CN116645011A - Quantitative index calculation method for evaluating building climate partition performance - Google Patents
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Abstract
The invention provides a quantitative index calculation method for evaluating building climate partition performance, which comprises the steps of obtaining all climate component data corresponding to a climate region to form climate data, and calculating a first partition performance index PI of the partition performance corresponding to the climate component; dividing the same region to be evaluated according to a plurality of standard partition indexes respectively, and acquiring overlapping regions of the reference climate regions to generate overlapping reference climate regions; and (3) acquiring overlapping climate data of the overlapping reference climate region, repeating the second step to calculate a second partition performance index PI0 corresponding to different climate components, defining the ratio of the second partition performance index PI0 corresponding to similar climate components to the first partition performance index PI as a performance index corresponding to the climate components, wherein the performance index is used for expressing the accuracy of dividing the target partition index into regions. The method can quantitatively and intuitively represent the accuracy index of the climate subareas, and the probability of influence of areas with insignificant boundaries on the index is low.
Description
Technical Field
The invention relates to the technical field of quantitative climate zoning, in particular to a quantitative index calculation method for evaluating performance of building climate zoning.
Background
The Chinese operators are wide, the topography is complex, and the climate difference of different areas is large due to different conditions such as dimension, topography and the like, and the energy-saving buildings of different areas need to be designed according to the climate areas. However, the climate division never has unified standards, different students have different emphasis on specific division of climate zones, or different areas have specific setting of climate division emphasis because of policy requirements. However, most of the methods are two kinds of cause classification and characterization classification, the classification method adopted in China is a combination of Zhou Shuzhen classification method and Cha Le classification method, and the influence of climate on production and life is considered, so that specific causes are not emphasized, and the defects are still remained.
Moreover, because the climate is transitive in geographical space, there is also occasional small variations for a particular area, which can affect the evaluation results when building climate division is performed in areas containing multiple climate zones.
Disclosure of Invention
In view of the above, the present invention aims to provide a quantitative index calculation method for evaluating the performance of a building climate zone, which can quantify an index indicating the accuracy of the climate zone, and the index is not affected by areas with insignificant demarcations.
In order to solve the technical problems, the invention adopts the following technical scheme:
a quantitative index calculation method for evaluating the performance of building climate zone comprises the following steps,
dividing a region to be evaluated into a plurality of climate areas through target partition indexes, acquiring all climate component data corresponding to each climate area and forming climate data of the climate area;
step two, climate data of all climate areas are grouped in pairs, overlapping values among the climate components of the same type in all groups are calculated, and the average value of the overlapping values of the climate components of the same type in all groups is a first partition performance index PI corresponding to the climate components;
dividing the same region to be evaluated according to at least two standard partition indexes respectively and generating corresponding reference region groups, wherein each reference region group comprises a plurality of reference climate regions, and acquiring reference climate regions with coincident geographic positions in each reference region group and recording the reference climate regions as overlapping reference climate regions;
acquiring climate data of overlapping reference climate areas, grouping the climate data of all overlapping reference climate areas in pairs, calculating overlapping values among the same type of climate components in all groups, and recording the average value of the overlapping values of the same type of climate components in all groups as a second partition performance index PI0 corresponding to the climate components;
fifthly, defining the ratio of the second partition performance index PI0 corresponding to the same type of climate components to the first partition performance index PI as the performance index corresponding to the climate components.
Further, the climate components include 1 month air temperature, 7 month relative humidity, HDD18, CDD26, days with air temperature less than 5 ℃, days with air temperature greater than 25 ℃, number of sunshine hours, and solar radiation.
Further, the standard partition indicators include GB50176, GB50178, and cluster analysis.
Further, the calculation method of the second partition performance index PI0 and the calculation method of the first partition performance index PI are the same, and the calculation formula of the first partition performance index PI is:
the PDFij is a Probability Density Function (PDF) of overlapping probability density between the climate zone i and the climate zone j, N is the number of the climate zone partitions, and N= (N-1) x N/2 is used for eliminating the influence of different numbers of the climate zone partitions.
The invention has the advantages and positive effects that:
dividing the region to be evaluated by using the target partition indexes, calculating first partition performance indexes PI corresponding to different climate components of the climate region, then performing climate partition on the same region to be evaluated by using a plurality of standard partition indexes, obtaining overlapping reference climate regions overlapped by the same type of climate regions under different standard partition methods, and calculating second partition performance indexes PI0 corresponding to different climate components of the overlapping reference climate regions for removing the influence of the unobvious region on performance evaluation. And calculating the performance index of the partition according to the second partition performance index PI0 and the first partition performance index PI so as to realize the accuracy index for quantitatively representing the climate partition, wherein the probability of the index being influenced by the areas with insignificant demarcations is low.
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The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is an overall flow chart of a method of quantitatively index calculation for assessing building climate zone performance of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a quantitative index calculation method for evaluating building climate zone performance, as shown in fig. 1, comprising the steps of firstly dividing a region to be evaluated into a plurality of climate areas through target zone indexes, acquiring all climate component data corresponding to each climate area and forming climate data of the climate area.
The target partition index is a partition index which is set by the user according to the policy index, or an existing partition index mode is selected. Dividing the region to be evaluated into a plurality of climate areas, wherein each climate area has a corresponding geographical range, each climate area comprises a plurality of sampling areas, and the climate sampling data in the climate area are adopted when the climate area is divided.
The acquisition method of the climate component data comprises the following steps: the sampling areas are internally provided with a plurality of weather stations, sampling component data corresponding to different weather components are respectively calculated according to the acquired data of the weather stations, the same kind of sampling component data of all the sampling areas jointly form weather component data of the weather areas, and the weather component data of all the weather areas jointly form weather data.
The weather stations in the city are distributed densely, and each sampling area (the geographical area participating in the regional characterization of the weather can be an aggregation area, a province, a part of provinces and a part of areas, etc.) can contain one or a plurality of weather stations, so that the accuracy of the demarcation of the sampling areas is high. The climate data is constructed by taking Tianjin city as an example: assuming that the whole Tianjin city is a climate area, the Tianjin city comprises 16 sampling areas, each sampling area comprises a plurality of types of sampling component data, and all the sampling component data of the 16 sampling areas jointly form the climate data.
And secondly, grouping the climate data of all the climate areas pairwise, and calculating the overlapping values among the climate components of the same type in all the groups, wherein the average value of the overlapping values of the climate components of the same type in all the groups is the first partition performance index PI corresponding to the climate components.
The climate data is composed of a plurality of climate component data, and the general climate components include 7 months of relative humidity (RH 7), 1 month of average air temperature (Temp 1), 7 months of average air temperature (Temp 7), heating degree Days (HDD 18), air conditioning degree Days (CDD 26), days of less than or equal to 5 ℃ (Days of less than or equal to 5 ℃), days of more than or equal to 25 ℃ (Days of more than or equal to 25 ℃), sunlight hours and solar radiation.
Taking 5 climatic regions as an example, examples are: and combining the sampling areas in the five climate areas in any pair, acquiring the climate component data of the same type in the combination in the climate area, and calculating the overlapping condition of the two groups of climate component data by using the probability density function f (x).
Climate component data of each sampling region is brought into probability density functionAnd calculating an overlapping value representing the data overlapping condition between similar climate components, and removing the minimum 5% and the maximum 5% of the climate component data for the universality of the climate partition in the process of calculating the overlapping value. Specifically, the probability density function describes the probability of the output value of a continuous random variable being near a certain value point. For the followingOne-dimensional random variable X, if a real-valued function is present +.>Satisfy->Is a piecewise continuous function; />；/>Then X is a continuous random variable (the climate component data of a sampling zone in the climate zone is the random variable X),>is a function of its probability density.
The overlapping values correspond to the climate components, overlapping values corresponding to the same type of climate components in all climate combinations are obtained, an average value is obtained to generate first partition performance indexes PI corresponding to the climate components, and the number of the climate components corresponds to the number of the first partition performance indexes PI.
The calculation formula of the first partition performance index PI is:
wherein the PDF ij The probability density function (corresponding to f (x) described above) for the overlapping probability density between climate zone i and climate zone j, N being the number of climate zone zones (n=5 in the embodiment of the invention), n= (N-1) x N/2, is used to eliminate the effect of the different number of zones of different methods.
Dividing the same region to be evaluated according to at least two standard partition indexes respectively, generating corresponding reference region groups, wherein each reference region group comprises a plurality of reference climate regions, and acquiring reference climate regions with coincident geographic positions in each reference region group and recording the reference climate regions as overlapping reference climate regions.
Standard partitioning indexes include GB50176 and GB50178 (which may also include clustering methods), which divide chinese regions using GB50176 and GB50178, respectively, and are named first and second reference region groups, respectively. The first reference area group (comprising a plurality of first reference climate areas) and the second reference area group (comprising a plurality of second reference climate areas) are not identical in area range corresponding to the same type of reference climate areas, the geographic areas where the same type of standard climate areas in the two reference area groups overlap are obtained by using a normalization method, and overlapping reference climate areas are generated (the overlapping reference climate areas are equally divided into five climate areas).
One example of this is: the temperate climate zone of the first reference zone group comprises: the temperature zone climate zone of the second reference area group comprises: the B, C and D sample areas, so the overlapping reference climate zones for the temperature zones include: the sampling area B, the sampling area C and the sampling area D, and the sampling area A is a region with unobvious climate boundary.
And fourthly, acquiring climate data of the overlapped reference climate areas, grouping the climate data of all the overlapped reference climate areas in pairs, calculating overlapping values among the climate components of the same type in all the groups, and recording the average value of the overlapping values of the same type of the climate components in all the groups as a second partition performance index PI0 corresponding to the climate components.
Fifthly, defining the ratio of the second partition performance index PI0 corresponding to the same type of climate components to the first partition performance index PI as the performance index corresponding to the climate components.
The ratio of the second partition performance index PI0 to the first partition performance index PI corresponding to the climate component is indicative of the accuracy of the partition under the climate component index. The average value of all class performance index ratios is used to evaluate the overall partition accuracy of the target partition index.
For example, after dividing China into five climate areas by the target zoning index, acquiring a basic weather element daily value data set acquired by a ground weather station according to a China weather bureau information center, wherein the basic weather element daily value data set comprises daily air temperature data and relative humidity data, and meanwhile, weather component data corresponding to different weather components, such as 7 months of relative humidity (RH 7), 1 month of average air temperature (Temp 1), 7 months of average air temperature (Temp 7), heating degree daily number (HDD 18), air conditioning degree daily number (CDD 26), days (Days less than or equal to 5) at a temperature of less than or equal to 5 ℃ and Days (Days more than or equal to 25) at a temperature of more than or equal to 25 ℃ can be calculated; the heating degree day number (HDD 18) is an accumulated value of products obtained by multiplying the number of degrees of difference between the day average temperature and 18 ℃ by 1 day in a year when the day average temperature outside a certain day is lower than 18 ℃, and the unit is °c.d; the number of empty scheduling days (CDD 26) is an accumulated value of products obtained by multiplying the number of degrees of difference between the daily average temperature and 26 ℃ by 1 day in a year when the daily average temperature outside a certain day is higher than 26 ℃, and the unit is °c.d.
If the climate zone comprises 9 climate components, the five climate zones thus correspond to 9 first partition performance indices PI, respectively, and the overlapping reference climate zone likewise corresponds to 9 second partition performance indices PI0. There are 9 performance indicators corresponding to the climate components. The second partition performance index PI0 is a theoretical minimum value because the area with the insignificant climate boundary is removed, the first partition performance index PI includes the area with the insignificant boundary, and the second partition performance index PI value is necessarily greater than the first partition performance index PI0. The influence of unobvious areas is removed from the performance index, so that the performance index has higher referenceable meaning. When the first partition performance index PI is closer to the second partition performance index PI0, that is, the performance index is closer to 1, the climate partition divided by the target partition index is more accurate.
In actual use, the range threshold of the performance index can be set, and the target partition index is adjusted according to the set fixed range threshold so as to obtain the optimal climate zone division, thereby facilitating the implementation of the floor for later planning and reference.
The foregoing describes the embodiments of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by this patent.
Claims (4)
1. The quantitative index calculation method for evaluating the building climate zone performance is characterized by comprising the following steps of firstly dividing a region to be evaluated into a plurality of climate areas through target zone indexes, acquiring all climate component data corresponding to each climate area and forming the climate data of the climate area;
step two, respectively grouping the climate data of all the climate areas in pairs, and calculating the overlapping values among the climate components of the same type in all the groups, wherein the average value of the overlapping values of the climate components of the same type in all the groups is a first partition performance index PI corresponding to the climate components;
dividing the same region to be evaluated according to at least two standard partition indexes respectively and generating corresponding reference region groups, wherein each reference region group comprises a plurality of reference climate regions, and acquiring reference climate regions with coincident geographic positions in each reference region group and recording the reference climate regions as overlapping reference climate regions;
acquiring climate data of overlapping reference climate areas, grouping the climate data of all overlapping reference climate areas in pairs, calculating overlapping values among the same type of climate components in all groups, and recording the average value of the overlapping values of the same type of climate components in all groups as a second partition performance index PI0 corresponding to the climate components;
fifthly, defining the ratio of the second partition performance index PI0 corresponding to the same type of climate components to the first partition performance index PI as the performance index corresponding to the climate components.
2. A method of calculating a quantitative index for assessing the performance of a partitioned area of a building according to claim 1 wherein said climate components include 1 month air temperature, 7 months relative humidity, HDD18, CDD26, days with air temperature less than 5 ℃, days with air temperature greater than 25 ℃, solar hours and solar radiation.
3. A method of quantitative index calculation for assessing the performance of a building climate zone according to claim 1 wherein the standard zone indicators include GB50176, GB50178 and cluster analysis.
4. The method for calculating the quantization index for evaluating the performance of a partition of a building climate according to claim 1, wherein the calculation method of the second partition performance index PI0 is the same as the calculation method of the first partition performance index PI, and the calculation formula of the first partition performance index PI is:
where PDFij is the probability density function of the overlap probability density between climate zone i and climate zone j, N is the number of climate zone zones, N= (N-1) x N/2.
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