CN103049612B - Building indoor environment optimization method based on model order reduction technology - Google Patents
Building indoor environment optimization method based on model order reduction technology Download PDFInfo
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- CN103049612B CN103049612B CN201210564269.3A CN201210564269A CN103049612B CN 103049612 B CN103049612 B CN 103049612B CN 201210564269 A CN201210564269 A CN 201210564269A CN 103049612 B CN103049612 B CN 103049612B
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Abstract
The invention discloses a building indoor environment optimization method based on the model order reduction technology. The method mainly includes the three steps: (1) using CFD (computational fluid dynamics) software for steady-state simulation of the indoor environment, and constructing variation spaces of various environmental parameters; 2) reconstructing low-order parameter variation subspaces by the aid of the POD (proper orthogonal decomposition) technology; and 3) searching the optimal air conditioner air supply temperature and the optimal air conditioner air supply speed by operating the genetic algorithm. The variation subspaces of the indoor environmental parameters are constructed by the aid of the POD technology, so that influences of spatial distribution on the environmental parameters are considered fully in an optimizing strategy, and optimization accuracy is improved. The POD model order reduction method maps a control equation in an original space into one orthogonal subspace, and accordingly mapping error can be guaranteed to minimum in the energy sense. Besides, compared with a present environment optimization strategy, the building indoor environment optimization method based on the model order reduction technology has the advantages of high optimization precision, high speed and the like.
Description
Technical field
The present invention relates to a kind of Building Indoor Environment optimization method based on model order reduction, belong to architectural environment and building energy saving field.
Background technology
Along with improving constantly that people require living quality and building energy conservation, how to coordinate and to optimize Building Indoor Environment comfort level and air conditioning energy consumption more and more receives publicity.Current numerous scholar has proposed the multiobjective optimal control strategy of multiple systems level.Optimized algorithm develops into all kinds of intelligent optimization algorithms (as evolutional programming, genetic algorithm etc.) that extensively use at present from the optimization method of early stage dependence gradient, Optimal Parameters has been contained the each side of indoor environment, comprises hot comfort, air quality and air conditioning energy consumption etc.
In environment optimization control strategy, how to resolve rapidly and accurately environmental response for candidate's control variable is a key problem.Because ready-made Building Indoor Environment model is difficult to meet real-time and the degree of accuracy requirement optimized simultaneously, current common way is that hypothesis room air mixes completely, ignore the impact of space distribution on environmental parameter, adopt the method for empirical model or half mechanism model to solve environmental response.And in fact, for most of air-conditioning systems, looking like especially displacement ventilation system, indoor environmental parameter spatially has larger difference.Ignore this species diversity and can cause the actual impression of effect of optimization and indoor each area people not to be inconsistent, cause various comfort level complaints.This respect research is very limited in the world at present.Reason is indoor multiparameter environmental modeling complexity, must be by CFD instrument, and be difficult to directly integrate with online system optimizing control.
2009, there is document to propose to obtain the simplified model of CFD model by the method for neural metwork training, for the rapid solving of environment optimization strategy parameter index.The method obtains abundant input/output data by CFD emulation.Set up the environmental index alternative model based on neural network by the training and testing that these data are right.In each iteration of optimized algorithm, utilize alternative model rapid solving objective function, reduce the complexity of optimized algorithm, improve real-time.The method has been considered the influence of spatial distribution of environmental parameter, but neural network model is empirical model in essence, can only do modeling to the environmental index of specifying.When performance index change, or the indoor user area change of paying close attention to, modeling again, adaptability to changes is poor.
Summary of the invention
For the existing above-mentioned defect of existing building indoor environment optimization method, the invention provides a kind of Building Indoor Environment optimization method based on model order reduction.Its feature is to change subspace by the environmental parameter of structure low order, and relevant indoor environment parameter model is directly embedded in searching process, realizes resolving accurately and fast of environmental response.
The model order reducing method that decomposes (POD) based on Proper Orthogonal is a kind of mapping method in essence.It is mapped to the governing equation in former space in certain orthogonal subspaces, and guarantees mapping error minimum on energy sense.Typical CFD model can be exchanged into the lower-order model about POD mode coefficient by POD order reducing method, can meet modeling accuracy and requirement of real-time simultaneously, is applicable to the task of resolving fast of environmental response in optimisation strategy.
Technical scheme of the present invention is:
A Building Indoor Environment optimization method based on model order reduction, comprises the steps:
(1) set up the indoor environment model based on CFD;
(2) variation range possible according to control variable, equidistantly selects control variable data point, makes corresponding CFD static Simulation; Extraction environment parameter distribution from CFD simulation result, constructing variable changes space; The parameter type extracting comprises indoor temperature, wind speed, pollutant levels and hot comfort index;
(3) utilize POD model order reduction to reconstruct the low order subspace in step (2) parameters obtained variation space;
(4) select indoor environment index and energy consumption index, for assessment of indoor environment and air conditioning energy consumption;
(5) Offered target function, utilizes optimized algorithm to carry out iteration optimization to control variable.In each Optimized Iterative process, obtain fast system responses by the multi-dimensional interpolation in Parameter Subspace, and then rapid solving objective function.
In described step (1), CFD emulation is used Airpak cfdrc; Indoor environment model is three-dimensional model; The establishment step of indoor environment model is as follows:
A, utilize Airpak software to set up the geometric model that go along with sb. to guard him in room; Determine position and the size of air conditioner air outlet and return air inlet; Determine position and the size of main furnishings in room;
B, to set up room model partition grid;
C, utilize Fluent solver coupling to ask for the steady state solution of quality, momentum, energy and pollutant levels equation.
In described step (2), control variable comprises air conditioner air outlet temperature and wind speed; The selection of control variable is spaced apart: 0.1 degree Celsius of air outlet temperature, air outlet wind speed 0.1 meter per second; Utilize the described indoor environment model of step (1) to carry out static Simulation to each group control variable; Extract Steady-state Parameters corresponding to each group control variable by the export function of Airpak software and distribute, form the variation space of all kinds of parameters.
In described step (3), the basic thought of POD model reduction is: in the H of n-dimensional vector space, have a group data set, find one group m dimension subset to form S subspace (m<n), make former data set be mapped to error minimum on energy sense of subset.The basic step of POD model reduction is as follows:
A, utilize each parameter to change spatial composing matrix:
Here
dimension
matrix representation parameter changes space,
room internal net point sum,
it is control variable group number;
for
transposition;
B, solution matrix
eigenwert
and proper vector
; Select suitable cutoff value
, before making
the system kinetic energy accounting that individual eigenwert comprises
be greater than 99%, here
be expressed as:
C, low order parameter change subspace and can be described as
the linear combination of individual proper vector and coefficient thereof:
Here
represent the
steady-state Parameters corresponding to group control variable distributes,
represent POD mode coefficient.
In described step (4), indoor environment index comprises hot comfort index and IAQ (indoor air quality) index.
Hot comfort index adopts prediction average ballot index (PMV).PMV index feels to be quantified as following seven grades by cold and hot human body: cold (3), cool (2), slightly cool (1), comfortable (0), slightly warm (1), warm (2), heat (3), and itself and air themperature, solar radiation, air velocity, air humidity, human body metabolism rate and human body to six factor functions such as clothing connect, be the most general hot comfort quantitative target in the world at present.
IAQ (indoor air quality) index employing ventilation effect index (
):
Here,
with
be respectively the pollutant levels of return air inlet for air-conditioner and air outlet,
for the pollutant mean concentration of indoor occupant height of head;
Air conditioning energy consumption is decomposed into two parts by the present invention: fan energy consumption and energy consumption for cooling, and do suitably to simplify, obtain energy consumption index and be:
Here,
for fan energy consumption,
for blower fan voltage rise,
for total air output,
for energy consumption for cooling,
for the energy consumption for cooling for except sensible heat load,
for new wind being carried out to the energy consumption of dehumidifying and cooling;
In described step (5), objective function is set to:
Here,
for the weighting coefficient of each index, subscript
refer to the maximal value of corresponding performance index, for the normalization of each index;
for penalty term, for reflecting the excessive impact on hot comfort of the indoor occupant head pin temperature difference and periphery wind speed.
Optimized algorithm adopts genetic algorithm, and the multi-dimensional interpolation algorithm in Parameter Subspace adopts spline interpolation.
The present invention proposes a kind of Building Indoor Environment optimization method based on model order reduction, take into full account the impact of space distribution on environmental index, by direct embedded environment optimized algorithm after " indoor environment " depression of order, to meet accuracy and the requirement of real-time of optimization.
Relatively current environment optimization method, advantage of the present invention shows:
1, the accuracy of optimizing.
The present invention no longer supposes room air " fully mix ", but utilizes CFD instrument to do Accurate Model to Building Indoor Environment, and method by model reduction is by the low order obtaining " indoor environment " embedded environment optimisation strategy, makes optimum results more accurate.In gymnasium, hall, hotel, hospital, the large space occasions such as school, the method that the present invention proposes especially possesses obvious precision advantage.
2, the rapidity of optimizing.
The present invention can rapid solving objective function by Parameter Subspace being made to multi-dimensional interpolation, makes optimized algorithm put forward high-precision while requirement of real time, can practice in on-line optimization occasion.
Accompanying drawing explanation
Fig. 1 is a 3D office model schematic diagram;
Fig. 2 is the indoor pollution concentration profile situation map before optimizing;
Fig. 3 is the indoor pollution concentration profile situation map after optimizing.
Embodiment
In order more specifically to describe the present invention, below in conjunction with the drawings and specific embodiments, the present invention is described in detail.
Fig. 1 is a 3D office model schematic diagram.High 5.16m × 3.65m × the 2.44m that is respectively of length and width of this office.Indoor design has the clerical workforce (c, d), two desks (g, h), two computers (d, e), two file cabinets (j, k) of two sitting postures and six daylight lamps (l-q).The window that room left side wall has one side 3.65m × 1.04m (i), the air outlet of displacement ventilation system (a) be arranged on window on face wall, return air inlet (b) is arranged in ceiling center.The volatilization mouth of air pollutants designs the head position in personnel.
Describe the implementation step of the inventive method below in detail:
Step 0. is set up the indoor environment model based on CFD.Modeling method is:
Step 0.1 is determined position and the size of the going along with sb. to guard him (wall, floor and ceiling etc.), air conditioner air outlet/return air inlet and furnishings (comprising personnel) that comprise room, utilizes Airpak software to build office's geometric model as shown in Figure 1;
Step 0.2, to the geometric model grid division of setting up, is divided 72282 irregular grids altogether in this example;
Step 0.3 utilizes the coupling of Fluent solver to ask for the steady state solution of quality, momentum, energy and pollutant levels equation; Before solving, relevant boundary condition arranges as follows: air conditioner air outlet is set to speed inlet boundary; Return air inlet is set to nature exit boundary; Wall, ground and ceiling are set to temperature boundary.Relevant model definition and solution strategies arrange as follows: indoor gas is assumed to be the incompressible viscous Newtonian fluid that low speed flows, and turbulence model is selected
master pattern, nearly wall is processed and is adopted Standard law of wall, buoyancy effect to adopt the approximate mode of Boussinesq, does not consider viscous heating, and pressure-speed coupling is calculated and is adopted SIMPLE algorithm.
Step 2.1 utilizes each parameter to change spatial composing matrix:
Here
dimension
matrix representation parameter changes space,
for
transposition;
Step 2.2 solution matrix
eigenwert
and proper vector
; Select suitable cutoff value
, before making
the system kinetic energy accounting that individual eigenwert comprises
be greater than 99%, wherein
be expressed as:
In this example, when
time, system kinetic energy accounting
be greater than 99%.
Step 2.3 low order parameter changes subspace and can be described as
the linear combination of individual proper vector and coefficient thereof:
Here
represent the
steady-state Parameters corresponding to group control variable distributes,
the coefficient of representation feature vector or be called POD mode coefficient;
Indoor environment index comprises hot comfort index and IAQ (indoor air quality) index;
Hot comfort index adopts prediction average ballot index (PMV).PMV index feels to be quantified as following seven grades by cold and hot human body: cold (3), cool (2), slightly cool (1), comfortable (0), slightly warm (1), warm (2), heat (3), and itself and air themperature, solar radiation, air velocity, air humidity, human body metabolism rate and human body to six factor functions such as clothing connect, be the most general hot comfort quantitative target in the world at present.
IAQ (indoor air quality) index employing ventilation effect index (
):
Here,
with
be respectively the pollutant levels of return air inlet for air-conditioner and air outlet,
for the pollutant mean concentration of indoor occupant height of head;
Air conditioning energy consumption is decomposed into two parts by the present invention: fan energy consumption and energy consumption for cooling, and do suitably to simplify, obtain energy consumption index and be:
Here,
for fan energy consumption,
for blower fan voltage rise,
for total air output,
for energy consumption for cooling,
for the energy consumption for cooling for except sensible heat load,
for new wind being carried out to the energy consumption of dehumidifying and cooling;
Here,
for the weighting coefficient of each index, subscript
refer to the maximal value of corresponding performance index, for the normalization of each index;
for penalty term, for reflecting the excessive impact on hot comfort of the indoor occupant head pin temperature difference and periphery wind speed;
In each iterative process of genetic algorithm, obtain fast system responses by the multi-dimensional interpolation in Parameter Subspace, and then solve objective function.The multi-dimensional interpolation algorithm here adopts Based on Interpolating Spline.
In order to contrast effect of optimization, select 17 degrees Celsius of air conditioner air outlet temperature, speed 0.1 meter per second is as the indoor environment base case before optimizing.Apply this optimization method, air conditioning energy consumption can reduce by 41.2% at most, and hot comfort can improve 48.6% at most, and IAQ (indoor air quality) can improve 38% at most.
Fig. 2 and Fig. 3 are the comparison diagram of indoor air pollutants concentration before and after optimizing.Fig. 2 has described the indoor pollution concentration profile situation before optimizing, and Fig. 3 has described the indoor pollution concentration profile situation after optimizing.As seen from the figure, the present invention, by take into full account the space distribution situation of environmental parameter in optimisation strategy, can obviously improve ventilation effect, improves IAQ (indoor air quality).
In conjunction with concrete implementation step, the present invention is described above, but for a person skilled in the art, can, not deviating under the prerequisite of the spirit and scope of the present invention, have made different improvement and modification to the present invention.Thereby fall into the various modifications and variations within the scope of claim of the present invention, within all should belonging to protection scope of the present invention.
Claims (6)
1. the Building Indoor Environment optimization method based on model order reduction, specifically comprises the steps:
(1) set up the indoor environment model based on CFD;
(2) variation range possible according to control variable, equidistantly selects control variable data point, makes corresponding CFD static Simulation; Extraction environment parameter distribution from CFD simulation result, constructing variable changes space;
(3) utilize POD model order reduction to reconstruct the low order subspace in step (2) parameters obtained variation space;
(4) select indoor environment index and energy consumption index, for assessment of indoor environment and air conditioning energy consumption;
(5) Offered target function, utilizes optimized algorithm to carry out iteration optimization to control variable.
2. a kind of Building Indoor Environment optimization method based on model order reduction according to claim 1, is characterized in that: in described step (1), CFD emulation is used Airpak cfdrc; Indoor environment model is three-dimensional model; The establishment step of indoor environment model is as follows:
(1) utilize Airpak software to set up the geometric model that go along with sb. to guard him in room; Determine position and the size of air conditioner air outlet and return air inlet; Determine position and the size of main furnishings in room;
(2) to the room model partition grid of setting up;
(3) utilize the coupling of Fluent solver to ask for the steady state solution of quality, momentum, energy and pollutant levels equation.
3. a kind of Building Indoor Environment optimization method based on model order reduction according to claim 1, is characterized in that: in described step (2), control variable comprises air conditioner air outlet temperature and wind speed; The selection of control variable is spaced apart: 0.1 degree Celsius of air outlet temperature, air outlet wind speed 0.1 meter per second; Utilize the described indoor environment model of step (1) to carry out static Simulation to each group control variable; Extract Steady-state Parameters corresponding to each group control variable by the export function of Airpak software and distribute, form the variation space of all kinds of parameters; The described environmental parameter type of extracting comprises indoor temperature, wind speed, pollutant levels and hot comfort index.
4. a kind of Building Indoor Environment optimization method based on model order reduction according to claim 1, is characterized in that: in described step (3), the basic step of POD model reduction is as follows:
(1) utilize each parameter to change spatial composing matrix:
Here
dimension
matrix representation parameter changes space,
room internal net point sum,
it is control variable group number;
for
transposition;
(2) solution matrix
eigenwert
and proper vector
; Select suitable cutoff value
, before making
the system kinetic energy accounting that individual eigenwert comprises
be greater than 99%, here
be expressed as:
(3) low order parameter variation subspace can be described as
the linear combination of individual proper vector and coefficient thereof:
Here
represent the
steady-state Parameters corresponding to group control variable distributes,
represent POD mode coefficient.
5. a kind of Building Indoor Environment optimization method based on model order reduction according to claim 1, it is characterized in that: in described step (4), indoor environment index comprises hot comfort index and IAQ (indoor air quality) index, and energy consumption index refers to air conditioning energy consumption index, wherein:
Hot comfort index adopts the average ballot of prediction index PMV;
IAQ (indoor air quality) index adopts ventilation effect index
:
Here,
with
be respectively the pollutant levels of return air inlet for air-conditioner and air outlet,
for the pollutant mean concentration of indoor occupant height of head;
Air conditioning energy consumption index is decomposed into two parts, i.e. fan energy consumption index and energy consumption for cooling index:
Here,
for fan energy consumption,
for blower fan voltage rise,
for total air output,
for energy consumption for cooling,
for the energy consumption for cooling for except sensible heat load,
for new wind being carried out to the energy consumption of dehumidifying and cooling.
6. a kind of Building Indoor Environment optimization method based on model order reduction according to claim 1, is characterized in that: in described step (5), objective function is set to:
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| CN103995548B (en) * | 2014-05-13 | 2016-06-15 | 江苏大学 | Indoor thermal environment control method based on model reduction and multiple model predictive control |
| CN104572246B (en) * | 2014-12-08 | 2017-11-17 | 江苏大学 | For improving the interactive optimization method of environment in building |
| TWI524293B (en) * | 2014-12-10 | 2016-03-01 | 財團法人工業技術研究院 | Environment control method and system thereof |
| US10145576B2 (en) * | 2015-03-06 | 2018-12-04 | Mitsubishi Electric Research Laboratories, Inc. | System and method for controlling operations of air-conditioning system |
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| CN107991964B (en) * | 2017-12-19 | 2021-01-08 | 超级智慧家(上海)物联网科技有限公司 | Intelligent monitoring control method for indoor environment and system for realizing method |
| CN109282443B (en) * | 2018-09-05 | 2021-03-09 | 广东工业大学 | Multi-mode low-energy-consumption indoor heat regulation method |
| CN109341681B (en) * | 2018-10-19 | 2023-10-27 | 珠海格力电器股份有限公司 | Method and system for determining position of equipment in building and air conditioner |
| CN109840307A (en) * | 2018-12-29 | 2019-06-04 | 重庆大学 | A kind of indoor temperature and humidity field quick predict system and method |
| CN112577161B (en) * | 2019-09-30 | 2022-05-27 | 北京国双科技有限公司 | Air conditioner energy consumption model training method and air conditioner system control method |
| CN111426034B (en) * | 2020-05-15 | 2021-06-15 | 北京草木元人工环境技术有限公司 | Indoor air conditioner ventilation air flow organization control system |
| CN112149364B (en) * | 2020-09-04 | 2022-11-29 | 同济大学 | Intelligent human residential environment airflow organization optimization method |
| CN114200986B (en) * | 2021-11-18 | 2023-03-21 | 江苏大学 | Greenhouse environment optimization design method considering crop production benefits and energy saving |
| CN114637350A (en) * | 2022-02-28 | 2022-06-17 | 青岛海尔空调电子有限公司 | Space environment control method, device and storage medium |
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| CN101908086A (en) * | 2010-07-09 | 2010-12-08 | 上海理工大学 | Analysis method for digitally and dynamically simulating indoor wind environment of building |
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