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

Abstract

The invention is suitable for a new generation of information technology industry and digital economy, and particularly relates to a building energy consumption simulation method, system and device based on big data and BIM, and the method comprises the steps: obtaining a building energy consumption data set based on the big data; analyzing according to the building energy consumption data set to obtain energy consumption fluctuation; based on the BIM, building design parameters to be tested are generated; analyzing the to-be-tested building design parameters and the building energy consumption data set to obtain analog building energy consumption; and performing simulation analysis according to the analog building energy consumption and the energy consumption fluctuation to obtain to-be-tested building energy consumption information. According to the method, building energy consumption is deeply analyzed in multiple dimensions, the limitation of a traditional evaluation method is overcome, multi-dimensional and dynamic energy consumption analysis is achieved by providing a regional and integral energy consumption evaluation view angle and combining refined modeling of the BIM technology, and therefore the precision and reliability of building energy consumption prediction are improved.

Description

Technical Field

[0001] The present application belongs to the field 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 Art

[0002] As the world pays more attention to energy efficiency and environmental protection, the management and optimization of building energy consumption has become an important topic in the construction industry. Buildings consume a lot 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, accurately predicting building energy consumption is of great significance to 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. In addition, traditional energy consumption assessments are usually based on data from a single building and lack a regional and holistic perspective. In addition, energy price fluctuations also have a significant impact on building operating costs, which leads to certain limitations in traditional building energy consumption assessment methods, making them inaccurate and inflexible. Summary of the invention

[0004] The embodiments of the present application provide a method, system and device for simulating building energy consumption 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, an embodiment of the present application provides a building energy consumption simulation method based on big data and BIM, comprising: Based on big data, a building energy consumption data set is obtained; wherein the building energy consumption data set is used to reflect the types and energy consumption data of other buildings in the area where the building to be tested is located; Analyzing the building energy consumption data set to obtain energy consumption fluctuations; wherein the energy consumption fluctuations are used to reflect the degree of fluctuation of energy prices; Based on BIM, generating design parameters of the building to be tested; wherein the design parameters of the building to be tested include geometric shape, material properties and system configuration; Analyze the design parameters of the building to be tested and the building energy consumption data set to obtain analog building energy consumption; wherein the analog building energy consumption is used to reflect the energy consumption of buildings in the building energy consumption data set that have the same design parameters as the building to be tested; A simulation analysis is performed based on the analog building energy consumption and the energy consumption fluctuation to obtain energy consumption information of the building to be tested; wherein the energy consumption information of the building to be tested is used to reflect the energy consumption situation of the building to be tested.

[0006] The above technical solutions in the embodiments of the present application have at least the following technical effects: The building energy consumption simulation method based on big data and BIM provided in this application obtains a building energy consumption data set based on big data to reflect the types and energy consumption data of other buildings in the area where the building to be tested is located; then analyzes the building energy consumption data set to obtain energy consumption fluctuations to reflect the degree of fluctuation of energy prices; then generates design parameters of the building to be tested including geometric shape, material properties and system configuration based on BIM; then analyzes the design parameters of the building to be tested with the building energy consumption data set to obtain analog building energy consumption to reflect the energy consumption of buildings similar to the building to be tested; simulates and analyzes the analog building energy consumption and energy consumption fluctuations to obtain energy consumption information of the building to be tested to reflect the energy consumption of the building to be tested. This method provides a regional and overall energy consumption assessment perspective by analyzing the data of other buildings in the region, which helps to identify commonalities and differences. Combined with the three-dimensional visualization and data correlation provided by BIM, it can conduct in-depth analysis of building energy consumption in multiple dimensions, and consider the fluctuation of energy prices to simulate building energy consumption, which not only overcomes the limitations of traditional evaluation methods, but also provides strong support for energy efficiency management and optimization of buildings, and promotes the process of green buildings and sustainable development. By providing regional and overall energy consumption assessment perspectives and combining them with refined modeling using BIM technology, multi-dimensional and dynamic energy consumption analysis is achieved, thereby improving the accuracy and reliability of building energy consumption forecasts.

[0007] In a possible implementation of the first aspect, analyzing the building energy consumption dataset to obtain energy consumption fluctuations includes: Analyze the building energy consumption data set to obtain a building energy consumption fluctuation group; Analyze the building energy consumption fluctuation group to obtain energy consumption fluctuation; The analysis based on the building energy consumption data set to obtain the building energy consumption fluctuation group includes: According to the building energy consumption data set, analysis is performed to obtain a first building energy consumption fluctuation group and a second building energy consumption fluctuation group of the building energy consumption fluctuation group; wherein the first building energy consumption fluctuation group includes energy consumption data of all buildings in the building energy consumption data set within a preset analysis interval, and the second building energy consumption fluctuation group includes energy consumption data of buildings whose similarity with the design parameters of the building to be tested is greater than a preset similarity within the preset analysis interval.

[0008] In a possible implementation of the first aspect, analyzing the building energy consumption dataset to obtain energy consumption fluctuations includes: Analyzing the first building energy consumption fluctuation group to obtain a first energy consumption fluctuation; 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; Analyzing the second building energy consumption fluctuation group to obtain a second energy consumption fluctuation; 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; An analysis is performed based on the first energy consumption fluctuation and the second energy consumption fluctuation to obtain energy consumption fluctuation.

[0009] In a possible implementation manner of the first aspect, the analyzing according to the first energy consumption fluctuation and the second energy consumption fluctuation to obtain the energy consumption fluctuation includes: Analyze the first energy consumption fluctuation to obtain a first current cycle fluctuation trend value and a first year-on-year cycle fluctuation trend value; 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; Analyze the second energy consumption fluctuation to obtain a second current cycle fluctuation trend value and a second year-on-year cycle fluctuation trend value; wherein the second current cycle fluctuation trend value is used to reflect the degree of energy consumption fluctuation of the 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 the buildings in the second building energy consumption fluctuation group in the previous preset analysis period; Energy consumption fluctuation is 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.

[0010] In a possible implementation manner of the first aspect, analyzing according to 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 fluctuation includes: Performing mean calculation based on the first current period fluctuation trend value and the second current period fluctuation trend value to obtain a first fluctuation index; Performing difference calculation based on the first year-on-year period fluctuation trend value and the second year-on-year period fluctuation trend value to obtain a second fluctuation index; Energy consumption fluctuation is obtained by performing weighted calculation according to the first fluctuation index and the second fluctuation index.

[0011] In a possible implementation of the first aspect, analyzing the design parameters of the building to be tested and the building energy consumption dataset to obtain the analog building energy consumption includes: Perform feature matching on the building design parameters to be tested and the building energy consumption data set to obtain geometric shape energy consumption, material property energy consumption and system configuration energy consumption; wherein the geometric shape energy consumption is used to indicate the average energy consumption of buildings in the building energy consumption data set with the same geometric shape as the building design parameters to be tested, the material property energy consumption is used to indicate the average energy consumption of buildings in the building energy consumption data set with the same material properties as the building design parameters to be tested, and the system configuration energy consumption is used to indicate the average energy consumption of buildings in the building energy consumption data set with the same system configuration as the building design parameters to be tested; The energy consumption of the analog building is obtained by analyzing the energy consumption of the geometric shape, the energy consumption of the material property and the energy consumption of the system configuration.

[0012] In a possible implementation manner of the first aspect, the analyzing according to the geometric shape energy consumption, the material property energy consumption, and the system configuration energy consumption to obtain the analog building energy consumption includes: Analyze the energy consumption of the material properties to obtain a material influence coefficient; wherein the material influence coefficient is used to reflect the degree to which the material properties affect the energy consumption; Analyze 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 the average value of the geometric shape energy consumption and the system configuration energy consumption; The energy consumption of the similar building is weighted based on the material influence coefficient to obtain the analog building energy consumption.

[0013] In a possible implementation of the first aspect, the 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 tested includes: Analyze the energy consumption fluctuation to obtain a correction value; wherein the correction value reflects the influence of the energy consumption fluctuation on the energy consumption of the analog building; The energy consumption of the analog building is corrected based on the correction value to obtain energy consumption information of the building to be measured.

[0014] In a second aspect, the embodiments of the present application provide a building energy consumption simulation system corresponding to any of the methods described in the first aspect, which may include: An acquisition module is used to acquire a building energy consumption data set based on big data; wherein the building energy consumption data set is used to reflect the types and energy consumption data of other buildings in the area where the building to be tested is located; A first analysis module is used to analyze the building energy consumption data set to obtain energy consumption fluctuations; wherein the energy consumption fluctuations are used to reflect the degree of fluctuation of energy prices; A generation module, used to generate design parameters of the building to be tested based on BIM; wherein the design parameters of the building to be tested include geometric shape, 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 data set to obtain analog building energy consumption; wherein the analog building energy consumption is used to reflect the energy consumption of buildings in the building energy consumption data set 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 analog building energy consumption and the energy consumption fluctuation to obtain energy consumption information of the building to be tested; wherein the energy consumption information of the building to be tested is used to reflect the energy consumption situation of the building to be tested.

[0015] In a third aspect, an embodiment of the present application provides a building energy consumption simulation device based on big data and BIM, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the methods described in the first aspect when executing the computer program.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above-mentioned first aspects is implemented.

[0017] In the fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a building energy consumption simulation device based on big data and BIM, the building energy consumption simulation device based on big data and BIM executes the building energy consumption simulation method based on big data and BIM described in any one of the first aspects above.

[0018] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 It is a flow chart of a building energy consumption simulation method based on big data and BIM provided in an embodiment of the present application; Figure 2 It is a schematic diagram of the implementation process of the building energy consumption simulation method based on big data and BIM provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of a building energy consumption simulation system based on big data and BIM provided in an embodiment of the present application; Figure 4 It is a structural schematic diagram of a building energy consumption simulation device based on big data and BIM provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may 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 to prevent unnecessary details from obstructing the description of the present application.

[0022] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0023] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0024] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if the described condition or event is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce the described condition or event is detected" or "in response to detecting the described condition or event" depending on the context.

[0025] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0026] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0027] As the world pays more attention to energy efficiency and environmental protection, the management and optimization of building energy consumption has become an important topic in the construction industry. Buildings consume a lot 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, accurately predicting building energy consumption is of great significance to achieving energy conservation and emission reduction goals.

[0028] However, traditional building energy consumption assessment methods often rely on static theoretical models and fail to fully consider dynamic factors in actual operation. In addition, traditional energy consumption assessments are usually based on data from a single building and lack a regional and holistic perspective. In addition, energy price fluctuations also have a significant impact on building operating costs, which leads to certain limitations in traditional building energy consumption assessment methods, making them inaccurate and inflexible.

[0029] To solve the above problems, the embodiments of the present application provide a method, system and device for simulating building energy consumption based on big data and BIM. In the method, based on big data, a building energy consumption data set is obtained to reflect the types and energy consumption data of other buildings in the area where the building to be tested is located; then, the building energy consumption data set is analyzed to obtain energy consumption fluctuations to reflect the degree of fluctuation of energy prices; then, based on BIM, design parameters of the building to be tested including geometric shape, material properties and system configuration are generated; then, the design parameters of the building to be tested are analyzed with the building energy consumption data set to obtain analog building energy consumption to reflect the energy consumption of buildings similar to the building to be tested; simulation analysis is performed based on the analog building energy consumption and energy consumption fluctuations to obtain energy consumption information of the building to be tested to reflect the energy consumption of the building to be tested. This method provides a regional and overall perspective on energy consumption assessment by analyzing the data of other buildings in the region, which helps to identify commonalities and differences. Combined with the three-dimensional visualization and data correlation provided by BIM, it can conduct in-depth analysis of building energy consumption in multiple dimensions, and consider the fluctuation of energy prices to simulate building energy consumption. It not only overcomes the limitations of traditional assessment 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 perspective on energy consumption assessment, combined with the refined modeling of BIM technology, a multi-dimensional and dynamic energy consumption analysis is achieved, thereby improving the accuracy and reliability of building energy consumption prediction.

[0030] The building energy consumption simulation method based on big data and BIM provided in the embodiment of the present application can be applied to the building energy consumption simulation equipment based on big data and BIM. At this time, the building energy consumption simulation equipment based on big data and BIM is the execution entity of the building energy consumption simulation method based on big data and BIM provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the building energy consumption simulation equipment based on big data and BIM.

[0031] For example, building energy consumption simulation equipment based on big data and BIM can be on terminal devices such as mobile phones, tablets, wearable devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), desktop computers, smart large screens, smart TVs, etc., handheld devices with wireless communication functions, computing devices or other processing devices connected to wireless modems, Internet of Things terminals, computers, laptops, handheld communication devices, handheld computing devices, satellite wireless devices, wireless modem cards, TV set top boxes (STB), customer premise equipment (CPE) and / or other devices used to communicate on wireless systems and next-generation communication systems, such as mobile terminals in 5G networks or mobile terminals in future evolved public land mobile networks (PLMN), etc.

[0032] In order to better understand the building energy consumption simulation method based on big data and BIM provided in the embodiment of the present application, the specific implementation process of the building energy consumption simulation method based on big data and BIM provided in the embodiment of the present application is exemplarily introduced below.

[0033] Figure 1 and Figure 2 A schematic flow chart of a building energy consumption simulation method based on big data and BIM provided in an embodiment of the present application is shown. The building energy consumption simulation method based on big data and BIM includes: S100, based on big data, obtaining a building energy consumption data set; wherein the building energy consumption data set is used to reflect the types and energy consumption data of other buildings in the area where the building to be tested is located.

[0034] It can be understood that the region refers to the geographical scope (such as South China, North China, etc.) or administrative region (such as the province, city, etc.) where the building to be tested is located. The set range of the region is pre-set and can be manually input or obtained from the building energy consumption database, etc., but is not limited to this. The building energy consumption database refers to a database that includes the scope of the region, the types of buildings collected in the region, and the corresponding energy consumption data. These data can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After obtaining, the collected data will be sorted, classified and archived, useful information and rules will be extracted, and the relevant data will be saved in the database to form a building energy consumption database. For example, the types and energy consumption data of other buildings in the area where the building to be tested is located can be obtained through questionnaires, on-site field measurements, or through web crawler technology to capture building energy consumption information on public websites, and integrate the obtained information to form a building energy consumption data set.

[0035] S200, analyzing the building energy consumption data set to obtain energy consumption fluctuations; wherein the energy consumption fluctuations are used to reflect the degree of fluctuation of energy prices.

[0036] It can be understood that the price of energy refers to the price of electric energy. This is the main form of energy consumption in buildings, and its price fluctuation has a significant impact on the overall energy consumption cost of buildings. When the price of electric energy decreases, the energy consumption fluctuation is negative, and when the price of electric energy increases, the energy consumption fluctuation is positive. Exemplarily, the building energy consumption data set can be classified to obtain the energy consumption of buildings similar to the building to be tested, and then the energy consumption fluctuation can be obtained by comprehensively measuring the energy consumption trend of all buildings and the periodic changes in the energy consumption of buildings similar to the building to be tested; the building energy consumption data set can also be input into the learning model, and the learning model outputs the corresponding energy consumption fluctuation, etc., but is not limited to this. The learning model is trained with multiple sets of training data, and each set of training data includes a building energy consumption data set and energy consumption fluctuations, that is, the energy consumption data of multiple buildings in a continuous time and the corresponding energy consumption fluctuations are used as training samples to train the learning model (such as a neural network, a decision tree, etc.).

[0037] In a possible implementation, in step S200, analyzing the building energy consumption data set to obtain energy consumption fluctuations includes: S210, analyzing the building energy consumption data set to obtain a building energy consumption fluctuation group.

[0038] It can be understood that the building energy consumption fluctuation group refers to a collection of all or part of the buildings in the building energy consumption data set of specific data on energy consumption changes. Exemplarily, by setting a specified time or interval, all building energy consumption data in the recent or specified time in the building energy consumption data set and energy consumption data of buildings with similar or identical design parameters to the building to be tested in the recent or specified time can be extracted as two different fluctuation groups in the building energy consumption fluctuation group; all buildings in the building energy consumption data set can also be divided into high energy consumption groups and low energy consumption groups in the building energy consumption fluctuation group according to the energy consumption of the buildings, etc., but not limited to this.

[0039] In a possible implementation, in step S210, the building energy consumption data set is analyzed to obtain a building energy consumption fluctuation group, including: S211, analyzing the building energy consumption data set to obtain a first building energy consumption fluctuation group and a second building energy consumption fluctuation group of the building energy consumption fluctuation group; wherein the first building energy consumption fluctuation group includes energy consumption data of all buildings in the building energy consumption data set within a preset analysis interval, and the second building energy consumption fluctuation group includes energy consumption data of buildings whose similarity with the design parameters of the building to be tested is greater than a preset similarity within a preset analysis interval.

[0040] It can be understood that the preset analysis interval is a preset time interval, which can be manually input by a person, or obtained from the building energy consumption database, etc., but is not limited to this. For example, the preset analysis interval can be 2 years, 3 years, etc., but is not limited to this. Similarity refers to the comprehensive similarity of the geometric shape, material properties and system configuration of the design parameters of the building. It can be obtained by calculating the similarity of the geometric shape, the similarity of the material properties and the similarity of the system configuration, and then calculating the comprehensive similarity through weight or mean, etc., but is not limited to this. The preset similarity is a preset similarity value, which can be manually input by a person, or obtained from the building energy consumption database, etc., but is not limited to this.

[0041] With this setting, without considering the design parameters of the building, the first building energy consumption fluctuation group can reflect the energy consumption characteristics and fluctuations of the entire building in the area, and capture the overall trend of the electricity energy price; and through the second building energy consumption fluctuation group with a high degree of similarity to the design parameters of the building to be tested, it can focus more on the specific use of electric energy in the building type to be tested, which not only reflects the inherent energy consumption pattern of this type of building, but also reveals the specific behavioral habits of building users to a certain extent, thereby providing strong support for accurate energy consumption prediction and personalized energy efficiency management.

[0042] S220, analyzing the building energy consumption fluctuation group to obtain energy consumption fluctuation.

[0043] Exemplarily, the energy consumption fluctuation degrees of the first building energy consumption fluctuation group and the second building energy consumption fluctuation group in the building energy consumption fluctuation group can be analyzed separately, and then the two energy consumption fluctuation degrees can be comprehensively analyzed to obtain the energy consumption fluctuation; the building energy consumption fluctuation group can also be input into the learning model, and the learning model can then output the corresponding energy consumption fluctuation, and so on, but not limited to this.

[0044] In a possible implementation, in step S220, analyzing the building energy consumption fluctuation group to obtain energy consumption fluctuations includes: S221, analyzing the first building energy consumption fluctuation group to obtain a first energy consumption fluctuation; 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.

[0045] Exemplarily, the mean calculation can be performed based on the energy consumption fluctuation of each building in the first building energy consumption fluctuation group, that is, the set of means within the preset analysis interval is taken as the first energy consumption fluctuation; the energy consumption fluctuation of a representative building in the first building energy consumption fluctuation group can also be taken as the first energy consumption fluctuation, and so on, but not limited to this.

[0046] S222, analyzing the second building energy consumption fluctuation group to obtain a second energy consumption fluctuation; 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.

[0047] It can be understood that the fluctuation of the average value of energy consumption of all buildings in the second building energy consumption fluctuation group within a preset analysis interval can be used as the second energy consumption fluctuation.

[0048] S223, analyzing the first energy consumption fluctuation and the second energy consumption fluctuation to obtain energy consumption fluctuation.

[0049] Exemplarily, the first energy consumption fluctuation and the second energy consumption fluctuation can be analyzed separately to obtain the degree of energy consumption fluctuation in the current analysis period in the preset analysis interval and the degree of energy consumption fluctuation in the previous analysis period in the preset analysis interval, and then the energy consumption fluctuation is obtained through comprehensive analysis; the first energy consumption fluctuation and the second energy consumption fluctuation can also be input into the learning model, and the learning model outputs the corresponding energy consumption fluctuation, and so on, but not limited to this.

[0050] In this way, the first energy consumption fluctuation reflects the electricity consumption of all buildings in the area and its overall trend, while the second energy consumption fluctuation focuses on the energy consumption characteristics of specific types of buildings similar to the building to be tested. By comparing and combining these two fluctuations, the energy consumption changes of the building to be tested can be predicted more comprehensively and accurately.

[0051] In a possible implementation, in step S223, analyzing the first energy consumption fluctuation and the second energy consumption fluctuation to obtain the energy consumption fluctuation includes: S2231, analyze according to the first energy consumption fluctuation to obtain a first current cycle fluctuation trend value and a first year-on-year cycle fluctuation trend value; 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.

[0052] It can be understood that the preset analysis period is a pre-set time period, which can be manually input or obtained from the building energy consumption database, etc., but is not limited to this. For example, 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 cycle is April to June of the current year, and the first year-on-year cycle is April to June of the previous year. The first current cycle fluctuation trend value is the degree of energy consumption fluctuation from April to June of the current year, and the first year-on-year cycle fluctuation trend value is the degree of energy consumption fluctuation from April to June of the previous year; assuming that the current month is May, the first current cycle is March to May of the current year, and the first year-on-year cycle is May to June of the previous year, and so on. The calculation of the first current cycle fluctuation trend value and the first year-on-year cycle fluctuation trend value can be quantitatively calculated by constructing a volatility index, for example, through an exponential function , respectively calculate the first current period fluctuation trend value and the first year-on-year period fluctuation trend value; where r i is the relative rate of change at each time point, r j is the average relative rate of change, 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 the learning model, and the learning model outputs the first current cycle fluctuation trend value and the first year-on-year cycle fluctuation trend value, etc., but is not limited to this.

[0053] S2232, analyze according to the second energy consumption fluctuation to obtain a second current cycle fluctuation trend value and a second year-on-year cycle fluctuation trend value; wherein, the second current cycle fluctuation trend value is used to reflect the degree of energy consumption fluctuation of the buildings in the second building energy consumption fluctuation group within 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 the buildings in the second building energy consumption fluctuation group within the previous preset analysis period.

[0054] It can be understood that the second current cycle fluctuation trend value and the second year-on-year cycle fluctuation trend value can be obtained in a similar manner to the first current cycle fluctuation trend value and the first year-on-year cycle fluctuation trend value in step S2231, which will not be repeated here.

[0055] S2233, analyzing according to 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 fluctuation.

[0056] Exemplarily, the mean can be calculated by using the first current cycle fluctuation trend value and the second current cycle fluctuation trend value, and then analysis and calculation can be performed based on the first year-on-year cycle fluctuation trend value and the second year-on-year cycle fluctuation trend value, and then the energy consumption fluctuation can be obtained by weighted summation; 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 can also be input into a learning model, and the learning model outputs the corresponding energy consumption fluctuation, and so on, but is not limited to this.

[0057] With this setting, by systematically 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, it can comprehensively reflect the consumption of building electric energy in the area and its overall trend, while focusing more on the specific usage of specific types of buildings similar to the building to be tested. This method significantly improves the accuracy and reliability of energy consumption forecasts and provides a scientific basis for building operating cost control and energy conservation and emission reduction. By combining the fluctuation trends of the current and year-on-year cycles, it can not only capture short-term fluctuations, but also reveal long-term change patterns, improving the timeliness and foresight of the analysis results.

[0058] In a possible implementation, in step S2233, the energy consumption fluctuation is 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, including: S22331, calculate the mean of the first current period fluctuation trend value and the second current period fluctuation trend value to obtain a first fluctuation index.

[0059] It can be understood that the first fluctuation index = (the first current period fluctuation trend value + the second current period fluctuation trend value) ÷ 2.

[0060] S22332, performing difference calculation based on the first year-on-year period fluctuation trend value and the second year-on-year period fluctuation trend value to obtain a second fluctuation index.

[0061] It can be understood that the second volatility index = the second year-on-year cycle volatility trend value - the first year-on-year cycle volatility trend value.

[0062] S22333: Perform weighted calculation based on the first fluctuation index and the second fluctuation index to obtain energy consumption fluctuation.

[0063] It can be understood that the weight of the first fluctuation index and the weight of the second fluctuation index can be manually input or obtained from a building energy consumption database, etc., but is not limited thereto.

[0064] With this setting, the first fluctuation index reflects the average energy consumption fluctuation degree between all buildings in the current cycle and specific types of buildings similar to the tested building, which helps to identify the comprehensive energy consumption characteristics and fluctuations of different building types in the current cycle; the second fluctuation index reflects the difference in energy consumption fluctuations between all buildings and specific types of buildings in the previous cycle, which provides historical background support for the energy consumption fluctuations of different building types in past cycles; by combining the fluctuation trends of the current cycle and the year-on-year cycle, it can capture the changing pattern of building energy consumption more comprehensively, which not only improves the accuracy and reliability of energy consumption forecasts, but also provides strong support for the energy efficiency management and optimization of buildings. This method significantly enhances the scientific nature and flexibility of decision-making.

[0065] S300, generating design parameters of the building to be tested based on BIM; wherein the design parameters of the building to be tested include geometric shape, material properties and system configuration.

[0066] It can be understood that BIM refers to data modeling technology used for building design, construction and management. It not only creates a three-dimensional geometric model of a building, but also integrates various building-related information, such as structure, materials, equipment, cost and schedule. Geometry refers to the physical form and spatial layout of the building to be tested, which can specifically include the building's appearance, internal structure, room layout, floor height, etc. Material properties refer to the various materials used in the building to be tested and the corresponding physical and chemical properties, such as wall materials that reflect thermal insulation performance and glass materials that reflect light transmittance. System configuration refers to the settings and operating parameters of various electromechanical systems and intelligent systems in the building to be tested, such as HVAC systems, water supply and drainage systems, electrical lighting systems, building automation systems, etc.

[0067] S400, analyzing the design parameters of the building to be tested and the building energy consumption data set to obtain analog building energy consumption; wherein the analog building energy consumption is used to reflect the energy consumption of buildings in the building energy consumption data set with the same design parameters as the building to be tested.

[0068] It can be understood that by matching the design parameters of the building to be tested with the building energy consumption data set, the analog building energy consumption can be obtained. Exemplarily, the geometry, material properties and system configuration of the building to be tested can be matched with the design parameters of all buildings in the building energy consumption data set, respectively, to obtain a set of buildings in the building energy consumption data set with the same geometry as the building to be tested, a set of buildings in the building energy consumption data set with the same material properties as the building to be tested, and a set of buildings in the building energy consumption data set with the same system configuration as the building to be tested, and then comprehensively analyze the energy consumption data of the buildings in each set to obtain the analog building energy consumption; the design parameters of the building to be tested and the building energy consumption data set can also be input into the learning model, and the learning model outputs the corresponding analog building energy consumption, etc., but is not limited to this.

[0069] In a possible implementation, in step S400, the design parameters of the building to be tested and the building energy consumption data set are analyzed to obtain the analog building energy consumption, including: S410, feature matching is performed on the design parameters of the building to be tested and the building energy consumption data set to obtain geometric shape energy consumption, material property energy consumption and system configuration energy consumption; wherein, the geometric shape energy consumption is used to indicate the average energy consumption of buildings in the building energy consumption data set that have the same geometric shape as the design parameters of the building to be tested, the material property energy consumption is used to indicate the average energy consumption of buildings in the building energy consumption data set that have the same material properties as the design parameters of the building to be tested, and the system configuration energy consumption is used to indicate the average energy consumption of buildings in the building energy consumption data set that have the same system configuration as the design parameters of the building to be tested.

[0070] It can be understood that after feature matching of the geometric shape of the building design parameters to be tested with the building energy consumption data set, a set of buildings with the same geometric shape as the building design parameters to be tested among all the buildings in the building energy consumption data set is obtained, and then the energy consumption data of each building in the set is summed up and the mean is calculated, which is the geometric shape energy consumption. After feature matching of the material properties of the building design parameters to be tested with the building energy consumption data set, a set of buildings with the same material properties as the building design parameters to be tested among all the buildings in the building energy consumption data set is obtained, and then the energy consumption data of each building in the set is summed up and the mean is calculated, which is the material property energy consumption. After feature matching of the system configuration of the building design parameters to be tested with the building energy consumption data set, a set of buildings with the same system configuration as the building design parameters to be tested among all the buildings in the building energy consumption data set is obtained, and then the energy consumption data of each building in the set is summed up and the mean is calculated, which is the system configuration energy consumption.

[0071] The method for judging whether the geometric shapes of all buildings in the building energy consumption data set are the same as the design parameters of the building to be tested can be comprehensively judged by the area, height and room layout reflected in the geometric shapes, that is, the area similarity, height similarity and room layout similarity are calculated respectively, and then the geometric shape similarity is obtained by averaging or weighted summing the area similarity, height similarity and room layout similarity. If the geometric shape similarity is greater than or equal to the preset geometric shape similarity, it is judged that the geometric shape of the building is the same as the design parameters of the building to be tested. If the geometric shape similarity is less than the preset geometric shape similarity, it is judged that the geometric shape of the building is different from the design parameters of the building to be tested. The method for judging whether the material properties of all buildings in the building energy consumption data set are the same as the design parameters of the building to be tested and the system configuration is the same is similar, which will not be repeated here.

[0072] S420, analyze the energy consumption of the analog building based on the energy consumption of the geometric shape, the energy consumption of the material properties and the energy consumption of the system configuration, and obtain the energy consumption of the analog building.

[0073] Exemplarily, an analysis can be performed based on the geometric shape energy consumption and the system configuration energy consumption to obtain a building energy consumption, and then the analysis can be performed based on the material property energy consumption to obtain a weight, and the building energy consumption can be weighted based on the weight to obtain an analog building energy consumption; the geometric shape energy consumption, material property energy consumption and system configuration energy consumption can also be calculated by taking the average or weighted sum, and the calculated value can be used as the analog building energy consumption, etc., but is not limited to this.

[0074] With this setup, by feature matching the design parameters of the building to be tested with the building energy consumption dataset, the average energy consumption of the geometric shape, material properties and system configuration are calculated respectively, and a comprehensive analysis based on these energy consumption values ​​is performed to obtain the analog building energy consumption, which significantly improves the accuracy and scientificity of energy consumption prediction: this method not only considers the specific impact of different factors on energy consumption in a refined manner, simplifies the complexity of the analysis process and improves calculation efficiency, but also enhances flexibility and adaptability, and can dynamically adjust to adapt to new data and technologies.

[0075] In a possible implementation, in step S420, the energy consumption of the analog building is obtained by analyzing the energy consumption of the geometric shape, the energy consumption of the material property, and the energy consumption of the system configuration, including: S421, analyzing the energy consumption according to the material properties to obtain the material influence coefficient; wherein the material influence coefficient is used to reflect the degree to which the material properties affect the energy consumption.

[0076] It can be understood that different material property energy consumptions correspond to a material influence coefficient. The material property energy consumption can be matched with the material property energy consumption interval in the building energy consumption database to obtain the material influence coefficient corresponding to the material property energy consumption interval; the material property energy consumption can also be input into the learning model, and the learning model outputs the corresponding material influence coefficient, etc., but not limited to this.

[0077] S422, analyzing 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 the average of the geometric shape energy consumption and the system configuration energy consumption.

[0078] It can be understood that building energy consumption = (geometric shape energy consumption + system configuration energy consumption) ÷ 2.

[0079] S423, weighting the energy consumption of similar buildings based on the material influence coefficient to obtain the analogous building energy consumption.

[0080] It can be understood that the energy consumption of analogous buildings = material impact coefficient × similar building energy consumption.

[0081] With this setting, material properties (such as thermal conductivity and light transmittance of wall, roof, window and other materials) have a direct and significant impact on building energy consumption. Differences in thermal performance of different materials can lead to significantly different energy consumption results for the same building under the same conditions of use. By analyzing the energy consumption of material properties separately and calculating the material influence coefficient, the specific impact of material properties on energy consumption can be more finely reflected. This method ensures that the prediction model can capture subtle changes caused by material selection, improving the accuracy and scientificity of the prediction; the geometry and system configuration provide a reference for the basic energy consumption level, while the material properties further refine the energy consumption impact, making the final analog building energy consumption closer to the actual situation. This method not only improves the prediction accuracy, but also enhances the reliability and practicality of the model.

[0082] S500, performing simulation analysis based on the analog building energy consumption and energy consumption fluctuation to obtain energy consumption information of the building to be tested; wherein the energy consumption information of the building to be tested is used to reflect the energy consumption situation of the building to be tested.

[0083] For example, the degree of influence of the current electricity price fluctuation on the energy consumption calculation can be obtained through energy consumption fluctuation analysis, and then the energy consumption information of the building to be tested can be obtained by combining the analog building energy consumption with analysis; the analog building energy consumption and energy consumption fluctuation can also be input into the learning model, and the learning model outputs the corresponding energy consumption information of the building to be tested.

[0084] In a possible implementation, in step S500, a simulation analysis is performed based on the analog building energy consumption and energy consumption fluctuation to obtain energy consumption information of the building to be tested, including: S510, analyzing the energy consumption fluctuation to obtain a correction value; wherein the correction value reflects the influence of the energy consumption fluctuation on the energy consumption of the analog building.

[0085] It is understandable that different energy consumption fluctuations correspond to a correction value. The energy consumption fluctuations can be matched with different energy consumption fluctuation intervals in the building energy consumption database to obtain a correction value corresponding to the energy consumption fluctuation interval that matches the energy consumption fluctuations.

[0086] S520, correcting the analog building energy consumption based on the correction value to obtain energy consumption information of the building to be tested.

[0087] It can be understood that the energy consumption information of the building to be tested = correction value + analog building energy consumption.

[0088] With this setting, by analyzing the fluctuation of energy consumption and calculating the correction value, this method can dynamically reflect the changes in energy consumption in actual operation, thereby improving the real-time and accuracy of the prediction results; secondly, the energy consumption of the analog building is corrected based on the correction value, ensuring that the energy consumption information of the building to be tested not only considers the influence of static design parameters, but also fully reflects the energy consumption characteristics under actual operating conditions.

[0089] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0090] 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, and the various modules of the system can implement the various steps of the building energy consumption simulation method based on big data and BIM. Figure 3 A structural block diagram of a building energy consumption simulation system based on big data and BIM provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0091] Reference Figure 3 , the building energy consumption simulation system based on big data and BIM includes: The acquisition module is used to acquire a building energy consumption data set based on big data; wherein the building energy consumption data set is used to reflect the types and energy consumption data of other buildings in the area where the building to be tested is located.

[0092] The first analysis module is used to analyze the building energy consumption data set to obtain energy consumption fluctuations; wherein the energy consumption fluctuations are used to reflect the degree of fluctuation of energy prices.

[0093] The generation module is used to generate design parameters of the building to be tested based on BIM; wherein the design parameters of the building to be tested include geometric shape, material properties and system configuration.

[0094] The second analysis module is used to analyze the design parameters of the building to be tested and the building energy consumption data set 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 data set with the same design parameters as the building to be tested.

[0095] The third analysis module is used to perform simulation analysis based on analog building energy consumption and energy consumption fluctuations to obtain energy consumption information of the building to be tested; wherein the energy consumption information of the building to be tested is used to reflect the energy consumption situation of the building to be tested.

[0096] It should be noted that the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0097] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above modules is used as an example for illustration. In actual applications, the above functions can be assigned to different modules as needed, that is, the internal structure of the system can be divided into different modules to complete all or part of the functions described above. The modules in the embodiment can be integrated into a processing unit, or each module can exist physically alone, or two or more modules can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the modules are only for the convenience of distinguishing from each other, and are not used to limit the scope of protection of this application. The specific working process of the modules in the above system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0098] An embodiment of the present application also provides a building energy consumption simulation device based on big data and BIM. Figure 4 This is a schematic diagram of the structure of a building energy consumption simulation device 6 based on big data and BIM provided in an embodiment of the present application. Figure 4 As shown, the building energy consumption simulation device 6 based on big data and BIM in this embodiment includes: at least one processor 60 ( Figure 4 Only one is shown), at least one memory 61 ( Figure 4Only one is shown in the figure) and a 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, the building energy consumption simulation device 6 based on big data and BIM implements the steps in any of the above-mentioned building energy consumption simulation method embodiments based on big data and BIM, or the building energy consumption simulation device 6 based on big data and BIM implements the functions of each module in the above-mentioned system embodiments.

[0099] Exemplarily, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the building energy consumption simulation device 6 based on big data and BIM.

[0100] The building energy consumption simulation device 6 based on big data and BIM can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The building energy consumption simulation device based on big data and BIM can include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 4 It is only an example of a building energy consumption simulation device 6 based on big data and BIM, and does not constitute a limitation of the building energy consumption simulation device 6 based on big data and BIM. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, buses, etc.

[0101] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0102] In some embodiments, the memory 61 may be an internal storage unit of the building energy consumption simulation device 6 based on big data and BIM, such as a hard disk or memory of the building energy consumption simulation device 6 based on big data and BIM. In other embodiments, the memory 61 may also be an external storage device of the building energy consumption simulation device 6 based on big data and BIM, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the building energy consumption simulation device 6 based on big data and BIM. Further, the memory 61 may also include both the internal storage unit of the building energy consumption simulation device 6 based on big data and BIM and an external storage device. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data and other programs, such as the program code 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.

[0103] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0104] An embodiment of the present application provides a computer program product. When the computer program product runs on a building energy consumption simulation device based on big data and BIM, the building energy consumption simulation device based on big data and BIM implements the steps in any of the above-mentioned method embodiments.

[0105] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the building energy consumption simulation equipment based on big data and BIM, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0106] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0107] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0108] In the embodiments provided in the present application, it should be understood that the disclosed building energy consumption simulation equipment 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 schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0109] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all 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 data set is obtained; wherein the building energy consumption data set is used to reflect the types and energy consumption data of other buildings in the area where the building to be tested is located; Analyzing the building energy consumption data set to obtain energy consumption fluctuations; wherein the energy consumption fluctuations are used to reflect the degree of fluctuation of energy prices; Based on BIM, generating design parameters of the building to be tested; wherein the design parameters of the building to be tested include geometric shape, material properties and system configuration; Analyze the design parameters of the building to be tested and the building energy consumption data set to obtain analog building energy consumption; wherein the analog building energy consumption is used to reflect the energy consumption of buildings in the building energy consumption data set that have the same design parameters as the building to be tested; A simulation analysis is performed based on the analog building energy consumption and the energy consumption fluctuation to obtain energy consumption information of the building to be tested; wherein the energy consumption information of the building to be tested is used to reflect the energy consumption situation of the building to be tested.

2. The building energy consumption simulation method based on big data and BIM as claimed in claim 1, characterized in that: The analyzing the building energy consumption data set to obtain energy consumption fluctuations includes: Analyze the building energy consumption data set to obtain a building energy consumption fluctuation group; Analyze the building energy consumption fluctuation group to obtain energy consumption fluctuation; The analysis based on the building energy consumption data set to obtain the building energy consumption fluctuation group includes: According to the building energy consumption data set, analysis is performed to obtain a first building energy consumption fluctuation group and a second building energy consumption fluctuation group of the building energy consumption fluctuation group; wherein the first building energy consumption fluctuation group includes energy consumption data of all buildings in the building energy consumption data set within a preset analysis interval, and the second building energy consumption fluctuation group includes energy consumption data of buildings whose similarity with the design parameters of the building to be tested is greater than a preset similarity within the preset analysis interval.

3. The building energy consumption simulation method based on big data and BIM as claimed in claim 2, characterized in that: The analyzing the building energy consumption data set to obtain energy consumption fluctuations includes: Analyzing the first building energy consumption fluctuation group to obtain a first energy consumption fluctuation; 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; Analyzing the second building energy consumption fluctuation group to obtain a second energy consumption fluctuation; 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; An analysis is performed based on the first energy consumption fluctuation and the second energy consumption fluctuation to obtain energy consumption fluctuation.

4. The building energy consumption simulation method based on big data and BIM as claimed in claim 3 is characterized in that: The analyzing the first energy consumption fluctuation and the second energy consumption fluctuation to obtain the energy consumption fluctuation includes: Analyze the first energy consumption fluctuation to obtain a first current cycle fluctuation trend value and a first year-on-year cycle fluctuation trend value; 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; Analyze the second energy consumption fluctuation to obtain a second current cycle fluctuation trend value and a second year-on-year cycle fluctuation trend value; wherein the second current cycle fluctuation trend value is used to reflect the degree of energy consumption fluctuation of the 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 the buildings in the second building energy consumption fluctuation group in the previous preset analysis period; Energy consumption fluctuation is 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.

5. The building energy consumption simulation method based on big data and BIM as claimed in claim 4, characterized in that: The analyzing according to 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 fluctuation includes: Performing mean calculation based on the first current period fluctuation trend value and the second current period fluctuation trend value to obtain a first fluctuation index; Performing difference calculation based on the first year-on-year period fluctuation trend value and the second year-on-year period fluctuation trend value to obtain a second fluctuation index; Energy consumption fluctuation is obtained by performing weighted calculation according to the first fluctuation index and the second fluctuation index.

6. The building energy consumption simulation method based on big data and BIM according to claim 1, characterized in that: The analyzing the design parameters of the building to be tested and the building energy consumption data set to obtain analog building energy consumption includes: Perform feature matching on the building design parameters to be tested and the building energy consumption data set to obtain geometric shape energy consumption, material property energy consumption and system configuration energy consumption; wherein the geometric shape energy consumption is used to indicate the average energy consumption of buildings in the building energy consumption data set with the same geometric shape as the building design parameters to be tested, the material property energy consumption is used to indicate the average energy consumption of buildings in the building energy consumption data set with the same material properties as the building design parameters to be tested, and the system configuration energy consumption is used to indicate the average energy consumption of buildings in the building energy consumption data set with the same system configuration as the building design parameters to be tested; The energy consumption of the analog building is obtained by analyzing the energy consumption of the geometric shape, the energy consumption of the material property and the energy consumption of the system configuration.

7. The building energy consumption simulation method based on big data and BIM as claimed in claim 6, characterized in that: The energy consumption of the analog building is obtained by analyzing the energy consumption of the geometric shape, the energy consumption of the material property and the energy consumption of the system configuration, including: Analyze the energy consumption of the material properties to obtain a material influence coefficient; wherein the material influence coefficient is used to reflect the degree to which the material properties affect the energy consumption; Analyze 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 the average value of the geometric shape energy consumption and the system configuration energy consumption; The energy consumption of the similar building is weighted based on the material influence coefficient to obtain the analog building energy consumption.

8. The building energy consumption simulation method based on big data and BIM as claimed in claim 1, characterized in that: The simulating and analyzing the analog building energy consumption and the energy consumption fluctuation to obtain the energy consumption information of the building to be tested includes: Analyze the energy consumption fluctuation to obtain a correction value; wherein the correction value reflects the influence of the energy consumption fluctuation on the energy consumption of the analog building; The energy consumption of the analog building is corrected based on the correction value to obtain energy consumption information of the building to be measured.

9. A building energy consumption simulation system based on big data and BIM, characterized in that: include: An acquisition module is used to acquire a building energy consumption data set based on big data; wherein the building energy consumption data set is used to reflect the types and energy consumption data of other buildings in the area where the building to be tested is located; A first analysis module is used to analyze the building energy consumption data set to obtain energy consumption fluctuations; wherein the energy consumption fluctuations are used to reflect the degree of fluctuation of energy prices; A generation module, used to generate design parameters of the building to be tested based on BIM; wherein the design parameters of the building to be tested include geometric shape, 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 data set to obtain analog building energy consumption; wherein the analog building energy consumption is used to reflect the energy consumption of buildings in the building energy consumption data set 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 analog building energy consumption and the energy consumption fluctuation to obtain energy consumption information of the building to be tested; wherein the energy consumption information of the building to be tested is used to reflect the energy consumption situation of the building to be tested.

10. A building energy consumption simulation device based on big data and BIM, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

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