CN107143981B - A kind of controlling system of central air conditioner and method - Google Patents
A kind of controlling system of central air conditioner and method Download PDFInfo
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- CN107143981B CN107143981B CN201710376155.9A CN201710376155A CN107143981B CN 107143981 B CN107143981 B CN 107143981B CN 201710376155 A CN201710376155 A CN 201710376155A CN 107143981 B CN107143981 B CN 107143981B
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Abstract
The invention discloses a kind of intelligent air condition energy-saving control system and its control method, which includes state detection unit, for obtaining the status information and power information of each device in air-conditioning system；Processor；Control unit, the Optimal Control Strategy for output processor；Wherein processor status information is clustered by gauss hybrid models cluster module, linear regression fit module, for each cluster labels, it establishes and is based on polynomial linear regression fit model, for being fitted each rating of set, and then predict the total power consumption of air-conditioning system, finally by Genetic algorithm searching module, control strategy is searched for.The present invention is based on the multi-model Fusion Models of gauss hybrid models, linear regression and genetic algorithm to have reliability stable and the apparent beneficial effect of effect of optimization so that the controlled variable control of intelligent air-conditioning system is contacted with the foundation of present apparatus status information.
Description
Technical field
The present invention relates to air conditioner energy saving fields, specially the sky based on gauss hybrid models, linear regression and genetic algorithm
Adjusting can control system and method.
Background technique
With the development of global warming and air-conditioning technical, more and more modern architectures use central air-conditioning regulation room
Interior temperature and humidity, shows according to document, and the energy consumption of central air-conditioning accounts about the 50%-70% of whole building energy consumption, along with " intelligent city
City " pace of construction quickly propels, and realizes that the intelligent control of central air-conditioning and energy conservation have also put on agenda.But be only according to
Experience by technical staff regulates and controls central air conditioner system, but effect is unobvious.
A technical problem that needs to be urgently solved by technical personnel in the field at present is: how to break through and relies only on technical staff
Experience to the limitation of central airconditioning control.
Summary of the invention
To solve the above-mentioned problems, the present invention provides one kind to be based on gauss hybrid models, linear regression and genetic algorithm
Multi-model merge intelligent air condition energy-saving control system, by being analyzed the correlation between each variable and proposing to reduce
Central air conditioner system total power consumption and the corresponding optimal control policy of system effectiveness；The multi-model convergence strategy makes central hollow
The information foundation that adjusting system unit state and device rotary speed are detected with condition checkout gear contacts, and has test accuracy
Height, the beneficial effect of the obvious safety of control strategy energy-saving effect.
The technical solution adopted by the present invention are as follows:
A kind of controlling system of central air conditioner, comprising:
Condition detecting device, for obtaining the switching-state information of each device, rotary speed information and function in central air conditioner system
Rate information；
Processor is connected with condition checkout gear, including gauss hybrid models cluster module, linear regression fit module and
Genetic algorithm searching module；
Controller equiment is connected with processor, the Optimal Control Strategy for output processor；
The gauss hybrid models cluster module, rotary speed information and power information based on acquisition, to central air conditioner system
In the switching-state information of each device clustered, obtain several cluster labels；
The linear regression fit module, for each cluster labels, using the accordingly corresponding revolving speed of each device as independent variable,
Using each rating of set as dependent variable, establishes and be based on polynomial linear regression fit model, for being fitted each rating of set, institute
Having the sum of rating of set is the total power consumption of the corresponding system of the cluster labels；
The Genetic algorithm searching module, in the pact for meeting central air conditioner system cooling and each equipment safety work of system
Under the conditions of beam, the control strategy that the total power consumption of system is minimum in global scope is searched for.
Further, processor further includes data preprocessing module.
Further, the device in the central air conditioner system includes cooling tower, cooling device, condensing tower, condensate pump.
Further, the Genetic algorithm searching module further comprises: carrying out to the revolving speed that air-conditioning system can be set
Globalization search, finds so that the total power consumption of system reaches the smallest control strategy.
According to another aspect of the present invention, a kind of energy-saving control method for central air conditioner is also provided, comprising the following steps:
Obtain switching-state information, rotary speed information and the power information of each device in central air conditioner system；
Rotary speed information and power information based on acquisition carry out the switching-state information of device each in central air conditioner system
Gauss hybrid models cluster, obtains several cluster labels；
For each cluster labels, using accordingly the corresponding revolving speed of each device is independent variable, using each rating of set as dependent variable,
It establishes and is based on polynomial linear regression fit model, for being fitted each rating of set, the sum of all rating of set are should
The total power consumption of the corresponding system of cluster labels；
Under the constraint condition for meeting central air conditioner system cooling and each equipment safety work of system, it is based on genetic algorithm
Search for the control strategy that the total power consumption of system is minimum in global scope.
Further, after the information for obtaining each device, data prediction has also been carried out.
Further, the device in the central air conditioner system includes cooling tower, cooling device, condensing tower, condensate pump.
Further, further based on the control strategy that can reduce total power consumption in Genetic algorithm searching global scope
Include: that globalization search is carried out to the revolving speed that air-conditioning system can be set, finds so that the total power consumption of system reaches the smallest
Control strategy.
Beneficial effects of the present invention:
1, model clusters data by the gauss hybrid models in machine learning, and Clustering Effect is preferable, clusters it
After can produce good linear fit effect.
2, optimal model is solved based on genetic algorithm, global search and fast speed may be implemented, have as one kind
The search of information avoids unnecessary operation.
3, the multi-model convergence strategy makes central air conditioner system unit state and device rotary speed and condition checkout gear
The information detected establishes connection, breaches the limitation for relying only on the experience of technical staff to central airconditioning control；And have
There are test accuracy height, the beneficial effect of the obvious safety of control strategy energy-saving effect.
Detailed description of the invention
The accompanying drawings constituting a part of this application is used to provide further understanding of the present application, and the application's shows
Meaning property embodiment and its explanation are not constituted an undue limitation on the present application for explaining the application.
Fig. 1 is information flow schematic diagram of the present invention；
Fig. 2 is the influence factor figure of the general power of cooling device of the present invention；
Fig. 3 is the influence factor figure of the general power of water supply pump of the present invention；
Fig. 4 is the influence factor figure of the general power of cooling tower of the present invention；
Fig. 5 is the relational graph between cooling device general power of the present invention and cooling load；
Fig. 6 is the relational graph after the present invention clusters between cooling device general power and cooling load；
Fig. 7 is the relational graph of water supply pump general power and revolving speed of the present invention；
Fig. 8 is the relational graph of water supply pump general power and revolving speed after present invention cluster；
Fig. 9 is cooling tower of the present invention and cooling tower rotation speed of the fan relational graph
Figure 10 is cooling tower and cooling tower rotation speed of the fan relational graph after present invention cluster.
Figure 11 is the solidifying pump general power of cold water of the present invention and condensate pump rotation speed relation figure
Figure 12 is the solidifying pump general power of cold water and condensate pump rotation speed relation figure after present invention cluster
Figure 13 is the relational graph of the present invention cooling load and total power consumption
Figure 14 is the relational graph of cooling tower general power of the present invention and cooling load
Specific embodiment
It is noted that following detailed description is all illustrative, it is intended to provide further instruction to the application.Unless another
It indicates, all technical and scientific terms used herein has usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singular
Also it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or their combination.
Embodiment 1
Fig. 1 is information flow schematic diagram of the present invention.
A kind of multi-model fusion intelligent air condition Energy Saving Control system based on gauss hybrid models, linear regression and genetic algorithm
System, including condition detecting device, for obtaining cooling tower, cooling device, condensing tower, the state of condensate pump and power；Place
Device is managed, is connected with condition checkout gear, there is gauss hybrid models cluster module, linear regression fit module and genetic algorithm to search
Rope module；Controller equiment is connected with processor, the Optimal Control Strategy for output processor；
The gauss hybrid models cluster module, establishes the cluster of each device state information and rating of set in air-conditioning system
Label, obtains several cluster labels, and identical cluster marks corresponding each device switch combination to constitute a kind of control strategy；
The linear regression fit module, using each rating of set as dependent variable, is built using the corresponding revolving speed of device as independent variable
Be based on polynomial linear regression fit module, and for being fitted each rating of set, the sum of all rating of set are air-conditioning
The total power consumption of system；
The Genetic algorithm searching module, for the sample to be kept cooling demand and equipment safety working condition
Constraint condition under, search for the minimum control strategy of power consumption in global scope.
Embodiment 2
By each appliance arrangement status information power of the central air conditioner system of Condition Monitoring Unit periodic monitor and revolving speed etc.
Sample, each sample includes acquisition time and status information 51 attributes in total, and the data that this implementation uses share 88840
Item.
1 sample explanation of field of table
Processor includes data preprocessing module, gauss hybrid models cluster module, linear regression fit module and heredity
Algorithm search module.
(1) data preprocessing module, including screening unit, fitting unit and converting unit:
Wherein, the screening module, first in the air-conditioning system device state information and power information sieve
It selects, the missing values in screening system device information；
Secondly, carrying out the deletion of Chang Bianliang, 51 column numeric type features are by calculating each numeric type feature in initial data
Standard deviation and average ratio value, reject the feature that varies less of part and rejected, herein for ratio especially close to 0
It is proposed room temperature and humidity, and changing in notebook data the most violent is switch, power and efficiency.
The fitting module is fitted above-mentioned air-conditioning system device loss of learning value.
Specifically, firstly, carrying out missing values cleaning, observation data calculate its missing ratio, determine the range of missing values.It presses
According to missing ratio and field importance, take different processing strategies: the feature high for importance, miss rate is low, pass through through
It tests or professional knowledge estimation is filled；The feature high for importance, miss rate is high, uses other more complicated model meters
Calculate completion.
The conversion module, for being formatted to the air-conditioning system device information after screening and fitting.
(2) the gauss hybrid models cluster module establishes the cluster label of each device state information in air-conditioning system:
This system uses GMM (gauss hybrid models), is clustered to obtain several marks to air-conditioning system device switching information
Label carry out regression analysis modeling to different labels respectively, and wherein GMM has following probability Distribution Model:
Wherein αkIt is coefficient, αk>=0,It is Gaussian distribution density,
It because state of a control information only has the switch of 12 equipment, and is the value of 0-1, it only need to be under corresponding conditions
Equipment state control information modify to obtain optimal system efficiency.
Constraint condition is the status information of 12 equipments herein, other universal constraining conditions are with before.
There is following cluster to mark later for different status information clusters
2 cooling device status information of table cluster label corresponds to
| Cooling device 1 switchs | Cooling device 2 switchs | Cooling device 3 switchs | Cooling device cluster label |
| 0 | 0 | 0 | 0 |
| 0 | 0 | 1 | 2 |
| 0 | 1 | 0 | 0 |
| 0 | 1 | 1 | 0 |
| 1 | 0 | 0 | 1 |
| 1 | 0 | 1 | 2 |
| 1 | 1 | 0 | 1 |
| 1 | 1 | 1 | 1 |
3 water supply pump status information of table cluster label corresponds to
4 condensate pump status information of table cluster label corresponds to
| Condensate pump 1 switchs | Condensate pump 2 switchs | Condensate pump 3 switchs | Condensate pump cluster mark |
| 0 | 0 | 0 | [0] |
| 1 | 0 | 0 | [1] |
| 0 | 1 | 0 | [0] |
| 0 | 0 | 1 | [2] |
| 1 | 1 | 0 | [1] |
| 1 | 0 | 1 | [2] |
| 0 | 1 | 1 | [2] |
| 1 | 1 | 1 | [2] |
5 cooling column status information of table cluster label corresponds to
| Cooling tower 1 switchs | Cooling tower 2 switchs | Cooling tower cluster mark |
| 0 | 0 | [0] |
| 0 | 1 | [1] |
| 1 | 0 | [0] |
| 1 | 1 | [1] |
(3) the linear regression fit module, for each cluster labels, using the corresponding revolving speed of device as independent variable, with
Each rating of set is dependent variable, establishes and is based on polynomial linear regression fit module, for being fitted each rating of set, is owned
The sum of rating of set is the total power consumption of air-conditioning system.
(4) the Genetic algorithm searching module, for before meeting air-conditioning system cooling and the work of each equipment safety
It puts, optimal sequence is found out based on genetic algorithm.
Genetic algorithm constraint condition:
Under the premise of meeting air-conditioning system cooling, total power consumption optimization model can indicate central air-conditioning are as follows:
Wherein, P (x) indicates that the total power consumption of central air-conditioning, x indicate that the parameter list for needing to optimize, S represent constraint
Condition.Constraint mainly includes constraint condition given in influencing each other between the control method of system, each module and material
It is identified.
Constraint condition 1: outer circulation supply water temperature is lower, and the operational energy efficiency of cooling device is lower, but outer circulation water temperature
Degree needs the fixed normal operation that can just guarantee water cooler in a certain range, and circulating water temperature is given herein, therefore does not examine
The limitation of worry water temperature, but what the power of the dehumidifying effect of central air conditioner system was determined with chilled water supply water temperature, consulting literatures
It is known that meet the central air conditioner system circulating water temperature condition of end environment comfort requirement, it is necessary to meet:
Wherein, Chwshdr indicates water temperature when flowing out cooling device；ChiKwSum indicates cooling device general power；
ChiKwSumIt is specifiedIndicate cooling device nominal total power.
Constraint condition 2: cooling device is easy to happen surge phenomenon when underload works, and reduces the service life of equipment,
So the constraint condition proposed is that rate of load condensate cannot be too low herein.
ChiKwmin≤ChiKw≤ChiKwIt is specified(work as ChiWhen Stat=1)
Wherein, ChiKw indicates the power of cooling device i, ChiStat=1 indicates that the status information of cooling device is to open.
More specifically, carrying out globalization search to the system and device revolving speed that system can be set, find so that system always consumes
Electricity reaches the smallest control strategy.
Finally, by the Optimal Control Strategy of controller equiment output processor.
It will be understood by those skilled in the art that each module of the above invention or each step can use general computer
Device realizes that optionally, they can be realized with the program code that computing device can perform, it is thus possible to which they are deposited
Storage be performed by computing device in the storage device, perhaps they are fabricated to each integrated circuit modules or by it
In multiple modules or step be fabricated to single integrated circuit module to realize.The present invention is not limited to any specific hardware
With the combination of software.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (8)
1. a kind of controlling system of central air conditioner characterized by comprising
Condition detecting device, for obtaining the switching-state information of each device in central air conditioner system, rotary speed information and power letter
Breath；
Processor is connected with condition detecting device, including gauss hybrid models cluster module, linear regression fit module and heredity
Algorithm search module；
Controller equiment is connected with processor, the Optimal Control Strategy for output processor；
The gauss hybrid models cluster module, rotary speed information and power information based on acquisition, to each in central air conditioner system
The switching-state information of device is clustered, and several cluster labels are obtained；
The linear regression fit module, for each cluster labels, using accordingly the corresponding revolving speed of each device is independent variable, with each
Rating of set is dependent variable, establishes and is based on polynomial linear regression fit model, for being fitted each rating of set, all dresses
Setting the sum of power is the total power consumption of the corresponding system of the cluster labels；
The Genetic algorithm searching module, in the constraint item for meeting central air conditioner system cooling and each equipment safety work of system
Under part, the smallest control strategy of the total power consumption of system in global scope is searched for.
2. system according to claim 1, it is characterised in that: processor further includes data preprocessing module.
3. system according to claim 1, it is characterised in that: the device in the central air conditioner system include cooling tower,
Cooling device, condensing tower, condensate pump.
4. system according to claim 1, it is characterised in that: Genetic algorithm searching module further comprises: to central hollow
The revolving speed that adjusting system can be set carries out globalization search, finds so that the total power consumption of system reaches the smallest control strategy.
5. a kind of energy-saving control method for central air conditioner, which comprises the following steps:
Obtain switching-state information, rotary speed information and the power information of each device in central air conditioner system；
Rotary speed information and power information based on acquisition carry out Gauss to the switching-state information of device each in central air conditioner system
Mixed model cluster, obtains several cluster labels；
Each cluster labels, using each rating of set as dependent variable, are established so that accordingly the corresponding revolving speed of each device is independent variable
Based on polynomial linear regression fit model, for being fitted each rating of set, the sum of all rating of set are the cluster
The total power consumption of the corresponding system of label；
Under the constraint condition for meeting central air conditioner system cooling and each equipment safety work of system, it is based on Genetic algorithm searching
The smallest control strategy of the total power consumption of system in global scope.
6. according to the method described in claim 5, it is characterized by: also having carried out data after obtaining the information of each device and having located in advance
Reason.
7. according to the method described in claim 5, it is characterized by: the device in the central air conditioner system include cooling tower,
Cooling device, condensing tower, condensate pump.
8. according to the method described in claim 5, it is characterized by: based on the total power consumption of system in Genetic algorithm searching global scope
The smallest control strategy of power further comprises: carrying out globalization search to the revolving speed that central air conditioner system can be set, finds
So that the total power consumption of system reaches the smallest control strategy.
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| CN109838875A (en) * | 2017-11-29 | 2019-06-04 | 李永红 | A kind of split-type air conditioner behavior perception energy-saving control method and system |
| US11243503B2 (en) * | 2018-07-20 | 2022-02-08 | Johnson Controls Tyco IP Holdings LLP | Building management system with online configurable system identification |
| CN111831871B (en) * | 2020-07-07 | 2023-10-24 | 海尔(深圳)研发有限责任公司 | Method, device and equipment for recommending air conditioner working mode |
| CN112257779A (en) * | 2020-10-22 | 2021-01-22 | 重庆中源绿蓝环境科技有限公司 | Method for acquiring self-learning working condition parameters of central air conditioner |
| CN112906184A (en) * | 2021-01-14 | 2021-06-04 | 合肥阳光新能源科技有限公司 | Temperature control method and system of battery energy storage system |
| CN115978722B (en) * | 2023-03-17 | 2023-05-23 | 四川港通医疗设备集团股份有限公司 | Artificial intelligence-based purification unit monitoring management method and system |
| CN116933681B (en) * | 2023-07-18 | 2024-01-26 | 东莞市鹏锦机械科技有限公司 | Calculation method for optimal working condition of NMP waste gas recovery system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1616897A (en) * | 2003-11-11 | 2005-05-18 | Lg电子株式会社 | Central control system of air conditioners and method for operating the same |
| CN201897294U (en) * | 2010-12-06 | 2011-07-13 | 北京星达技术开发公司 | Energy-saving control system for refrigeration station of central air conditioner |
| CN103853927A (en) * | 2014-03-12 | 2014-06-11 | 北京理工大学 | Clustering overall optimization algorithm-based method for predicting material mechanical behaviors |
| CN103912966A (en) * | 2014-03-31 | 2014-07-09 | 武汉科技大学 | Optimal control method for ground source heat pump refrigerating system |
| CN106021961A (en) * | 2016-06-20 | 2016-10-12 | 吉林大学 | Urban standard cyclic working condition constructing method based on genetic algorithm optimization |
| CN106209618A (en) * | 2016-07-27 | 2016-12-07 | 国网辽宁省电力有限公司 | A kind of communication mixed networking method and system improving intelligence adapted electric energy effect |
| CN106453294A (en) * | 2016-09-30 | 2017-02-22 | 重庆邮电大学 | Security situation prediction method based on niche technology with fuzzy elimination mechanism |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7328194B2 (en) * | 2005-06-03 | 2008-02-05 | Aspeed Software Corporation | Method and system for conditioning of numerical algorithms for solving optimization problems within a genetic framework |
-
2017
- 2017-05-24 CN CN201710376155.9A patent/CN107143981B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1616897A (en) * | 2003-11-11 | 2005-05-18 | Lg电子株式会社 | Central control system of air conditioners and method for operating the same |
| CN201897294U (en) * | 2010-12-06 | 2011-07-13 | 北京星达技术开发公司 | Energy-saving control system for refrigeration station of central air conditioner |
| CN103853927A (en) * | 2014-03-12 | 2014-06-11 | 北京理工大学 | Clustering overall optimization algorithm-based method for predicting material mechanical behaviors |
| CN103912966A (en) * | 2014-03-31 | 2014-07-09 | 武汉科技大学 | Optimal control method for ground source heat pump refrigerating system |
| CN106021961A (en) * | 2016-06-20 | 2016-10-12 | 吉林大学 | Urban standard cyclic working condition constructing method based on genetic algorithm optimization |
| CN106209618A (en) * | 2016-07-27 | 2016-12-07 | 国网辽宁省电力有限公司 | A kind of communication mixed networking method and system improving intelligence adapted electric energy effect |
| CN106453294A (en) * | 2016-09-30 | 2017-02-22 | 重庆邮电大学 | Security situation prediction method based on niche technology with fuzzy elimination mechanism |
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Inventor after: Wang Hong Inventor after: Zhou Dongliang Inventor after: Fang Shijie Inventor after: Ma Xiaobin Inventor after: Yu Xiaomei Inventor before: Zhou Dongliang Inventor before: Fang Shijie Inventor before: Ma Xiaobin Inventor before: Wang Hong Inventor before: Yu Xiaomei |
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