CN111723456A - Central air-conditioning system energy efficiency optimization method based on NSGA-II algorithm - Google Patents
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Abstract
The invention discloses a central air-conditioning system energy efficiency optimization method based on NSGA-II algorithm, comprising the following steps: data preprocessing, namely filtering abnormal data by using a data preprocessing method aiming at the collected historical operating data of the central air-conditioning system to obtain available data; dividing operation conditions, namely dividing the operation conditions and determining experimental data of each condition on the basis of finishing data preprocessing; modeling an air conditioning system, namely establishing an air conditioning system model by taking refrigerating capacity and energy consumption of a central air conditioning system as optimization targets and taking operation parameters as decision variables; parameter fitting, namely fitting the experimental data into a mathematical model according to working conditions to obtain a specific target function; and solving the multi-objective optimization model by using an NSGA-II algorithm. Based on the method, the Pareto optimal solution set of each working condition can be obtained, the optimal operation parameters of the air conditioning system are obtained, and the system operation energy efficiency is further improved.
Description
Technical Field
The invention relates to the field of energy efficiency optimization of central air-conditioning systems, in particular to an energy efficiency optimization method of a central air-conditioning system based on an NSGA-II algorithm.
Background
With the global climate change problem being more and more emphasized by people, the problem of shortage of energy resources is more and more prominent, and the building energy saving is more and more important in the process. According to the latest Chinese building energy-saving annual development research report 2020, the total building energy consumption and the power consumption in the building are greatly increased from 2001 to 2018; in 2018, the energy consumption of public buildings accounts for 44% of the energy consumption of civil buildings in China. The energy consumption of the central air-conditioning system accounts for half of the energy consumption of large-scale public buildings, and the energy-saving optimization of the central air-conditioning system is the key point of building energy conservation. Therefore, the improvement of the operation energy efficiency of the central air conditioner has important significance for building energy conservation.
The high-efficiency operation is always the target of the energy-saving research field of the central air conditioner, and the energy efficiency ratio of the system is improved by mainly optimizing equipment configuration and operation parameters, so that the comprehensive energy consumption of the system is reduced, and the aim of energy-saving research is fulfilled. However, at present, there are few multi-objective optimization researches on optimization of the central air-conditioning system, and especially, there is little concern in the industry to establish a proper modeling method to be incorporated into a proper multi-objective optimization algorithm.
Disclosure of Invention
The invention mainly aims to provide a central air-conditioning system energy efficiency optimization method based on NSGA-II algorithm, which can integrate the optimization problem of the central air-conditioning into multi-objective optimization, and analyze Pareto optimal solution set results from an air-conditioning system mechanism model to obtain optimal operation parameters so as to improve the system operation energy efficiency. In order to achieve the purpose, the invention adopts the following technical scheme:
the method is mainly realized from two aspects of model establishment and optimization operation, and the established model needs to be input into the optimization operation to complete optimization. The method specifically comprises the following steps:
data preprocessing, namely filtering abnormal data of the collected historical operation data of the central air-conditioning system by using a data preprocessing method to obtain available data;
dividing operation conditions, dividing the available data obtained after data preprocessing into operation conditions and determining experimental data of each condition by taking the load rate as an index;
modeling the air conditioning system, namely establishing a multi-objective optimization model of the central air conditioning system by taking refrigerating capacity and energy consumption of the central air conditioning system as optimization targets, taking operating parameters of the central air conditioning system as decision variables and taking numerical relationships among threshold values of the decision variables and the operating parameters as constraint conditions;
parameter fitting, namely fitting the experimental data into a mathematical model according to working conditions to obtain a specific target function under each working condition;
and solving the multi-objective optimization model by using an NSGA-II algorithm, and integrating the mathematical model for parameter fitting with the maximum refrigerating capacity and the minimum total system energy consumption as objective functions into the NSGA-II algorithm for optimization to obtain a Pareto optimal solution set.
Further, the data preprocessing method specifically comprises:
data elimination, namely eliminating data of a missing part of data loss caused by transmission errors in the data transmission process;
attribute reduction, wherein a plurality of air conditioner operating parameters are adopted, and the attribute reduction is used for removing the mutual influence among the operating parameters;
and eliminating abnormal data with large errors by using a Laplace criterion.
Further, the operation condition division specifically includes:
dividing different operation conditions according to the operation state of the air conditioner, and analyzing the load rate parameters by adopting an equal-width discretization method;
and taking different clustering results as an operation working condition, and dividing historical data of different working conditions into data groups to form experimental data of each working condition.
Further, the modeling of the air conditioning system specifically comprises:
respectively establishing a multi-element nonlinear energy consumption model of each module of a water chilling unit, a chilled water pump, a cooling water pump and a cooling tower of the central air conditioner according to an air conditioner operation mechanism;
establishing a function expression according to the relation between the refrigerating capacity and the operation parameters of the air conditioning system;
and establishing a multi-objective optimization model for optimizing the energy efficiency of the central air conditioner according to the obtained multi-element nonlinear energy consumption model and the function expression.
Further, the establishment basis and the specific expression of the multivariate nonlinear energy consumption model of each module are as follows:
the water chilling unit model fits the energy consumption of the water chilling unit to a function of the freezing backwater temperature and the cooling backwater temperature, and adopts a form of product of two quadratic polynomials:
wherein f is1The power consumption of the water chilling unit is reduced; t isclThe cooling return water temperature;
the average value of the regression cooling water inlet temperature parameters is obtained; t iselThe temperature of the freezing backwater is adopted;
the average value of the regression chilled water inlet temperature parameters is obtained; dijIs a regression coefficient;
the chilled water pump model obtains the relation between water energy consumption and flow according to the variable frequency operation data of the water pump:
wherein f is2Power consumption of the chilled water pump; v. ofcwhIs the flow rate of the chilled water; a is0、a1、a2、a3Is a regression coefficient;
cooling water pump model:
wherein f is3Power consumption for cooling the water pump; v. ofcwIs the cooling water flow rate; b0、b1、b2、b3Is a regression coefficient;
the cooling tower model can control the rotating speed of a fan of the cooling tower by controlling the rotating speed of a motor in a central air-conditioning operation control system, and the energy consumption model of the fan of the cooling tower is expressed as follows:
f4＝Pfan，nom(c0+c1PLR+c2PLR2+c3PLR3)
wherein f is4Power consumption for cooling towers; f. ofaThe actual frequency of the cooling tower fan; f. ofa，nomRated frequency for the cooling tower fan; pfan，nomRated power for the cooling tower fan; PLR is the partial load rate of the cooling tower fan; c. C0、c1、c2、c3Are regression coefficients.
Further, the central air-conditioning system energy efficiency multi-objective optimization model comprises a minimum system total energy consumption model and a maximum refrigerating capacity model; the refrigerating capacity is a definite expression about the operation parameter, the total energy consumption of the system is equal to the sum of the energy consumption of each module, and the expressions are respectively as follows:
Q＝c·vcwh·(Tel－Tchws)
fmin(Tcl，Tel，vcwh，vcw)＝f1+f2+f3+f4
wherein Q is the refrigeration capacity; c is the specific heat capacity of water; v. ofcwhIs the flow rate of the chilled water; t ischwsIs the freezing water outlet temperature; f. ofminTo minimize the total system energy consumption.
Further, the parameter fitting specifically includes:
and the experimental data grouped under the working conditions are used for regressing the energy consumption model and the refrigerating capacity model, the least square method is used for regression on a Matlab platform or python to obtain each coefficient of the target function, the corresponding experimental data groups are adopted for model regression under different working conditions, and each obtained specific model represents the running state of the same central air-conditioning system under different external environments and running modes.
Further, the specific process of integrating the NSGA-II algorithm for optimization is as follows:
reading in system operation parameters and target variable values, and inputting a multi-target function after parameter fitting;
randomly generating an initial population and setting a maximum evolution algebra; inputting a constraint condition expression of a decision variable, and correcting an infeasible solution;
sorting the initial population by non-dominated solutions, correcting the non-feasible solutions, and then obtaining a first generation offspring population by three basic operations of selection, crossover and variation of a genetic algorithm;
from the second generation, merging the parent population and the child population, and performing rapid non-dominated sorting to obtain a merged population;
carrying out crowding degree calculation on the individuals in each non-dominant layer, and selecting proper individuals to form a new parent population according to the non-dominant relationship and the crowding degree of the individuals;
and (4) after the condition of meeting the end of the program is met, stopping the algorithm, otherwise, randomly generating the initial population again and repeating the subsequent process.
Further, the constraint condition is based on the regulations of the actual central air-conditioning manufacturer and the actual operating parameters, and the parameter values are continuity values and are expressed by upper and lower limit values of the variables, which can be specifically expressed as follows:
Tcl，min≤Tcl≤Tcl，max
Tel，min≤Tel≤Tel，max
vcwh，min≤vcwh≤vcwh，max
vcw，min≤vcw≤vcw，max
Tchws，min≤Tchws≤Tchws，max
fa,min≤fa≤fa,max。
furthermore, each solution of the Pareto optimal solution set can be used as an optimized value of an air conditioning system operation parameter under the current working condition.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the invention divides the operation working conditions according to the external environment condition of the air conditioner operation and the real-time operation parameters of the air conditioner, can practically express the actual operation condition of the air conditioner, improves the reduction capability of the model, and can greatly improve the accuracy of optimization.
(2) The invention optimizes the refrigerating capacity and the total system energy consumption as a multi-objective function aiming at the system energy efficiency, realizes more comprehensive expression of the operation principle of the central air-conditioning system, and better excavates the hidden operation parameters in the operation of the air-conditioning system.
(3) The invention establishes the multi-element nonlinear function of the target function according to the mechanism relation among the operating parameters of the central air conditioner, and realizes a better parameter optimization result by applying the NSGA-II algorithm with stronger global optimization capability.
Drawings
FIG. 1 is a flow chart illustration of the present invention;
FIG. 2 is a detailed flow chart of the data preprocessing steps of the present invention;
FIG. 3 is a flow chart of the multi-objective optimization algorithm of the present invention incorporated into NSGA-II;
FIG. 4 is a flow chart of the present invention for establishing a multi-objective optimization model and for performing optimization solution by blending into NSGA-II algorithm;
FIG. 5 is a graph of Pareto front at 80% -90% duty load rate.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
The NSGA-II algorithm, namely the non-dominated sorting genetic algorithm II, is a classical algorithm which is commonly used for solving the multi-objective optimization problem, has the characteristics of high running speed and avoidance of falling into local optimization, and is often used as a reference for the performance of other multi-objective optimization algorithms. The NSGA-II algorithm is superior to the NSGA algorithm: the method adopts a rapid non-dominated sorting algorithm, and the calculation complexity is greatly reduced compared with that of NSGA; the crowdedness and crowdedness comparison operator is adopted to replace the shared radius shareQ required to be specified, and the shared radius shareQ is used as a winning standard in the peer comparison after the rapid sequencing, so that the individuals in the quasi-Pareto domain can be expanded to the whole Pareto domain and are uniformly distributed, and the diversity of the population is kept; an elite strategy is introduced, the sampling space is enlarged, the loss of the optimal individual is prevented, and the operation speed and the robustness of the algorithm are improved.
This embodiment will take a central air conditioning system of a large mall as an example to describe the implementation process of the present invention in detail. In the scheme, the historical operating data of the air conditioning system in 2018 is selected. As shown in fig. 1, the energy efficiency optimization method for a central air conditioning system based on the NSGA-ii algorithm in this embodiment mainly includes the following steps:
s1, preprocessing data, and filtering abnormal data by using a data preprocessing method aiming at a large amount of historical operating data collected by the energy-saving supervision platform of the central air-conditioning system to obtain available data, wherein the specific method comprises the steps of attribute reduction, abnormal data elimination and abnormal data elimination by using Lauda criteria, and is shown in figure 2. The central air-conditioning system records 18 rows of parameters including a freezing water outlet temperature, a freezing water inlet temperature, a freezing water flow rate, a freezing water supply and return water pressure difference, a condensing pressure, an evaporating pressure, a cooling water outlet temperature, a cooling water inlet temperature, a cooling water flow rate and a cooling tower fan power, the data amount accounts for 52125 pieces, and finally, the effective data accounts for 17380 pieces after pretreatment.
S2, dividing the operation conditions, wherein on the basis of finishing data preprocessing, the specific contents comprise:
and dividing various operation conditions by considering the operation state of the air conditioner, and analyzing the load rate parameters by adopting an equal-width discretization method.
The working condition division result is four working conditions of which the load rates are 60% -70%, 70% -80%, 80% -90% and 90% -100% in sequence, and the accuracy of model establishment cannot be guaranteed due to the fact that the data quantity of the rest working conditions is too small, so that the data are removed.
S3, modeling the air conditioning system, as shown in figure 4, taking the refrigerating capacity and the energy consumption of the central air conditioning system as optimization targets, taking 6 running parameters of the freezing water outlet temperature, the freezing water return temperature, the cooling water return temperature, the freezing water flow, the cooling water flow and the fan frequency of the cooling tower of the central air conditioning system as decision variables, and simultaneously taking the threshold value of each decision variable and the numerical relationship among the running parameters as constraint conditions, and establishing the multi-objective optimization model of the air conditioning system. The concrete model is established as follows:
(1) the water chilling unit model fits the energy consumption of the water chilling unit to a function of the freezing backwater temperature and the cooling backwater temperature, and adopts a form of product of two quadratic polynomials:
wherein f is1The power consumption of the water chilling unit is reduced; t isclThe cooling return water temperature;
the average value of the regression cooling water inlet temperature parameters is obtained; t iselThe temperature of the freezing backwater is adopted;
the average value of the regression chilled water inlet temperature parameters is obtained; dijAre regression coefficients.
(2) In the energy consumption model of the refrigeration water pump, theoretically, the performance parameters of the water pump under the variable frequency condition follow a similar law, but in actual operation, the operating condition point of the refrigeration water pump does not completely follow the similar law. According to the variable frequency operation data of the water pump, the relation between the power consumption and the flow of the water pump can be obtained:
wherein f is2Power consumption of the chilled water pump; v. ofcwhIs the flow rate of the chilled water; a is0、a1、a2、a3Are regression coefficients.
(3) The energy consumption model of the cooling water pump is similar to the energy consumption model of the freezing water pump and is fit into a cubic polynomial of flow:
wherein f is3Power consumption for cooling the water pump; v. ofcwFor cooling the water flowAn amount; b0、b1、b2、b3Are regression coefficients.
(4) The cooling tower energy consumption model can control the rotating speed of a fan of the cooling tower by controlling the rotating speed of a motor in a central air-conditioning operation control system, and is expressed as follows;
f4＝Pfan,nom(c0+c1PLR+c2PLR2+c3PLR3)
wherein f is4Power consumption for cooling towers; f. ofaThe actual frequency of the cooling tower fan; f. ofa,nomRated frequency for the cooling tower fan; pfan，nomRated power for the cooling tower fan; PLR is the partial load rate of the cooling tower fan; c. C0、c1、c2、c3Are regression coefficients.
(5) The multi-objective optimization model is a multi-objective optimization model, the multi-objective optimization energy consumption model is the sum of energy consumption of the water chilling unit module, the cooling pump module, the freezing pump module and the cooling tower module of the air conditioning system, the refrigerating capacity model is a determined expression of relevant operating parameters, and the expressions are respectively:
fmin(Tcl，Tel，vcwh，vcw)＝f1+f2+f3+f4
Q＝c·vcwh·(Tel－Tchws)
wherein f isminMinimum total system energy consumption; q is refrigerating capacity; c is the specific heat capacity of water; v. ofcwhIs the flow rate of the chilled water; t ischwsIs the freezing water outlet temperature;
(6) according to the operation parameter value under the standard condition specified by the manufacturer of the central air-conditioning unit and the setting range of the actual operation parameter in the central air-conditioning control system of the market, the constraint condition of the decision variable is expressed as follows:
24≤Tcl≤35
10≤Tel≤14.5
150≤vcwh≤300
200≤vcw≤500
7≤Tchws≤10
30≤fa≤50
and S4, performing parameter fitting on the model, and fitting the experimental data to the mathematical model according to the working conditions by using a least square method to obtain a specific objective function under each working condition. Because the refrigerating capacity model is a definite equation about the operation parameters of the air conditioning system, the energy consumption model under each working condition is only fitted when the parameters are fitted. The coefficients of each module function were regressed on the Matlab platform using the least squares method, and the detailed parameter fitting results are shown in the following table:
and S5, solving the model by using an NSGA-II algorithm, and integrating the model obtained by parameter fitting into the NSGA-II algorithm for optimization by taking the maximum refrigerating capacity and the minimum total system energy consumption as objective functions to obtain a Pareto optimal solution set. As shown in fig. 3, a specific implementation of the solving process is described, and the multi-objective optimization process integrated into the NSGA-ii algorithm specifically includes the following steps:
s51, reading system operation parameters and target variable values, and inputting a multi-target function after parameter fitting;
s52, randomly generating an initial population P with the initial population size of 5000The maximum evolution algebra is set to 300 generations; inputting a constraint condition expression of a decision variable, and correcting an infeasible solution;
s53, sorting the initial population by non-dominated solutions, correcting the non-feasible solutions, and then obtaining a first generation offspring population Q through three basic operations of selection, crossover and variation of a genetic algorithm1；
S54, from the secondStarting generation, merging the parent population and the offspring population, and performing rapid non-dominant sequencing to obtain a merged population Rt；
S55, carrying out crowding degree calculation on the individuals in each non-dominant layer, and selecting proper individuals to form a new parent population according to the non-dominant relationship and the crowding degree of the individuals;
and S56, terminating the algorithm when the condition of program end is met, otherwise, returning to the step S52.
The model under each working condition can present a Pareto frontier map after being optimized by an NSGA-II algorithm, and the result after one working condition is optimized is taken as an example for explanation. As shown in fig. 5, the Pareto frontier diagram is obtained after multi-objective optimization when the load factor operating condition is 80% -90%, which shows the conditions of two objective function optimization values, each point on the curve shown in fig. 5 is an optimal solution after multi-objective algorithm optimization, and the corresponding decision variables can be used as the optimization values of the operating parameters of the air conditioning system under the current operating condition. In the figure, the horizontal axis represents the total power of the system, the vertical axis represents the refrigerating capacity, the slope of the curve can represent the COP of the state point, and the state point with the maximum slope is selected as an optimization parameter for improving the operation energy efficiency of the central air-conditioning system. The multi-objective optimized operation parameters are beneficial to energy conservation of the air conditioning system, the operation energy efficiency of the air conditioner is obviously improved, and meanwhile, the requirements of different users can be better met.
It should also be noted that in this specification, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A central air-conditioning system energy efficiency optimization method based on an NSGA-II algorithm is characterized by comprising the following steps:
data preprocessing, namely filtering abnormal data of the collected historical operating data of the central air-conditioning system by using a data preprocessing method to obtain available data;
dividing operation conditions, dividing the available data obtained after data preprocessing into operation conditions and determining experimental data of each condition by taking the load rate as an index;
modeling the air conditioning system, namely establishing a multi-objective optimization model of the central air conditioning system by taking refrigerating capacity and energy consumption of the central air conditioning system as optimization targets, taking operating parameters of the central air conditioning system as decision variables and taking numerical relationships among threshold values of the decision variables and the operating parameters as constraint conditions;
parameter fitting, namely fitting the experimental data into a mathematical model according to working conditions to obtain a specific target function under each working condition;
and solving the multi-objective optimization model by using an NSGA-II algorithm, and integrating the mathematical model for parameter fitting with the maximum refrigerating capacity and the minimum total system energy consumption as objective functions into the NSGA-II algorithm for optimization to obtain a Pareto optimal solution set.
2. The NSGA-II algorithm-based energy efficiency optimization method for the central air-conditioning system according to claim 1, wherein the data preprocessing method specifically comprises the following steps:
data elimination, namely eliminating data of a missing part of data loss caused by transmission errors in the data transmission process;
attribute reduction, wherein a plurality of air conditioner operating parameters are adopted, and the attribute reduction is used for removing the mutual influence among the operating parameters;
and eliminating abnormal data with large errors by using a Laplace criterion.
3. The NSGA-II algorithm-based energy efficiency optimization method for the central air-conditioning system according to claim 1, wherein the operation condition division is specifically as follows:
dividing different operation conditions according to the operation state of the air conditioner, and analyzing the load rate parameters by adopting an equal-width discretization method;
and taking different clustering results as an operation working condition, and dividing historical data of different working conditions into data groups to form experimental data of each working condition.
4. The NSGA-II algorithm-based energy efficiency optimization method for the central air-conditioning system according to claim 1, wherein the air-conditioning system modeling specifically comprises the following steps:
respectively establishing a multi-element nonlinear energy consumption model of each module of a water chilling unit, a chilled water pump, a cooling water pump and a cooling tower of the central air conditioner according to an air conditioner operation mechanism;
establishing a function expression according to the relation between the refrigerating capacity and the operation parameters of the air conditioning system;
and establishing a multi-objective optimization model for optimizing the energy efficiency of the central air conditioner according to the obtained multi-element nonlinear energy consumption model and the function expression.
5. The NSGA-II algorithm-based energy efficiency optimization method for the central air-conditioning system according to claim 4, wherein the establishment basis and the specific expression of the multivariate nonlinear energy consumption model of each module are as follows:
the water chilling unit model fits the energy consumption of the water chilling unit as a function of the freezing return water temperature and the cooling return water temperature:
wherein f is1The power consumption of the water chilling unit is reduced; t isclThe cooling return water temperature;
the average value of the regression cooling water inlet temperature parameters is obtained; t iselThe temperature of the freezing backwater is adopted;
the average value of the regression chilled water inlet temperature parameters is obtained; dijIs a regression coefficient;
the chilled water pump model obtains the relation between water energy consumption and flow according to the variable frequency operation data of the water pump:
wherein f is2Power consumption of the chilled water pump; v. ofcwhIs the flow rate of the chilled water; a is0、a1、a2、a3Is a regression coefficient;
cooling water pump model:
wherein f is3Power consumption for cooling the water pump; v. ofcwIs the cooling water flow rate; b0、b1、b2、b3Is a regression coefficient;
the model of the cooling tower and the model of the energy consumption of the fan of the cooling tower are expressed as follows:
f4＝Pfan，nom(c0+c1PLR+c2PLR2+c3PLR3)
wherein f is4Power consumption for cooling towers; f. ofaThe actual frequency of the cooling tower fan; f. ofa，nomRated frequency for the cooling tower fan; pfan，nomRated power for the cooling tower fan; PLR is the partial load rate of the cooling tower fan; c. C0、c1、c2、c3Are regression coefficients.
6. The NSGA-II algorithm-based central air-conditioning system energy efficiency optimization method according to claim 5, wherein the central air-conditioning system energy efficiency multi-objective optimization model comprises a minimum system total energy consumption model and a maximum cooling capacity model; the refrigerating capacity is a definite expression about the operation parameter, the total energy consumption of the system is equal to the sum of the energy consumptions of all the modules, and the expressions are respectively as follows:
Q＝c·vcwh·(Tel-Tchws)
fmin(Tcl，Tel，vcwh，vcw)＝f1+f2+f3+f4
wherein Q is the refrigeration capacity; c is the specific heat capacity of water; v. ofcwhIs the flow rate of the chilled water; t ischwsIs the freezing water outlet temperature; f. ofminTo minimize the total system energy consumption.
7. The NSGA-II algorithm-based energy efficiency optimization method for the central air-conditioning system according to claim 1, wherein the parameter fitting specifically comprises the following steps:
regression is carried out on a Matlab platform or python by using experimental data regression energy consumption model and refrigerating capacity model which finish working condition grouping to obtain each coefficient of the target function; model regression under different working conditions adopts corresponding experimental data sets, and each obtained specific model represents the running state of the same central air-conditioning system under different external environments and running modes.
8. The central air-conditioning system energy efficiency optimization method based on the NSGA-II algorithm according to claim 1, wherein the specific process of integrating the NSGA-II algorithm for optimization is as follows:
reading in system operation parameters and target variable values, and inputting a multi-target function after parameter fitting;
randomly generating an initial population and setting a maximum evolution algebra; inputting a constraint condition expression of a decision variable, and correcting an infeasible solution;
sorting the initial population by non-dominated solutions, correcting the non-feasible solutions, and then obtaining a first generation offspring population by three basic operations of selection, crossover and variation of a genetic algorithm;
from the second generation, merging the parent population and the child population, and performing rapid non-dominated sorting to obtain a merged population;
carrying out crowding degree calculation on the individuals in each non-dominant layer, and selecting proper individuals to form a new parent population according to the non-dominant relationship and the crowding degree of the individuals;
and (4) after the condition of meeting the end of the program is met, stopping the algorithm, otherwise, randomly generating the initial population again and repeating the subsequent process.
9. The NSGA-II algorithm-based energy efficiency optimization method for the central air-conditioning system according to claim 1, wherein the constraint condition is expressed by upper and lower limit values of a variable according to the regulation of an actual central air-conditioning manufacturer and actual operation parameters, because parameter values are continuity values, which can be specifically expressed as follows:
Tcl，min≤Tcl≤Tcl，max
Tel，min≤Tel≤Tel，max
vcwh，min≤vcwh≤vcwh，max
vcw，min≤vcw≤vcw，max
Tchws，min≤Tchws≤Tchws，max
fa，min≤fa≤fa，max。
10. the NSGA-II algorithm-based energy efficiency optimization method for the central air-conditioning system according to claim 1, wherein a corresponding decision variable of each solution of the Pareto optimal solution set can be used as an optimized value of an operation parameter of the air-conditioning system under the current working condition.
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