CN106461251B - Utilize the indoor cooling and heating load prediction technique of prediction insolation amount - Google Patents
Utilize the indoor cooling and heating load prediction technique of prediction insolation amount Download PDFInfo
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- CN106461251B CN106461251B CN201580030276.7A CN201580030276A CN106461251B CN 106461251 B CN106461251 B CN 106461251B CN 201580030276 A CN201580030276 A CN 201580030276A CN 106461251 B CN106461251 B CN 106461251B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2130/00—Control inputs relating to environmental factors not covered by group F24F2110/00
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2130/00—Control inputs relating to environmental factors not covered by group F24F2110/00
- F24F2130/10—Weather information or forecasts
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2140/00—Control inputs relating to system states
- F24F2140/50—Load
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Abstract
The present invention provides a kind of indoor cooling and heating load prediction technique using prediction insolation amount, it is characterized in that, distinguishing window and wall body when finding out insolation load and heat transfer load, and it is applicable in the part throttle characteristics coefficient different by orientation, so as to which indoor cooling and heating load is more accurately predicted out.
Description
Technical field
The present invention relates to a kind of indoor cooling and heating load prediction techniques, relate more specifically to following indoor cooling and heating load predictions
Method is more accurately predicted using automatic control device possessed in indoor cold-heating system and is suitably maintained as adjusting
Required indoor cooling and heating load when the room temperature of the building of object is saved, so as to efficiently and economically using indoor cold and hot system
System.
Background technique
Recently, in every country, in order to cope with fossil energy peter out and in order to improve earth environment, competitively into
The effort that the utilization of the energy more rationalizes is exercised, as a scheme of the rationalization for realizing this using energy source, Ke Yiju
Out: indoor cooling and heating load accurately predicted the building as controlled plant, the indoor cooling and heating load based on the prediction, most preferably
Ground runs indoor cold-heating system.
But due to being not easy indoor cooling and heating load of the prediction as the building of controlled plant, all the time,
The operation of indoor cold-heating system relies primarily on the experience of operator, as a result, often occurring because of operator in many cases,
Error in judgement and operation it is unskilled and consume unnecessary electric power or indoor cold and hot supply amount is insufficient and bring to user
Inconvenient situation.
In order to more economic run indoor cold-heating system while solving the problem above-mentioned, actively carried out about
The research of indoor cooling and heating load prediction technique, still, existing interior cooling and heating load prediction technique are mainly all based on complicated
Mathematics and statistical concepts and the method controlled it, accordingly, there exist the operators of not relevant professional knowledge to be difficult to transport
Capable problem.Also, there are following other problems: since the building for needing much to be suitable for indoor cooling and heating load prediction is special
Property input value, or past operation data is depended on to a large extent, accordingly, it is difficult to be suitable for not finding out and building
Build the building or the insufficient building of past operation data of the input value of object characteristic.
Therefore, the inventors of the present invention are in order to solve to ask possessed by existing indoor cooling and heating load prediction technique as described above
Topic, proposes new indoor cooling and heating load prediction technique, and obtain patent right (Korean Patent No. 10-1301123), at this
In indoor cooling and heating load prediction technique, as following mathematical expressions 1 to mathematical expression 3, the conduct adjusting pair of building is found out
After the sensible heat load and latent heat load in the space of elephant, these loads are added and calculate indoor refrigeration duty, at this point, utilizing mathematics
Formula 2 finds out sensible heat load, and finds out latent heat load using mathematical expression 3, at this point, according to the load inventory of building or as tune
Calculate sensible heat load coefficient (P with saving the area univocality in the space of objects) and insolation coefficient of discharge (Psol)。
(mathematical expression 1)
Here,It is indoor refrigeration duty,It is sensible heat load,It is latent heat load,It is solar radiant heat,It is conduction heat,It is heat caused by intrusion outside air and importing outside air,Internal heat generation with
And other thermic loads.
(mathematical expression 2)
Here,It is sensible heat load, PsIt is sensible heat load coefficient, ToIt is external air temperature, TiIt is room temperature, Psol
It is insolation coefficient of discharge, IsolIt is insolation amount, s is the Exposure degree rate of air interchanger,It is to be flowed by ventilation from outside
The amount of air, hioIt is indoor humidity ratio and the enthalpy of the air of point that external air temperature meets, hiIt is the air under indoor conditions
Enthalpy,It is the amount for invading outside air, CsIt is sensible heat load constant.
(mathematical expression 3)
Here,It is latent heat load, i is the recovery of latent heat rate of air interchanger,It is to be flowed by ventilation from outside
Air amount, hioIt is indoor humidity ratio and the enthalpy of the air of point that external air temperature meets, hiIt is the sky under indoor conditions
The enthalpy of gas, minfIt is the amount for invading outside air, ClIt is latent heat load constant.
That is, in above-mentioned patent, using the load inventory according to building or as the area in the space of controlled plant
The calculated sensible heat load coefficient (P in univocality grounds) and insolation coefficient of discharge (Psol), calculate sensible heat loadAnd in practice, window
The characteristic of the heat transfer load and insolation load of family and wall body is quite different, nevertheless, not distinguishing window as described above also
With wall body, obtain a sensible heat load coefficient (Ps) and insolation coefficient of discharge (Psol) and use, then existing may be with sizable
The problem of error.
Also, the insolation characteristic of window and wall body is quite different by orientation, and heat-transfer character also presses orientation may be quite different,
Nevertheless, using a load system for building entirety or single area with not distinguishing orientation like that also like above-mentioned patent
Number is inappropriate.Further, since the load inventory of building is made based on peak load, therefore, do not consider in winter
Insolation load is also difficult to accurately reflect insolation load in summer, thus the case where obtaining load coefficient according to load inventory
Under, it is also possible to sizable error.
Therefore, it is necessary to develop consider window and wall body heat transfer load and insolation load characteristic and by orientation
The new indoor cooling and heating load prediction technique of insolation characteristic etc..
Summary of the invention
The invention technical task to be solved
The present invention is to solve the problems, such as to propose, purpose possessed by existing indoor cooling and heating load prediction technique
Be, a kind of indoor cooling and heating load prediction technique be provided, distinguish the building as controlled plant window and wall body and examine
Consider the characteristic of heat transfer load and insolation load, and reflect the insolation characteristic etc. for pressing orientation, is found out negative independent of load inventory
Lotus characteristic coefficient, thus using insolation amount is more accurately predicted compared with the existing.
For solving the means of technical task
Purpose present invention as described above is by providing a kind of following indoor cooling and heating load using prediction insolation amount
Prediction technique is realized, wherein is calculated indoor cooling and heating load using mathematical expression 4, also, is utilized respectively mathematical expression 6 and mathematics
Formula 7 calculates insolation load and heat transfer load.
(mathematical expression 4)
(mathematical expression 6)
(mathematical expression 7)
Also, it is a feature of the present invention that the heat-transfer character coefficient (P of windowHt, win) and wall body heat-transfer character coefficient
(PHt, wall) it is the function that summation heat conveys coefficient respectively, the linear formula for being utilized respectively mathematical expression 8 and mathematical expression 9 is found out.
(mathematical expression 8)
Pht,win(i, j)=C1Uwin(i,j)+C2
(mathematical expression 9)
PHt, wall(i, j)=C3Uwall(i, j)+C4
In addition, it is a further feature of the invention that the insolation characteristic coefficient of window is the function that insolation obtains coefficient, benefit
It is found out with mathematical expression 10.
(mathematical expression 10)
PSol, win(i, j)=[C5SHGC (i, j)2+C6SHGC (i, j)+C7]SCwin(i, j)
In addition, it is a further feature of the invention that the insolation characteristic coefficient of wall body is the solar absorptance of wall body and total
The function that coefficient is conveyed with heat, is found out using mathematical expression 11.
(mathematical expression 11)
Psol,wall(i, j)=[C8α (i, j)nUwall(i, j)m+C9]SCwall(i, j)
In addition, it is a further feature of the invention that finding out the prediction day by each of orientation hour using mathematical expression 17
The amount of penetrating.
(mathematical expression 17)
Isol(i)=Csol(i)Idh+Idiff
Here, CsolIt is direct projection insolation orientation coefficient, is found out by mathematical expression 18.
(mathematical expression 18)
In addition, it is a further feature of the invention that using simple genetic algorithms with regulation coefficient and the insolation adjustment system of conducting heat
Number is respectively adjusted the heat-transfer character coefficient and insolation characteristic coefficient of window and wall body.
The effect of invention
The present invention is by distinguishing the window of the building as controlled plant and conduct heat load and the insolation load of wall body
Characteristic, and reflect the insolation characteristic etc. for pressing orientation, indoor cooling and heating load can be more accurately calculated compared with the existing.
Also, the present invention uses simple genetic algorithms respectively with conduct heat regulation coefficient and insolation regulation coefficient to window and wall body
Heat-transfer character coefficient and insolation characteristic coefficient be adjusted, so as to make predict load and survey load between it is inconsistent
It minimizes.
Detailed description of the invention
Fig. 1 is the chart for indicating heat-transfer character coefficient and changing as the summation heat of window conveys the variation of coefficient；
Fig. 2 is the chart for indicating heat-transfer character coefficient and changing as the summation heat of wall body conveys the variation of coefficient；
Fig. 3 is to indicate insolation characteristic coefficient with the chart that the insolation of window obtains the variation of coefficient and changes；
Fig. 4 is predicting indoor cooling and heating load prediction technique according to the present invention for period one month January in 2014
Indoor heat load and the chart being compared using the indoor heat load that EnergyPlus is analyzed；
Fig. 5 is predicting indoor cooling and heating load prediction technique according to the present invention for period one month July in 2014
Indoor refrigeration duty and the chart being compared using the indoor refrigeration duty that EnergyPlus is analyzed.
Specific embodiment
Composition and effect of the invention are explained in more detail below based on the attached drawing for showing the preferred embodiment of the present invention.
Also, integrated control ands with microprocessor (microprocessor), communication device, input unit and display etc.
The computer (PC) or integrated manipulator of entire interior cold-heating system execute the indoor cooling and heating load prediction of invention as described below
Method.
The present invention to be offered is using more accurately the indoor cooling and heating load of prediction insolation amount is pre- compared with the existing
Survey method, for this purpose, the present invention distinguish as controlled plant building window and wall body and by orientation consideration heat-transfer character system
The part throttle characteristics coefficients such as several and insolation characteristic coefficient and predict indoor cooling and heating load.Window and wall body have mutually not between floors
The type of identical interior cooling and heating load characteristic, structural body may be different by orientation, also, act on the insolation on wall body simultaneously
Not instead of directly effect be building indoor cooling and heating load, be accumulated on the surface of wall body and cause the rising of temperature, by
This caused temperature difference will form load, and on the other hand, window makes insolation penetrate and be fed directly to it in building, to be
The indoor cooling and heating load of building is more accurately found out, as described above, is distinguished in the present invention as controlled plant
The window and wall body of building simultaneously consider heat-transfer character coefficient and insolation characteristic coefficient by orientation.
Below, the room for distinguishing window and wall body and part throttle characteristics coefficient is considered by orientation of the invention is explained in detail
Interior cooling and heating load prediction technique.
In general, as in order to freeze and heat the interior of building and required indoor cooling and heating load brings shadow
Loud load (heat), have through the solar radiant heat of glass and wall body, the heat transmitted due to outside air and the indoor temperature difference,
It invades outside air and imports the internal heat generation including steam line of heat caused by outside air, human body or indoor utensil
Other loads of loss etc. be generally as follows the mathematical expression 4 stated (with above-mentioned mathematical expression 1 and when calculating indoor cooling and heating load
It is substantially the same) shown in distinguish and calculate.
(mathematical expression 4)
Here,Indicate indoor cooling and heating load,Indicate sensible heat load,Indicate latent heat load,Indicate day
Load is penetrated,Indicate heat transfer load,Indicate ventilation load,Indicate internal load.
In the present invention, using existing negative including method disclosed in above-mentioned Korean Patent No. 10-1301123
Lotus calculation method finds out the ventilation load of above-mentioned mathematical expression 4And internal loadFor example, utilizing following numbers
Formula 5 finds out ventilation loadAnd internal loadSum on the other hand distinguish window and wall body and be applicable in
By the different part throttle characteristics coefficient in orientation (heat-transfer character coefficient and insolation characteristic coefficient), following mathematical expressions 6 and mathematics are utilized
Formula 7 finds out insolation load respectivelyWith heat transfer load
(mathematical expression 5)
Here,It is ventilation volume, hioIt is indoor humidity ratio and the enthalpy of the air of point that external air temperature meets, hiIt is
The enthalpy of air under indoor conditions, s are the Exposure degree rate of air interchanger, CsIt is sensible heat load constant, hoIt is external air conditions
Under air enthalpy, l is the recovery of latent heat rate of air interchanger, ClIt is latent heat load constant.
(mathematical expression 6)
Here, PHt, winAnd PHt, wallIt is the heat-transfer character coefficient of window and wall body, A respectivelywinAnd AwallBe respectively window and
The area of wall body, i indicate the orientation in 6 faces around the cold and hot space in interior of building, and j indicates to constitute a side of building
The wall body or window species number of plane.ToIt is each hour prediction external air temperature, TdIt is the Indoor Temperature in indoor cold and hot space
Degree.
(mathematical expression 7)
Here, PSol, winAnd PSol, wallIt is the insolation characteristic coefficient of window and wall body, I respectivelysolIt is small by each of orientation
When prediction insolation amount, AwinAnd AwallDeng identical as above-mentioned mathematical expression 6.
In the indoor cooling and heating load calculation method of existing patent (Korean Patent No. 10-1301123), for building
Object entirety or single area find out part throttle characteristics coefficient according to load inventory, still, distinguish window and wall as in the present invention
The body and part throttle characteristics coefficient different by orientation cannot be found out as the prior art according to load inventory.Therefore, in this hair
In bright, part throttle characteristics coefficient is found out using the architectural resource simulation program according to Energy Sources Equilibrium method, is built used in the present invention
Building object energy simulation program is to be most widely used now and EnergyPlus that accuracy of analysis is outstanding.
Below, illustrate the heat-transfer character for finding out the heat-transfer character coefficient of window, wall body respectively using EnergyPlus
The method of the insolation characteristic coefficient of coefficient, the insolation characteristic coefficient of window and wall body, and the water according to prediction is described in detail
The method that plane global solar radiation amount finds out the prediction insolation amount by orientation.
(1) the heat-transfer character coefficient (P of windowHt, win)
In the present invention, in order to find out the heat-transfer character coefficient of window, for what is provided in the database of EnergyPlus
The window of multiple types (being 23 in this test), observes the pass between summation heat reception and registration coefficient and heat-transfer character coefficient
System, as a result, confirmed: as shown in Figure 1, summation heat conveys coefficient and heat-transfer character coefficient to show linear pass in window
System.To if formulated to this, can be expressed as following mathematical expressions 8, as a result, by the window of actual setting
Summation heat conveys coefficient (Uwin) it is updated to mathematical expression 8, it will be able to easily find out the heat-transfer character coefficient (P of windowHt, win)。
(mathematical expression 8)
PHt, win(i, j)=C1Uwin(i, j)+C2
Here, UwinIt is the summation heat reception and registration coefficient of window, has been documented in Building Design book etc., or if
Know the type of window, is then easily calculated by calculating, constant C1、C2Be respectively the straight line in the chart of Fig. 1 slope and cut
Away from can use the constant that architectural resource simulation program finds out the structure of various windows, i indicates to surround the interior of building
The orientation in 6 faces in cold and hot space, j indicate to constitute the window species number of an azimuth plane of building.
(2) the heat-transfer character coefficient (P of wall bodyHt, wall)
In the present invention, in order to find out consider constitute building a variety of wall bodies structure part throttle characteristics coefficient, will
The structure of the wall body of building is constituted as parameter, and each parameter is divided into 3 kinds of conditions and is analyzed, still, at this time such as
Fruit converts the condition of parameter every time and is analyzed, then the quantity of situation becomes excessively, therefore, in order to solve this problem, utilizes
The wall body of a certain number of (being 27 in this test) is set as analysis model by test plan method, and analyzes mould for these
Type when with the heat-transfer character coefficient for finding out window identically observes summation heat and conveys between coefficient and heat-transfer character coefficient
Relationship, as a result, confirmed: as shown in Fig. 2, also in the same manner as window, summation heat conveys coefficient and heat transfer special in wall body
Property coefficient shows linear relationship.To can be expressed as following mathematical expressions 9, as a result, if formulated to this
Convey the summation heat of the wall body of actual setting to coefficient (Uwall) it is updated to mathematical expression 9, it will be able to easily find out the biography of wall body
Thermal characteristics coefficient (PHt, wall)。
(mathematical expression 9)
PHt, wall(i, j)=C3Uwall(i, j)+C4
Here, UwallIt is the summation heat reception and registration coefficient of wall body, has been documented in Building Design book etc., or if
Know the structure of wall body, is then easily calculated by calculating, constant C3、C4Be respectively the straight line in the chart of Fig. 2 slope and cut
Away from can use the constant that architectural resource simulation program finds out the structure of various wall bodies, i indicates to surround the interior of building
The orientation in 6 faces in cold and hot space, j indicate to constitute the wall body species number of an azimuth plane of building.
(3) the insolation characteristic coefficient (P of windowSol, win)
In the present invention, in order to find out the insolation characteristic coefficient of window, for what is provided in the database of EnergyPlus
The window of multiple types (being 207 in this test), observes insolation and obtains coefficient (SHGC) and insolation characteristic coefficient
(PSol, win) between relationship, and its result is shown in FIG. 3.
If carried out curve fitting (curve fitting) to chart shown in Fig. 3, the insolation characteristic coefficient of window
(PSol, win) the shown quadratic expression for being expressed as insolation and obtaining coefficient (SHGC) of mathematical expression 9 described as follows, as a result, by actual setting
Window insolation obtain coefficient (SHGC) be updated to mathematical expression 10, it will be able to easily find out the insolation characteristic coefficient of window
(PSol, win)。
(mathematical expression 10)
PSol, win(i, j)=[C5SHGC (i, j)2+C6SHGC (i, j)+C7]SCwin(i, j)
Here, can use the constant C that architectural resource simulation program finds out various windows respectively5、C6And C7, SCwin
It is the insolation sheltering coefficient for the external solar protection devices being arranged on window, the geometry and orientation of solar protection devices is accounted for
And calculate, in the case where no solar protection devices, becoming 1, i is indicated around 6 faces in the cold and hot space in interior of building
Orientation, j indicate to constitute the window species number of an azimuth plane of building.
(4) the insolation characteristic coefficient (P of wall bodySol, wall)
In the present invention, in order to find out the insolation characteristic coefficient of wall body, using test plan method by a certain number of (in this examination
In testing be 27) wall body be set as Accurate analysis model, on one side convert wall body structure, carried out zooming test on one side.It is logical
Cross zooming test confirmation as a result, the insolation characteristic coefficient (P of wall bodySol, wall) be expressed as following mathematical expressions 11 too
Positive absorptivity (α) and summation heat convey coefficient (Uwall) exponential function, as a result, by the solar absorptance of the wall body of actual setting
(α) and summation heat convey coefficient (Uwall) it is updated to mathematical expression 11 respectively, it will be able to find out the insolation characteristic coefficient of wall body
(PSol, wall)。
(mathematical expression 11)
PSol, wall(i, j)=[C8α (i, j)nUwall(i, j)m+C9]SCwall(i, j)
Here, α is the solar absorptance of wall body, UwallIt is the summation heat reception and registration coefficient of wall body, these values have been documented in
In Building Design book etc., or if it is known that wall body structure, then by calculate easily calculate, can use building energy
Source simulation program finds out coefficient C8、C9And index n and m, SCwallIt is the insolation screening for the external solar protection devices being arranged on wall body
Coefficient is covered, the geometry and orientation of solar protection devices are accounted for and is calculated, i and j are identical as above-mentioned mathematical expression 10.
(5) the insolation amount in orientation is pressed
In order to predict the indoor cooling and heating load of building, other than part throttle characteristics coefficient, it is also necessary to external air temperature,
The predictive information of humidity, insolation amount, in these predictive information, external air temperature, humidity can pass through the meteorologies such as the meteorological Room
It is obtained in the weather forecast that forecast system provides, still, for insolation amount, meteorological Ting Deng mechanism will not be mentioned as forecast information
For, therefore, the inventors of the present invention in order to obtain the information for insolation amount, the cloud amount (CA) by the hour that obtains that the meteorological Room provides and
Relative humidity (RH) and/or daily difference by the hour, and clearness index (Kt) by the hour is calculated, to develop utilization
The clearness index (Kt) by the hour of the calculating and predict horizontal plane global solar radiation amount (I by the hourT) method, and propose specially
Benefit application (Korean Patent Application No.: 10-2014-0194891), below, to predicting by the hour for the inventors of the present invention's exploitation
Horizontal plane global solar radiation amount (IT) method be illustrated.
In order to predict horizontal plane global solar radiation amount (I by the hourT), meteorological data is obtained from the meteorological Room first, is obtained according to this
The meteorological data obtained finds out cloud amount (CA) by the hour and relative humidity (RH) by the hour and daily difference (Δ t), to calculate
Clearness index (Kt) out by the hour.
Here, clearness index (Kt) refers to when extraatmospheric insolation amount farthest reaches horizontal plane and actually
The ratio between the insolation amount of horizontal plane is reached, such clearness index (Kt) mathematical expression 12 can be defined like that described as follows.
(mathematical expression 12)
Here, ITIt is horizontal plane global solar radiation amount, IoIt is extraatmospheric insolation amount, h is the height of the sun.
In above-mentioned mathematical expression 12, clearness index (Kt), extraatmospheric insolation amount I can useoAnd the sun
Height h finds out horizontal plane global solar radiation amount IT, here, extraatmospheric insolation amount IoHeight h with the sun is known value.
The present inventor is in order to confirm in a variety of meteorological datas which meteorological data with clearness index (Kt) by the hour most
Correlation analyzes Pearson came according to the crop field local weather Room measured data in past 5 years (2009~2013)
(Pearson) correlation, shown in result table 1 described as follows.
Pearson came correlation is the coefficient for indicating the degree of two linear dependences between variable X, Y, closer to 1, more
With strong positive correlation, closer -1, more there is strong negative correlation, on the other hand, coefficient indicates no phase closer to 0
Guan Xing.
[table 1]
| It distinguishes | With the related coefficient of clearness index (Kt) by the hour |
| Cloud amount by the hour | -0.800 |
| Average cloud amount | -0.755 |
| 12 cloud amount | -0.732 |
| Temperature by the hour | 0.02 |
| Maximum temperature | 0.02 |
| Minimum temperature | -0.179 |
| Daily difference | 0.601 |
| Humidity by the hour | -0.699 |
| Highest humidity | -0.334 |
| Minimum humidity | -0.627 |
| Psychrometric difference | 0.572 |
By Pearson came correlation, from above-mentioned table 1 may validate that clearness index (Kt) by the hour in cloud amount by small
When cloud amount (CA), the relative humidity (RH) by the hour in humidity, the daily difference (Δ T) in temperature have strong correlation.
Therefore, in the present invention, the cloud amount (CA) by the hour and by the hour of maximum influence will be brought on insolation amount
Relative humidity (RH) is chosen to be undependent variable, finds out by the hour fine using the correlativity formula as following mathematical expressions 13
Empty index (Kt).
(mathematical expression 13)
Kt=D1+D2CA+D3CA2+D4CA3+D5RH+D6RH2+D7RH3
Here, Kt is clearness index, CA is cloud amount by the hour, and RH is relative humidity by the hour.
In above-mentioned mathematical expression 13, the coefficient of correlativity formula may be different according to region, in the present invention, will be big
The past 5 years meteorological Room measured datas in field domain are used as input data, so as to find out the coefficient of correlativity formula, knot
Shown in fruit table 2 described as follows, at this point, the meteorological Room provides cloud amount with the interval of 3 hours, therefore, in the present invention, in order to find out
Cloud amount by the hour and used interpolation method.
[table 2]
| It distinguishes | Coefficient |
| D1 | 0.8277 |
| D2 | -0.1185e-1 |
| D3 | 0.6370e-3 |
| D4 | -0.3739e-3 |
| D5 | -0.5191e-2 |
| D6 | 0.9571e-4 |
| D7 | -0.8066e-6 |
If determined by above-mentioned process for reflecting cloud amount by the hour and by the hour in insolation amount by the hour
Relative humidity clearness index (Kt) correlativity formula, then meteorological Room forecast is substituted into the correlativity formula by the hour
Cloud amount and relative humidity, thus find out clearness index (Kt) by the hour.
If calculating clearness index (Kt) by above-mentioned process, being then updated to the clearness index (Kt) by the hour
Following mathematical expressions 14 predicts horizontal plane global solar radiation amount (I by the hourT)。
(mathematical expression 14)
IT=KtIosin(h)
Here, ITIt is horizontal plane global solar radiation amount by the hour, Kt is clearness index, IoIt is extraatmospheric insolation amount, h is
The height of the sun.
Here, therefore the meteorological Room of South Korea in the present invention, uses interpolation with the interval forecast relative humidity of 3 hours
Method finds out relative humidity by the hour.
Also, cloud amount is not forecast in the meteorological Room of South Korea, alternatively, with the interval forecast sky conditions of 3 hours (it is fine,
It is partly cloudy, cloudy, negative), therefore, by these sky conditions be scaled as following Table 3 0~10 cloud amount and use, and make
The cloud amount at 3 hour intervals is transformed to cloud amount by the hour with interpolation method.
[table 3]
| Sky condition | It is fine | It is partly cloudy | It is cloudy | Yin |
| CA | 1 | 4 | 7 | 9.5 |
Also, it, is illustrating to find out by the hour based on the meteorological Room with the sky condition of the interval forecast of 3 hours above
Cloud amount, still, unlike this, since weather information organ Accuweather provides cloud amount forecast for 0~100%, because
The cloud amount, can be used as 0~10 cloud amount by this divided by 10.
As illustrated in front, by Pearson came correlation, clearness index (Kt) and cloud amount by the hour by the hour
(CA), there is relative humidity (RH) and daily difference (Δ T) by the hour strong correlation to be therefore described above: upper
It states in three kinds of variables with strong correlation, cloud amount (CA) by the hour and relative humidity (RH) by the hour is chosen to be solely
Vertical parameter, thus finds out clearness index (Kt) by the hour.
But such as observed by above, daily difference (Δ T) in one day is also with these cloud amount (CA) by the hour and by small
When relative humidity (RH) similarly, bring big influence to insolation amount, also, daily difference, compared with relative humidity, forecast is quasi-
Exactness is high.Therefore, as other embodiments, when calculating clearness index (Kt), by by the hour cloud amount (CA) and daily difference select
It is set to undependent variable, thus finds out clearness index (Kt) by the hour, at this point, clearness index (Kt) by the hour can also passes through
Following mathematical expressions 15 are found out.
(mathematical expression 15)
Kt=D1+D2CA+D3CA2+D4CA3+D5ΔT+D6ΔT2+D7ΔT3
Here, Kt is clearness index, CA is cloud amount by the hour, and Δ T is daily difference.
In above-mentioned mathematical expression 15, the coefficient of correlativity formula may be different according to region, in the present invention, and preceding
Face similarly, the past 5 years meteorological Room measured datas of crop field region is used as input data, so as to find out correlativity
The coefficient of formula, shown in result table 4 described as follows, at this point, the meteorological Room provides cloud amount with the interval of 3 hours, therefore, in this hair
In bright, interpolation method has been used in order to find out cloud amount by the hour.
[table 4]
| It distinguishes | Coefficient |
| D1 | 0.8277 |
| D2 | -0.1185e-1 |
| D3 | 0.6370e-3 |
| D4 | -0.3739e-3 |
| D5 | -0.5191e-2 |
| D6 | 0.9571e-4 |
| D7 | -0.8066e-6 |
On the other hand, direct projection insolation amount and scattering insolation amount, direct projection can be divided into the insolation amount that building affects
Insolation amount refers to extraatmospheric insolation through the insolation amount for directly reaching building after atmosphere, and scattering insolation amount refers to by big
Vapor, dust of gas etc. scatter and reach the insolation amount of building, with isotropism.If using what is had been known
Erbs model etc. directly dissipates disjunctive model, then horizontal plane global solar radiation amount can be separated into direct projection insolation amount and scattering insolation amount, this
When, since the direct projection insolation amount distinguished and directly dissipating separation is horizontal plane direct projection insolation amount, in order to find out for vertical
The direct projection insolation amount in face, it is also necessary to horizontal plane direct projection insolation amount be converted as following mathematical expressions 16.
(mathematical expression 16)
Here, IdvIndicate the direct projection insolation amount of vertical plane, IdhIndicate horizontal plane direct projection insolation amount, h indicates altitude of the sun, A
Indicate the azimuth of the sun, AwIndicate the azimuth of building wall, i indicates 6 faces around the cold and hot space in interior of building
Orientation.
Therefore, the global solar radiation amount (I that building receivessol) it is the direct projection insolation amount and scattering received by each orientation
The sum of insolation amount is calculated by following mathematical expressions 17.
(mathematical expression 17)
Isol(i)=Csol(i)Idh+Idiff
Here, CsolIt is direct projection insolation orientation coefficient, is found out by following mathematical expressions 18, IdhIt is horizontal plane direct projection insolation
Amount, IdiffIt is scattering insolation amount.
(mathematical expression 18)
Here, h indicates that altitude of the sun, A indicate the azimuth of the sun, AwIndicate the azimuth of building wall, i expression surrounds
The orientation in 6 faces in the cold and hot space in interior of building.
The inventors of the present invention are closed to confirm the validity of above-described indoor cooling and heating load prediction technique of the invention
During each one month of the January as the month for representing winter and the July as the month for representing summer by the hour
Indoor cooling and heating load, to the result calculated according to the present invention and using EnergyPlus analyze result be compared, and
Its result is respectively illustrated in Fig. 4 and Fig. 5.
In figures 4 and 5, solid line is that the interior that indoor cooling and heating load prediction technique according to the present invention calculates is cold and hot negative
Lotus, dotted line are using the indoor cooling and heating load of EnergyPlus analysis, and+(just) indicates indoor heat load, and (negative) expression of ﹣ is indoor cold
Load can be confirmed that load variations tendency and payload by the hour are all particularly well coincide.
It is above-mentioned the result is that the indoor cooling and heating load for the building predicted according to the present invention, the interior of this prediction are cold and hot negative
Lotus may be variant with actual load (actual measurement load), therefore, in the present invention, if using simple genetic algorithms to load spy
Property coefficient is corrected, then can significantly reduce the error between these values, for this purpose, the heat-transfer character coefficient of window and wall body
(PHt, win(i, j), PHt, wall(i, j)) respectively multiplied by heat transfer regulation coefficient (CHt, win、CHt, wall), the insolation of window and wall body is special
Property coefficient (PSol, win(i, j), CSol, wall(i, j)) respectively multiplied by insolation regulation coefficient (CSol, win、CSol, wall) after, use base
Because algorithm finds out the prediction load of building and surveys heat transfer adjustment system of the error as the smallest window and wall body of load
Number (CHt, win、CHt, wall) and insolation regulation coefficient (CSol, win、CSol, wall) and be corrected, it is drilled due to this using gene
The step of algorithm is corrected parameter (being in the present invention load characteristic coefficient) and method have been known, therefore, omission pair
This detailed description.
As described above, the present invention is to the window of the building as controlled plant and the heat transfer part throttle characteristics of wall body and day
It penetrates part throttle characteristics to distinguish, and reflects the insolation characteristic etc. by orientation, it is indoor cold and hot so as to be more accurately predicted
Load.
Claims (6)
1. a kind of indoor cooling and heating load prediction technique using prediction insolation amount is being connect and integrated control with indoor cold-heating system
In the integrated manipulator for making indoor cold-heating system, carries out worthwhile to sensible heat load and latent heat load and predicts indoor cooling and heating load,
It is characterized in that,
Calculate the indoor cooling and heating load using mathematical expression 4, also, be utilized respectively mathematical expression 6 and mathematical expression 7 to calculate insolation negative
LotusWith heat transfer loadTo distinguish window and wall body and be applicable in the part throttle characteristics system different by orientation
Number,
(mathematical expression 4)
Here,Indicate indoor cooling and heating load,Indicate sensible heat load,Indicate latent heat load,Indicate that insolation is negative
Lotus,Indicate heat transfer load,Indicate ventilation load,Indicate internal load,
(mathematical expression 6)
Here, PHt, winAnd PHt, wallIt is the heat-transfer character coefficient of window and wall body, A respectivelywinAnd AwallIt is window and wall body respectively
Area, i indicates the orientation in 6 faces around the cold and hot space in interior of building, and j indicates to constitute an azimuth plane of building
Wall body or window species number, ToIt is each hour prediction external air temperature, TdIt is the room temperature in indoor cold and hot space,
(mathematical expression 7)
Here, PSol, winAnd PSol, wallIt is the insolation characteristic coefficient of window and wall body, I respectivelysolIt is by each of orientation hour
Predict insolation amount.
2. the indoor cooling and heating load prediction technique according to claim 1 using prediction insolation amount, which is characterized in that
Heat-transfer character coefficient (the P of the windowHt, win) and wall body heat-transfer character coefficient (PHt, wall) it is that summation heat is conveyed respectively
The function of coefficient, the linear formula for being utilized respectively mathematical expression 8 and mathematical expression 9 are found out,
(mathematical expression 8)
PHt, win(i, j)=C1Uwin(i, j)+C2
Here, UwinIt is the summation heat reception and registration coefficient of window, i indicates the side in 6 faces around the cold and hot space in interior of building
Position, j indicates to constitute the window species number of an azimuth plane of building, for various windows, simulates journey using architectural resource
Sequence finds out constant C1、C2,
(mathematical expression 9)
PHt, wall(i, j)=C3Uwall(i, j)+C4
Here, UwallIt is the summation heat reception and registration coefficient of wall body, i indicates the side in 6 faces around the cold and hot space in interior of building
Position, j indicate that the wall body species number for constituting an azimuth plane of building utilizes architectural resource for the structure of various wall bodies
Simulation program finds out constant C3、C4。
3. the indoor cooling and heating load prediction technique according to claim 1 using prediction insolation amount, which is characterized in that
Insolation characteristic coefficient (the P of the windowSol, win) it is the function that insolation obtains coefficient (SHGC), it is found out using mathematical expression 10,
(mathematical expression 10)
PSol, win(i, j)=[C5SHGC (i, j)2+C6SHGC (i, j)+C7]SCwin(i, j)
Here, the insolation that SHGC is window obtains coefficient, SCwinIt is the insolation masking for the external solar protection devices being arranged on window
Coefficient, i indicate the orientation in 6 faces around the cold and hot space in interior of building, and j indicates to constitute an azimuth plane of building
Window species number finds out constant C using architectural resource simulation program for various windows5、C6And C7。
4. the indoor cooling and heating load prediction technique according to claim 1 using prediction insolation amount, which is characterized in that
Insolation characteristic coefficient (the P of the wall bodySol, wall) it is that the solar absorptance (α) of wall body and summation heat convey coefficient (Uwall)
Function, found out using mathematical expression 11,
(mathematical expression 11)
PSol, wall(i, j)=[C8α (i, j)nUwall(i, j)m+C9]SCwall(i, j)
Here, α is the solar absorptance of wall body, UwallIt is the summation heat reception and registration coefficient of wall body, SCwallIt is arranged on wall body
The insolation sheltering coefficient of external solar protection devices, i indicate the orientation in 6 faces around the cold and hot space in interior of building, and j indicates structure
The structure of various wall bodies is asked using architectural resource simulation program at the window species number of an azimuth plane of building
Constant C out8、C9And index n and m.
5. the indoor cooling and heating load prediction technique according to claim 1 using prediction insolation amount, which is characterized in that
The prediction insolation amount (I by each of orientation hour is found out using mathematical expression 17sol),
(mathematical expression 17)
Isol(i)=Csol(i)Idh+Idiff
Here, CsolIt is direct projection insolation orientation coefficient, is found out by mathematical expression 18, IdhIt is horizontal plane direct projection insolation amount, IdiffIt is scattered
Insolation amount is penetrated, according to the horizontal plane global solar radiation amount of prediction, is found out using the straight disjunctive model that dissipates,
(mathematical expression 18)
Here, h indicates that altitude of the sun, A indicate the azimuth of the sun, AwIndicate the azimuth of building wall, i is indicated around building
The orientation in 6 faces in the cold and hot space in interior of object.
6. the indoor cooling and heating load prediction technique according to claim 1 using prediction insolation amount, which is characterized in that
Using simple genetic algorithms with the heat transfer regulation coefficient (C of window and wall bodyHt, win、CHt, wall) and insolation regulation coefficient
(CSol, win、CSol, wall) respectively to the heat-transfer character coefficient (P of the window and wall bodyHt, win、PHt, wall) and insolation characteristic coefficient
(PSol, win、PSol, wall) be adjusted.
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| PCT/KR2015/011953 WO2016114477A1 (en) | 2015-01-16 | 2015-11-06 | Method of estimating indoor heating and cooling loads by using estimated insolation |
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| KR101571806B1 (en) * | 2015-09-21 | 2015-11-25 | (주)가교테크 | Self-operating Optimal Control Method for Air Conditioning System |
| CN105758028B (en) * | 2016-02-02 | 2018-01-16 | 福建师范大学 | A kind of hot water reserves control method applied to solar energy central hot-water heating system |
| KR101840738B1 (en) * | 2017-09-28 | 2018-03-21 | (주)가교테크 | Method for predicting heating and cooling load of buildings based on actual load of air conditioning equipments |
| JPWO2019146067A1 (en) * | 2018-01-26 | 2020-06-11 | 三菱電機株式会社 | Control systems, air conditioners and servers |
| KR101898412B1 (en) | 2018-04-04 | 2018-10-29 | 한밭대학교 산학협력단 | Control system for supplying heat based on solar insolation and use pattern |
| CN109359782B (en) * | 2018-11-23 | 2021-05-04 | 中冶西北工程技术有限公司 | Method for predicting heat load required by unit building area in building in heating period |
| JP6893262B2 (en) * | 2019-02-08 | 2021-06-23 | 日東電工株式会社 | Smart window control device, smart window control method and smart window control program |
| CN110334366B (en) * | 2019-03-14 | 2023-07-14 | 华电电力科学研究院有限公司 | Building instantaneous cold load prediction method based on Monte Carlo method using Latin hypercube sampling |
| CN110704892B (en) * | 2019-08-16 | 2022-09-27 | 西安建筑科技大学 | Air conditioner cooling load calculation method and system |
| KR102325989B1 (en) | 2020-10-19 | 2021-11-12 | 성한 주식회사 | Prediction Method of Building Heating and Cooling Load Based on Multiple Artificial Intelligence Learning Models |
| CN113685962B (en) * | 2021-10-26 | 2021-12-31 | 南京群顶科技有限公司 | Machine room temperature efficient control method and system based on correlation analysis |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1892205A (en) * | 2005-07-07 | 2007-01-10 | 日本油漆株式会社 | Heat amount prediction method, heat amount prediction system, and recording medium having heat amount prediction program |
| CN101021914A (en) * | 2006-03-22 | 2007-08-22 | 侯春海 | Heating ventilating and air conditioner load predicting method and system |
| CN102095591A (en) * | 2010-12-01 | 2011-06-15 | 西安建筑科技大学 | Energy-saving performance estimating method of driven solar heating building |
| CN102128481A (en) * | 2010-01-20 | 2011-07-20 | 珠海格力电器股份有限公司 | Air conditioner as well as control method and device thereof |
| CN102705957A (en) * | 2012-06-07 | 2012-10-03 | 华南理工大学 | Method and system for predicting hourly cooling load of central air-conditioner in office building on line |
| KR101301123B1 (en) * | 2012-05-31 | 2013-12-31 | 충남대학교산학협력단 | Prediction method for cooling and heating load |
| CN104236020A (en) * | 2014-09-30 | 2014-12-24 | 张迎春 | Method and device for controlling air conditioning system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2582447B2 (en) * | 1989-12-26 | 1997-02-19 | 三菱重工業株式会社 | Load prediction device for air conditioner |
| JPH0960945A (en) * | 1995-08-24 | 1997-03-04 | Toshiba Corp | Thermal load estimating device for building air conditioning |
| KR100830095B1 (en) | 2007-11-12 | 2008-05-20 | 충남대학교산학협력단 | Prediction method for cooling load |
-
2015
- 2015-01-16 KR KR1020150007941A patent/KR101506215B1/en active IP Right Grant
- 2015-11-06 WO PCT/KR2015/011953 patent/WO2016114477A1/en active Application Filing
- 2015-11-06 CN CN201580030276.7A patent/CN106461251B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1892205A (en) * | 2005-07-07 | 2007-01-10 | 日本油漆株式会社 | Heat amount prediction method, heat amount prediction system, and recording medium having heat amount prediction program |
| CN101021914A (en) * | 2006-03-22 | 2007-08-22 | 侯春海 | Heating ventilating and air conditioner load predicting method and system |
| CN102128481A (en) * | 2010-01-20 | 2011-07-20 | 珠海格力电器股份有限公司 | Air conditioner as well as control method and device thereof |
| CN102095591A (en) * | 2010-12-01 | 2011-06-15 | 西安建筑科技大学 | Energy-saving performance estimating method of driven solar heating building |
| KR101301123B1 (en) * | 2012-05-31 | 2013-12-31 | 충남대학교산학협력단 | Prediction method for cooling and heating load |
| CN102705957A (en) * | 2012-06-07 | 2012-10-03 | 华南理工大学 | Method and system for predicting hourly cooling load of central air-conditioner in office building on line |
| CN104236020A (en) * | 2014-09-30 | 2014-12-24 | 张迎春 | Method and device for controlling air conditioning system |
Non-Patent Citations (2)
| Title |
|---|
| 关于空调冷负荷技术方法的讨论;王飞腾;《甘肃科技》;20140320;第25卷(第13期);第83-84页以及第102页 |
| 建筑围护结构能耗的计算机仿真设计;吕政，唐海萍;《制冷与空气》;20070615(第2期);第101-104页 |
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