Typical meteorological year data generation method and equipment for building overheating evaluation

Abstract

The invention discloses a typical meteorological year data generation method and equipment for building overheating evaluation, and relates to the technical field of building comprehensive energy and intelligent control. The method comprises the steps that long-term meteorological data are acquired, and the long-term meteorological data at least comprise data related to outdoor parameters of the following items; dry-bulb temperature, relative humidity, wind speed and global solar radiation; based on the long-term meteorological data, overheat simulation in a free operation mode is conducted on the target building through a building energy simulation tool, so that indoor temperature data are obtained; calculating a correlation coefficient of each outdoor parameter in the indoor temperature data and the long-term meteorological data; based on the correlation coefficient, distributing a dynamic weight for each outdoor parameter; and generating typical meteorological year data and typical month selection based on the dynamic weight of each outdoor parameter. According to the invention, precision and target guidance of typical meteorological year data are realized.

Description

Technical Field

[0001] This invention relates to the field of integrated building energy and intelligent control technology, specifically to a method and device for generating typical meteorological year data for building overheating assessment. Background Technology

[0002] Existing technologies involve methods for generating typical meteorological year (TMY) data for building energy simulation and overheating assessment. Traditional solutions are based on the Finkelstein-Schafer (FS) statistical method, proposed by Sandia Laboratories and the European Committee for Standardization (ISO). Its core principle is to select typical months using fixed weighting factors to represent the distribution characteristics of long-term meteorological data. These methods are widely used in building energy consumption and overheating risk simulation, but they do not consider the correlation between indoor thermal environment and outdoor parameters.

[0003] The existing general method for generating TMY involves data preparation, building simulation, selection of typical months, and fixed weight allocation, mainly including the following four steps:

[0004] Data preparation: Parameters such as dry-bulb temperature, relative humidity, wind speed and global solar radiation are extracted from long-term meteorological databases, but raw observation data or simple correction methods are usually used without bias correction to adapt to future climate scenarios.

[0005] Typical month selection: The FS statistic is calculated by comparing the annual cumulative distribution function (CDF) with the long-term CDF. For example, the formula for calculating the FS statistic is shown below:

[0006]

[0007] in, This represents the distribution of meteorological parameter p values ​​in the mo-th month; Indicates the first The annual distribution of meteorological parameter p values; Indicates the day; This indicates the rank in the daily average sequence for a predetermined number of years; N represents the number of years. This indicates the rank within the daily average sequence for each year; Indicates the number of days; Let p represent the daily distribution of meteorological parameter p in the mo-th month of year y; This represents the final composite score for the mo-th month in year y. This represents the weight, which is a fixed value; Indicates the number of parameters.

[0008] Weighting: Fixed weights are used. For example, in the Sandia method, the average solar radiation has a weight of 12 / 24, while the average dry-bulb temperature has a weight of only 2 / 24; in the ISO method, dry-bulb temperature, relative humidity, and solar radiation each have a weight of 1 / 3. These fixed weights are based on historical data experience and are not adjusted according to climate zone or simulation target, as shown.

[0009] surface Traditional methods use fixed weights for each parameter.

[0010]

[0011] Output TMY data: The final output generates typical meteorological year data for building simulation, but the simulation results only reflect the average level of outdoor parameters and are not correlated with the indoor thermal environment.

[0012] Overall, existing technical solutions rely on static weights and general processes, lacking adaptability to specific building requirements. Summary of the Invention

[0013] This invention addresses the problem that existing technical solutions rely on static weights and general processes, lacking adaptability to specific building needs. It provides a method, device, storage medium, and program product for generating typical meteorological year data for building overheating assessment, achieving accuracy and target orientation of TMY data, thereby enabling its use in building overheating risk assessment.

[0014] The present invention is achieved through the following technical solution.

[0015] In a first aspect, a method for generating typical meteorological year data for building overheating assessment is provided, the method comprising:

[0016] Acquire long-term meteorological data, wherein the long-term meteorological data includes at least data involving the following outdoor parameters: dry-bulb temperature, relative humidity, wind speed, and global solar radiation;

[0017] Based on the aforementioned long-term meteorological data, an overheating simulation of the target building under free-running mode was performed using a building energy simulation tool to obtain indoor temperature data.

[0018] Calculate the correlation coefficient between the indoor temperature data and each outdoor parameter in the long-term meteorological data;

[0019] Based on the correlation coefficient, dynamic weights are assigned to each outdoor parameter;

[0020] Based on the dynamic weights of the outdoor parameters, typical meteorological year data and typical month selection are generated.

[0021] In some embodiments, based on the long-term meteorological data, a building energy simulation tool is used to perform overheating simulation of the target building under free-running mode to obtain indoor temperature data, including:

[0022] Select a standard prototype and configure the enclosure parameters and internal loads of the selected standard prototype;

[0023] The long-term meteorological data is input into the selected standard prototype, and the indoor thermal environment response is calculated based on the heat balance equation to obtain the indoor temperature time series.

[0024] In some embodiments, calculating the correlation coefficient between the indoor temperature data and each outdoor parameter in the long-term meteorological data includes calculating the correlation coefficient of each outdoor parameter using the following formula:

[0025] ;

[0026] in, This indicates the average or maximum value of outdoor parameters. Indicates indoor temperature. Describing covariance, Indicates standard deviation; This represents the average value of the outdoor parameters. This indicates the values ​​of outdoor parameters.

[0027] In some embodiments, based on the correlation coefficient, dynamic weights are assigned to each outdoor parameter, including:

[0028] The correlation coefficients of each outdoor parameter are normalized using the following formula to obtain the dynamic weights of each outdoor parameter:

[0029] ;

[0030] in, This represents the correlation coefficient between the p-th outdoor meteorological parameter and the indoor temperature. This indicates the number of meteorological parameters.

[0031] In some embodiments, typical meteorological year data and typical month selection are generated based on the dynamic weights of the outdoor parameters, including:

[0032] The long-term cumulative distribution function and annual cumulative distribution function of each outdoor parameter were calculated using the FS statistical method.

[0033] Calculate the sum of the daily absolute differences between the interpolation of the long-term cumulative distribution function of each outdoor parameter and the interpolation of the annual cumulative distribution function;

[0034] By using the dynamic weights of each outdoor parameter and the sum of the daily absolute differences between the interpolation of the long-term cumulative distribution function of each outdoor parameter and the interpolation of the annual cumulative distribution function, monthly scores are calculated, and the month with the smallest summation result is taken as the typical month, so as to assemble complete typical meteorological year data.

[0035] In some embodiments, the method further includes: validating the complete typical meteorological year data, wherein the validation includes: assessing the deviation between the complete typical meteorological year data and the long-term meteorological data, and using the root mean square error of the coefficient of variation to quantify the error and using overheating index simulation to cross-validate the complete typical meteorological year data.

[0036] Secondly, a device for generating typical meteorological year data for building overheating assessment is provided, the device comprising:

[0037] The long-term meteorological data acquisition module is used to: acquire long-term meteorological data, wherein the long-term meteorological data includes at least data involving the following outdoor parameters: dry-bulb temperature, relative humidity, wind speed, and global solar radiation;

[0038] The indoor temperature data acquisition module is used to: based on the long-term meteorological data, use a building energy simulation tool to perform overheating simulation on the target building in a free-running mode to obtain indoor temperature data;

[0039] The correlation coefficient calculation module is used to calculate the correlation coefficient between the indoor temperature data and each outdoor parameter in the long-term meteorological data.

[0040] The dynamic weight allocation module is used to: allocate dynamic weights for each outdoor parameter based on the correlation coefficient;

[0041] The typical meteorological year data generation module is used to generate typical meteorological year data and select typical months based on the dynamic weights of the outdoor parameters.

[0042] Thirdly, a device for generating typical meteorological year data for building overheating assessment is provided, the device comprising:

[0043] At least one processor;

[0044] At least one memory is coupled to the at least one processor and stores instructions for execution by the at least one processor, which, when executed by the at least one processor, implement the method described above.

[0045] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform the methods described above.

[0046] Fifthly, a computer program product is provided, the computer program product including instructions, which, when executed by a computer, cause the computer to perform the methods described above.

[0047] Compared with the prior art, the present invention has the following advantages and beneficial effects: through dynamic weight allocation and systematic technical process, significant progress has been made in the representativeness, accuracy and adaptability of TMY data, providing more reliable technical support for building overheating assessment. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of a method for generating typical meteorological year data for building overheating assessment according to an embodiment of the present invention.

[0050] Figure 2 This is a schematic diagram showing the distribution results of TMY data obtained from different methods.

[0051] Figure 3 This is a diagram illustrating the comparison of overheating upgrade factors (TMY data and long-term data).

[0052] Figure 4 This is a diagram showing the relationship between indoor superheat and superheat progression factor (TMY and long-term data).

[0053] Figure 5 This is a structural block diagram of a typical meteorological year data generation device for building overheating assessment according to an embodiment of the present invention.

[0054] Figure 6 This is a schematic diagram of a typical meteorological year data generation device for building overheating assessment according to an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0056] On the one hand, the present invention provides a method for generating typical meteorological year data for building overheating assessment. Figure 1 This is a flowchart illustrating a method for generating typical meteorological year data for building overheating assessment according to an embodiment of the present invention. (Reference) Figure 1 The method for generating typical meteorological year data for building overheating assessment includes S10 to S50. S10 to S50 are described in detail below with reference to the accompanying drawings.

[0057] In S10, long-term meteorological data is acquired. This long-term meteorological data includes at least the following outdoor parameters: dry-bulb temperature, relative humidity, wind speed, and global solar radiation. Long-term meteorological data can be sourced from public meteorological databases or local observation stations, including hourly or daily series of parameters such as dry-bulb temperature, relative humidity, wind speed, and global solar radiation. The data does not need to be specific to future climate predictions but is generalized to any long-term historical or measured data. To ensure data consistency, basic preprocessing of the meteorological data can be performed, such as format normalization or missing value imputation, but without involving complex bias corrections. S10 provides a clean long-term meteorological dataset as input for subsequent simulations. Data acquisition can be achieved through an API (Application Programming Interface) or file import.

[0058] In S20, based on long-term meteorological data, a building energy simulation tool is used to perform overheating simulation of the target building under free-running mode to obtain indoor temperature data. For example, a standard prototype can be selected, and its building envelope parameters and internal loads can be configured. Then, long-term meteorological data is input into the selected standard prototype, and the indoor thermal environment response is calculated based on the heat balance equation to obtain the indoor temperature time series.

[0059] Specifically, building energy simulation tools can be used to perform overheating simulations of the target building in a free-running mode. The building model can be a standard prototype (such as a passive house), configured with envelope parameters (such as U-value and solar heat gain coefficient) and internal loads (personnel and equipment schedules). During simulation, long-term meteorological data acquired in S10 is input, dynamic thermal simulation is run, and indoor temperature time series is output. The simulation process is based on the heat balance equation, calculating the indoor thermal environment response without the need for a heating and cooling system, thus focusing on overheating risks. In S20, indoor temperature data is generated through batch processing to ensure synchronization with outdoor parameters, providing a basis for correlation analysis. Automated simulations can be achieved by writing scripts, for example, using EnergyPlus's IDF files for parametric operation.

[0060] In S30, the correlation coefficients between indoor temperature data and various outdoor parameters in long-term meteorological data are calculated. Pearson can be used to calculate the correlation coefficients for each outdoor parameter.

[0061] ;

[0062] in, This indicates the average or maximum value of outdoor parameters. Indicates indoor temperature. Describing covariance, Indicates standard deviation; This represents the average value of the outdoor parameters. This indicates the values ​​of outdoor parameters.

[0063] In some embodiments, the Spearman correlation coefficient can be used instead of the Pearson correlation coefficient. Spearman calculates correlation based on the ordinal order of variables rather than their actual numerical values, making it particularly suitable for non-normally distributed data or cases with outliers. It better handles nonlinear relationships and improves the robustness of weight allocation. Alternatively, correlation can be quantified by calculating the mutual information values ​​between indoor temperature and various meteorological parameters. This captures any statistical dependence between variables, including nonlinear relationships, and provides a more comprehensive measure of correlation, especially suitable for complex climate-building interaction analyses. Furthermore, dividing long-term data by season, calculating the correlation coefficients for each season, and then weighting them to obtain the final weights—for example, assigning higher weights to summer data—is beneficial because overheating assessments primarily focus on the high-temperature season, providing a more nuanced reflection of the differences in parameter influence across seasons and improving the seasonal representativeness of TMY data.

[0064] In S40, dynamic weights are assigned to each outdoor parameter based on the correlation coefficient.

[0065] Based on the correlation coefficients output by S30, the weights of each meteorological parameter in TMY generation are dynamically allocated. The weights are generated through normalization.

[0066] ;

[0067] in, This represents the correlation coefficient between the p-th outdoor meteorological parameter and the indoor temperature. This indicates the number of meteorological parameters. When assigning weights, the original proportional relationships between parameter subclasses (such as the maximum and minimum values ​​of dry-bulb temperature) are preserved to ensure consistency. For example, within the Sandia methodology framework, the dry-bulb temperature weight is adjusted from a fixed value to a dynamic value based on correlation. This step outputs a weight table for subsequent FS statistical calculations. Normalization can be achieved through simple arithmetic operations, such as using Excel or a programming script.

[0068] In S50, typical meteorological year data and typical month selection are generated based on the dynamic weights of each outdoor parameter. Specifically, the generation of typical meteorological year data and typical month selection based on the dynamic weights of each outdoor parameter includes the following steps S51 to S53.

[0069] In S51, the FS statistical method is used to calculate the long-term cumulative distribution function and annual cumulative distribution function of each outdoor parameter:

[0070] ;

[0071] ;

[0072] in, This represents the distribution of meteorological parameter p values ​​in month mo (long-term). Indicates the first Annual distribution of meteorological parameter p values ​​(year); Indicates the day; This indicates the rank in the daily average sequence for a predetermined number of years; N represents the number of years. This indicates the rank within the daily average sequence for each year; Indicates the number of days.

[0073] In S52, the summation of the daily absolute differences between the interpolation of the long-term cumulative distribution function and the interpolation of the annual cumulative distribution function for each outdoor parameter is calculated. For example, the summation is performed using the following formula:

[0074] ;

[0075] in, Let p represent the daily distribution of meteorological parameter p in the mo-th month of year y.

[0076] In S53, the monthly score is calculated by summing the daily absolute differences between the interpolations of the long-term cumulative distribution functions of each outdoor parameter and the interpolations of the annual cumulative distribution functions, using the dynamic weights of each outdoor parameter and the sum of these differences. The month with the smallest sum is then selected as the typical month to assemble complete typical meteorological year data. For example, the monthly score is calculated based on the following formula:

[0077] ;

[0078] in, This represents the final composite score for the mo-th month in year y. This indicates the dynamic weights for outdoor parameters; This indicates the number of parameters. The month with the smallest WS value is selected as the typical month, and the data is assembled into complete TMY data. This step outputs a TMY file, which can be in EPW or CSV format and directly used in building simulation software. A standard FS algorithm implementation can be referenced; for example, an existing library (such as PVLib for Python) can be used for CDF calculation.

[0079] In some embodiments, the typical meteorological year data generation method further includes S50: validating the complete typical meteorological year data, wherein the validation includes: assessing the deviation between the complete typical meteorological year data and long-term meteorological data, quantifying the error using the coefficient of variation and root mean square error (CVRMSE), and cross-validating the complete typical meteorological year data using overheating index simulation. Exemplarily, in S50, the representativeness of the TMY data is verified by comparing its deviation with long-term data; the error is quantified using the coefficient of variation root mean square error (CVRMSE).

[0080] ;

[0081] ;

[0082] Wherein, RMSE represents the root mean square error; Mean; Indicates the number of data items; Indicates the predicted value; This represents the actual value. Simultaneously, overheating index simulations (such as Unmet Degree Hours (UDH)) can be run for cross-validation. This step ensures that the TMY data accurately reflects the long-term trend; validation scripts can be written to automate error calculation.

[0083] Compared with related technologies, this invention has achieved significant progress in the generation method and application effect of Typical Meteorological Year (TMY) data. The advantages and technical effects of this invention are explained in detail below using specific examples and reasoning (taking calculation results from Harbin, Beijing, and Chengdu as examples).

[0084] 1. Improved the representativeness and accuracy of TMY data in building overheating assessment.

[0085] Reasoning and Analysis: Existing technologies use fixed weights to allocate meteorological parameters (e.g., solar radiation has a weight of 12 / 24 in the Sandia method). However, the degree of influence of each parameter on indoor overheating varies greatly across different climate zones and building types. This invention uses steps S30 and S40 to dynamically allocate weights based on the Pearson correlation coefficient between indoor temperature and outdoor parameters. As shown in Table 2, the correlation coefficient between dry-bulb temperature and indoor temperature reaches 0.75-0.99, while that of solar radiation is only 0.37-0.70. Therefore, this invention correspondingly increases the weight of dry-bulb temperature and decreases the weight of solar radiation.

[0086] This weight optimization makes the generated TMY data more closely match the actual overheating simulation requirements. As can be seen from the CVRMSE data in Table 3, the improved Sandia-T method reduces the indoor temperature simulation error by an average of 33%-44% compared to the traditional Sandia method. This indicates that this patent significantly improves the representativeness of TMY data in overheating assessment through correlation-driven weight allocation.

[0087] surface Long-term correlation between outdoor parameters and indoor temperature

[0088]

[0089] surface Comparison of CVRMSE errors of different TMY methods with long-term data (those with the -T suffix are from this invention).

[0090]

[0091] like As shown, the TMY data generated by this invention is closer to long-term data in terms of cumulative distribution characteristics, especially in terms of dry-bulb temperature distribution, which directly improves the reliability of overheating simulation results.

[0092] 2. Significantly reduced the calculation error of overheat assessment indicators.

[0093] Reasoning and Analysis: The traditional TMY method suffers from systematic bias in calculating overheating indicators due to unreasonable weight allocation. The complete process optimization method proposed in this invention ensures accuracy throughout the entire chain, from data input to result verification.

[0094] This paper compares the performance of different TMY methods in calculating Unsatisfied Hours (UDH), Indoor Overheat (IOD), and Overheating Escalation Factor (αIOD). The results of conventional methods often deviate from the distribution range of long-term data, while the results of the method of this invention closely follow the average distribution of long-term data.

[0095] According to the documentation, this invention reduces the error rate of the αIOD metric by 63%-67%. This improvement stems from the S60 verification mechanism, which continuously optimizes weight allocation through CVRMSE to ensure the calculation accuracy of the overheating assessment metric.

[0096] 3. Enhanced the adaptability of TMY data to different climate zones and building types.

[0097] Reasoning and Analysis: Existing fixed-weight methods cannot adapt to the differences in characteristics across different climate zones. This invention automatically identifies the degree of influence of key parameters in each climate zone through correlation analysis in S30. For example... As shown, Harbin, Beijing, and Chengdu received different weightings, reflecting the actual characteristics of each climate zone.

[0098] surface Comparison of parameter weights in TMY (comparison between traditional methods and this invention)

[0099]

[0100] The TMY data generated by this invention exhibits good consistency across different climate zones. It can be seen that the CVRMSE errors of the three cities in terms of various meteorological parameters and indoor temperature have been significantly reduced, which proves that the method has strong versatility and adaptability.

[0101] 4. Improved the accuracy and reliability of overheat risk assessment.

[0102] Reasoning and analysis: This invention enables TMY data to better reflect the interaction between building thermal performance and outdoor climate through indoor temperature-driven weight allocation. The relationship between indoor superheat and superheat progression factor is shown, and the results of this invention are more closely distributed near the fitting curve of long-term data.

[0103] This improvement makes building overheating risk assessment more accurate and reliable, providing a more scientific basis for building design, energy management, and policy making. In particular, when assessing the overheating risk of passive buildings, this invention can more accurately predict indoor thermal environment conditions.

[0104] 5. Improved the scientific rigor and repeatability of the TMY data generation process.

[0105] Reasoning and Analysis: This invention establishes a complete TMY data generation and quality control system through standardized processes and quantitative evaluation indicators (such as CVRMSE). Compared with traditional methods, the weight allocation in this patent is based on objective statistical correlation, rather than subjective experience.

[0106] This improvement makes the generation process of TMY data more transparent and repeatable, allowing different users to obtain consistent and reliable results. Meanwhile, the correlation coefficient-based weight allocation method provides a clear theoretical basis for optimizing TMY data.

[0107] In summary, this invention, through its dynamic weighting method and systematic technical process, has achieved significant improvements in the representativeness, accuracy, and adaptability of TMY data, providing more reliable technical support for building overheating assessment. These improvements are fully validated in the data and charts provided in the document, demonstrating the technical advantages and application value of this invention.

[0108] On the other hand, the present invention provides a typical meteorological year data generation device for building overheating assessment. Figure 5 This is a structural block diagram of a typical meteorological year data generation device for building overheating assessment according to an embodiment of the present invention. (Reference) Figure 5 The device includes: a long-term meteorological data acquisition module, an indoor temperature data acquisition module, a correlation coefficient calculation module, a dynamic weight allocation module, and a typical meteorological year data generation module.

[0109] The long-term meteorological data acquisition module is used to acquire long-term meteorological data, which includes at least the following outdoor parameters: dry-bulb temperature, relative humidity, wind speed, and global solar radiation.

[0110] The indoor temperature data acquisition module is used to: based on long-term meteorological data, use building energy simulation tools to perform overheating simulation of the target building in free operation mode to obtain indoor temperature data.

[0111] The correlation coefficient calculation module is used to calculate the correlation coefficient between indoor temperature data and various outdoor parameters in long-term meteorological data.

[0112] The dynamic weight allocation module is used to allocate dynamic weights for each outdoor parameter based on the correlation coefficient.

[0113] The typical meteorological year data generation module is used to generate typical meteorological year data and select typical months based on the dynamic weights of various outdoor parameters.

[0114] In some embodiments, the device further includes: a typical meteorological year data verification module, used to: verify complete typical meteorological year data, wherein the verification includes: assessing the deviation between complete typical meteorological year data and long-term meteorological data, and using root mean square error of coefficient of variation to quantify the error and using overheating index simulation to cross-verify the complete typical meteorological year data.

[0115] Other implementation details of the typical meteorological year data generation device are described in the relevant description of the typical meteorological year data generation method, and will not be repeated here.

[0116] For further details regarding the terahertz image denoising system based on multi-scale dilated convolutional residual networks, please refer to the previous description of the terahertz image denoising method based on multi-scale dilated convolutional residual networks, which will not be repeated here.

[0117] In implementing the functions of the integrated modules described above in hardware, this embodiment of the invention provides a structure for a typical meteorological year data generation device for building overheating assessment as described in the above embodiments. Figure 6This is a schematic diagram of a typical meteorological year data generation device for building overheating assessment according to an embodiment of the present invention. (Reference) Figure 6 The typical meteorological year data generation device for building overheating assessment includes: at least one processor; and at least one memory. The at least one memory is coupled to the at least one processor and stores instructions for execution by the at least one processor, which, when executed by the at least one processor, implement the method described above.

[0118] A processor can be a set of logic blocks, modules, and circuits that implement or execute the various exemplary logic blocks, modules, and circuits described in connection with embodiments of the present invention. The processor can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in connection with embodiments of the present invention. A processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc.

[0119] The memory may be read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0120] In one implementation, the memory can exist independently of the processor. The memory can be connected to the processor via a bus and used to store instructions or program code. When the processor calls and executes the instructions or program code stored in the memory, it can implement the method provided in the embodiments of the present invention. In another implementation, the memory can also be integrated with the processor.

[0121] On the other hand, the present invention also provides a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) storing computer program instructions that, when executed on a computer, cause the computer to perform the method as described in any of the above embodiments.

[0122] Exemplary examples show that the aforementioned computer-readable storage media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical discs (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (EPROMs), cards, sticks, or key drives, etc.). The various computer-readable storage media described in this invention may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0123] This invention provides a computer program that, when run on a computer, causes the computer to perform the method of any of the above embodiments.

[0124] This invention provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the method of any of the above embodiments.

[0125] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for generating design meteorological year data for building overheating assessment, characterized in that, The method comprises: obtaining long-term meteorological data, wherein the long-term meteorological data at least comprises data of outdoor parameters related to the following items: dry-bulb temperature, relative humidity, wind speed, global solar radiation; based on the long-term meteorological data, performing overheating simulation on a target building in a free running mode by using a building energy simulation tool to obtain indoor temperature data; calculating the correlation coefficient of the indoor temperature data and each outdoor parameter in the long-term meteorological data; based on the correlation coefficient, assigning a dynamic weight for each outdoor parameter; based on the dynamic weight of each outdoor parameter, generating typical meteorological year data and typical month selection.

2. The method of claim 1, wherein, based on the long-term meteorological data, performing overheating simulation on a target building in a free running mode by using a building energy simulation tool to obtain indoor temperature data, comprising: selecting a standard prototype and configuring the building envelope parameters and internal loads of the selected standard prototype; inputting the long-term meteorological data into the selected standard prototype and calculating the indoor thermal environment response based on the heat balance equation to obtain the indoor temperature time series.

3. The method of claim 1, wherein, calculating the correlation coefficient of the indoor temperature data and each outdoor parameter in the long-term meteorological data, comprising calculating the correlation coefficient of each outdoor parameter using the following formula: ； wherein, represents an average or maximum value of an outdoor parameter, represents an indoor temperature, represents a covariance, represents a standard deviation; represents a mean value of a value of an outdoor parameter; represents a value of an outdoor parameter.

4. The method according to any one of claims 1 to 3, characterized in that, based on the correlation coefficient, assigning a dynamic weight for each outdoor parameter, comprising: normalizing the correlation coefficient of each outdoor parameter using the following formula to obtain the dynamic weight of each outdoor parameter: ； wherein, represents the correlation coefficient of the pth outdoor meteorological parameter and the indoor temperature, represents the number of meteorological parameters.

5. The method of claim 4, wherein, based on the dynamic weight of each outdoor parameter, generating typical meteorological year data and typical month selection, comprising: using the FS statistical method to calculate the long-term cumulative distribution function and the annual cumulative distribution function of each outdoor parameter; calculating the sum of the absolute value differences of the interpolation of the long-term cumulative distribution function and the interpolation of the annual cumulative distribution function of each outdoor parameter on a daily basis; using the dynamic weight of each outdoor parameter and the sum of the absolute value differences of the interpolation of the long-term cumulative distribution function and the interpolation of the annual cumulative distribution function of each outdoor parameter on a daily basis, calculating the month score and selecting the month with the smallest sum as the typical month to assemble the complete typical meteorological year data.

6. The method of claim 5, wherein, The method further comprises verifying the complete typical meteorological year data, wherein the verification comprises evaluating the deviation of the complete typical meteorological year data from the long-term meteorological data, and using the root mean square error of the coefficient of variation to quantify the error and using the overheating index simulation to cross-verify the complete typical meteorological year data.

7. An apparatus for generating design meteorological year data for building overheating assessment, characterized by The device comprises: a long-term meteorological data acquisition module for acquiring long-term meteorological data, wherein the long-term meteorological data at least comprises data of outdoor parameters related to the following items: dry-bulb temperature, relative humidity, wind speed, global solar radiation; an indoor temperature data acquisition module for performing overheating simulation on a target building in a free running mode by using a building energy simulation tool based on the long-term meteorological data to obtain indoor temperature data; a correlation coefficient calculation module for calculating the correlation coefficient of the indoor temperature data and each outdoor parameter in the long-term meteorological data; a dynamic weight distribution module, configured to distribute a dynamic weight for each outdoor parameter based on the correlation coefficient; a typical meteorological year data generation module, configured to generate typical meteorological year data and a typical month selection based on the dynamic weight of each outdoor parameter.

8. An equipment for generating data of a typical meteorological year for building overheating assessment, characterized by The device comprises: at least one processor; at least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor, the instructions, when executed by the at least one processor, implementing the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores instructions, which, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer program product comprises instructions, which, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 6.

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