CN111597609B - Basic operation unit containing solar radiation and building energy consumption rapid simulation method applying same - Google Patents
Basic operation unit containing solar radiation and building energy consumption rapid simulation method applying same Download PDFInfo
- Publication number
- CN111597609B CN111597609B CN202010351293.3A CN202010351293A CN111597609B CN 111597609 B CN111597609 B CN 111597609B CN 202010351293 A CN202010351293 A CN 202010351293A CN 111597609 B CN111597609 B CN 111597609B
- Authority
- CN
- China
- Prior art keywords
- rad
- wall
- area
- energy consumption
- basic operation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Computational Mathematics (AREA)
- Biomedical Technology (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Structural Engineering (AREA)
- Civil Engineering (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Architecture (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Pure & Applied Mathematics (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention relates to the technical field of energy consumption prediction, in particular to a basic operation unit containing solar radiation and a building energy consumption rapid simulation method applying the same. A basic arithmetic Unit containing solar radiation, wherein the basic arithmetic Unit is one of 5 basic types, and the 5 basic types are units ground‑ceiling 、Unit ground‑roof 、Unit floor‑ceiling 、Unit floor‑roof And Unit exposed‑ceiling The basic operation unit comprises geometric parameters and solar radiation quantity parameters. A building energy consumption rapid simulation method comprises the step that parameters of the basic operation unit are used for machine learning algorithm generation and building energy consumption rapid simulation feedback. The invention brings the annual radiation calculation into the machine learning training parameters for the first time, and solves the defect that the influence factors of the environment outside the building cannot be considered when the current machine learning algorithm simulates the building energy consumption; the defect is solved, so that the accuracy of the machine learning algorithm is greatly improved, and the practicability of the machine learning algorithm is improved.
Description
Technical Field
The invention relates to the technical field of energy consumption prediction, in particular to a basic operation unit containing solar radiation and a building energy consumption rapid simulation method applying the same.
Background
The building energy consumption accounts for an important proportion in the energy consumption of China, and the optimization aiming at the building energy consumption in the building design stage has important significance for realizing the sustainable development of cities under the large background of smart cities, energy conservation and emission reduction. However, the current building energy consumption simulation accuracy and the operation speed cannot be simultaneously considered:
on one hand, the traditional physical simulation method is extremely time-consuming in operation, and for a monomer building or a building group with a complex form, one-time accurate energy consumption prediction has to take hours or even days; a part of operation time can be reduced through combined calculation of similar floors and rooms, complicated external environment radiation is caused due to shadow of an external building or self-shielding of a building body type in a real environment, and the model without considering the external environment radiation is simplified and reduces the energy consumption prediction accuracy.
On the other hand, machine learning algorithms typified by neural network techniques offer the possibility of rapid energy consumption simulation. The machine learning algorithm extracts knowledge from existing sample data and predicts the performance of the newly input sample with acceptable accuracy. Due to the complexity of building form control parameters and the complex interaction between the building form and the surrounding environment, the previous research of energy consumption simulation by means of machine learning is only suitable for the parameter optimization of simple block volume or the optimization of the building envelope structure under the premise of fixed volume (CN 109255472A, CN104134097A, CN 109409605A). These simulation methods are not suitable for comparing a large number of building schemes with complex shapes in the actual building design process. The only research (CN 109033595 a) trying to solve this problem uses a shape cutting method to add up the energy consumption of simple cuboid units to obtain the total energy consumption of the building, but this method only guesses the energy consumption through the basic geometric dimensions (length, width, height, window-wall ratio) of the cuboid units, ignores the influence of external short-wavelength radiation factors on the energy consumption, and potentially affects the energy consumption prediction accuracy.
The neglect of the radiation of the external environment by the machine learning algorithm can cause large-scale deviation of energy consumption, and a distorted energy consumption simulation result cannot effectively guide a designer to optimize a building scheme, so that the feasibility of introducing actual project practice by the machine learning algorithm is directly influenced.
Disclosure of Invention
The invention provides a basic operation unit containing solar radiation to solve the problem that the accuracy is low due to the fact that external environment radiation cannot be considered in a building energy consumption prediction model in the prior art.
In order to solve the technical problems, the invention adopts the technical scheme that: a basic operation Unit containing solar radiation, wherein the basic operation Unit is one of 5 basic types, and the 5 basic types are units ground-ceiling 、Unit ground-roof 、Unit floor-ceiling 、Unit floor-roof And Unit exposed-ceiling (ii) a Wherein Unit ground-ceiling The lower surface of the basic operation unit is a terrace surface which generates heat exchange with the ground, and the upper surface is an interlayer ceiling; unit ground-roof The lower surface of the basic arithmetic unit is a terrace surface which generates heat exchange with the ground, and the upper surface is a roof surface which is exposed to the external environment; unit floor-ceiling The lower surface of the basic operation unit is a floor surface between layers, and the upper surface is a ceiling surface between layers; unit floor-roof The lower surface of the basic operation unit is a floor surface between floors, and the upper surface is a roof surface exposed to the external environment; unit exposed-ceiling The lower surface of the basic operation unit is an overhead layer surface exposed to the external environment, and the upper surface is an interlayer ceiling surface; the basic operation unit comprises geometric parameters and solar radiation quantity parameters.
Preferably, the basic operation unit is a cuboid, and the geometric parameters of the basic operation unit comprise the length X, the width Y and the height Z of the cuboid; taking the plane where the length and the height of the cuboid are located as a reference plane, wherein the azimuth angle between the reference plane and the local north direction is Rotate; the ratio of the inner wall part of each surface of the basic operation unit to the total area of the surface is Adiabalic i The window-wall ratio of the external wall part on each surface of the basic operation unit is WWR i I.e. the ratio of the window area to the external wall surface area, where i denotes each side of the basic arithmetic unit, i =1 to 4.
Preferably, the solar radiation quantity parameter includes the solar radiation quantity Rad _ wall received by any side external wall of the basic operation unit i And the amount of solar radiation Rad _ win received by the outer window on the side i 。
Preferably, the amount Rad _ wall of solar radiation received by the outer wall i Including Rad _ wall i (low)、Rad_wall i (mid) and Rad _ wall i (high), wherein Rad _ wall i (low) is the amount of solar radiation received by the side surface when the outdoor air temperature is lower than the lowest indoor set temperature; rad _ wall i (mid) is the amount of solar radiation received by the side when the outdoor air temperature is in the indoor set temperature zone; rad _ wall i (high) is outdoor air temperature higher thanThe amount of solar radiation received by the side when the temperature is set indoors; the Rad _ win i Including Rad _ win i (low)、Rad_win i (mid) and Rad _ win i (high), wherein Rad _ win i (low) is the solar radiation quantity received by the external window on the side surface when the outdoor air temperature is lower than the lowest indoor set temperature; rad _ win i (mid) is the amount of solar radiation received by the external window on the side when the outdoor air temperature is in the indoor set temperature zone; rad _ win i (high) is the amount of solar radiation received by the exterior window on that side when the outdoor air temperature is higher than the indoor set temperature.
Preferably, the basic operation Unit is a Unit ground-roof Or Unit floor-roof When the solar dosimetry parameters further comprise top surface solar radiation Rad _ roof, the Rad _ roof comprising Rad _ roof (low), rad _ roof (mid), and Rad _ roof (high); wherein, rad _ roof (low) is the solar radiation quantity received by the top surface when the outdoor air temperature is lower than the lowest indoor set temperature; rad _ roof (mid) is the amount of solar radiation received by the top surface when the outdoor air temperature is in the indoor set temperature interval; rad _ roof (high) is the amount of solar radiation received by the ceiling when the outdoor air temperature is higher than the indoor set temperature.
The invention also provides a building energy consumption rapid simulation method, which comprises the step of using the parameters of the basic operation unit for machine learning algorithm generation and building energy consumption rapid simulation feedback.
Preferably, the machine learning algorithm generation comprises training sample generation, machine learning algorithm training and optimal machine learning algorithm screening;
the training sample generation is to combine the parameters of the basic operation units and randomly supercube sampling, the basic operation units with different geometric parameters and solar radiation quantity parameters are generated in batch by adopting experimental design and serve as the training samples, and the basic operation units are led into energy consumption simulation software to carry out accurate physical simulation so as to obtain energy consumption data of the basic operation units;
the machine learning algorithm training includes two steps:
a: because the selection of the input parameters greatly influences the prediction precision of the machine learning algorithm, the input parameters with the best prediction effect need to be explored, and j groups of input parameters are obtained through a parameter transformation formula;
b: dividing the j training sample combinations after the parameter transformation of the previous step into training sets D according to the proportion 0 Test set D 1 Test set D 2 And will train set D 0 Respectively inputting the training data into k machine learning algorithms, setting different hyper-parameters p for each machine learning algorithm, and obtaining j x k groups in total, wherein each group has a plurality of machine learning models with hyper-parameter setting combinations
It should be noted that j × K is a numerical value obtained by multiplying j by K;
the optimal machine learning algorithm screening comprises the following steps:
s1: using various types of machine learning algorithm models
Computing test set D 1 Energy consumption value of, and test set D 1 Comparing the actual energy consumption to obtain the product of R 2 Expressed accuracy value->
S2: using this accuracy value
Screening optimal hyper-parameter setting model in each type of machine learning algorithm
S3: then using the j x k group candidate machine learning algorithm to the test set D 2 The prediction accuracy of the model F is calculated to obtain a machine learning algorithm prediction model F with the highest prediction accuracy R2 best 。
It should be noted that the machine learning algorithm is artificial neural network, gaussiaThe method comprises the steps of n process regression Gaussian process regression algorithm, kriging agent model algorithm, random Forest algorithm, MARS multivariate adaptive regression algorithm and SVM support vector machine. Each of the machine learning algorithm prediction models F best The branch prediction model comprises 5 basic operation units. The optimal hyper-parameter combinations p obtained by the machine learning algorithm prediction model are different for different basic arithmetic units.
Preferably, in the step a, the parameter information obtained from the basic operation unit can be converted into other associated parameters, and the parameter transformation formula is as follows:
Area＝X*Y
Area_win i +Area_wall i ＝(Z*Length)*(1-Adiabatic i )
(Length＝X，if i＝1，3；Length＝Y，if i＝2，4)
Area_win i ＝WWR i *(Area_win i +Area_wall i )
Area_wall i ＝(1-WWR i )*(Area_win i +Area_wall i )
Rad_wall_average i ＝Rad_wall i /Area_wall i ；
Rad_win_average i ＝Rad_win i /Area_win i
Rad_roof_average＝Rad_roof/Area
wherein, area _ win i Represents the Area of the outer window on the ith side of the basic arithmetic unit, area _ wall i The area of an outer wall on the ith side of the basic operation unit is shown; rad _ wall _ average g e i Rad _ win _ average, the amount of solar radiation received by any side wall of the basic arithmetic unit i Representing the amount of solar radiation received per unit area of the outer window on the side; rad _ roof-average represents the amount of solar radiation received per unit area of the top surface; the j sets of input parameters include Configuration1, configuration2, configuration3, configuration4, configuration5, and Configuration6; wherein Configuration1 comprises X, Y, Z, area, rotate, rad _ wall i ，Rad_win i Configuration2 includes X, Y, Z, area, rotate, adiabaltic i ，Rad_wall i ，Rad_win i The Configuration3 includes X, Y, Z, area, rotate, adiabaltic i ，WWR i ，Rad_wall i ，Rad_win i Configuration4 includes X, Y, Z, area, rotate, adiabaltic i ，WWR i ，Rad_wall_average i ，Rad_win_average i The Configuration5 includes X, Y, Z, area, rotate, area _ wall i ，Area_win i ，Rad_wall i ，Rad_win i The Configuration6 includes X, Y, Z, area, rotate, area _ wall i ，Area_win i ，Rad_wall_avera g e i And Rad _ win _ average i
Preferably, the basic operation Unit is a Unit ground-roof Or Unit floor-roof Configuration1, configuration2, configuration3 and Configuration5 further include Rad _ roof, configuration4 and Configuration6 further include Rad _ roof _ average, and Rad _ roof _ average is top surface unit area solar radiation.
Preferably, the building energy consumption rapid simulation feedback comprises the following steps:
a: and (3) building decomposition: the building is decomposed into the building volume which is firstly divided into volume slices of different layers according to the actual building floor height, and then each layer of slices is decomposed into a plurality of cuboid basic operation units;
b: calculating the radiation value of the basic operation unit: using Accelard to carry out fast calculation on the sunshine radiation parameters accelerated by the GPU on the basic operation unit obtained in the step A, wherein the operation speed of the calculation algorithm is not in direct proportion to the fineness degree and the surface complexity of a calculation grid;
c: fast calculation of energy consumption: inputting the geometric parameters and solar radiation quantity parameters of the basic operation unit into a machine learning algorithm prediction model F in batches best Obtaining the energy consumption values E of all basic operation units of the whole building idx The total energy consumption value of the building is
Wherein n is the number of basic arithmetic units forming the whole building;
d: fast energy consumption feedback: because the machine learning algorithm is approximately the real-time energy consumption feedback speed, the calculated total energy consumption value can be rapidly displayed in the building model through a visualization method; the energy consumption numerical values of all the basic operation units forming the building volume can be directly colored on the surface of each basic operation unit in a pseudo-color graph mode, and an architect can conveniently and quickly find out the unfavorable building energy consumption area to carry out targeted form optimization.
Compared with the prior art, the beneficial effects are that:
firstly, the annual radiation calculation is taken into the machine learning training parameters for the first time, and the defect that influence factors of the environment outside a building cannot be considered when the current machine learning algorithm simulates the building energy consumption is overcome; the defect is solved, so that the accuracy of the machine learning algorithm is greatly improved, and the practicability of the machine learning algorithm is improved.
Secondly, by means of the GPU accelerated solar radiation calculation and the quick feedback of a machine learning algorithm, the simulation time of building energy consumption is reduced from hours in the past to seconds, the accuracy is guaranteed, and the feasibility of introducing actual project practice is achieved.
Thirdly, three groups of radiation parameters Rad associated with the indoor design temperature interval are provided low ，Rad mid ，Rad high The relation between the building energy consumption and the external solar radiation to which the building energy consumption is subjected can be accurately described.
And fourthly, a building combination concept including a basic operation unit of solar radiation is provided, so that the building energy consumption prediction precision is improved, and meanwhile, an energy consumption numerical value of each combination unit of the building can be provided and visually presented, so that an architect can visually judge an energy consumption unfavorable area and pertinently improve a scheme.
And fifthly, the machine learning algorithm generation module can screen out the optimal machine learning algorithm without manual screening.
Drawings
FIG. 1 is a schematic diagram of a method for rapid simulation of building energy consumption;
FIG. 2 is a schematic diagram of a machine learning algorithm of the present invention;
FIG. 3 is a diagram of a basic arithmetic unit according to the present invention;
FIG. 4 is an experimental design of a basic arithmetic unit training sample in accordance with the present invention;
FIG. 5 is an exploded view of the construction of the present invention;
FIG. 6 is a schematic surface view of the detailed architectural energy consumption of the present invention, which is colored in a pseudo-color manner on each basic operation unit;
FIG. 7 is a schematic view showing the building size in example 3 of the present invention;
FIG. 8 is a graph showing the total annual solar radiation received by a building surface as quickly calculated in example 3 of the present invention;
FIG. 9 is a regression analysis chart of predicted energy consumption and physical simulation energy consumption under Beijing climate conditions;
FIG. 10 is a regression analysis plot of predicted energy consumption versus physically modeled energy consumption under Guangzhou climate conditions;
fig. 11 is a schematic diagram showing the predicted building energy consumption value of each basic operation unit in the form of a pseudo-color chart on the surface of a three-dimensional building model in the invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there are orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "long", "short", etc., based on the orientations or positional relationships shown in the drawings, the description is merely for convenience of description and simplification, but it is not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed in a specific orientation and be operated, and therefore, the terms describing the positional relationships in the drawings are only used for illustrative purposes and are not to be construed as limitations of the present patent, and specific meanings of the terms described above can be understood according to specific situations by those skilled in the art.
The technical scheme of the invention is further described in detail by the following specific embodiments in combination with the attached drawings:
example 1
As shown in fig. 3, 4 and 5, a basic arithmetic Unit containing solar radiation is one of 5 basic types, and 5 basic types are units ground-ceiling 、Unit ground-roof 、Unit floor_ceiling 、Unit floor-roof And Unit exposed-ceiling (ii) a Wherein Unit ground-ceiling The lower surface of the basic operation unit is a terrace surface which generates heat exchange with the ground, and the upper surface is an interlayer ceiling; unit ground-roof The lower surface of the basic arithmetic unit is a terrace surface which generates heat exchange with the ground, and the upper surface is a roof surface which is exposed to the external environment; unit floor-ceiling The lower surface of the basic operation unit is a floor surface between layers, and the upper surface is a ceiling surface between layers; unit floor-roof The lower surface of the basic operation unit is a floor surface between floors, and the upper surface is a roof surface exposed to the external environment; unit exposed-ceiling The lower surface of the basic operation unit is an overhead layer surface exposed to the external environment, and the upper surface is an interlayer ceiling surface; the basic operation unit comprises geometric parameters and solar radiation quantity parameters.
The basic operation unit is a cuboid, and the geometric parameters of the basic operation unit comprise the length X, the width Y and the height Z of the cuboid; taking the plane where the length and the height of the cuboid are located as a reference plane, wherein the azimuth angle between the reference plane and the local north direction is Rotate; the ratio of the inner wall part of each surface of the basic operation unit to the total area of the surface is Adiabaltic i The ratio of the window to the wall of the outer wall portion on each side of the basic arithmetic unit is WWR i I.e. window surfacesAnd (c) the ratio of the area of the outer wall surface, wherein i represents each side of the basic operation unit, and i =1 to 4.
In addition, the solar radiation quantity parameter comprises the solar radiation quantity Rad _ wall received by any side outer wall of the basic operation unit i And the solar radiation amount Rad _ win received by the outer window on the side surface i 。
Wherein the solar radiation quantity Rad _ wall received by the outer wall i Including Rad _ wall i (low)、Rad_wall i (mid) and Rad _ wall i (high), wherein Rad _ wall i (low) is the amount of solar radiation received by the side surface when the outdoor air temperature is lower than the lowest indoor set temperature; rad _ wall i (mid) is the amount of solar radiation received by the side when the outdoor air temperature is in the indoor set temperature zone; rad _ wall i (high) is the amount of solar radiation received by the side when the outdoor air temperature is higher than the indoor set temperature; rad _ win i Including Rad _ win i (low)、Rad_win i (mid) and Rad _ win i (high), wherein Rad _ win i (low) is the amount of solar radiation received by the external window on the side when the outdoor air temperature is lower than the lowest indoor set temperature; rad _ win i (mid) is the amount of solar radiation received by the external window on the side when the outdoor air temperature is in the indoor set temperature zone; rad _ win i (high) is the amount of solar radiation received by the exterior window on that side when the outdoor air temperature is higher than the indoor set temperature.
In addition, the basic arithmetic Unit is a Unit ground-roof Or Unit floor-roof Meanwhile, the solar radiation quantity parameters further comprise top surface solar radiation Rad _ roof, wherein the Rad _ foof comprises Rad _ roof (low), rad _ roof (mid) and Rad _ roof (high); wherein Rad _ roof (low) is the solar radiation quantity received by the top surface when the outdoor air temperature is lower than the lowest indoor set temperature; rad _ roof (mid) is the amount of solar radiation received by the top surface when the outdoor air temperature is in the indoor set temperature interval; rad _ roof (high) is the amount of solar radiation received by the ceiling when the outdoor air temperature is higher than the indoor set temperature.
Example 2
As shown in FIG. 1, a building energy consumption rapid simulation method includes that parameters of the basic operation unit are used for machine learning algorithm generation and building energy consumption rapid simulation feedback.
As shown in fig. 2, the machine learning algorithm generation includes training sample generation, machine learning algorithm training, and optimal machine learning algorithm screening;
the method comprises the steps that training samples are generated by combining parameters of basic operation units and randomly sampling super cubes, the basic operation units with different geometric parameters and solar radiation parameters are generated in batches by adopting experimental design and serve as the training samples, and the basic operation units are led into energy consumption simulation software to be subjected to accurate physical simulation to obtain energy consumption data of the basic operation units;
machine learning algorithm training includes two steps:
a: because the selection of the input parameters greatly influences the prediction precision of the machine learning algorithm, the input parameters with the best prediction effect need to be explored, and j groups of input parameters are obtained through a parameter transformation formula;
b: dividing the j training sample combinations after the parameter transformation of the last step into a training set D according to the proportion 0 Test set D 1 Test set D 2 And will train set D 0 Respectively inputting the parameters into k machine learning algorithm training, setting different hyper-parameters p for each machine learning algorithm, totally obtaining j x k groups, each group having a plurality of hyper-parameter setting combinations
The screening of the optimal machine learning algorithm comprises the following steps:
s1: using various types of machine learning algorithm models
Computing test set D 1 Energy consumption value of, and test set D 1 Comparing the actual energy consumption to obtain the product of R 2 Expressed accuracy value->
S2: using this accuracy value
Screening optimal hyper-parameter setting model in each type of machine learning algorithm
S3: then using the j x k group candidate machine learning algorithm to the test set D 2 The prediction accuracy of the model F is calculated to obtain a machine learning algorithm prediction model F with the highest prediction accuracy R2 best 。
In addition, in step a, the parameter information obtained from the basic operation unit may be converted into other associated parameters, and the parameter transformation formula is as follows: area = X Y
Area_win i +Area_wall i ＝(Z*Length)*(1-Adiabatic i )
(Length＝X，if i＝1，3；Length＝Y，if i＝2，4)
Area_win i ＝WWR i *(Area_win i +Area_wall i )
Area_wall i ＝(1-WWR i )*(Area_win i +Area_wall i )
Rad_wall_average i ＝Rad_wall i /Area_wall i ；
Rad_win_average i ＝Rad_win i /Area_win i
Rad_roof_average＝Rad roof /Area
Wherein, area _ win i Represents the Area of the outer window on the ith side of the basic arithmetic unit, area _ wall i The area of an outer wall on the ith side of the basic operation unit is shown; rad _ wall _ average i Rad _ win _ average, the amount of solar radiation received by any side wall of the basic arithmetic unit i Representing the amount of solar radiation received per unit area of the outer window on the side; rad _ of _ average represents the amount of solar radiation received per unit area of the top surface; j =6,6 the set of input parameters includes Configuration1, configuration2, and,Configuration3, configuration4, configuration5 and Configuration6; wherein Configuration1 includes X, Y, Z, area, rotate, rad _ wall i ，Rad_win i Configuration2 includes X, Y, Z, area, rotate, adiabaltic i ，Rad_wall i ，Rad_win i The Configuration3 includes X, Y, Z, area, rotate, adiabaltic i ，WWR i ，Rad_wall i ，Rad_win i Configuration4 includes X, Y, Z, area, rotate, adiabaltic i ，WWR i ，Rad_wall_average i ，Rad_win_average i The Configuration5 includes X, Y, Z, area, rotate, area _ wall i ，Area_win i ，Rad_wall i ，Rad_win i Configuration6 includes X, Y, Z, area, rotate, area _ wall i ，Area_win i ，Rad_wall_average i And Rad _ win _ average i 。
In addition, the basic arithmetic Unit is a Unit ground-roof Or Unit floor-roof Configuration1, configuration2, configuration3 and Configuration5 further include Rad _ roof, configuration4 and Configuration6 further include Rad _ roof _ operation, and Rad _ roof _ operation is solar radiation per unit area of the top surface.
The quick simulation feedback of the building energy consumption comprises the following steps:
a: and (3) building decomposition: the building is decomposed into slices of the building volume which are divided into different layers according to the actual building layer height, and the slices of each layer are further decomposed into cuboid basic operation units;
b: calculating the radiation value of the basic operation unit: using Accelard to carry out fast calculation on the sunshine radiation parameters accelerated by the GPU on the basic operation unit obtained in the step A, wherein the operation speed of the calculation algorithm is not in direct proportion to the fineness degree and the surface complexity of a calculation grid; compared with a CPU (central processing unit) calculation method, the calculation speed and the model complexity are not in a linear relation, so that the calculation time is shortened from minutes to seconds when the radiation value containing a large number of sampling points is calculated;
c: fast calculation of energy consumption:inputting the geometric parameters and solar radiation quantity parameters of the basic operation unit into a machine learning algorithm prediction model F in batches best Obtaining the energy consumption value E of all basic operation units of the whole building iax The total energy consumption value of the building is
Wherein n is the number of basic arithmetic units constituting the whole building;
d: fast energy consumption feedback: because the machine learning algorithm is close to the real-time energy consumption feedback speed, the calculated total energy consumption value can be quickly displayed in the building model through a visualization method; the energy consumption values of the basic operation units forming the building volume can be directly colored on the surface of each basic operation unit in a pseudo-color graph mode, and architects can conveniently and quickly find out areas with unfavorable building energy consumption to conduct targeted form optimization.
Example 3:
as shown in fig. 7, energy consumption prediction is performed on a city block with a side length of about 140 × 95 m, the total building area of an office building is 33500 square meters, the buildings are all oriented in the north-south direction, the height of the first floor is 4.5m, the height of the standard floor is 4.2m, the north window-wall ratio of all the buildings is 0.4, the south window-wall ratio is 0.6, and the window-wall ratios of the east-west sides and the west-west sides are 0.2. The building is formed by combining a plurality of high floors and a plurality of floors, the lowest floor of each building group is 3, the highest floor is 15, the complex forms such as block connection, overhead bottom, staggered roof and the like are provided, and the building has a more complex energy consumption simulation environment compared with a single building. Assuming that all building spaces inside the building are in an open office function and have the same building physical properties (surface thermal engineering coefficients), establishing a model in a Rhinoceros3D platform, respectively using weather files of Beijing and Guangzhou for prediction accuracy verification under different climate zones, and performing building shape decomposition and calculation of related parameters of a basic operation unit in a grasshopper platform.
First, 15000 training samples are generated by using weather files of Guangzhou and Beijing, and two machine learning prediction algorithms are trained. And then, importing the relevant parameters of the basic operation unit acquired by the grasshopper platform into a machine learning algorithm and EnergyPlus respectively to calculate and count the running time of the basic operation unit.
Comparing the machine learning predicted energy consumption with the energy consumption obtained by the Energyplus simulation shows that the prediction errors of the refrigeration and heating energy consumption in the two climate partitions of Guangzhou and Beijing are mostly within the range of 5%, wherein the deviation of the heating energy consumption is slightly larger, but the reference significance is still provided, as shown in FIG. 9 and FIG. 10. The typical prediction time of the machine learning algorithm is 40s (including the annual radiation calculation time), the total annual solar radiation received by the building surface is shown in fig. 8, while the accurate energy consumption simulation takes 2.5h, and the speed difference is 220 times. The traditional machine learning prediction model can only estimate the whole building energy consumption value, and the rapid simulation algorithm is used for decomposing a building into a large number of basic operation units and then respectively predicting corresponding energy consumption, so that the annual heating and cooling energy consumption of each building unit can be displayed on the model, as shown in fig. 11, an architect can rapidly and accurately determine the unfavorable position of energy consumption optimization and make targeted form optimization (including adjustment of window-wall ratio, change of building form, movement of building blocks and the like) and verify the optimization effect in near real time; and the method of calculating energy consumption by physical simulation consumes too much time, and the possibility of carrying out repeated and rapid body optimization aiming at the building energy-saving target is lost.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (7)
1. A basic arithmetic unit comprising solar radiation, characterized by: the basic operation unit is one of 5 basic types, 5 basic classesType is Unit ground-ceiling 、Unit ground-roof 、Unit floor-ceiling 、Unit floor-roof And Unit exposed-ceiling (ii) a Wherein Unit ground-ceiling The lower surface of the basic operation unit is a terrace surface which generates heat exchange with the ground, and the upper surface is an interlayer ceiling; unit ground-roof The lower surface of the basic operation unit is a terrace surface which generates heat exchange with the ground, and the upper surface is a roof surface exposed to the external environment; unit floor-ceiling The lower surface of the basic operation unit is a floor surface between layers, and the upper surface is a ceiling surface between layers; unit floor-roof The lower surface of the basic operation unit is a floor surface between floors, and the upper surface is a roof surface exposed to the external environment; unit exposed-ceiling The lower surface of the basic operation unit is an overhead layer surface exposed to the external environment, and the upper surface is an interlayer ceiling surface; the basic operation unit comprises geometric parameters and solar radiation quantity parameters; the solar radiation quantity parameter comprises solar radiation quantity Rad _ wall received by any side outer wall of the basic operation unit i And the solar radiation amount Rad _ win received by the outer window on the side surface i (ii) a Solar radiation Rad _ wall received by outer wall i Including Rad _ wall i (low)、Rad_wall i (mid) and Rad _ wall i (high), wherein Rad _ wall i (low) is the amount of solar radiation received by the side surface when the outdoor air temperature is lower than the lowest indoor set temperature; rad _ wall i (mid) is the amount of solar radiation received by the side when the outdoor air temperature is in the indoor set temperature zone; rad _ wall i (high) is the amount of solar radiation received by the side when the outdoor air temperature is higher than the indoor set temperature; the Rad _ win i Including Rad _ win i (low)、Rad_win i (mid) and Rad _ win i (high), wherein Rad _ win i (low) is the solar radiation quantity received by the external window on the side surface when the outdoor air temperature is lower than the lowest indoor set temperature; rad _ win i (mid) is the amount of solar radiation received by the external window on the side when the outdoor air temperature is in the indoor set temperature zone; rad _ win i (high) when the outdoor air temperature is higher than the indoor set temperature, the side surfaceThe amount of solar radiation received by the outer window; the basic operation Unit is a Unit ground-roof Or Unit floor-roof When the solar dosimetry parameters further comprise top surface solar radiation Rad _ roof, the Rad _ roof comprising Rad _ roof (low), rad _ roof (mid), and Rad _ roof (high); wherein, rad _ roof (low) is the solar radiation quantity received by the top surface when the outdoor air temperature is lower than the lowest indoor set temperature; rad _ roof (mid) is the amount of solar radiation received by the top surface when the outdoor air temperature is in the indoor set temperature interval; rad _ roof (high) is the amount of solar radiation received by the ceiling when the outdoor air temperature is higher than the indoor set temperature.
2. A basic arithmetic unit containing solar radiation according to claim 1, characterized in that: the basic operation unit is a cuboid, and the geometric parameters of the basic operation unit comprise the length X, the width Y and the height Z of the cuboid; taking the plane where the length and the height of the cuboid are located as a reference plane, wherein the azimuth angle between the reference plane and the local north direction is Rotate; the ratio of the inner wall part of each surface of the basic operation unit to the total area of the surface is Adiabalic i The ratio of the window to the wall of the outer wall part on each side of the basic operation unit is WWR i I.e. the ratio of the window area to the external wall surface area, where i denotes each side of the basic arithmetic unit, i =1 to 4.
3. A building energy consumption rapid simulation method is characterized by comprising the following steps: comprising using the parameters of the basic arithmetic unit of claim 1 or 2 for machine learning algorithm generation and building energy consumption fast simulation feedback.
4. The building energy consumption rapid simulation method according to claim 3, characterized in that: the machine learning algorithm generation comprises training sample generation, machine learning algorithm training and optimal machine learning algorithm screening;
the training sample is generated by combining the parameters of the basic operation units and randomly supercube sampling, the basic operation units with different geometric parameters and solar radiation quantity parameters are generated in batches by adopting experimental design and are used as the training samples, and the basic operation units are led into energy consumption simulation software to carry out accurate physical simulation so as to obtain energy consumption data of the basic operation units;
the machine learning algorithm training includes two steps:
a: because the selection of the input parameters greatly influences the prediction precision of the machine learning algorithm, the input parameters with the best prediction effect need to be explored, and j groups of input parameters are obtained through a parameter transformation formula;
b: dividing the j training sample combinations after the parameter transformation of the previous step into training sets D according to the proportion 0 Test set D 1 Test set D 2 And will train set D 0 Respectively inputting the training data into k machine learning algorithms, setting different hyper-parameters p for each machine learning algorithm, and obtaining j x k groups in total, wherein each group has a plurality of machine learning models with hyper-parameter setting combinations
The optimal machine learning algorithm screening comprises the following steps:
s1: using various types of machine learning algorithm models
Computing test set D 1 Energy consumption value of, and test set D 1 Comparing the actual energy consumption to obtain the product of R 2 Accuracy value of representation
S2: using this accuracy value
Screening optimal hyper-parameter setting model in each type of machine learning algorithm
S3: then divide intoUsing the j x k group candidate machine learning algorithm to test set D 2 The prediction accuracy of the model F is calculated to obtain a machine learning algorithm prediction model F with the highest prediction accuracy R2 best 。
5. The building energy consumption rapid simulation method according to claim 4, characterized in that: in the step a, the parameter information obtained from the basic operation unit can be converted into other associated parameters, and the parameter transformation formula is as follows:
Area＝X*Y
Area_win i +Area_wall i ＝(Z*Length)*(1-Adiabatic i )
(Length＝X，if i＝1，3；Length＝Y，if i＝2，4)
Area_win i ＝WWR i *(Area_win i +Area_wall i )
Area_wall i ＝(1-WWR i )*(Area_win i +Area_wall i )
Rad_wall_average i ＝Rad_wall i /Area_wall i ；
Rad_win_average i ＝Rad_win i /Area_win i
Rad_roof_average＝Rad_roof/Area
wherein, area _ win i Represents the Area of the outer window on the ith side of the basic arithmetic unit, area _ wall i The area of an outer wall on the ith side of the basic operation unit is shown; rad _ wall _ average i Rad _ win _ average, the amount of solar radiation received by any side wall of the basic arithmetic unit i Representing the amount of solar radiation received per unit area of the outer window on the side; rad _ roof _ average represents the amount of solar radiation received per unit area of the top surface; the j sets of input parameters include Configuration1, configuration2, configuration3, configuration4, configuration5, and Configuration6; wherein Configuration1 comprises X, Y, Z, area, rotate,
Rad_win i configuration2 includes X, Y, Z, area, rotate, adiabaltic i ，Rad_wall i ，Rad_win i The Configuration3 includes X, Y, Z, area, rotate, adiabaltic i ，WWR i ，Rad_wall i ，Rad_win i Configuration4 includes X, Y, Z, area, rotate, adiabaltic i ，WWR i ，Rad_wall_average i ，Rad_win_average i The Configuration5 includes X, Y, Z, area, rotate, area _ wall i ，Area_win i ，Rad_wall i ，Rad_win i The Configuration6 includes X, Y, Z, area, rotate, area _ wall i ，Area_win i ，Rad_wall_average i And Rad _ win _ average i 。
6. The building energy consumption rapid simulation method according to claim 5, characterized in that: the basic operation Unit is a Unit ground-roof Or Unit floor-roof Configuration1, configuration2, configuration3 and Configuration5 further include Rad _ roof, configuration4 and Configuration6 further include Rad _ roof _ operation, and Rad _ roof _ operation is solar radiation per unit area of the top surface.
7. The building energy consumption rapid simulation method according to claim 6, characterized in that: the quick simulation feedback of the building energy consumption comprises the following steps:
a: and (3) building decomposition: the building is decomposed into building volume slices which are firstly divided into different layers according to the actual building floor height, and the slices of each layer are further decomposed into a cuboid basic operation unit;
b: calculating the radiation value of the basic operation unit: b, using Accelerad to carry out GPU accelerated sunlight radiation parameter fast calculation on the basic operation unit obtained in the step A, wherein the operation speed of the calculation algorithm is not in direct proportion to the fineness and the surface complexity of a calculation grid;
c: fast calculation of energy consumption: inputting the geometric parameters and solar radiation quantity parameters of the basic operation unit into the machine in batchesLearning algorithm prediction model F best Obtaining the energy consumption value E of all basic operation units of the whole building idx The total energy consumption value of the building is
Wherein n is the number of basic arithmetic units forming the whole building;
d: fast energy consumption feedback: because the machine learning algorithm is close to the real-time energy consumption feedback speed, the calculated total energy consumption value can be quickly displayed in the building model through a visualization method; the energy consumption values of the basic operation units forming the building volume can be directly colored on the surface of each basic operation unit in a pseudo-color graph mode, and architects can conveniently and quickly find out areas with unfavorable building energy consumption to conduct targeted form optimization.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010351293.3A CN111597609B (en) | 2020-04-28 | 2020-04-28 | Basic operation unit containing solar radiation and building energy consumption rapid simulation method applying same |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010351293.3A CN111597609B (en) | 2020-04-28 | 2020-04-28 | Basic operation unit containing solar radiation and building energy consumption rapid simulation method applying same |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111597609A CN111597609A (en) | 2020-08-28 |
| CN111597609B true CN111597609B (en) | 2023-03-24 |
Family
ID=72189470
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010351293.3A Active CN111597609B (en) | 2020-04-28 | 2020-04-28 | Basic operation unit containing solar radiation and building energy consumption rapid simulation method applying same |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111597609B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112270137B (en) * | 2020-11-24 | 2022-06-17 | 同济大学 | Method and device for dynamically generating sunshade behavior based on building energy consumption simulation software |
| CN112801357B (en) * | 2021-01-21 | 2024-04-02 | 长江慧控科技(武汉)有限公司 | Solar radiation quantity prediction method, device, equipment and storage medium |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4425983B1 (en) * | 2009-03-18 | 2010-03-03 | 株式会社パスコ | Method and apparatus for evaluating solar radiation |
| FR2975206B1 (en) * | 2011-05-11 | 2014-04-04 | Commissariat Energie Atomique | METHOD FOR PREDICTING THE ENERGY CONSUMPTION OF A BUILDING |
| CN104133938B (en) * | 2014-07-01 | 2015-12-30 | 哈尔滨工业大学 | A kind of severe cold area office building exterior window geometric Parameters Optimization method considered based on heating energy consumption |
| CN109033595B (en) * | 2018-07-16 | 2024-03-29 | 清华大学 | New method and device for building green performance simulation analysis |
| CN109255472A (en) * | 2018-08-20 | 2019-01-22 | 天津科技大学 | A kind of Office Building Energy Consumption prediction technique based on Multivariate adaptive regression splines batten |
| CN110046743B (en) * | 2019-03-06 | 2021-06-04 | 上海交通大学 | Public building energy consumption prediction method and system based on GA-ANN |
-
2020
- 2020-04-28 CN CN202010351293.3A patent/CN111597609B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111597609A (en) | 2020-08-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yang et al. | Influence of urban morphological characteristics on thermal environment | |
| Wang et al. | Multi-objective optimization (MOO) for high-rise residential buildings’ layout centered on daylight, visual, and outdoor thermal metrics in China | |
| Sanaieian et al. | Review of the impact of urban block form on thermal performance, solar access and ventilation | |
| Gros et al. | Cool materials impact at district scale—Coupling building energy and microclimate models | |
| CN111597609B (en) | Basic operation unit containing solar radiation and building energy consumption rapid simulation method applying same | |
| CN108399470B (en) | Indoor PM2.5 prediction method based on multi-example genetic neural network | |
| Ding et al. | Optimization approach of passive cool skin technology application for the Building’s exterior walls | |
| De la Torre-Gea et al. | Computational fluid dynamics in greenhouses: A review | |
| CN105117787A (en) | Model optimization prediction method of greenhouse energy consumption | |
| Rodrigues et al. | An approach to urban quarter design using building generative design and thermal performance optimization | |
| Wu et al. | Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions | |
| CN111310257A (en) | Regional building energy consumption prediction method under BIM environment | |
| You et al. | Improving building facade design using integrated simulation of daylighting, thermal performance and natural ventilation | |
| CN110334366A (en) | A kind of instantaneous cooling load prediction method of building based on the Monte Carlo Method with Latin Hypercube Sampling | |
| Gholami et al. | A modelling methodology for a solar energy-efficient neighbourhood | |
| Liu et al. | Building information modelling-enabled multi-objective optimization for energy consumption parametric analysis in green buildings design using hybrid machine learning algorithms | |
| Mokrzecka | Influence of building shape and orientation on heating demand: simulations for student dormitories in temperate climate conditions | |
| Zheng et al. | Optimization of exterior wall insulation in office buildings based on wall orientation: Economic, energy and carbon saving potential in China | |
| Forouzandeh | Parametric analysis of influence of courtyard microclimate on diminution of convective heat transfer through building’s envelope | |
| JP4437187B2 (en) | Method and apparatus for predicting thermal environment inside and outside buildings | |
| Li et al. | Comprehensive evaluation of combining CFD simulation and entropy weight to predict natural ventilation strategy in a sliding cover solar greenhouse | |
| Baranyai et al. | Planning and research of smart buildings and constructions with the ‘Energydesign Roadmap’method | |
| Peters | Defining environments: Understanding architectural performance through modelling, simulation and visualisation | |
| CN115660338A (en) | Typical building cooling, heating and power load model construction method for southwest border area | |
| Pan et al. | Multi-objective optimization for building performance design considering thermal comfort and energy consumption |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |