CN116150859A - Wetland park cooling effect and building energy consumption prediction system, method and device - Google Patents
Wetland park cooling effect and building energy consumption prediction system, method and device Download PDFInfo
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Abstract
The invention aims to provide a wetland park cooling effect and building energy consumption prediction system, method and device, which comprise the following steps: the climate dividing module, the preprocessing module, the acquisition module, the prediction module and the output module are respectively and electrically connected with the micro-processing module; the invention combines the local climate zone, the cooling effect of the wetland park and the building energy consumption, provides a method for the cooling effect of the wetland park on the climate zone of different local areas around the wetland park, predicts the building energy consumption of the climate zone of different local areas around the wetland park, can optimize the running scheme of the building air conditioning system around the wetland park, and provides a strategy for planning the urban landscape pattern.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a data processing system, a method and a device for a geographic remote sensing image, which are used for acquiring the cooling effect of an urban wetland park and the prediction of the influence of the wetland park on building energy consumption.
Background
The urban land is greatly changed in urban space patterns, various building lands are pulled up, and a great amount of natural land surfaces such as greenbelts, water bodies, wetlands and the like gradually disappear in the urban process due to the continuous extension of the non-permeable asphalt hard road surface. Accompanying this is various urban problems, a more typical urban climate is the urban heat island effect.
In the traditional urban green land park and urban water body urban heat island effect alleviation research, most of analysis and research are carried out on the whole heat environment around the target urban green land park or the urban water body. For different living or working demands, a large number of different buildings are built around the green land park or the urban water body of the target city, the original natural topography is changed, the underlying surface around the target research area is complex and changeable, and the great heterogeneity exists. Because people have different demands on building functions, different buildings in different underlying surfaces can also generate building energy consumption of different degrees.
In recent years, the local climate zone (Local Climate Zone, LCZ for short) theory has been widely paid attention to, and the theory divides different suburban floors into different zones according to different urban space forms, so that the urban space forms and urban climate researches are closely connected. As is known, most research is now directed to a target urban green park or urban water body having a cooling effect on the surrounding global thermal environment, but a relationship between how the target research area has a temperature change law for the surrounding different local climate zones and the building energy consumption remains to be studied.
Therefore, the exploration of the climate zone cooling effect of different surrounding areas for the urban wetland park is particularly needed for predicting the energy consumption of the building.
Disclosure of Invention
The invention aims to provide a wetland park cooling effect and building energy consumption prediction system, method and device, which are used for solving at least one technical problem in the prior art by utilizing remote sensing image data.
The technical scheme of the invention is as follows:
a wetland park cooling effect and building energy consumption prediction system for carrying out data processing on remote sensing images, acquiring the wetland park cooling effect and predicting building energy consumption, comprising:
a microprocessor module;
the climate dividing module is electrically connected with the micro-processing module and is used for dividing local climate zones in the cooling range of the urban wetland park to obtain self factors of the urban wetland park, local climate zone earth surface structural parameters and earth surface coverage parameters;
the preprocessing module is electrically connected with the micro-processing module and is used for acquiring the remote sensing images corresponding to the urban wetland park to obtain the cooling effect of the urban wetland park on the climate zones of different local areas around the urban wetland park;
the acquisition module is electrically connected with the micro-processing module and is used for movably acquiring the air temperature of the local climate zone, and the micro-processing module performs verification of the inversion accuracy of the surface temperature according to the air temperature and acquires the air temperature in the cooling range of the urban wetland park to obtain meteorological data of building energy consumption;
the prediction module is electrically connected with the micro-processing module and is used for establishing a building model according to building types in the cooling range of the urban wetland park, obtaining a preliminary prediction result through the building model and the meteorological data, carrying out correlation analysis on the preliminary prediction result and the cooling index of the wetland park, and predicting building energy consumption to obtain a prediction result aiming at the building energy consumption;
and the output module is electrically connected with the micro-processing module and is used for outputting the prediction result.
The self factors of the urban wetland park comprise: park area, park perimeter, perimeter area ratio, greenbelt area and water area;
the local climate zone surface structure parameters comprise: one or more of sky visual angle coefficient, street height-width ratio and building density;
the surface coverage parameters include: one or more of building height, water-permeable area ratio and water-impermeable area ratio.
The "obtaining the cooling effect of the urban wetland park on the climate zones of different local areas around" includes:
selecting a remote sensing image corresponding to the cooling range of the urban wetland park, and inverting the surface temperature of the cooling range of the urban wetland park;
obtaining the earth surface temperature change rule of different local climate zones around the urban wetland park to obtain the wetland park cooling index;
and performing correlation analysis and linear regression analysis on the cooling indexes and the cooling factors to obtain cooling effects of the wetland park on climate zones of different local areas around the wetland park.
The method for obtaining the cooling effect of the urban wetland park on the climate zones of different local areas on the periphery specifically comprises the following steps:
inversion of the surface temperature is carried out by adopting a single window algorithm;
combining the urban wetland park and the local climate zone boundary vector data to obtain the surface temperatures of the selected urban wetland park and the local climate zone;
equally dividing the ranges of the urban wetland park and the local climate zones into N small rings, extracting the surface temperature inside each small ring, and fitting by taking the cooling distance as an independent variable and the surface temperature of each small ring as a dependent variable to obtain surface temperature change curves of the urban wetland park and the peripheral local climate zones;
obtaining the cooling index according to the surface temperature change curve;
performing correlation analysis on the cooling index and the cooling factor to obtain the relation between the cooling effect and the influence factor of the climate zones of different places around the urban wetland park;
and performing linear regression analysis on the cooling index and the cooling factor to obtain a model between the cooling index and the cooling factor of the urban wetland park.
The inversion of the surface temperature by adopting the single window algorithm comprises the following steps:
in the formula ,
is the surface temperature;
and
Is a coefficient fitted according to the relationship between the heat radiation intensity and the brightness temperature;
、
Is an intermediate variable;
The average action temperature of the atmosphere;
The temperature is bright temperature;
in the formula ,
for the atmospheric transmittance of the day, +.>
Is the atmospheric moisture content;
Representing the emissivity of the earth's surface; said->
The method comprises the following steps of: />
wherein ,
、
and
Emissivity of water surface, natural surface and town surface respectively;
the vegetation coverage is obtained by the following steps:
wherein, NDVI is normalized vegetation index,
and
The 5 th and 4 th wave bands of the remote sensing image are respectively, < + >>
And
NDVI values for bare soil and vegetation, respectively;
The average action temperature of the atmosphere;
wherein ,
is the air temperature of the nearby ground.
The mobile acquisition of the air temperature micro-processing module of the local climate zone performs verification of the surface temperature inversion precision according to the air temperature and acquires the air temperature in the cooling range of the urban wetland park, and comprises the following steps:
selecting a partition with better road conditions in the surrounding local climate partitions of the urban wetland park and more types of the surrounding local climate partitions for mobile observation;
selecting any traffic tool to check road conditions, and constructing a smooth moving observation route on a flat ground;
moving and observing each meteorological data acquisition point, and recording the air temperature of each meteorological data acquisition point;
the time for collecting the air temperature is consistent with the imaging time of the remote sensing image;
setting the size of each pixel in the remote sensing image, taking the average value of the air temperature in the pixel range as an air temperature value of the corresponding pixel range, performing correlation fitting with the earth surface temperature pixel value obtained by inversion of the remote sensing image, and verifying the earth surface temperature inversion accuracy;
and reversely calculating the relation obtained by fitting the air temperature and the corresponding surface temperature to obtain an air temperature value of the whole research area, wherein the air temperature value is used as meteorological data for calculating building energy consumption in the later period.
Building a building model according to building types in the cooling range of the urban wetland park, obtaining a preliminary prediction result through the building model and the meteorological data, and carrying out correlation analysis on the preliminary prediction result and the cooling index of the wetland park to predict building energy consumption, wherein the building model comprises the following steps of:
building models corresponding to urban village residential buildings and low residential buildings in the urban wetland park cooling range, and acquiring internal parameters of the buildings in the urban wetland park cooling range and building areas corresponding to the urban village residential buildings and the low residential buildings respectively;
introducing meteorological data and a building model into Energy Plus software to perform Energy consumption calculation to obtain time-by-time load values of various buildings in different local climate areas, counting the time-by-time load values, and selecting the time-by-time load values located in a remote sensing image imaging time interval as local climate area building Energy consumption calculation data to obtain building Energy consumption simulation results;
according to the areas of village residential buildings and low residential buildings in a city in a local climate zone of a research area, carrying out weighted assignment calculation on the building energy consumption simulation result according to the total area of corresponding building types in the local climate zone to obtain energy consumption results of each local climate zone in remote sensing image imaging time;
and carrying out correlation analysis on the energy consumption results of the climate zones of each local area and the cooling indexes of the wetland park to obtain the relation between the cooling effect of the wetland park and the energy consumption of the building, and predicting the energy consumption of the building of the climate zones of different local areas around the wetland park.
A method for predicting building energy consumption by using a cooling effect of an urban wetland park and the wetland park based on remote sensing images comprises the following steps:
dividing local climate zones in a selected wetland park cooling range; determining a wetland park self factor which is a wetland park cooling influence factor: park area, park perimeter, perimeter area ratio, green area and water area; local climate zone surface structure parameters: sky view angle coefficient, street aspect ratio, building density; the surface coverage parameters include building height, water permeable area ratio and water impermeable area ratio;
preprocessing the selected remote sensing image, inverting the earth surface temperature of a research area, obtaining earth surface temperature change rules of climate zones of different local areas around the wetland park through a buffer area analysis method, calculating to obtain a cooling index of the wetland park, performing correlation analysis and linear regression analysis on the cooling index and a cooling factor, and obtaining cooling effects of the wetland park on the climate zones of different local areas around the wetland park;
performing mobile observation on a local wetland park, obtaining local air temperature of a research area during remote sensing image imaging, verifying ground surface temperature inversion accuracy and performing back-calculation on the air temperature of the whole research area, and taking the air temperature as meteorological data of building energy consumption;
building a building model according to building types in the cooling range of the urban wetland park, obtaining a preliminary prediction result through the building model and the meteorological data, carrying out correlation analysis on the preliminary prediction result and the cooling index of the wetland park, and predicting building energy consumption.
An electronic device, comprising:
a storage medium for storing a computer program,
and the processing unit is used for carrying out data exchange with the storage medium and carrying out the steps of the method for forecasting the building energy consumption by the urban wetland park cooling effect and the wetland park when forecasting the building energy consumption by the urban wetland park cooling effect and the wetland park.
A computer readable storage medium having a computer program stored therein;
the computer program, when run, performs the steps of the method of urban wetland park cooling effect and wetland park energy consumption prediction as described above.
The beneficial effects of the invention at least comprise:
according to the system, a typical wetland park is selected as a study object to carry out regional climate division, and a wetland park cooling effect influence factor is determined; then carrying out inversion on the earth surface temperature of the research area to obtain the change rule of the earth surface temperature around the wetland park, calculating a cooling index, and carrying out factor analysis and linear regression analysis on the cooling factors and the cooling index of the wetland park to obtain the cooling effect of the wetland park; then, carrying out mobile observation to obtain the air temperature of the local research area, verifying the inversion of the surface temperature, and carrying out reverse calculation to obtain the air temperature of the whole research area; finally, building energy consumption calculation of different local climate zones around the wetland park is carried out, and factor analysis is carried out by combining all cooling indexes of the wetland park, so that the influence relationship of the wetland park on the building energy consumption is obtained;
the invention combines the local climate zone, the cooling effect of the wetland park and the building energy consumption, more comprehensively reflects the cooling effect of the wetland park and predicts the building energy consumption around the wetland park, and provides references for the planning and design of climate-adapted buildings and cities.
Drawings
FIG. 1 is a system block diagram of a system according to the present invention;
FIG. 2 is a flow chart for predicting the influence of a cooling effect of an urban wetland park and the energy consumption of a building by the wetland park based on remote sensing images;
FIG. 3 is a schematic view of the climate zone division of the surrounding land of the wetland park;
FIG. 4 is a schematic diagram of a surface temperature inversion process;
FIG. 5 is a schematic diagram of a mobile observation;
FIG. 6 is a three-dimensional model of a typical village home in city;
FIG. 7 is a three-dimensional model of a typical self-building house;
FIG. 8 is a schematic diagram of energy consumption calculation of a climate zone building around a wetland park;
FIG. 9 is a plot of constructed energy consumption distribution results for a constructed wetland park perimeter area LCZ;
FIG. 10 is a graph of cooling distance (PCD) versus as-built zone LCZ building energy consumption analysis results;
FIG. 11 is a graph of the results of a cooling amplitude (PCR) versus build-up zone LCZ building energy consumption correlation analysis;
FIG. 12 is a graph of the results of a cooling gradient (PCG) versus build zone LCZ building energy consumption correlation analysis;
it should be noted that: 10-12 in the drawing respectively show the fitting result graphs between the building energy consumption of the LCZ of the constructed environmental area around the wetland park and the PCG, the PCR and the PCG corresponding to the building energy consumption, wherein PCD represents the cooling distance of the wetland park to the LCZ of the constructed environmental area around the wetland park, and reflects the range of the cooling effect of the wetland park; PCR is used as another important index of the cooling effect of the wetland park, and represents the cooling intensity of the wetland park on the LCZ of the surrounding built environment area;
specific: by means of figures 10 and 11, it can be shown that the energy consumption of LCZ construction in different built-up environment areas around the wetland park is linearly inversely related to PCG and PCR; the correlation R2 values are 0.8210 and 0.5307, respectively; meanwhile, in fig. 10 and 11, the larger the PCG value is, the more favorable the construction energy consumption of the constructed environmental zone LCZ around the wetland park is reduced, and the smaller the construction energy consumption of the constructed environmental zone LCZ around the wetland park is, the description that the cooling effect of the large-scale wetland park is favorable for relieving the construction energy consumption is;
FIG. 12 shows that the energy consumption of LCZ buildings in different built-up environment areas around a wetland park is linearly and positively correlated with PCG, and the R2 value is 0.5531; PCG represents the cooling efficiency of the wetland park on the LCZ of the surrounding built environment area, and the larger the PCG value is, the better the cooling effect of the wetland park on the LCZ in a short distance is.
Detailed Description
The present application is further described below with reference to the accompanying drawings.
Specific example I:
the invention provides a data processing system for remote sensing images, as shown in figure 1, which is used for predicting the cooling effect of urban wetland parks and the influence of the wetland parks on building energy consumption, and comprises the following steps: the system comprises a micro-processing module 100, a climate dividing module 200, a preprocessing module 300, an acquisition module 400, a prediction module 500 and an output module 600; the climate dividing module 200 is electrically connected with the micro-processing module 100, and is configured to divide local climate zones in the cooling range of the urban wetland park, and obtain self factors of the urban wetland park, local climate zone earth surface structural parameters and earth surface coverage parameters; the preprocessing module 300 is electrically connected with the micro-processing module 100, and is configured to obtain the remote sensing image corresponding to the urban wetland park, so as to obtain a cooling effect of the urban wetland park on climate zones of different local areas around the urban wetland park; the acquisition module 400 is electrically connected with the micro-processing module 100, and is used for movably acquiring the air temperature of the local climate zone, and the micro-processing module 100 performs verification of the inversion accuracy of the surface temperature according to the air temperature and acquires the air temperature in the cooling range of the urban wetland park to obtain the meteorological data of building energy consumption; the prediction module 500 is electrically connected with the microprocessor module 100, and is configured to establish a building model according to a building type in the cooling range of the urban wetland park, obtain a preliminary prediction result through the building model and the meteorological data, perform correlation analysis on the preliminary prediction result and the cooling index of the wetland park, and predict building energy consumption to obtain a prediction result for the building energy consumption; the output module 600 is electrically connected to the microprocessor module 100, and is configured to output the prediction result; the microprocessor module 100 may be any programmable element;
specifically, the micro-processing module 100 may be any microprocessor, and performs data processing with the climate dividing module 200, the preprocessing module 300, the acquisition module 400 and the prediction module 500, and may perform the following method for predicting building energy consumption by using the urban wetland park cooling effect and the wetland park based on remote sensing images; the method comprises the following specific steps:
(1) And (5) carrying out regional climate partition in the selected wetland park cooling range. Determining a wetland park self factor which is a wetland park cooling influence factor: park area, park perimeter, perimeter area ratio, green area and water area; local climate zone surface structure parameters: sky view angle coefficient, street aspect ratio, building density; the surface coverage parameters include building height, water permeable area ratio and water impermeable area ratio;
further, the step (1) includes the steps of:
(11) The selected geographic position of the wetland park should be in the region with obvious urban heat island effect, and different local climate zones are distributed around the wetland park. The wetland park boundary vector data is obtained by online investigation and on-site field investigation;
(12) Parameters of the wetland park: park area, park perimeter area ratio, greenbelt area and water area are obtained in Google Earth images and field investigation;
(13) The local climate zone parameter acquisition method comprises the following steps: the sky view angle coefficient is obtained by carrying out field view finding shooting through a fisheye camera, and importing Rayman software for area conversion; estimating the building height through the shadow length in the Google Earth image, and checking in the field; the street height-width ratio, the building density, the water permeable area ratio and the water impermeable area ratio are obtained through high-resolution satellite image statistical calculation, and are subjected to field check;
(14) Carrying out block loading and field labeling on local climate subareas in a geographic information system, and linking the local climate subareas into characteristic indexes corresponding to each local climate subarea to obtain characteristic parameter data atlas of different local climate subareas around the wetland park;
(15) The selected typical local climate zone is of an international general type of local climate zone, such as: a neighborhood morphology classification within 9 typical urban areas of LCZ1 (compact high-rise building area), LCZ2 (compact medium-rise building area), LCZ3 (compact low-rise building area), LCZ4 (open high-rise building area), LCZ5 (open medium-rise building area), LCZ6 (open low-rise building area), LCZ7 (light low-rise building area), LCZ8 (large low-rise building area) and LCZ9 (sparse building area);
(16) The method for dividing the climate zones of the surrounding areas of the wetland park is as follows: firstly, classifying different underlying surfaces around the wetland park through field investigation and by means of Google map, and taking the underlying surfaces which are uniformly distributed and have the same type as an LCZ partition. Because the cooling effect of the wetland park on the peripheral neighborhood is considered, whether the range of the LCZ partition meets the cooling distance of the case wetland park on the peripheral neighborhood or not needs to be fully considered during the LCZ partition, and the local climate partition is partitioned in the cooling distance range of the wetland park according to the calculation of the cooling distance of the wetland park in the later stage.
(2) Preprocessing a selected Landsat8 remote sensing image, inverting the earth surface temperature of a research area, obtaining earth surface temperature change rules of climate zones of different local areas around the wetland park through a buffer area analysis method, calculating to obtain a cooling index of the wetland park, performing correlation analysis and linear regression analysis on the cooling index and a cooling factor, and obtaining cooling effects of the wetland park on the climate zones of different local areas around the wetland park.
Further, the step (2) includes the steps of:
(21) The selected Landsat8 remote sensing image is acquired in the USGS functional network (https:// earthorpore. USGS. Gov /) and the geospatial data cloud. The quality requirement of the remote sensing image is that no cloud exists or the cloud load is below 10%;
(22) Inversion of the surface temperature is carried out by adopting a single window algorithm, and the calculation principle is as follows.
(22) Inversion of the surface temperature is carried out by adopting a single window algorithm, and the calculation principle is as follows.
in the formula ,
the unit is K, which is the surface temperature;
and
Is a coefficient fitted according to the relation between the heat radiation intensity and the bright temperature, and is between 0 and 70℃ +.>
=-67.355 35，
=0.458 608；
、
Is an intermediate variable;
The average action temperature of the atmosphere is shown as K;
The bright temperature is given by K.
in the formula ,
for the current day's atmospheric transmittance, at atmospheric moisture content +.>
The unit is g/cm 2 At 1.6-3.0 g/cm 2 During the variation interval, according to the water vapor condition of the imaging date of the selected Landsat8 remote sensing data, < >>
The value is 1.6 g/cm 2 ；
Representing the emissivity of the earth's surface, which is generally divided into a water surface, a natural surface and a town surface, ++>
The calculation principle of (2) is as follows:
in the above formula
、
and
Representing the emissivity of the water surface, natural surface and town surface, respectively.
For vegetation coverage, the calculation method is as follows:
wherein, NDVI is normalized vegetation index,
and
The 5 th and 4 th wave bands of the remote sensing image are respectively, < ->
And
NDVI values for bare soil and vegetation, respectively. Atmospheric mean action temperature->
Can be calculated according to a tropical average atmospheric empirical formula.
In the formula (i),
is the air temperature (in K) of the nearby ground.
(3) And (3) carrying out mobile observation on the local wetland park, obtaining the local air temperature of a research area during the imaging of the remote sensing image, verifying the inversion accuracy of the surface temperature and carrying out back-calculation on the air temperature of the whole research area, and taking the verification result as meteorological data of the energy consumption of the later building.
Further, the step (3) includes the steps of:
(31) Selecting a wetland park with good road conditions and more types of surrounding local climate zones for mobile observation;
(32) Selecting proper vehicles (such as automobiles, sharing bicycles and electric vehicles) for road condition inspection, and designing a smooth moving observation route with flat ground;
(33) In the mobile observation process, the longitude and latitude of each meteorological data acquisition point of the handheld GPS positioning instrument are recorded by the HOBO sensor;
(34) The remote sensing image is generally imaged at a certain moment, so that the requirement of the mobile observation time is consistent with the imaging time of the remote sensing image, a fixed weather station is required to be set for synchronous monitoring with the mobile observation, and finally, the weather data is corrected to be consistent with the imaging moment of the remote sensing image;
(35) The size of each pixel in the remote sensing image is 30 x 30m, the average value of moving observation meteorological data in the pixel range is taken as a corresponding air temperature value, correlation fitting is carried out on the average value and the ground surface temperature pixel value obtained by inversion of the remote sensing image, and the ground surface temperature inversion accuracy is verified;
(36) And reversely calculating the relation obtained by fitting the air temperature obtained by the mobile observation and the corresponding surface temperature to obtain an air temperature value of the whole research area, wherein the air temperature value is used as meteorological data for calculating building energy consumption in the later period.
(4) Building a building model according to the main building type of the research area, importing the building model and meteorological data into building energy consumption simulation calculation software to calculate building energy consumption, performing correlation analysis on settlement results and wetland park cooling indexes, and predicting building energy consumption.
Further, the step (4) includes the steps of:
(41) Building two building models of a village residential building in a city and a low residential building, and setting internal parameters such as a building enclosure structure, an air conditioning system and the like according to design specifications and standards of corresponding building thermal partitions of various buildings;
(42) Introducing meteorological data and a building model into Energy Plus software to perform Energy consumption calculation to obtain time-by-time load values of various buildings in different local climate areas, counting the time-by-time load values, and selecting the load value positioned in a remote sensing image imaging time interval as later local climate area building Energy consumption calculation data;
(43) And counting the areas of two types of buildings in the local climate zone of the research area, and carrying out weighted assignment calculation on the building energy consumption simulation result according to the total area of the corresponding building types in the local climate zone to obtain the energy consumption result of each local climate zone in the imaging time of the remote sensing image.
(44) And (3) performing correlation analysis on the calculated building energy consumption and each cooling index obtained in the step (25) to obtain a relation between the cooling effect of the wetland park and the building energy consumption, and predicting the building energy consumption of different local climate zones around the wetland park.
Specific example II:
in this embodiment, ten typical wetland parks in a city are taken as research areas, and specific steps are shown in fig. 2:
(1) Ten wetland parks such as a certain wetland park, a certain lake park, a certain river wetland park, a certain bead park, a certain national wetland park section, a certain bank wetland park, a certain park wetland park, a certain lake wetland park, a certain ridge wetland park and the like are selected as research objects, and the periphery of the ten wetland parks is divided into local climate zones. The division results are shown in fig. 3, and the periphery of the 10 wetland parks is divided into 51 LCZ partitions, wherein the built environment types are 38, and the natural surface types are 13. The LCZ types are 6 kinds of LCZ2, LCZ5, LCZ6, LCZ9, LCZ D and LCZ G. Determining a wetland park self factor which is a wetland park cooling influence factor: park area, park perimeter, perimeter area ratio, green area and water area; local climate zone surface structure parameters: sky view angle coefficient, street aspect ratio, building density; the surface coverage parameters are building height, water permeable area ratio and water impermeable area ratio.
(2) Landsat8 remote sensing images are selected as research data, the imaging time is 2019, 9, 27 days, the cloud cover is 4.73%, the stripe number is 122, and the row number is 44. And performing preprocessing such as geometric correction, radiation calibration, study area cutting and the like on the remote sensing image by using ENVI5.3 software, and performing surface temperature inversion by adopting a single window algorithm.
Combining the boundary vector data of the wetland park and the climate zones of different peripheral places, and extracting a mask in geographic information analysis software Arcgis to obtain the surface temperatures of the selected wetland park and the climate zones of different peripheral places;
dividing the wetland park into N small rings at equal intervals according to the range of the climate zone of the periphery of the wetland park. Extracting the surface temperature of the interior of each small ring in Arcgis software by using a mask, and performing mathematical model fitting by using the cooling distance as an independent variable and the surface temperature of each small ring as a dependent variable to obtain a surface temperature change curve of a surrounding local climate zone of the wetland park;
according to the earth surface temperature change curve of the surrounding local climate zone of the wetland park, performing integral operation in Origin software, and calculating to obtain cooling indexes such as cooling distance, cooling amplitude, cooling gradient and the like;
performing correlation analysis on the cooling index and the cooling factors to obtain the relation between the cooling effect and each influencing factor of different local climate zones around the wetland park;
and performing linear regression analysis on each cooling index and the cooling factor respectively to obtain mathematical expressions between the cooling indexes and the cooling factors of the wetland park.
(3) In a certain lake park, two park peripheral land climate zones of a certain national wetland park section are used for mobile observation, a shared bicycle is used for mobile observation, and a fixed meteorological site is arranged in the certain lake park;
in the mobile observation process, a handheld GPS positioning instrument is used for recording the longitude and latitude of each meteorological data acquisition point, and an HOBO U23-002 sensor is used for recording the air temperature, wherein the precision of the two instruments is as follows: GPS positioning (precision: single point positioning 2-5 m; differential positioning 1-3 m); HOBO U23-002 (precision: + -0.21 ℃ C.; resolution: 0.02 ℃ C.);
the remote sensing image is generally imaged at a certain moment, so that the requirement of the mobile observation time is consistent with the imaging time of the remote sensing image, a fixed weather station is required to be set for synchronous monitoring with the mobile observation, and finally, the weather data is corrected to be consistent with the imaging moment of the remote sensing image;
the size of each pixel in the remote sensing image is 30 x 30m, the average value of moving observation meteorological data in the pixel range is taken as a corresponding air temperature value, correlation fitting is carried out on the average value and the ground surface temperature pixel value obtained by inversion of the remote sensing image, and the ground surface temperature inversion accuracy is verified;
and reversely calculating the relation obtained by fitting the air temperature obtained by the mobile observation and the corresponding surface temperature to obtain an air temperature value of the whole research area, wherein the air temperature value is used as meteorological data for calculating the energy consumption of the building.
The case movement observation roadmap is shown in fig. 5.
(4) Since the main building of the selected study area is a village residential building and a low residential building, building models of the village residential building and the low residential building in the city are built, as shown in fig. 6-7; setting internal parameters such as building enclosure structures, air conditioning systems and the like according to design specifications and standards of corresponding building thermal partitions of various buildings;
the thermal parameters of the building envelope are shown in the following tables 1 and 2:
TABLE 1 construction model roofing, exterior wall construction and major thermal parameters
TABLE 2 major thermal parameters for exterior windows of building models
The indoor heat disturbance setting parameters of the air conditioning system are referred to GB 50189-2015 public building energy-saving design standard, JGJ 449-2018 civil building green performance calculation standard and GB 50034-2013 building illumination design standard. The specific parameters are as follows: the personnel density in living room and bedroom are 4 m and 2m respectively 2 The heat dissipation capacity of the person is 134W/person, and the illumination power density is 6W/m 2 Power density of electrical equipment 10W/m 2 。
As shown in fig. 8, firstly, introducing meteorological data and a building model into Energy Plus software to perform Energy consumption calculation to obtain time-by-time load values of various buildings in different local climate zones, counting the time-by-time load values, and selecting the load values in a remote sensing image imaging time interval as Energy consumption calculation data of buildings in later local climate zones;
then, the areas of two types of buildings in the local climate zone of the research area are counted, the building energy consumption simulation result is calculated according to the total area of the corresponding building types in the local climate zone, and the energy consumption result of each local climate zone in the remote sensing image imaging time is obtained as shown in fig. 9;
from fig. 9, it can be derived that: the energy consumption of the building in different types of built environment areas around the wetland park is distributed at 112 Wh/m 2 ~ 375 Wh/m 2 Between different types of local climate zones have different building energy consumption distributions, including:
the highest building energy consumption value is LCZ2, the interval distribution is minimum, and the value is 273 Wh/m 2 ~ 375 Wh/m 2 Is distributed among the two layers and has the average value of 319.92 Wh/m 2 The method comprises the steps of carrying out a first treatment on the surface of the Therefore, the local climate zone of the compact middle-layer building has higher building energy consumption, and the distribution among the buildings is relatively close, so that the difference of the building energy consumption distribution is not large;
LCZ6 as a local climate zone of the open low-rise building is larger than LCZ5 in distribution interval and is respectively distributed in 209.03 Wh/m 2 ~ 324.55 Wh/m 2 And 146.89 Wh/m 2 ~ 341.07 Wh/m 2 The average value is not greatly different from each other, and is respectively 250.4. 250.4 Wh/m 2 And 251.71 Wh/m 2 The method comprises the steps of carrying out a first treatment on the surface of the Although LCZ6 is larger than LCZ5 in distribution interval, the building area is larger and the distribution is more dispersed, so that the building energy consumption distribution interval is larger than LCZ 5;
LCZ9 as super open building type local climate zone has smaller internal building distribution, so that the building energy consumption is minimum in four local climate zones, and the average value is 224.15 Wh/m 2 ；
And finally, carrying out correlation analysis on the calculated building energy consumption and each cooling index obtained in the step (25) to obtain the relation between the cooling effect of the wetland park and the building energy consumption, and predicting the building energy consumption of different local climate zones around the wetland park.
The invention also provides an embodiment:
an electronic device, comprising: a storage medium and a processing unit; the method comprises the steps of storing a computer program in a storage medium, performing data exchange between a processing unit and the storage medium, and performing the steps of the method for predicting the building energy consumption of the wetland park and the urban wetland park based on remote sensing images in the specific embodiment I by executing the computer program by the processing unit when performing urban local scale carbon emission calculation and carbon neutralization prediction.
A computer-readable storage medium having a computer program stored therein; the computer program, when run, performs the steps of the remote sensing image-based method for urban wetland park cooling effect and wetland park energy consumption prediction for buildings as described in embodiment I.
In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The invention introduces the method for the cooling effect of the climate zone of the different places around the wetland park in detail, predicts the energy consumption of the climate zone building of the different places around the wetland park, can optimize the running scheme of the air conditioning system of the building around the wetland park, and provides a strategy for planning the urban landscape pattern.
The foregoing disclosure is merely illustrative of some embodiments of the invention, and the invention is not limited thereto, as modifications may be made by those skilled in the art without departing from the scope of the invention. The above-mentioned inventive sequence numbers are merely for description and do not represent advantages or disadvantages of the implementation scenario.
Claims (10)
1. The utility model provides a wetland park cooling effect and building energy consumption prediction system for carry out data processing to remote sensing image, acquire wetland park cooling effect and predict building energy consumption, its characterized in that includes:
a microprocessor module;
the climate dividing module is electrically connected with the micro-processing module and is used for dividing local climate zones in the cooling range of the urban wetland park to obtain self factors of the urban wetland park, local climate zone earth surface structure parameters and earth surface coverage parameters;
the preprocessing module is electrically connected with the micro-processing module and is used for acquiring the remote sensing images corresponding to the urban wetland park to obtain the cooling effect of the urban wetland park on the climate zones of different local areas around the urban wetland park;
the acquisition module is electrically connected with the micro-processing module and is used for movably acquiring the air temperature of the local climate zone, and the micro-processing module performs verification of the inversion accuracy of the surface temperature according to the air temperature and acquires the air temperature in the cooling range of the urban wetland park to obtain meteorological data of building energy consumption;
the prediction module is electrically connected with the micro-processing module and is used for establishing a building model according to building types in the cooling range of the urban wetland park, obtaining a preliminary prediction result through the building model and the meteorological data, carrying out correlation analysis on the preliminary prediction result and the cooling index of the wetland park, and predicting building energy consumption to obtain a prediction result aiming at the building energy consumption;
and the output module is electrically connected with the micro-processing module and is used for outputting the prediction result.
2. The wetland park cooling effect and building energy consumption prediction system according to claim 1, wherein:
the self factors of the urban wetland park comprise: park area, park perimeter, perimeter area ratio, greenbelt area and water area;
the local climate zone surface structure parameters comprise: one or more of sky visual angle coefficient, street height-width ratio and building density;
the surface coverage parameters include: one or more of building height, water-permeable area ratio and water-impermeable area ratio.
3. The system for predicting cooling effect and building energy consumption of a wetland park according to claim 1, wherein said obtaining cooling effect of the urban wetland park on climate zones of different local areas around comprises:
selecting a remote sensing image corresponding to the cooling range of the urban wetland park, and inverting the surface temperature of the cooling range of the urban wetland park;
obtaining the earth surface temperature change rule of different local climate zones around the urban wetland park to obtain the wetland park cooling index;
and performing correlation analysis and linear regression analysis on the cooling indexes and the cooling factors to obtain cooling effects of the wetland park on climate zones of different local areas around the wetland park.
4. A wetland park cooling effect and building energy consumption prediction system according to claim 3, wherein:
inversion of the surface temperature is carried out by adopting a single window algorithm;
combining the urban wetland park and the local climate zone boundary vector data to obtain the surface temperatures of the selected urban wetland park and the local climate zone;
equally dividing the ranges of the urban wetland park and the local climate zones into N small rings, extracting the surface temperature inside each small ring, and fitting by taking the cooling distance as an independent variable and the surface temperature of each small ring as a dependent variable to obtain surface temperature change curves of the urban wetland park and the peripheral local climate zones;
obtaining the cooling index according to the surface temperature change curve;
performing correlation analysis on the cooling index and the cooling factor to obtain the relation between the cooling effect and the influence factor of the climate zones of different places around the urban wetland park;
and performing linear regression analysis on the cooling index and the cooling factor to obtain a model between the cooling index and the cooling factor of the urban wetland park.
5. The system for predicting the cooling effect and the building energy consumption of the wetland park according to claim 4, wherein the inversion of the surface temperature by adopting the single window algorithm comprises:
，
in the formula ,
is the surface temperature;
and
Is a coefficient fitted according to the relationship between the heat radiation intensity and the brightness temperature;
、
Is an intermediate variable;
The average action temperature of the atmosphere;
The temperature is bright temperature;
in the formula ,
for the atmospheric transmittance of the day, +.>
Is the atmospheric moisture content;
Representing the emissivity of the earth's surface; said->
The method comprises the following steps of:
wherein ,
、
and
Emissivity of water surface, natural surface and town surface respectively;
wherein, NDVI is normalized vegetation index,
and
The 5 th and 4 th wave bands of the remote sensing image are respectively, < + >>
and
NDVI values for bare soil and vegetation, respectively;
The average action temperature of the atmosphere;
wherein ,
is the air temperature of the nearby ground.
6. The system of claim 1, wherein the mobile collection of air temperature micro-processing module of the local climate zone performs verification of surface temperature inversion accuracy according to the air temperature and obtains the air temperature in the cooling range of the urban wetland park, comprising:
selecting a partition with better road conditions in the surrounding local climate partitions of the urban wetland park and more types of the surrounding local climate partitions for mobile observation;
selecting any traffic tool to check road conditions, and constructing a smooth moving observation route on a flat ground;
moving and observing each meteorological data acquisition point, and recording the air temperature of each meteorological data acquisition point;
the time for collecting the air temperature is consistent with the imaging time of the remote sensing image;
setting the size of each pixel in the remote sensing image, taking the average value of the air temperature in the pixel range as an air temperature value of the corresponding pixel range, performing correlation fitting with the earth surface temperature pixel value obtained by inversion of the remote sensing image, and verifying the earth surface temperature inversion accuracy;
and reversely calculating the relation obtained by fitting the air temperature and the corresponding surface temperature to obtain an air temperature value of the whole research area, wherein the air temperature value is used as meteorological data for calculating building energy consumption in the later period.
7. The system according to claim 1, wherein the building model is built according to building types in the cooling range of the urban wetland park, a preliminary prediction result is obtained through the building model and the meteorological data, and the preliminary prediction result and the cooling index of the wetland park are subjected to correlation analysis, so as to predict building energy consumption, and the system comprises:
building models corresponding to various residential buildings in the cooling range of the urban wetland park, and acquiring internal parameters of the buildings in the cooling range of the urban wetland park and building areas corresponding to the various residential buildings respectively;
introducing meteorological data and a building model into Energy Plus software to perform Energy consumption calculation to obtain time-by-time load values of various buildings in different local climate areas, counting the time-by-time load values, and selecting the time-by-time load values located in a remote sensing image imaging time interval as local climate area building Energy consumption calculation data to obtain building Energy consumption simulation results;
according to the areas of various residential buildings in the local climate zone of the research area, carrying out weighted assignment calculation on the building energy consumption simulation result according to the total area of the corresponding building types in the local climate zone to obtain the energy consumption result of each local climate zone in the remote sensing image imaging time;
and carrying out correlation analysis on the energy consumption results of the climate zones of each local area and the cooling indexes of the wetland park to obtain the relation between the cooling effect of the wetland park and the energy consumption of the building, and predicting the energy consumption of the building of the climate zones of different local areas around the wetland park.
8. The method for predicting the building energy consumption by the urban wetland park cooling effect and the wetland park based on the remote sensing image is characterized by comprising the following steps of:
dividing local climate zones in a selected wetland park cooling range; determining a wetland park self factor which is a wetland park cooling influence factor: park area, park perimeter, perimeter area ratio, green area and water area; local climate zone surface structure parameters: sky view angle coefficient, street aspect ratio, building density; the surface coverage parameters include building height, water permeable area ratio and water impermeable area ratio;
preprocessing the selected remote sensing image, inverting the earth surface temperature of a research area, obtaining earth surface temperature change rules of climate zones of different local areas around the wetland park through a buffer area analysis method, calculating to obtain a cooling index of the wetland park, performing correlation analysis and linear regression analysis on the cooling index and a cooling factor, and obtaining cooling effects of the wetland park on the climate zones of different local areas around the wetland park;
performing mobile observation on a local wetland park, obtaining local air temperature of a research area during remote sensing image imaging, verifying ground surface temperature inversion accuracy and performing back-calculation on the air temperature of the whole research area, and taking the air temperature as meteorological data of building energy consumption;
building a building model according to building types in the cooling range of the urban wetland park, obtaining a preliminary prediction result through the building model and the meteorological data, carrying out correlation analysis on the preliminary prediction result and the cooling index of the wetland park, and predicting building energy consumption.
9. An electronic device, comprising:
a storage medium for storing a computer program,
and the processing unit is used for carrying out data exchange with the storage medium and executing the computer program by the processing unit when carrying out the urban wetland park cooling effect and the wetland park energy consumption prediction on the building, so as to carry out the steps of the method for carrying out the urban wetland park cooling effect and the wetland park energy consumption prediction on the building according to claim 8.
10. A computer-readable storage medium, characterized by:
the computer readable storage medium has a computer program stored therein;
the computer program, when run, performs the steps of the method of urban wetland park cooling effect and wetland park to building energy consumption prediction as claimed in claim 8.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007104972A (en) * | 2005-10-14 | 2007-04-26 | Misawa Homes Co Ltd | Greening method of citizen-based town planning considering microclimate |
| CN104298828A (en) * | 2014-10-13 | 2015-01-21 | 河海大学 | Method for simulating influence of urban green space patterns on thermal environments |
| CN113987306A (en) * | 2021-11-03 | 2022-01-28 | 广东工业大学 | City block energy consumption time-varying graph set construction method, device, equipment and storage medium |
| CN114626966A (en) * | 2022-03-18 | 2022-06-14 | 杭州师范大学 | Method for quantizing large-scale urban blue-green space cooling scale based on landscape pattern |
| CN115860238A (en) * | 2022-12-09 | 2023-03-28 | 东莞理工学院 | Green land type optimal configuration method for relieving urban heat island effect |
-
2023
- 2023-04-20 CN CN202310425169.0A patent/CN116150859B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007104972A (en) * | 2005-10-14 | 2007-04-26 | Misawa Homes Co Ltd | Greening method of citizen-based town planning considering microclimate |
| CN104298828A (en) * | 2014-10-13 | 2015-01-21 | 河海大学 | Method for simulating influence of urban green space patterns on thermal environments |
| CN113987306A (en) * | 2021-11-03 | 2022-01-28 | 广东工业大学 | City block energy consumption time-varying graph set construction method, device, equipment and storage medium |
| CN114626966A (en) * | 2022-03-18 | 2022-06-14 | 杭州师范大学 | Method for quantizing large-scale urban blue-green space cooling scale based on landscape pattern |
| CN115860238A (en) * | 2022-12-09 | 2023-03-28 | 东莞理工学院 | Green land type optimal configuration method for relieving urban heat island effect |
Non-Patent Citations (4)
| Title |
|---|
| LIN LIU 等: "Landscape pattern optimization strategy of coastal mountainside greenway from a microclimatic comfort view in hot and humid areas", URBAN CLIMATE, pages 1 - 19 * |
| XIAOSHAN YANG 等: "Assessing the Impact of Urban Heat Island Effect on Building Cooling Load based on the Local Climate Zone Scheme", 10TH INTERNATIONAL SYMPOSIUM ON HEATING, VENTILATION AND AIR CONDITIONING, pages 2839 - 1846 * |
| 孟凡超;任国玉;郭军;张雷;张瑞雪;: "城市热岛效应对天津市居住建筑供暖和制冷负荷的影响", 地理科学进展, no. 08, pages 50 - 61 * |
| 潘鑫沛 等: "基于LCZ的湿热地区典型城市 局部空间格局特性研究", 广东工业大学学报, vol. 38, no. 2, pages 107 - 114 * |
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