CN115540215B - Method for realizing energy conservation by algorithm scheduling of central air conditioner based on causal reasoning - Google Patents
Method for realizing energy conservation by algorithm scheduling of central air conditioner based on causal reasoning Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 18
- 230000001364 causal effect Effects 0.000 title claims abstract description 14
- 238000004134 energy conservation Methods 0.000 title claims description 12
- 238000001816 cooling Methods 0.000 claims abstract description 53
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 39
- 239000000498 cooling water Substances 0.000 claims abstract description 21
- 238000004378 air conditioning Methods 0.000 claims abstract description 10
- 238000010845 search algorithm Methods 0.000 claims abstract description 6
- 238000005057 refrigeration Methods 0.000 claims abstract description 4
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 24
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 12
- 239000001569 carbon dioxide Substances 0.000 claims description 12
- 238000005265 energy consumption Methods 0.000 claims description 7
- 230000006698 induction Effects 0.000 claims description 3
- 238000012417 linear regression Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 230000007613 environmental effect Effects 0.000 abstract description 12
- 238000011217 control strategy Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
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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
-
- 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
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- 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
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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
-
- 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
- F24F2110/12—Temperature of the outside air
-
- 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
- F24F2110/20—Humidity
-
- 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
- F24F2110/50—Air quality properties
- F24F2110/65—Concentration of specific substances or contaminants
- F24F2110/70—Carbon dioxide
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2120/00—Control inputs relating to users or occupants
- F24F2120/10—Occupancy
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/08—Probabilistic or stochastic CAD
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- 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
- G06F2119/08—Thermal analysis or thermal optimisation
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Abstract
The invention relates to the technical field of central air conditioner energy-saving control, in particular to a method for realizing energy saving by designing an algorithm scheduling of a central air conditioner based on causal reasoning, which comprises the following steps: s1, acquiring environment information, water chilling unit information, chilled water pump information, cooling water pump information and cooling tower information; s2, calculating the relation between the indoor environment temperature change and the environment information; s3, calculating indoor required cooling capacity Q by combining an indoor temperature change value delta Temp room, an indoor volume V, an optimal indoor temperature Temp best, a current indoor environment temperature Temp room and an air specific heat capacity C air; s4, formulating an optimal refrigeration strategy according to the cooling capacity Q provided by the need; s5, controlling the central air conditioning equipment according to the optimal strategy; according to the intelligent energy-saving intelligent central air conditioner, the intelligent algorithm is designed to collect environmental information and equipment information historical data through the AI intelligent algorithm, an indoor environmental temperature change prediction model is constructed, and intelligent energy saving of the central air conditioner is realized through the random search algorithm according to the predicted environmental temperature change trend.
Description
Technical Field
The invention relates to the technical field of central air conditioner energy-saving control, in particular to a method for realizing energy saving by algorithm scheduling of a central air conditioner based on causal reasoning.
Background
The state of the central air conditioner can only be adjusted simply according to the physical parameters of the cold water side, the environment in the building and the history data of equipment information are not fully considered, and due to the defects of time lag, large inertia and the like of the central air conditioner, when the cold load of the system changes, the traditional adjusting system cannot respond quickly and effectively, so that unnecessary energy waste or indoor comfort level is caused, correspondingly, the energy consumption of the central air conditioner is increased, and the energy consumption is wasted.
In summary, the invention solves the existing problems by designing a method for realizing energy conservation by algorithm scheduling of a central air conditioner based on causal reasoning.
Disclosure of Invention
The invention aims to provide a method for realizing energy conservation by algorithm scheduling of a central air conditioner based on causal reasoning, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method for realizing energy saving by central air conditioner algorithm scheduling based on causal reasoning comprises the following steps:
S1, acquiring environment information, water chilling unit information, chilled water pump information, cooling water pump information and cooling tower information;
The environment information comprises indoor environment temperature Temp room, outdoor environment temperature Temp out, air humidity Humidity, personnel Flow rate Flow and carbon dioxide concentration An indoor volume V;
the water chilling unit information comprises chilled water supply temperature Temp dsw, chilled water return temperature Temp dbw, cooling water supply temperature Temp qsw and cooling water return temperature Temp qbw;
the chilled water pump information comprises a chilled water pump lift L d and a chilled water pump flow F d;
The cooling water pump information comprises a cooling pump lift L q and a cooling pump flow F q;
The cooling tower information comprises cooling tower fan energy P t and cooling tower fan flow F t.
S2, calculating the relation between the indoor environment temperature change and the environment information, wherein the calculation formula is as follows:
S3, combining an indoor temperature change value delta Temp room, an indoor volume V, an optimal indoor temperature Temp best, a current indoor environment temperature Temp room and an air specific heat capacity C air, and calculating indoor required cooling capacity Q, wherein the formula is specifically as follows:
Q＝(Temproom-Tempbest+ΔTemproom)×V×Cair
s4, formulating an optimal refrigeration strategy according to the cooling capacity Q provided as required, wherein the formula is as follows:
Q＝f(Tempdsw,Tempdbw,Tempqsw,Tempqbw,Ld,Fd,Lq,Fq,Pt,Ft)
The optimal solution of all relevant parameters when the cooling capacity Q is reached is found out through an AI random search algorithm, namely, when the cooling capacity Q is met, the corresponding chilled water supply temperature Temp dsw, chilled water return temperature Temp dbw, cooling water supply temperature Temp qsw, cooling water return temperature Temp qbw, cooling pump lift L d, cooling pump flow, cooling pump lift L q, cooling pump flow F q, cooling tower fan energy consumption P t and cooling tower fan flow F t parameter combination is found out, so that the energy consumption of the central air conditioning equipment is minimum when the cooling capacity Q is met, the constraint range can be set according to user definition by each parameter data, the optimal solution found out through the algorithm is used as an optimal strategy, and the optimal solution is correspondingly set in the central air conditioning equipment;
S5, controlling the central air conditioning equipment according to the optimal strategy.
As a preferable scheme of the invention, the indoor environment temperature Temp room, the outdoor environment temperature Temp out and the air humidity Humidity in the environment information in the S1 are obtained by a temperature and humidity sensor; carbon dioxide concentrationAcquiring through a carbon dioxide sensor; the indoor volume V is a user-defined value according to the actual scene.
As a preferable scheme of the invention, the personnel Flow in the S1 is obtained in two modes, namely, the personnel quantity in the area is identified through a camera, and a heat source induction sensor is adopted.
As a preferable scheme of the invention, the information of the cold water unit, the information of the cold water pump and the information of the cooling tower in the S1 can be obtained through a sensor.
As a preferable scheme of the invention, delta Temp room in S2 is an indoor environment temperature change value, by collecting historical indoor volume, indoor environment temperature, outdoor environment temperature, indoor air humidity, personnel flow, indoor carbon dioxide concentration and indoor environment temperature change data, and by fitting the relation between parameters and indoor environment temperature change value through an AI linear regression prediction algorithm.
Compared with the prior art, the invention has the beneficial effects that:
1. According to the method for realizing energy conservation by designing the causal reasoning-based algorithm scheduling of the central air conditioner, the intelligent energy conservation of the central air conditioner is realized by collecting environmental information and equipment information historical data through an AI intelligent algorithm, constructing an indoor environmental temperature change prediction model, dynamically adjusting an optimal control strategy through a random search algorithm according to the predicted environmental temperature change trend.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
In order that the invention may be readily understood, several embodiments of the invention will be described more fully hereinafter with reference to the accompanying drawings, in which embodiments of the invention are shown, but in which the invention may be embodied in many different forms and is not limited to the embodiments described herein, but instead is provided to provide a more thorough and complete disclosure of the invention.
It will be understood that when an element is referred to as being "mounted" on another element, it can be directly on the other element or intervening elements may also be present, and when an element is referred to as being "connected" to the other element, it may be directly connected to the other element or intervening elements may also be present, the terms "vertical", "horizontal", "left", "right" and the like are used herein for the purpose of illustration only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, and the terms used herein in this description of the invention are for the purpose of describing particular embodiments only and are not intended to be limiting of the invention, with the term "and/or" as used herein including any and all combinations of one or more of the associated listed items.
Referring to fig. 1, the present invention provides a technical solution:
S1, acquiring environment information, water chilling unit information, chilled water pump information, cooling water pump information and cooling tower information;
The environment information comprises indoor environment temperature Temp room, outdoor environment temperature Temp out, air humidity Humidity, personnel Flow rate Flow and carbon dioxide concentration An indoor volume V;
the water chilling unit information comprises chilled water supply temperature Temp dsw, chilled water return temperature Temp dbw, cooling water supply temperature Temp qsw and cooling water return temperature Temp qbw;
The chilled water pump information comprises a chilled water pump lift L d and a chilled water pump flow F d; the cooling water pump information comprises a cooling pump lift L q and a cooling pump flow F q;
The cooling tower information comprises cooling tower fan energy P t and cooling tower fan flow F t;
Further, in the environmental information, the indoor environmental temperature Temp room, the outdoor environmental temperature Temp out and the air humidity Humidity are obtained by a temperature and humidity sensor; the personnel Flow is obtained in two ways, namely, the personnel quantity in the area is identified through a camera, and the personnel quantity is obtained through a heat source induction sensor; carbon dioxide concentration Acquiring through a carbon dioxide sensor; the indoor volume V is a user-defined value according to an actual scene;
Further, the water chilling unit information, the chilled water pump information, the cooling water pump information and the cooling tower information can be obtained through sensors;
s2, calculating the relation between the indoor environment temperature change and the environment information, wherein the formula is as follows:
Delta Temp room is an indoor environment temperature change value, by collecting historical indoor volume, indoor environment temperature, outdoor environment temperature, indoor air humidity, personnel flow, indoor carbon dioxide concentration and indoor environment temperature change data, and by an AI linear regression prediction algorithm, fitting the relation between parameters and the indoor environment temperature change value;
S3, combining an indoor temperature change value delta Temp room, an indoor volume V, an optimal indoor temperature Temp best, a current indoor environment temperature Temp room and an air specific heat capacity C air, and calculating indoor required cooling capacity Q, wherein the formula is specifically as follows:
Q＝(Temproom-Tempbest+ΔTemproom)×V×Cair
s4, formulating an optimal refrigeration strategy according to the cooling capacity Q provided as required, wherein the formula is as follows:
Q＝f(Tempdsw,Tempdbw,Tempqsw,Tempqbw,Ld,Fd,Lq,Fq,Pt,Ft)
And finding out the optimal solution of all the related parameters when the cooling capacity Q is reached through an AI random search algorithm. When the cooling capacity Q is met, the corresponding parameter combinations such as the chilled water supply temperature Temp dsw, the chilled water return temperature Temp dbw, the cooling water supply temperature Temp qsw, the cooling water return temperature Temp qbw, the chilled pump lift L d, the chilled pump flow, the chilled pump lift L q, the chilled pump flow F q, the cooling tower fan energy P t, the cooling tower fan flow F t and the like are found, so that the energy consumption is minimum when the central air conditioning equipment meets the cooling capacity Q, wherein each parameter data can be used for setting a constraint range according to user definition, and the optimal solution found through an algorithm is used as an optimal strategy to be correspondingly set in the central air conditioning equipment;
S5, controlling the central air conditioning equipment according to the optimal strategy;
The method for realizing energy conservation by the algorithm scheduling based on causal reasoning of the central air conditioner is designed, environmental information and equipment information historical data can be collected through an AI intelligent algorithm, an indoor environmental temperature change prediction model is constructed, and an optimal control strategy is dynamically adjusted through a random search algorithm according to the predicted environmental temperature change trend, so that intelligent energy conservation of the central air conditioner is realized.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. A method for realizing energy saving by central air conditioner algorithm scheduling based on causal reasoning comprises the following steps:
S1, acquiring environment information, water chilling unit information, chilled water pump information, cooling water pump information and cooling tower information;
The environment information comprises indoor environment temperature Temp room, outdoor environment temperature Temp out, air humidity Humidity, personnel Flow, carbon dioxide concentration C co2 and indoor volume V;
the water chilling unit information comprises chilled water supply temperature Temp dsw, chilled water return temperature Temp dbw, cooling water supply temperature Temp qsw and cooling water return temperature Temp qbw;
the chilled water pump information comprises a chilled water pump lift L d and a chilled water pump flow F d;
The cooling water pump information comprises a cooling pump lift L q and a cooling pump flow F q;
The cooling tower information comprises cooling tower fan energy P t and cooling tower fan flow F t;
s2, calculating the relation between the indoor environment temperature change and the environment information, wherein the calculation formula is as follows:
S3, combining an indoor temperature change value delta Temp room, an indoor volume V, an optimal indoor temperature Temp best, a current indoor environment temperature Temp room and an air specific heat capacity C air, and calculating indoor required cooling capacity Q, wherein the formula is specifically as follows:
Q＝(Temproom-Tempbest+ΔTemproom)×V×Cair
s4, formulating an optimal refrigeration strategy according to the cooling capacity Q provided as required, wherein the formula is as follows:
Q＝f(Tempdsw,Tempdbw,Tempqsw,Tempqbw,Ld,Fd,Lq,Fq,Pt,Ft)
The optimal solution of all relevant parameters when the cooling capacity Q is reached is found out through an AI random search algorithm, namely, when the cooling capacity Q is met, the corresponding chilled water supply temperature Temp dsw, chilled water return temperature Temp dbw, cooling water supply temperature Temp qsw, cooling water return temperature Temp qbw, cooling pump lift L d, cooling pump flow, cooling pump lift L q, cooling pump flow F q, cooling tower fan energy consumption P t and cooling tower fan flow F t parameter combination is found out, so that the energy consumption of the central air conditioning equipment is minimum when the cooling capacity Q is met, the constraint range can be set according to user definition by each parameter data, the optimal solution found out through the algorithm is used as an optimal strategy, and the optimal solution is correspondingly set in the central air conditioning equipment;
S5, controlling the central air conditioning equipment according to the optimal strategy.
2. The method for realizing energy conservation by algorithm scheduling based on causal reasoning of the central air conditioner according to claim 1, wherein the method comprises the following steps: indoor environment temperature Temp room, outdoor environment temperature Temp out and air humidity Humidity in the environment information in the S1 are obtained through a temperature and humidity sensor; the carbon dioxide concentration C co2 is obtained by a carbon dioxide sensor; the indoor volume V is a user-defined value according to the actual scene.
3. The method for realizing energy conservation by algorithm scheduling based on causal reasoning of the central air conditioner according to claim 1, wherein the method comprises the following steps: the personnel Flow in the S1 is obtained in two modes, namely, the personnel quantity in the area is identified through a camera, and the personnel quantity is obtained through a heat source induction sensor.
4. The method for realizing energy conservation by algorithm scheduling based on causal reasoning of the central air conditioner according to claim 1, wherein the method comprises the following steps: and the S1 cold water unit information, the chilled water pump information, the cooling water pump information and the cooling tower information can be obtained through sensors.
5. The method for realizing energy conservation by algorithm scheduling based on causal reasoning of the central air conditioner according to claim 1, wherein the method comprises the following steps: and in the step S2, delta Temp room is an indoor environment temperature change value, historical indoor volume, indoor environment temperature, outdoor environment temperature, indoor air humidity, personnel flow, indoor carbon dioxide concentration and indoor environment temperature change data are collected, and the relation between fitting parameters and the indoor environment temperature change value is obtained through an AI linear regression prediction algorithm.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101424436A (en) * | 2008-11-29 | 2009-05-06 | 深圳市奥宇控制系统有限公司 | Intelligent optimizing control system and method for central air-conditioning |
| CN111486552A (en) * | 2020-04-24 | 2020-08-04 | 辽宁工程技术大学 | Method for identifying water supply temperature strategy of chilled water of air conditioner based on subentry metering data |
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| GB2498275B (en) * | 2010-10-13 | 2018-02-28 | Weldtech Tech Shanghai Co Ltd | Energy-saving optimized control system and method for chiller plant room |
| TWI644062B (en) * | 2017-06-26 | 2018-12-11 | 群光電能科技股份有限公司 | Adjusting system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101424436A (en) * | 2008-11-29 | 2009-05-06 | 深圳市奥宇控制系统有限公司 | Intelligent optimizing control system and method for central air-conditioning |
| CN111486552A (en) * | 2020-04-24 | 2020-08-04 | 辽宁工程技术大学 | Method for identifying water supply temperature strategy of chilled water of air conditioner based on subentry metering data |
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