based on the analysis of the aforementioned building energy consumption dataset, a building energy consumption fluctuation group is obtained; The energy consumption fluctuations are obtained by analyzing the aforementioned building energy consumption fluctuation groups; The step of analyzing the building energy consumption dataset to obtain the building energy consumption fluctuation group includes: Based on the analysis of the building energy consumption dataset, a first building energy consumption fluctuation group and a second building energy consumption fluctuation group are obtained; wherein, the first building energy consumption fluctuation group includes the energy consumption data of all buildings in the building energy consumption dataset within a preset analysis interval, and the second building energy consumption fluctuation group includes the energy consumption data of buildings whose design parameters are more similar to the building under test than a preset similarity within the preset analysis interval.

2. The building energy consumption simulation method based on big data and BIM as described in claim 1, characterized in that, The analysis based on the building energy consumption dataset to obtain energy consumption fluctuations includes: The first energy consumption fluctuation is obtained by analyzing the first building energy consumption fluctuation group; wherein, the first building energy consumption fluctuation is used to reflect the energy consumption fluctuation of all buildings in the first building energy consumption fluctuation group. The second energy consumption fluctuation is obtained by analyzing the second building energy consumption fluctuation group; wherein, the second building energy consumption fluctuation is used to reflect the energy consumption fluctuation of the buildings in the second building energy consumption fluctuation group; Based on the analysis of the first energy consumption fluctuation and the second energy consumption fluctuation, the energy consumption fluctuation is obtained.

3. The building energy consumption simulation method based on big data and BIM as described in claim 2, characterized in that, The step of analyzing the energy consumption fluctuations based on the first and second energy consumption fluctuations to obtain the energy consumption fluctuations includes: Based on the analysis of the first energy consumption fluctuation, a first current cycle fluctuation trend value and a first year-on-year cycle fluctuation trend value are obtained; wherein, the first current cycle fluctuation trend value is used to reflect the degree of energy consumption fluctuation of all buildings in the current preset analysis cycle, and the first year-on-year cycle fluctuation trend value is used to reflect the degree of energy consumption fluctuation of all buildings in the previous preset analysis cycle. Based on the analysis of the second energy consumption fluctuation, a second current cycle fluctuation trend value and a second year-on-year cycle fluctuation trend value are obtained; wherein, the second current cycle fluctuation trend value is used to reflect the degree of energy consumption fluctuation of buildings in the second building energy consumption fluctuation group in the current preset analysis period, and the second year-on-year cycle fluctuation trend value is used to reflect the degree of energy consumption fluctuation of buildings in the second building energy consumption fluctuation group in the previous preset analysis period. Energy consumption fluctuations are obtained by analyzing the first current cycle fluctuation trend value, the first year-on-year cycle fluctuation trend value, the second current cycle fluctuation trend value, and the second year-on-year cycle fluctuation trend value.

4. The building energy consumption simulation method based on big data and BIM as described in claim 3, characterized in that, The analysis based on the first current cycle fluctuation trend value, the first year-on-year cycle fluctuation trend value, the second current cycle fluctuation trend value, and the second year-on-year cycle fluctuation trend value to obtain energy consumption fluctuations includes: The first volatility index is obtained by averaging the first current cycle volatility trend value and the second current cycle volatility trend value. The second volatility index is obtained by calculating the difference between the first year-on-year cycle volatility trend value and the second year-on-year cycle volatility trend value. Energy consumption fluctuation is obtained by weighting the first fluctuation index and the second fluctuation index.

5. The building energy consumption simulation method based on big data and BIM as described in claim 1, characterized in that, The step of analyzing the building design parameters to be measured with the building energy consumption dataset to obtain analogous building energy consumption includes: The design parameters of the building to be tested are matched with the building energy consumption dataset to obtain geometric shape energy consumption, material property energy consumption, and system configuration energy consumption. The geometric shape energy consumption indicates the average energy consumption of buildings in the building energy consumption dataset with the same geometry as the building to be tested in the design parameters. The material property energy consumption indicates the average energy consumption of buildings in the building energy consumption dataset with the same material properties as the building to be tested in the design parameters. The system configuration energy consumption indicates the average energy consumption of buildings in the building energy consumption dataset with the same system configuration as the building to be tested in the design parameters. Based on the analysis of the energy consumption of the geometric shape, the energy consumption of the material properties, and the energy consumption of the system configuration, the energy consumption of the analog building is obtained.

6. The building energy consumption simulation method based on big data and BIM as described in claim 5, characterized in that, The analysis based on the energy consumption of the geometric shape, the energy consumption of the material properties, and the energy consumption of the system configuration yields the analogous building energy consumption, including: Based on the analysis of energy consumption according to the material properties, a material influence coefficient is obtained; wherein, the material influence coefficient is used to reflect the degree to which material properties affect energy consumption; Based on the analysis of the energy consumption of the geometric shape and the energy consumption of the system configuration, the energy consumption of similar buildings is obtained; wherein, the energy consumption of similar buildings is used to indicate the average of the energy consumption of the geometric shape and the energy consumption of the system configuration; The energy consumption of the analogous building is obtained by weighting the energy consumption of the similar building based on the material influence coefficient.

7. The building energy consumption simulation method based on big data and BIM as described in claim 1, characterized in that, The simulation analysis based on the analog building energy consumption and the energy consumption fluctuations yields the energy consumption information of the building under test, including: The energy consumption fluctuations are analyzed to obtain correction values; wherein, the correction values ​​reflect the degree of impact of the energy consumption fluctuations on the energy consumption of the analog building. The energy consumption of the analog building is corrected based on the correction value to obtain the energy consumption information of the building to be measured.

8. A building energy consumption simulation system based on big data and BIM, characterized in that, include: The acquisition module is used to acquire a building energy consumption dataset based on big data; wherein, the building energy consumption dataset is used to reflect the types and energy consumption data of other buildings in the area where the building under test is located; The first analysis module is used to analyze the building energy consumption dataset to obtain energy consumption fluctuations; wherein, the energy consumption fluctuations are used to reflect the degree of fluctuation in energy prices. The generation module is used to generate design parameters for a building under test based on BIM; wherein the design parameters for a building under test include geometry, material properties, and system configuration. The second analysis module is used to analyze the design parameters of the building to be tested and the building energy consumption dataset to obtain the analog building energy consumption; wherein, the analog building energy consumption is used to reflect the energy consumption of buildings in the building energy consumption dataset that have the same design parameters as the building to be tested. The third analysis module is used to perform simulation analysis based on the energy consumption of the analog building and the energy consumption fluctuation to obtain the energy consumption information of the building under test; wherein, the energy consumption information of the building under test is used to reflect the energy consumption of the building under test. Specifically, the first analysis module is used for: Based on the analysis of the aforementioned building energy consumption dataset, a building energy consumption fluctuation group is obtained; The energy consumption fluctuations are obtained by analyzing the aforementioned building energy consumption fluctuation groups; The step of analyzing the building energy consumption dataset to obtain the building energy consumption fluctuation group includes: Based on the analysis of the building energy consumption dataset, a first building energy consumption fluctuation group and a second building energy consumption fluctuation group are obtained; wherein, the first building energy consumption fluctuation group includes the energy consumption data of all buildings in the building energy consumption dataset within a preset analysis interval, and the second building energy consumption fluctuation group includes the energy consumption data of buildings whose design parameters are more similar to the building under test than a preset similarity within the preset analysis interval.

9. A building energy consumption simulation device based on big data and BIM, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1 to 7.

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