CN110390470A - Climate region of building method and apparatus - Google Patents
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Abstract
This application provides a kind of climate region of building method and apparatus, this method comprises: obtaining multiple position datas and meteorological data corresponding with each position data；Nondimensionalization processing is carried out to the meteorological data, using the meteorological data after nondimensionalization as sample data；The sample data is clustered, to obtain cluster numbers；Obtain corresponding relationship, the cluster numbers and the puppet t of the cluster numbers and Pseudo F-Statistics2The corresponding relationship of the corresponding relationship of statistic, the cluster numbers and cube clustering criteria；According to the corresponding relationship of the cluster numbers and Pseudo F-Statistics, the cluster numbers and puppet t2The corresponding relationship of the corresponding relationship of statistic, the cluster numbers and cube clustering criteria, obtains preferable clustering number；Final cluster numbers are determined according to the preferable clustering number and preset condition.
Description
Technical Field
The application relates to the technical field of building climate zoning, and particularly provides a building climate zoning method and equipment.
Background
At present, the climate zoning of buildings in China mainly considers the climate characteristics of land, the annual average value of climate elements or indexes is used as a first-level index and a second-level index, the distribution of the climate in a region space is divided, only static climate characteristic geographical distribution is represented, and the boundary zone is determined by a plurality of human factors, so that the climate zoning requirement of the island buildings which are growing in China cannot be met.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present application provides a building climate partitioning method and apparatus.
In a first aspect, the present application provides a method for partitioning a building climate, comprising: acquiring a plurality of position data and meteorological data corresponding to each position data; carrying out non-dimensionalization processing on the meteorological data, and taking the meteorological data subjected to non-dimensionalization as sample data; clustering the sample data to obtain a clustering number; acquiring the corresponding relation between the cluster number and the pseudo F statistic, and the cluster number and the pseudo t2The corresponding relation of the statistics and the corresponding relation of the clustering number and the cubic clustering criterion; according to the corresponding relation between the cluster number and the pseudo F statistic, the cluster number and the pseudo t2Obtaining the optimal clustering number according to the corresponding relation of the statistics and the corresponding relation of the clustering number and the cubic clustering criterion; and determining a final clustering number according to the optimal clustering number and preset conditions, and partitioning the building climate according to the final clustering number.
In some embodiments, the location data includes a city location at which the weather station is located; the meteorological data comprise average temperature of 1 month, average temperature of 7 months, average relative humidity of 7 months, number of days with average temperature of less than or equal to 5 ℃ per year and day, number of days with average temperature of more than or equal to 25 ℃ per year and day, maximum wind speed and average temperature day difference of 7 months.
In some embodiments, said clustering said sample data comprises: and adopting the Euclidean distance as the similarity measurement between two sample points in the sample data, and calculating the distance between classes by using a sum of squared deviations method to finish clustering the sample data.
In some embodiments, the clustering number and the pseudo-t are determined according to the corresponding relationship between the clustering number and the pseudo-F statistic2Obtaining the optimal clustering number according to the corresponding relation of the statistics and the corresponding relation of the clustering number and the cubic clustering criterion, wherein the optimal clustering number comprises the following steps: searching a local peak value in the corresponding relation between the cluster number and the pseudo F statistic to obtain a first cluster number corresponding to the local peak value; searching a local peak value in the corresponding relation between the cluster number and the cubic clustering criterion to obtain a second cluster number corresponding to the local peak value; interpolating the first and/or second cluster numbers between the cluster numbers and the pseudo-t using interpolation2In the corresponding relation of statistics, if the pseudo t corresponding to the first cluster number and/or the second cluster number2The pseudo-t corresponding to a statistic less than the next cluster number2And (5) taking the current clustering number as the optimal clustering number.
In some embodiments, the clustering number and the pseudo-t are determined according to the corresponding relationship between the clustering number and the pseudo-F statistic2Obtaining the optimal clustering number according to the corresponding relation of the statistics and the corresponding relation of the clustering number and the cubic clustering criterion, wherein the optimal clustering number comprises the following steps: searching a local peak value in the corresponding relation between the cluster number and the pseudo F statistic to obtain a first cluster number corresponding to the local peak value; searching a local peak value in the corresponding relation between the cluster number and the cubic clustering criterion to obtain a second cluster number corresponding to the local peak value; finding the cluster number and the pseudo t2Local minimum value in the correspondence of the statistics, the pseudo t corresponding to the cluster number next to the cluster number corresponding to the local minimum value2If the statistic is larger, taking the current clustering number as a third clustering number; and searching the optimal cluster number according to the first cluster number, the second cluster number and the third cluster number.
In some embodiments, the preset conditions include: the result of the climate partition of the building in the current standard, the special climate conditions of the island region and the building design requirements of the island region.
In a second aspect, the present application provides a building climate zone apparatus, the apparatus comprising: a memory for storing executable program code; one or more processors configured to read executable program code stored in the memory to perform the building climate partitioning method of the first aspect.
The method, the device and the equipment for partitioning the building climate provided by the embodiment of the application have the following beneficial effects:
in the aspect of overall planning of sea and land, the special climatic characteristics of island buildings are considered, and the ocean land is brought into the national division range;
on the basis of deep research on the climate characteristics and design principles of buildings, the influence of climate conditions on the heat and humidity transfer process of the buildings, the heat load of the buildings and the like is fully considered, and a multi-element and multi-level index system is established.
The method integrates multivariate mathematical statistical analysis and the traditional grading and partitioning method, updates the partitioning method system, enhances the objectivity of the partitioning process and improves the accuracy of the partitioning result.
Drawings
Fig. 1 is a schematic flow chart of a building climate partitioning method according to an embodiment of the present application.
Fig. 2 is a graph of clustering number criteria provided in an embodiment of the present application.
Fig. 3 is a graph of a clustering lineage provided in an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant application and are not limiting of the application. It should be noted that, for convenience of description, only the portions related to the present application are shown in the drawings.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Fig. 1 is a schematic flow chart of a building climate partitioning method according to an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:
step 101, acquiring a plurality of position data and meteorological data corresponding to each position data.
The location data may be, for example, the location of the city where the weather station is located, or the location of the weather station.
The meteorological data may include, for example, a 1-month average air temperature, a 7-month average relative humidity, a number of days with a year-day average temperature of not more than 5 ℃, a number of days with a year-day average air temperature of not less than 25 ℃, a maximum wind speed, and a 7-month average air temperature day-to-day difference.
The maximum wind speed is a maximum wind speed in a certain period of time, and may be, for example, a maximum wind speed occurring within 30 years.
The following description will be made in detail by taking an example in which the location data is the location of the city where the weather station is located.
In a specific example, 36 sea island stations in the bohai sea, the yellow sea, the east sea and the south sea are selected, and 40 land stations in provinces adjacent to the four sea areas are selected, so that a plurality of obtained position data and meteorological data corresponding to each position data can be shown in table 1:
TABLE 1
In the case of this example of the present invention,the average air temperature over 1 month is shown,represents the average temperature in 7 months, d≤5Representing the number of days at an average annual daily temperature of less than or equal to 5℃, d≥25Representing the number of days with the annual average temperature of more than or equal to 25 ℃, VmaxRepresenting maximum wind speed, DR7Indicating 7 months of worse average temperature day, RH7Indicating a 7 month average relative humidity.
And 102, carrying out non-dimensionalization on the meteorological data, and taking the non-dimensionalized meteorological data as sample data.
For example, the meteorological data may be processed by an averaging method to obtain non-dimensionalized meteorological data such that the mean value of the non-dimensionalized meteorological data is 1 and the standard deviation is the coefficient of variation of the original meteorological data. By carrying out dimensionless processing on the meteorological data, the difference information on the variation degree of each index of the original meteorological data can be kept while the dimensional and magnitude influence is eliminated, so that the important information of the original meteorological data is well embodied.
In a specific example, the meteorological data in table 1 is processed by averaging to obtain dimensionless meteorological data as shown in table 2:
TABLE 2
In the case of this example of the present invention,shows a dimensionless value after the average air temperature is equalized for 1 month,shows a dimensionless value after 7-month average air temperature equalization,represents a dimensionless value, Z, after 7-month average air temperature day difference equalizationd≤5Representing a dimensionless value, Z, of a daily number average of not more than 5 DEG Cd≥25A dimensionless value, Z, representing the daily average of the annual average temperature of not less than 25 DEG CVmaxRepresenting a dimensionless value after averaging the maximum wind speed,represents a dimensionless value after 7 months average relative humidity equalization.
And 103, clustering the sample data to obtain a cluster number.
In the present embodiment, for example, the meteorological data in table 2 may be used as an input of an sas (static ANALYSIS system) to perform cluster ANALYSIS on the meteorological data.
In some alternative embodiments, for example, euclidean distance may be used as a similarity measure between two sample points in the sample data, and a distance between classes is calculated by using a sum of squared deviations method, so as to complete the clustering of the sample data.
Specifically, the euclidean distance may be calculated by the following formula:
wherein d isijRepresenting the distance, x, of objects i and jihThe value of the variable h (h ═ 1,2, …, p), x, for the object ijhThe value of the variable h representing the object j (h ═ 1,2, …, p), and h represents the variable contained in the object (h ═ 1,2, …, p).
The distance between classes can be calculated by the following formula:
wherein,represents the square of the distance between the J-th class and the M-th class (new class merged by K and J),represents the square of the distance between the K-th and J-th classes,represents the square of the distance between the L and J types,represents the square of the distance between the K and L classes, nJDenotes the number of samples contained in class J, nKDenotes the number of samples contained in class K, nMIndicating the number of samples (n) contained in the Mth class of the new classM＝nI+nK)，nLIndicating the number of samples contained in the L-th class,representing the center of gravity of the sample in class K,representing the sample centroid for class L.
104, acquiring the corresponding relation between the cluster number and the pseudo F statistic, and the cluster number and the pseudo t2The corresponding relation of the statistics, the corresponding relation of the clustering number and the cubic clustering criterion.
According to the embodiment of the application, the clustering ANALYSIS is carried out on the meteorological data by using the SAS (static ANALYSIS System), so that the corresponding relation between the clustering number and the pseudo F statistic, the clustering number and the pseudo t can be obtained2The corresponding relation of the statistics, the corresponding relation of the clustering number and the cubic clustering criterion.
Step 105, according to the polymerizationCorresponding relation between class number and pseudo F statistic, cluster number and pseudo t2And obtaining the optimal clustering number according to the corresponding relation of the statistics and the corresponding relation of the clustering number and the cubic clustering criterion.
In an example, the correspondence of the number of clusters to the pseudo F statistic, the number of clusters to the pseudo t, for example, can be2The corresponding relation of the statistic, the corresponding relation of the cluster number and the cubic clustering criterion are respectively drawn in the same coordinate system to form three curves, the abscissa of the coordinate system is the cluster number, and the ordinate is the pseudo F statistic and the pseudo t statistic respectively2Statistics, cubic clustering criterion, as shown in FIG. 2, CCC denotes cubic clustering criterion, PSF denotes pseudo F statistics, PST2 denotes pseudo t2Statistics are obtained.
In this example, the optimal cluster number may be determined by:
step 201, a local peak value in the corresponding relation between the cluster number and the pseudo F statistic is searched, and a first cluster number corresponding to the local peak value is obtained.
Step 202, finding a local peak value in the corresponding relation between the cluster number and the cubic clustering criterion, and obtaining a second cluster number corresponding to the local peak value.
Step 203, insert the first cluster number and/or the second cluster number into the cluster number and the pseudo-t by interpolation2In the corresponding relation of the statistics, if the first clustering number and/or the second clustering number correspond to the pseudo t2The statistic is less than the pseudo t corresponding to the next cluster number2And (5) taking the current clustering number as the optimal clustering number.
In this example, the optimal cluster number may also be determined by:
step 301, find a local peak in the correspondence between the cluster number and the pseudo F statistic to obtain a first cluster number corresponding to the local peak.
Step 302, find the local peak in the corresponding relationship between the cluster number and the cubic clustering criterion, and obtain the second cluster number corresponding to the local peak.
Step 303, find the cluster number and the false t2A local minimum in the correspondence of the statistics,pseudo t corresponding to the next cluster number of the cluster number corresponding to the local minimum2If the statistic is larger, the cluster number at this time is taken as a third cluster number.
And step 304, searching the optimal cluster number according to the first cluster number, the second cluster number and the third cluster number.
Specifically, fig. 2 shows a clustering criterion diagram of 36 sea-island stations and 40 land stations of four-sea-area adjacent provinces, in which there is no obvious local peak in a corresponding relationship curve of the cluster number and the cubic clustering criterion, there is a local maximum when the cluster number is equal to 3 in a corresponding relationship curve of the cluster number and the pseudo F statistic, and the cluster number and the pseudo t are2In the statistical correspondence curve, when the cluster number is equal to 4,6 and 8, a local minimum value exists, and when the cluster number is equal to 3,5 and 7, the local minimum value is obviously increased, so that 3,4,6 and 8 are selected as the optimal cluster number, and in consideration of the fact that 76 stations are selected in total, when the cluster number is 3, the sample number in each class is not uniform, the obtained clustering result does not conform to the climate characteristics of a building, and therefore 4,6 and 8 are selected as the optimal cluster number.
And 106, determining a final clustering number according to the optimal clustering number and preset conditions, and partitioning the building climate according to the final clustering number.
The preset conditions comprise the building climate partition result in the current standard, the special climate conditions of the island region and the building design requirements of the island region.
In combination with the existing building climate subareas of 76 weather stations, it can be seen that all the weather stations belong to the building climate subareas II, III and IV, and some of the cities in Hainan province and the weather stations such as Xisha and coral do not belong to any existing climate subarea, so that a new climate subarea needs to be added, and at least one weather station needs to be added, so that the cluster number is set to 4, and the subareas of the 76 weather stations are shown in FIG. 3.
In a second aspect, the present application also provides a building climate zone apparatus comprising: a memory for storing executable program code; one or more processors configured to read executable program code stored in the memory to perform the building climate partitioning method described above.
The building climate partition equipment can integrate multivariate mathematical statistical analysis and a traditional grading partition method, update a partition method system, enhance the objectivity of a partition process and improve the accuracy of a partition result.
Moreover, although the steps of the methods herein are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware.
Claims (7)
1. A building climate zoning method comprising:
acquiring a plurality of position data and meteorological data corresponding to each position data;
carrying out non-dimensionalization processing on the meteorological data, and taking the meteorological data subjected to non-dimensionalization as sample data;
clustering the sample data to obtain a clustering number;
acquiring the corresponding relation between the cluster number and the pseudo F statistic, and the cluster number and the pseudo t2The corresponding relation of the statistics and the corresponding relation of the clustering number and the cubic clustering criterion;
according to the corresponding relation between the cluster number and the pseudo F statistic, the cluster number and the pseudo t2Obtaining the optimal clustering number according to the corresponding relation of the statistics and the corresponding relation of the clustering number and the cubic clustering criterion;
and determining a final clustering number according to the optimal clustering number and preset conditions, and partitioning the building climate according to the final clustering number.
2. The method of claim 1, wherein the location data includes a civic location at which the weather station is located; the meteorological data comprise average temperature of 1 month, average temperature of 7 months, average relative humidity of 7 months, number of days with average temperature of less than or equal to 5 ℃ per year and day, number of days with average temperature of more than or equal to 25 ℃ per year and day, maximum wind speed and average temperature day difference of 7 months.
3. The method of claim 1, wherein said clustering said sample data comprises:
and adopting the Euclidean distance as the similarity measurement between two sample points in the sample data, and calculating the distance between classes by using a sum of squared deviations method to finish clustering the sample data.
4. The method of claim 1, wherein the clustering is performed according to the correspondence between the cluster number and the pseudo-F statistic, and the cluster number and the pseudo-t2Obtaining the optimal clustering number according to the corresponding relation of the statistics and the corresponding relation of the clustering number and the cubic clustering criterion, wherein the optimal clustering number comprises the following steps:
searching a local peak value in the corresponding relation between the cluster number and the pseudo F statistic to obtain a first cluster number corresponding to the local peak value;
searching a local peak value in the corresponding relation between the cluster number and the cubic clustering criterion to obtain a second cluster number corresponding to the local peak value;
interpolating the first and/or second cluster numbers between the cluster numbers and the pseudo-t using interpolation2In the corresponding relation of statistics, if the pseudo t corresponding to the first cluster number and/or the second cluster number2The pseudo-t corresponding to a statistic less than the next cluster number2And (5) taking the current clustering number as the optimal clustering number.
5. The method of claim 1, wherein the basis isThe corresponding relation between the cluster number and the pseudo F statistic, and the cluster number and the pseudo t2Obtaining the optimal clustering number according to the corresponding relation of the statistics and the corresponding relation of the clustering number and the cubic clustering criterion, wherein the optimal clustering number comprises the following steps:
searching a local peak value in the corresponding relation between the cluster number and the pseudo F statistic to obtain a first cluster number corresponding to the local peak value;
searching a local peak value in the corresponding relation between the cluster number and the cubic clustering criterion to obtain a second cluster number corresponding to the local peak value;
finding the cluster number and the pseudo t2Local minimum value in the correspondence of the statistics, the pseudo t corresponding to the cluster number next to the cluster number corresponding to the local minimum value2If the statistic is larger, taking the current clustering number as a third clustering number;
and searching the optimal cluster number according to the first cluster number, the second cluster number and the third cluster number.
6. The method according to claim 1, wherein the preset condition comprises:
the result of the climate partition of the building in the current standard, the special climate conditions of the island region and the building design requirements of the island region.
7. A building climate zone apparatus, comprising:
a memory for storing executable program code;
one or more processors for reading executable program code stored in the memory to perform the building climate zoning method of any of claims 1 to 6.
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| US20140365128A1 (en) * | 2011-12-29 | 2014-12-11 | Gagyotech Co., Ltd. | Method for predicting hourly climatic data to estimate cooling/heating load |
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