CN115755628A - Central air-conditioning energy-saving control method based on genetic algorithm - Google Patents
Central air-conditioning energy-saving control method based on genetic algorithm Download PDFInfo
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Abstract
The invention discloses a central air-conditioning energy-saving control method based on a genetic algorithm, which comprises the following steps: constructing a central air conditioner BP neural network model and a central air conditioner association rule model, and outputting the characteristic subset as the model; in the aspect of load distribution optimization of a central air-conditioning system, a genetic algorithm is used for optimizing a BP neural network model of the central air-conditioning system, a central air-conditioning association rule model is introduced, an optimization strategy based on an improved genetic algorithm is provided, and the feasibility of an optimization result is verified through actual data; from the angle of the iteration times and the energy saving rate of the optimization result, analyzing and comparing the optimization process of the hybrid genetic association algorithm and the genetic algorithm; and (3) analyzing the parameter trend of the optimized central air-conditioning control system by fitting the optimized data of the hybrid genetic association algorithm. The invention analyzes the system operation data information through an artificial intelligence modeling technology and a learning theory, provides support and comparison for air conditioner operation decision and judgment, and realizes intelligent energy saving.
Description
Technical Field
The invention relates to the technical field of intelligent energy conservation of air conditioning equipment, in particular to a central air conditioning energy-saving control method based on a genetic algorithm.
Background
The central air-conditioning refrigeration system is divided into two categories, namely a direct refrigeration system and an indirect refrigeration system, wherein the indirect refrigeration system is a cold source system circulation, a chilled water circulation, an indoor air circulation and a cooling water circulation system of the central air-conditioning, and the working principle of the system is shown in the attached figure 1.
In the operation process of the system, factors such as hydraulic unbalance caused by any change, such as complex structure of a central air conditioner pipe network, continuous change of outdoor meteorological conditions and indoor load, incapability of quick self-adaptive adjustment of the system and the like finally cause high energy consumption of the central air conditioner. In order to achieve the purpose of energy saving and consumption reduction, the most conventional solution is to perform frequency conversion control on equipment such as a chilled water pump, a cooling water pump and a cooling tower, and actually, the energy consumption of the whole central air conditioning system cannot be reduced only by performing independent frequency conversion control on the equipment, and even the energy consumption may be counterproductive. In the existing operation and maintenance scene, due to the fact that user engineering personnel are high in mobility and large in capacity and quality difference, the field problems are difficult to find and judge due to inexperience, and manual misjudgment often occurs. In addition, because the device drawing and the point information are often not corresponding, or the device is disconnected, the data is incomplete, and great inconvenience and high energy consumption operation are brought to operation and maintenance.
In the system that adopts central air conditioning at present, because domestic design custom and standard, two sets of systems often can appear: the system comprises two sets of data acquisition systems, namely a building automation control system (BAS) and an air conditioning control system (BMS), and the systems are stored in a specified database (such as an Oracle database). Most software and hardware in the market at present need authorization to obtain a data source, and due to various factors, the authorization is relatively complicated to open, and even extra cost is also provided.
The real-time monitoring data volume of the central air conditioner is huge due to the development of informatization, but the data is rarely fully utilized. While current database systems can efficiently import, query, and count databases, these traditional database manipulation techniques are simply obtaining statistical information about the data surface that requires expertise in engineering to analyze and utilize. It is difficult for the average worker to find deep, seemingly unrelated information.
Disclosure of Invention
The invention provides a central air-conditioning energy-saving control method based on a genetic algorithm, which adopts a data mining technology, is based on probability theory and statistics, analyzes system operation data information through an artificial intelligence modeling technology and a learning theory, provides support and comparison for air-conditioning operation decision and judgment, and thus quickly judges system faults and provides an optimization strategy.
The energy-saving control method of the central air conditioner based on the genetic algorithm comprises the following steps:
data mining and modeling of the central air conditioner: preprocessing original data of the central air conditioner, screening out a characteristic subset through analysis, constructing a central air conditioner BP neural network model and a central air conditioner association rule model, and outputting the characteristic subset as a model;
the energy-saving strategy optimization algorithm of the central air conditioner is as follows: in the aspect of load distribution optimization of a central air-conditioning system, a genetic algorithm is used for optimizing a BP neural network model of the central air-conditioning system, a central air-conditioning association rule model is introduced, an optimization strategy based on an improved genetic algorithm is provided, the optimization strategy is called as a hybrid genetic association algorithm, the hybrid genetic association algorithm improves the correlation of chromosomes in genetic operation through the association rule model, and the feasibility of an optimization result is verified through actual data;
comparing and analyzing the energy-saving strategy of the central air conditioner: optimizing the control parameters of the two sets with the typical load rates of 50%, 75% and 90%, and analyzing and comparing the optimization processes of the hybrid genetic association algorithm and the genetic algorithm from the aspects of the iteration times and the energy saving rate of the optimization result;
analyzing the energy-saving strategy result of the central air conditioner: and fitting the optimized data of the hybrid genetic association algorithm to obtain a trend graph of different control variables changing along with the load rate and the energy consumption ratio, analyzing the parameter trend of the optimized central air-conditioning control system, and performing optimization control.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the energy-saving control method of the central air conditioner based on the genetic algorithm analyzes the system operation data information through an artificial intelligence modeling technology and a learning theory, provides support and comparison for the decision and judgment of the air conditioner operation, thereby quickly judging the system fault and providing an optimization strategy and realizing intelligent energy-saving control;
2. the energy-saving control method of the central air conditioner based on the genetic algorithm realizes the collection and analysis of the energy consumption data of the air conditioner in each system with different running states, and further realizes the judgment of the running states of single equipment and the whole system under different load rates by adopting Matlab, thereby providing important improvement measures and decision-making bases for the energy analysis and the energy-saving control of the central air conditioning system.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a schematic diagram of a conventional central air conditioning system;
FIG. 2 is a topological diagram of an application of the energy-saving control method of the present invention;
FIG. 3 is a system optimization graph;
fig. 4 is a system operation trend analysis chart.
Detailed Description
The invention is further described below with reference to examples and figures. The following examples are only a few specific examples of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by the design concept should fall within the scope of infringing on the protection scope of the present invention.
Examples
The energy-saving control method of the central air conditioner based on the genetic algorithm is combined with the attached figure 2, and comprises the following steps:
data mining and modeling of the central air conditioner: preprocessing original data of the central air conditioner, screening out a characteristic subset through analysis, constructing a central air conditioner BP neural network model and a central air conditioner association rule model, and outputting the characteristic subset as a model;
the energy-saving strategy optimization algorithm of the central air conditioner is as follows: in the aspect of load distribution optimization of a central air-conditioning system, a genetic algorithm is used for optimizing a BP neural network model of the central air-conditioning system, a central air-conditioning association rule model is introduced, an optimization strategy based on an improved genetic algorithm is provided, the optimization strategy is called as a hybrid genetic association algorithm, the hybrid genetic association algorithm improves the correlation of chromosomes in genetic operation through the association rule model, and the feasibility of an optimization result is verified through actual data;
comparing and analyzing the energy-saving strategy of the central air conditioner: optimizing the control parameters of the two sets with the typical load rates of 50%, 75% and 90%, and analyzing and comparing the optimization processes of the hybrid genetic association algorithm and the genetic algorithm from the aspects of the iteration times and the energy saving rate of the optimization result;
analyzing the energy-saving strategy result of the central air conditioner: and fitting the optimized data of the hybrid genetic association algorithm to obtain a trend graph of different control variables changing along with the load rate and the energy consumption ratio, analyzing the parameter trend of the optimized central air-conditioning control system, and performing optimized control.
The improved genetic algorithm is a method which takes the association rule as a chromosome generation constraint condition in the genetic algorithm, and the feasibility of an optimization result is ensured by enhancing the association between genes so as to achieve system optimization and energy conservation.
The following examples illustrate specific embodiments of the invention:
1. central air-conditioning data mining modeling
The data are collected before the data of the central air conditioner are preprocessed, the collected data are derived from energy consumption data, meteorological data and expert variables, and the original data of the central air conditioner are obtained after the data are collected. The method comprises the steps of preprocessing original data of the central air conditioner, including data merging, data cleaning, data normalization, abnormal data removing, data lost due to various reasons and repeated data, and finally obtaining stable operation data of the central air conditioner.
The data feature extraction adopts a Boruta feature selection algorithm which is an encapsulation type feature selection algorithm based on random forests, the random forests are used for evaluating each feature subset, namely the importance of each feature subset is calculated, and then effective feature subsets are selected according to the importance of each feature subset.
The BP neural network prediction model is established by aiming at calculating the energy efficiency ratio EER of the refrigeration system, selecting a result according to the energy consumption characteristics of the system, and adopting an energy consumption characteristic subset as input variables of the BP neural network prediction model, namely the system load rate, the freezing water pump frequency, the host water outlet temperature and the cooling water pump frequency, and passing through the established central air-conditioning BP neural network prediction model.
Operating by the Weka platform software: including load, supply and return water temperature, water pump frequency, etc. obtain the association rules.
2. Data integration and optimization recommendations
In the aspect of load distribution optimization of a central air-conditioning system, a genetic algorithm is used for optimizing a BP neural network model of the central air-conditioning system, a central air-conditioning association rule model is introduced, an optimization strategy based on an improved genetic algorithm is provided, the optimization strategy is called as a hybrid genetic association algorithm, the hybrid genetic association algorithm improves the correlation of chromosomes in genetic operation through the association rule model, and the feasibility of an optimization result is verified through actual data.
Optimizing the control parameters of the two sets with the typical load rates of 50%, 75% and 90%, and analyzing and comparing the optimization processes of the hybrid genetic association algorithm and the genetic algorithm from the aspects of the iteration times and the energy saving rate of the optimization result;
and fitting the optimized data of the hybrid genetic association algorithm to obtain a trend graph of different control variables changing along with the load rate and the energy consumption ratio, analyzing the parameter trend of the optimized central air-conditioning control system, and performing optimized control.
Performing data fitting based on a central air conditioner BP neural network model and a central air conditioner association rule model to obtain a system optimization curve chart, wherein the ordinate of the graph 3 is the water outlet temperature of a host, the frequency of a chilled water pump and the frequency of a cooling water pump respectively as shown in the graph 3; the system operation trend analysis chart is shown in fig. 4, and the abscissa of fig. 4 is the host water outlet temperature, the chilled water pump frequency and the cooling water pump frequency respectively.
The prefabricated data are applied to analysis software, and the on-site actual data are combined to realize on-site rapid judgment and strategy adjustment suggestions. The learning cost of field workers is reduced, the unordered increase of hardware equipment is reduced, and the modification cost is reduced. The high-efficiency, safe and energy-saving operation is realized.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only examples of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (1)
1. The energy-saving control method of the central air conditioner based on the genetic algorithm is characterized by comprising the following steps:
data mining and modeling of the central air conditioner: preprocessing original data of the central air conditioner, screening out a characteristic subset through analysis, constructing a central air conditioner BP neural network model and a central air conditioner association rule model, and outputting the characteristic subset as a model;
the energy-saving strategy optimization algorithm of the central air conditioner is as follows: in the aspect of load distribution optimization of a central air-conditioning system, a genetic algorithm is used for optimizing a BP neural network model of the central air-conditioning system, a central air-conditioning association rule model is introduced, an optimization strategy based on an improved genetic algorithm is provided, the optimization strategy is called as a hybrid genetic association algorithm, the hybrid genetic association algorithm improves the correlation of chromosomes in genetic operation through the association rule model, and the feasibility of an optimization result is verified through actual data;
comparing and analyzing the energy-saving strategy of the central air conditioner: optimizing the control parameters of the two sets with the typical load rates of 50%, 75% and 90%, and analyzing and comparing the optimization processes of the hybrid genetic association algorithm and the genetic algorithm from the aspects of the iteration times and the energy saving rate of the optimization result;
analyzing the energy-saving strategy result of the central air conditioner: and fitting the optimized data of the hybrid genetic association algorithm to obtain a trend graph of different control variables changing along with the load rate and the energy consumption ratio, analyzing the parameter trend of the optimized central air-conditioning control system, and performing optimization control.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107044710A (en) * | 2016-12-26 | 2017-08-15 | 深圳达实智能股份有限公司 | Energy-saving control method for central air conditioner and system based on joint intelligent algorithm |
| CN108489013A (en) * | 2018-01-30 | 2018-09-04 | 深圳市新环能科技有限公司 | Central air-conditioner control method based on genetic algorithm and load on-line amending and device |
| CN110805997A (en) * | 2019-11-14 | 2020-02-18 | 中金新源（天津）科技有限公司 | Energy-saving control method for central air-conditioning system |
| WO2021063033A1 (en) * | 2019-09-30 | 2021-04-08 | 北京国双科技有限公司 | Energy consumption model training method for air conditioner and air conditioning system control method |
| CN114383299A (en) * | 2021-12-22 | 2022-04-22 | 武汉理工大学 | Central air-conditioning system operation strategy optimization method based on big data and dynamic simulation |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107044710A (en) * | 2016-12-26 | 2017-08-15 | 深圳达实智能股份有限公司 | Energy-saving control method for central air conditioner and system based on joint intelligent algorithm |
| CN108489013A (en) * | 2018-01-30 | 2018-09-04 | 深圳市新环能科技有限公司 | Central air-conditioner control method based on genetic algorithm and load on-line amending and device |
| WO2021063033A1 (en) * | 2019-09-30 | 2021-04-08 | 北京国双科技有限公司 | Energy consumption model training method for air conditioner and air conditioning system control method |
| CN110805997A (en) * | 2019-11-14 | 2020-02-18 | 中金新源（天津）科技有限公司 | Energy-saving control method for central air-conditioning system |
| CN114383299A (en) * | 2021-12-22 | 2022-04-22 | 武汉理工大学 | Central air-conditioning system operation strategy optimization method based on big data and dynamic simulation |
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