CN111811111A - Central air conditioner energy consumption control method based on improved particle swarm algorithm - Google Patents

Central air conditioner energy consumption control method based on improved particle swarm algorithm Download PDF

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CN111811111A
CN111811111A CN202010554051.4A CN202010554051A CN111811111A CN 111811111 A CN111811111 A CN 111811111A CN 202010554051 A CN202010554051 A CN 202010554051A CN 111811111 A CN111811111 A CN 111811111A
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钱虹
潘跃凯
张栋良
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Shanghai University of Electric Power
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract

The invention relates to a central air-conditioning energy consumption control method based on an improved particle swarm algorithm, which comprises the following steps: obtaining COP curves of different units of the central air conditioner, and fitting to obtain an energy consumption function of each unit; constructing a central air conditioner energy consumption optimization model based on the energy consumption function of each unit and combining an external point punishment mode; solving an energy consumption optimization model of the central air conditioner by adopting an improved particle swarm algorithm to obtain the optimal load distribution rate of each unit; and controlling load switching of each unit according to the optimal load distribution rate of each unit to finish energy consumption optimization control of the central air conditioner. Compared with the prior art, the method has the advantages that the particle swarm optimization is improved, the previous ergodicity of the particle swarm optimization is increased by utilizing the sine chaotic sequence, the sine chaotic disturbance is added, the particle swarm has a mechanism of escaping from a local optimal point, the particle inertia weight is adjusted adaptively in the optimization process, the optimization speed and the optimization precision can be effectively improved, the complex operation condition of the central air conditioner is adapted, and the minimum energy consumption of the air conditioner in operation is ensured.

Description

Central air conditioner energy consumption control method based on improved particle swarm algorithm
Technical Field
The invention relates to the technical field of energy conservation of central air conditioners, in particular to a central air conditioner energy consumption control method based on an improved particle swarm algorithm.
Background
The central air conditioner can meet the requirements of people on the indoor air quality in the new period, but when the central air conditioner is used, a large amount of energy consumption can be generated, and the requirements of energy conservation and environmental protection are not met. Air conditioning units are manufactured and designed to handle extreme conditions and are designed to operate at full load, whereas in normal circumstances the air conditioner is operating at partial load for 90% of the time, with the 50% of the time being at half the design load. Therefore, adjusting the settings of the air conditioner as needed can greatly reduce energy consumption.
With the development of computer technology, computer simulation is often used to model and identify parameters of a central air-conditioning system, global search is performed through an optimization algorithm, and finally a parameter collocation with the lowest total energy consumption is found out, so that an optimal control scheme of the central air-conditioning system is determined. The existing algorithm for energy consumption control of a central air conditioner mainly comprises a genetic algorithm, a simulated annealing algorithm and a Particle Swarm Optimization (PSO), wherein the PSO is an Optimization algorithm based on population intelligence, the core idea of the PSO is derived from research of simulating foraging behavior of a biological population, if a group of birds arrives at an area with only one piece of food, the optimal strategy for quickly finding the food is to search the surrounding area of the bird closest to the food, in the algorithm, the position of each bird is regarded as a possible solution (namely one Particle), the position of the food is a global optimal solution, an adaptive value of the current position is calculated according to an objective function, each Particle is close to the optimal solution according to a certain flight speed, and finally the global optimal solution is searched.
The PSO algorithm has the advantages of simple algorithm structure, less dependence on key parameters, strong robustness and easy engineering realization, is widely applied to the field of industrial optimization production and is also applied to the optimization control of the air conditioning system, but the PSO algorithm easily has the problem that the algorithm falls into the local optimal solution due to low population diversity, the optimization speed and precision of the PSO algorithm cannot adapt to complex working conditions, and as most central air conditioning systems comprise a plurality of units, the load distribution of the units is not uniform, the operating working conditions are complex, and the PSO algorithm is adopted, the load distribution of the units cannot be rapidly and accurately optimized, so that the effective energy consumption saving and energy consumption saving cannot be guaranteed
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a central air conditioner energy consumption control method based on an improved particle swarm algorithm, which is based on a sinusoidal chaotic sequence and self-adaptive inertia weight to improve the optimization speed and precision of the particle swarm algorithm and can adapt to the complex operation condition of the central air conditioner, thereby realizing the purposes of optimizing the load distribution of a unit and effectively saving energy consumption.
The purpose of the invention can be realized by the following technical scheme: a central air conditioner energy consumption control method based on an improved particle swarm algorithm comprises the following steps:
s1, obtaining COP (Coefficient Of Performance) curves Of different units Of the central air conditioner, and fitting to obtain an energy consumption function Of each unit;
s2, constructing a central air-conditioning energy consumption optimization model based on energy consumption functions of all units and in combination with an external point punishment mode, wherein the constraint conditions of the central air-conditioning energy consumption optimization model comprise a system load balance constraint condition and a unit output constraint condition, and the central air-conditioning energy consumption optimization model takes the minimum total unit energy consumption as a target function;
s3, solving an energy consumption optimization model of the central air conditioner by adopting an improved particle swarm algorithm to obtain the optimal load distribution rate of each unit;
and S4, controlling the load switching of each unit according to the optimal load distribution rate of each unit, namely finishing the energy consumption optimization control of the central air conditioner.
Further, the energy consumption function of the unit is specifically as follows:
Figure BDA0002543638470000021
0≤Pα≤Pαmax
wherein, F (P)α) As a function of the energy consumption of the alpha-th unit, PαIs the current load of the alpha unit, a0, a1, a2 and a3 are the energy consumption coefficient of the unit, PαmaxThe maximum load capacity of the alpha machine set.
Further, the system load balancing constraint conditions in step S2 are specifically:
Figure BDA0002543638470000022
wherein, PLThe total load of the central air-conditioning system, and D is the total number of units;
the unit output constraint conditions are specifically as follows:
Pαmin≤Pα≤Pαmax
wherein, PαminThe minimum load of the alpha machine set.
Further, the objective function of the central air conditioner energy consumption optimization model in step S2 is specifically:
Figure BDA0002543638470000031
wherein, P is the total energy consumption of the unit under the determined working condition, and lambda is the penalty coefficient of the exterior point.
Further, the specific process of solving the central air-conditioning energy consumption optimization model by using the improved particle swarm optimization in the step S3 is as follows:
s31, initializing the positions and the speeds of the particles by adopting a sine chaotic sequence, wherein the positions of the particles are used for representing a solution containing the load distribution rate of each unit;
s32, calculating the initialized individual fitness values of the particles, and selecting the first N particles with the optimal fitness values from the M populations as initial populations, wherein the fitness values are used for representing the total energy consumption of the unit under the condition corresponding to the position of one particle;
s33, updating an individual extreme value and a global extreme value based on the individual fitness value of the particle, wherein the individual extreme value is the minimum fitness value calculated from a single particle to the current generation, and the global extreme value is the minimum fitness value calculated from all particles of the population to the current generation;
s34, adaptively adjusting the inertia weight of particles according to the fitness values of all the particles of the current generation, and updating the speed and the position of the particles of the current generation by combining the individual extreme value and the global extreme value of the previous generation;
s35, calculating the average distance between particles, if the average distance is smaller than a preset threshold value, executing a step S36, otherwise executing a step S39;
s36, judging whether the current global optimal fitness value is smaller than a preset global optimal fitness value, if so, executing a step S39, otherwise, executing a step S37;
s37, judging whether the current iteration number is larger than or equal to the preset dangerous iteration number, if so, indicating that the particle population is locally optimal, executing a step S38, otherwise, executing a step S39;
s38, randomly selecting N/2 particles from the contemporary population, adding the sinusoidal chaotic disturbance to the positions of the N/2 particles, returning the particles added with the sinusoidal chaotic disturbance to the population again, and then returning to the step S34;
s39, judging whether the current iteration number is larger than or equal to the preset maximum iteration number, if so, ending the solution, and outputting the position and the fitness value of the current optimal particle, namely the optimal load distribution rate of each unit and the corresponding total energy consumption of the unit; otherwise, returning to step S33, and continuing the solving.
Further, the sinusoidal chaotic sequence in step S31 specifically includes:
zi+1＝sin(5.65/zi)-1≤zi≤1,z1≠0
wherein z isi+1And ziRespectively (i +1) th and ith sinusoidal chaotic individuals.
Further, the specific process of initializing the position and the velocity of the particle in step S31 is as follows: generating N chaotic individuals in [0,1] intervals based on a sinusoidal chaotic sequence, and then transforming the chaotic individuals to the whole search space through carrier transformation:
xi＝xmin+(xmax-xmin)·zi,0＜zi＜1,i＝1,2,......,N
vi＝vmin+(vmax-vmin)·zi,0＜zi＜1,i＝1,2,......,N
in the formula, xiIs the position of the ith particle, xminAnd xmaxRespectively a set minimum and maximum particle position, viIs the velocity of the ith particle, vminAnd vmaxRespectively, the set minimum value and the maximum value of the particle speed, and N is the number of particles.
Further, the specific process of adaptively adjusting the inertial weight of the particle in step S34 is as follows:
s341, calculating the average value of the fitness values of all particles in the current-generation population:
Figure BDA0002543638470000041
wherein f isiFitness value of the ith particle of the contemporary population, favgThe average value of all the particle fitness degrees of the contemporary population is the average particle fitness degree;
s342, selecting the population of the current generation with the fitness value larger than or equal to favgThe average value of the particle fitness degrees is calculated again and is recorded as the preferred particle fitness degree favg ·；
S343, based on the fitness of the particles in the contemporary population, matching the fitness f with the preferred particleavg ·Average particle fitness favgA comparison is made to adjust the inertial weight of the updated particle.
Further, the specific process in step S343 is:
if the particle fitness is less than the preferred particle fitness favg ·Then the particle is a good particle in the population, and the inertia weight of the particle is adjusted as follows:
Figure BDA0002543638470000042
wherein f isgFor the current optimum fitness value, w, of the populationmaxFor a set maximum value of the inertial weight, wminIs the set inertia weight minimum;
if the particle fitness is greater than or equal to the preferred particle fitness favg ·And less than the average particle fitness favgThen the particle is a general particle in the population, and the inertia weight of the particle is adjusted to be:
Figure BDA0002543638470000043
wherein T is the current iteration frequency, and T is the maximum iteration frequency;
if the particle fitness is greater than or equal to the average particle fitness favgIf the particle is a poor particle in the population, the inertia weight of the particle is adjusted to be:
Figure BDA0002543638470000051
wherein, | fg-favg ·And | is used for representing the dispersion degree of the current particle swarm, the smaller the value of the | is, the more concentrated the particle swarm is, the larger the inertia weight of the particle is at the moment, and k is an inertia weight control parameter and is used for controlling the speed of the inertia weight changing along with the dispersion degree of the particle swarm.
Further, the calculation formula of the average distance between the particles in the step S35 is as follows:
Figure BDA0002543638470000052
Figure BDA0002543638470000053
wherein,
Figure BDA0002543638470000054
is the j-th dimension coordinate value of the ith particle,
Figure BDA0002543638470000055
for all particlesThe average value of the j-th dimension coordinate value, div (t), is the average distance between the particles of the t-th generation population, and n is the dimension of the individual particles and corresponds to the total unit number in the air-conditioning system.
Further, the specific process of step S38 is as follows:
s381, randomly selecting N/2 particles from the contemporary population, mapping the N/2 particle individuals to a [0,1] interval to obtain mapped particle individuals:
Figure BDA0002543638470000056
wherein psii *Mapping to [0,1] for the ith particle](ii) post-compartmentalized individual particles;
s382, generating N/2 sine chaotic individual z according to the sine chaotic seriesi *Adding chaotic disturbance to the current particle position:
xi *＝(1-β)·ψi *+βzi *
wherein beta is a chaotic control parameter for controlling the degree of chaotic disturbance, xi *The position of the ith particle added with the chaotic disturbance is obtained;
and then, the particles added with the chaotic disturbance are classified into the population again, namely:
xi＝xmin+(xmax-xmin)·xi *
wherein x isiIs to mix xi *Mapping back to the particle position of the original optimization space;
finally, the process returns to step S34.
Compared with the prior art, the invention has the following advantages:
the particle swarm optimization is applied to the solution of the central air conditioner energy consumption optimization model, can well adapt to the complex operation working condition of multiple units of the central air conditioner, thereby quickly and automatically obtaining the optimal load distribution rate of each unit and ensuring the minimum operation energy consumption of the central air conditioner.
In addition, when the example population falls into the local optimum, the population has the capability of escaping from the local optimum point by adding sinusoidal chaotic disturbance into the population, the accuracy of an optimization result is further ensured, and the optimal load distribution rate of each unit of the central air conditioner can really realize the aim of lowest energy consumption.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of a solving process of an improved particle swarm optimization algorithm;
FIG. 3 is a COP graph of each unit of the central air conditioner in the embodiment;
FIG. 4 is a graph comparing the performance test results of the present invention and the existing particle swarm optimization in the embodiment;
FIG. 5 is a comparison graph of the optimizing accuracy of the PSO algorithm of the present invention in the embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
As shown in fig. 1, a method for controlling energy consumption of a central air conditioner based on an improved particle swarm algorithm includes the following steps:
s1, obtaining COP (Coefficient Of Performance) curves Of different units Of the central air conditioner, and fitting to obtain an energy consumption function Of each unit;
s2, constructing a central air-conditioning energy consumption optimization model based on energy consumption functions of all units and in combination with an external point punishment mode, wherein the constraint conditions of the central air-conditioning energy consumption optimization model comprise a system load balance constraint condition and a unit output constraint condition, and the central air-conditioning energy consumption optimization model takes the minimum total unit energy consumption as a target function;
s3, solving the energy consumption optimization model of the central air conditioner by adopting an improved particle swarm algorithm to obtain the optimal load distribution rate of each unit, wherein the specific process of improving the particle swarm algorithm is shown in figure 2;
and S4, controlling the load switching of each unit according to the optimal load distribution rate of each unit, namely finishing the energy consumption optimization control of the central air conditioner.
The central air conditioner of this embodiment includes five different units, and a COP curve of each unit is shown in fig. 3, where a COP value means a ratio between a cooling capacity (heating capacity) and an input power that can be realized by an air conditioning system, and a larger value indicates that the higher efficiency of the air conditioning system is, the more energy is saved, and according to a principle of a least square method, the COP curves of each unit are fitted based on an MATLAB platform to obtain an energy consumption function of each unit, as shown in table 1:
TABLE 1
Figure BDA0002543638470000071
The method of the present invention is applied to this embodiment, and the specific process includes:
step one, constructing an air conditioner energy consumption optimization model:
the method is characterized in that the minimum total energy consumption value of a unit is taken as a target, and according to the two aspects of unit start-stop and load distribution of the unit combination problem, a target function is defined as follows:
Figure BDA0002543638470000072
wherein P is the total energy consumption of the unit under the determined working condition, D is the number of the units of the total unit, PαIs the current load of the alpha machine set, F (P)α) And the energy consumption function of the unit takes the load as a variable.
For air conditioning systems, industries and commercial occasions, there is usually no strict requirement on factors such as the starting speed and the starting time of units, so that an objective function mainly considers two constraints of system load balance and upper and lower limits of unit output:
Figure BDA0002543638470000073
wherein P isLIs the total load of the current system. The upper limit and the lower limit of the unit output are restricted by limiting an algorithm search space, an external point penalty function method is adopted for restricting a system load balance condition, the load balance restriction is added into a target function in a penalty item mode, and therefore an optimization target function established in an example is as follows:
Figure BDA0002543638470000081
and when a certain particle does not meet the load balance condition, the punishment item enables the particle to obtain a large fitness value, so that the particle which does not meet the constraint condition is eliminated in the next iteration.
Step two, solving an energy consumption optimization model by adopting an improved particle swarm optimization:
the invention adopts two strategies of sine chaotic disturbance and self-adaptive inertia weight to improve the traditional particle swarm, and specifically comprises the following steps:
1. initializing particle swarm algorithm parameters: population size N, i.e. number of particles; the maximum iteration number T; the variation range (including the maximum value and the minimum value) of the inertia weight w, the learning factor, the search space (the maximum value and the minimum value of the particle position), the speed range (the maximum value and the minimum value of the particle speed), and the attributes of the particle in the population include the position, the speed and the fitness value;
2. initializing the position and the speed of the population particles by using a sine chaotic sequence, and comprising the following steps of:
generating chaotic individuals in N [0,1] intervals according to a sinusoidal chaotic sequence formula:
zi+1＝sin(5.65/zi)-1≤zi≤1,z1≠0
wherein z isi+1And ziRespectively the (i +1) th sinusoidal chaotic individual and the ith sinusoidal chaotic individual,
then the carrier wave is converted to the whole search space by the following formula
xi＝xmin+(xmax-xmin)·zi,0＜zi＜1,i＝1,2,......,N
vi＝vmin+(vmax-vmin)·zi,0＜zi＜1,i＝1,2,......,N
Wherein z isiRepresenting sinusoidal chaotic individuals; x is the number ofiIndicating the position of the ith particle; v. ofiRepresents the velocity of the ith particle; n represents the number of particles.
Calculating the individual fitness value of the chaotic particles, and selecting the first N particles with the optimal fitness values from the particle population as an initial population.
3. Calculating the individual optimal value of each particle and the global optimal value of the current population, and calculating the average fitness value f of the current-generation population according to the following formulaavg：
Figure BDA0002543638470000082
Selecting out fitness value superior to f from individuals in contemporary populationavgThe fitness values of the individuals are averaged according to the formula and are recorded as the preferred particle fitness favg ·。
4. Determining the inertial weight of the particle according to the fitness value of the individual particle, and updating the velocity and position of the particle:
when the adaptability value of the particle is superior to favg ·Then, the particle is known to be excellent in the population and close to the optimal value of the target, so that the particle should be given smaller inertial weight to increase the local searching capability, and the expression of the inertial weight is：
Figure BDA0002543638470000091
In the formula (f)gFor the current optimum fitness value, w, of the populationmaxFor a set maximum value of the inertial weight, wminIs the set inertia weight minimum.
When the adaptability of the particle is inferior to favg ·But is superior to favgThen, it is known that the particle is a general particle in the population, and the global and local search capabilities of the particle should be considered, where the inertial weight expression is:
Figure BDA0002543638470000092
wherein T is the current iteration number, and T is the maximum iteration number.
Third, the adaptability of the particle is inferior to favgIf the particle is a poor particle in the population, a larger inertia weight should be given to the particle to enhance the global search capability, and the expression of the inertia weight is:
Figure BDA0002543638470000093
wherein | fg-favg ·And | represents the dispersion degree of the current particle swarm, the smaller the value of the | is, the more concentrated the particle swarm is, the larger the inertia weight of the particles is at the moment, the global searching capability of the particles is enhanced, and the speed of the change of the inertia weight along with the dispersion degree of the particle swarm is controlled by the k parameter.
5. Calculating the average particle spacing of the current-generation particle swarm according to a formula, judging whether the average particle spacing is smaller than a set threshold value or not, simultaneously judging whether the current-generation global optimum value is inferior to (smaller than) a set theoretical optimum value (the optimization goal of the invention is that the energy consumption value of an air conditioner is minimum, when the current-generation optimal fitness value is larger than a preset global optimal fitness value, the current-generation optimal fitness value has a space which can be optimized, namely, the current-generation optimal fitness value is not a real optimal solution), if the two conditions are met, accumulating the iteration times, when the iteration times are seven times, judging that a group falls into a local optimum point, and turning to step 6, otherwise, executing step 7.
6. Adding sinusoidal chaotic disturbance from individuals of random N/2 in the contemporary population, and the strategy is as follows:
firstly, mapping N/2 particle individuals to a [0,1] interval according to the following formula:
Figure BDA0002543638470000094
and N/2 sine chaotic individuals z are generated according to the sine chaotic sequencei *Adding the chaotic disturbance to the current particle position and returning the particles added with the chaotic disturbance to the original solution space according to the following formula:
xi *＝(1-β)·ψi *+βzi *
xi＝xmin+(xmax-xmin)·xi *
wherein the parameter beta is used to control the degree of chaotic disturbance.
And secondly, returning the particles added with the chaotic disturbance into the population again, recalculating the average particle spacing of the current population, if the value is smaller than a set threshold value, operating the step 6 again, increasing the chaotic disturbance degree until the average particle spacing meets the requirement, and executing the step 7.
7. And if the maximum iteration number T is not reached, executing the step 3, otherwise, ending the solution and outputting the optimization result.
Comparing the method with the traditional PSO, chaotic PSO and adaptive PSO algorithms, the finally obtained total energy consumption result of the unit is shown in FIG. 4, and the method is superior to other algorithms in the aspects of optimizing speed and accuracy and can achieve the minimization of the total energy consumption of the unit.
In this example, five common working conditions of the central air-conditioning system, 5174kW, 6608kW, 7028kW, 9500kW, and 10067kW, were selected and compared with the PSO algorithm and the actual operating conditions to further verify the energy-saving optimization effect of the present invention, and the specific results are shown in table 2, and in addition, to illustrate the superiority of the improved algorithm of the present invention, the results are shown in fig. 5 in comparison with the conventional particle swarm algorithm (PSO).
TABLE 2
Figure BDA0002543638470000101
As can be seen from table 2, the present invention can optimize the load distribution of the current startup unit under the condition of satisfying the external cooling load demand. Taking 7028kW as an example, the rated power of each unit in the example is 2800kW, according to the COP curve of the unit, when the load rate of the unit is 65% to 70%, the unit efficiency is the highest, and in the actual operation mode, the load distribution rate is 44%, 47%, 47%, 57% and 56%, and the efficiency of each unit is known to be low, after the optimization by the invention, the load distribution rate is 60.46%, 67.29%, 58.14% and 65.11%, which is close to the optimal load rate, and the energy consumption is saved by about 24.53kWH, thereby illustrating the effectiveness and the practicability of the invention. As can be seen from fig. 5, the optimization accuracy of the present invention is obviously superior, and the advantage is not obvious when the total load is low, but the complexity of the working condition increases with the increase of the load, and the advantage of the optimization accuracy of the present invention is more and more obvious, taking the total load of 10067kW as an example, the energy consumption saved by the traditional particle swarm algorithm is only 0.43kWH, and the energy consumption saved by the algorithm of the present invention is 7.30kWH, which illustrates the superiority of the improved algorithm of the present invention in adapting to the complex working condition.

Claims (10)

1. A central air conditioner energy consumption control method based on an improved particle swarm algorithm is characterized by comprising the following steps:
s1, obtaining COP curves of different units of the central air conditioner, and fitting to obtain energy consumption functions of the units;
s2, constructing a central air-conditioning energy consumption optimization model based on energy consumption functions of all units and in combination with an external point punishment mode, wherein the constraint conditions of the central air-conditioning energy consumption optimization model comprise a system load balance constraint condition and a unit output constraint condition, and the central air-conditioning energy consumption optimization model takes the minimum total unit energy consumption as a target function;
s3, solving an energy consumption optimization model of the central air conditioner by adopting an improved particle swarm algorithm to obtain the optimal load distribution rate of each unit;
and S4, controlling the load switching of each unit according to the optimal load distribution rate of each unit, namely finishing the energy consumption optimization control of the central air conditioner.
2. The method for controlling energy consumption of a central air conditioner based on the improved particle swarm algorithm according to claim 1, wherein the energy consumption function of the unit is specifically as follows:
Figure FDA0002543638460000011
wherein, F (P)α) As a function of the energy consumption of the alpha-th unit, PαIs the current load of the alpha unit, a0, a1, a2 and a3 are the energy consumption coefficient of the unit, PαmaxThe maximum load capacity of the alpha machine set.
3. The method for controlling energy consumption of a central air conditioner based on the improved particle swarm algorithm according to claim 2, wherein the system load balance constraint conditions in the step S2 are specifically:
Figure FDA0002543638460000012
wherein, PLThe total load of the central air-conditioning system, and D is the total number of units;
the unit output constraint conditions are specifically as follows:
Pαmin≤Pα≤Pαmax
wherein, PαminThe minimum load of the alpha set of units;
the objective function of the central air-conditioning energy consumption optimization model in the step S2 is specifically:
Figure FDA0002543638460000013
wherein, P is the total energy consumption of the unit under the determined working condition, and lambda is the penalty coefficient of the exterior point.
4. The method for controlling energy consumption of a central air conditioner based on the improved particle swarm optimization as claimed in claim 1, wherein the specific process of solving the energy consumption optimization model of the central air conditioner by the improved particle swarm optimization in the step S3 is as follows:
s31, initializing the positions and the speeds of the particles by adopting a sine chaotic sequence, wherein the positions of the particles are used for representing a solution containing the load distribution rate of each unit;
s32, calculating the initialized individual fitness values of the particles, and selecting the first N particles with the optimal fitness values from the M populations as initial populations, wherein the fitness values are used for representing the total energy consumption of the unit under the condition corresponding to the position of one particle;
s33, updating an individual extreme value and a global extreme value based on the individual fitness value of the particle, wherein the individual extreme value is the minimum fitness value calculated from a single particle to the current generation, and the global extreme value is the minimum fitness value calculated from all particles of the population to the current generation;
s34, adaptively adjusting the inertia weight of particles according to the fitness values of all the particles of the current generation, and updating the speed and the position of the particles of the current generation by combining the individual extreme value and the global extreme value of the previous generation;
s35, calculating the average distance between particles, if the average distance is smaller than a preset threshold value, executing a step S36, otherwise executing a step S39;
s36, judging whether the current global optimal fitness value is smaller than a preset global optimal fitness value, if so, executing a step S39, otherwise, executing a step S37;
s37, judging whether the current iteration number is larger than or equal to the preset dangerous iteration number, if so, indicating that the particle population is locally optimal, executing a step S38, otherwise, executing a step S39;
s38, randomly selecting N/2 particles from the contemporary population, adding the sinusoidal chaotic disturbance to the positions of the N/2 particles, returning the particles added with the sinusoidal chaotic disturbance to the population again, and then returning to the step S34;
s39, judging whether the current iteration number is larger than or equal to the preset maximum iteration number, if so, ending the solution, and outputting the position and the fitness value of the current optimal particle, namely the optimal load distribution rate of each unit and the corresponding total energy consumption of the unit; otherwise, returning to step S33, and continuing the solving.
5. The method for controlling energy consumption of a central air conditioner based on the improved particle swarm algorithm according to claim 4, wherein the sinusoidal chaotic sequence in the step S31 is specifically as follows:
zi+1＝sin(5.65/zi)-1≤zi≤1,z1≠0
wherein z isi+1And ziRespectively (i +1) th and ith sinusoidal chaotic individuals.
6. The method for controlling energy consumption of a central air conditioner based on the improved particle swarm optimization algorithm according to claim 5, wherein the specific process of initializing the particle position and velocity in the step S31 is as follows: generating N chaotic individuals in [0,1] intervals based on a sinusoidal chaotic sequence, and then transforming the chaotic individuals to the whole search space through carrier transformation:
xi＝xmin+(xmax-xmin)·zi,0＜zi＜1,i＝1,2,......,N
vi＝vmin+(vmax-vmin)·zi,0＜zi＜1,i＝1,2,......,N
in the formula, xiIs the position of the ith particle, xminAnd xmaxRespectively a set minimum and maximum particle position, viIs the velocity of the ith particle, vminAnd vmaxRespectively, the set minimum value and the maximum value of the particle speed, and N is the number of particles.
7. The method for controlling energy consumption of a central air conditioner based on an improved particle swarm algorithm according to claim 6, wherein the specific process of adaptively adjusting the inertia weight of the particle in the step S34 is as follows:
s341, calculating the average value of the fitness values of all particles in the current-generation population:
Figure FDA0002543638460000031
wherein f isiFitness value of the ith particle of the contemporary population, favgThe average value of all the particle fitness degrees of the contemporary population is the average particle fitness degree;
s342, selecting the population of the current generation with the fitness value larger than or equal to favgThe average value of the particle fitness degrees is calculated again and is recorded as the preferred particle fitness degree favg ·；
S343, based on the fitness of the particles in the contemporary population, matching the fitness f with the preferred particleavg ·Average particle fitness favgA comparison is made to adjust the inertial weight of the updated particle.
8. The method for controlling energy consumption of a central air conditioner based on an improved particle swarm algorithm according to claim 7, wherein the specific process of the step S343 is as follows:
if the particle fitness is less than the preferred particle fitness favg ·Then the particle is a good particle in the population, and the inertia weight of the particle is adjusted as follows:
Figure FDA0002543638460000032
wherein f isgFor the current optimum fitness value, w, of the populationmaxFor a set maximum value of the inertial weight, wminIs the set inertia weight minimum;
if the particle fitness is greater than or equal to the preferred particle fitness favg ·And less than the average particle fitness favgThen the particle is a general particle in the population, and the inertia weight of the particle is adjusted to be:
Figure FDA0002543638460000033
wherein T is the current iteration frequency, and T is the maximum iteration frequency;
if the particle fitness is greater than or equal to the average particle fitness favgIf the particle is a poor particle in the population, the inertia weight of the particle is adjusted to be:
Figure FDA0002543638460000041
wherein, | fg-favg ·And | is used for representing the dispersion degree of the current particle swarm, the smaller the value of the | is, the more concentrated the particle swarm is, the larger the inertia weight of the particle is at the moment, and k is an inertia weight control parameter and is used for controlling the speed of the inertia weight changing along with the dispersion degree of the particle swarm.
9. The method as claimed in claim 8, wherein the calculation formula of the average particle spacing in step S35 is as follows:
Figure FDA0002543638460000042
Figure FDA0002543638460000043
wherein,
Figure FDA0002543638460000044
is the j-th dimension coordinate value of the ith particle,
Figure FDA0002543638460000045
the average value of j-th dimension coordinate values of all the particles is Div (t), the average distance between the particles of the t-th generation population is Div (t), and n is the dimension of the individual particles and corresponds to the total number of units in the air conditioning system.
10. The method for controlling energy consumption of a central air conditioner based on the improved particle swarm algorithm according to claim 9, wherein the specific process of the step S38 is as follows:
s381, randomly selecting N/2 particles from the contemporary population, mapping the N/2 particle individuals to a [0,1] interval to obtain mapped particle individuals:
Figure FDA0002543638460000046
wherein psii *Mapping to [0,1] for the ith particle](ii) post-compartmentalized individual particles;
s382, generating N/2 sine chaotic individual z according to the sine chaotic seriesi *Adding chaotic disturbance to the current particle position:
xi *＝(1-β)·ψi *+βzi *
wherein beta is a chaotic control parameter for controlling the degree of chaotic disturbance, xi *The position of the ith particle added with the chaotic disturbance is obtained;
and then, the particles added with the chaotic disturbance are classified into the population again, namely:
xi＝xmin+(xmax-xmin)·xi *
wherein x isiIs to mix xi *Mapping back to the particle position of the original optimization space;
finally, the process returns to step S34.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112347597A (en) * 2020-11-13 2021-02-09 西安工程大学 Method for improving LED illumination uniformity based on improved particle swarm optimization
CN112484257A (en) * 2020-12-04 2021-03-12 东南大学 Double-layer consistency control method for large-scale variable-frequency air conditioner group
CN112733433A (en) * 2020-12-25 2021-04-30 北京航天测控技术有限公司 Equipment testability strategy optimization method and device
CN114440404A (en) * 2022-02-09 2022-05-06 重庆美的通用制冷设备有限公司 Load distribution method and device of air conditioning system and electronic equipment
CN115654786A (en) * 2022-12-28 2023-01-31 北京绿建软件股份有限公司 Method and device for automatically determining operation strategy of water chilling unit
CN116203830A (en) * 2022-11-21 2023-06-02 广东石油化工学院 Air flow uncontrolled type heating furnace temperature PID optimization control method

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104036320A (en) * 2014-05-30 2014-09-10 国家电网公司 Dynamic economical dispatch method for microgrid system on the basis of improved particle swarm optimization
CN104850908A (en) * 2015-05-22 2015-08-19 上海电力学院 Chaos particle swarm based thermal power unit load optimal distribution method
US20150248118A1 (en) * 2014-02-26 2015-09-03 Board Of Trustees Of The University Of Alabama Systems and methods for modeling energy consumption and creating demand response strategies using learning-based approaches
CN106447024A (en) * 2016-08-31 2017-02-22 上海电机学院 Particle swarm improved algorithm based on chaotic backward learning
CN108168030A (en) * 2017-12-14 2018-06-15 南京师范大学 A kind of intelligent control method based on refrigeration performance curve
CN109447393A (en) * 2018-09-13 2019-03-08 西安理工大学 A kind of modified particle swarm optiziation of Power System Economic Load Dispatch
CN110110380A (en) * 2019-04-11 2019-08-09 上海电力学院 A kind of piezo actuator Hysteresis Nonlinear modeling method and application

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150248118A1 (en) * 2014-02-26 2015-09-03 Board Of Trustees Of The University Of Alabama Systems and methods for modeling energy consumption and creating demand response strategies using learning-based approaches
CN104036320A (en) * 2014-05-30 2014-09-10 国家电网公司 Dynamic economical dispatch method for microgrid system on the basis of improved particle swarm optimization
CN104850908A (en) * 2015-05-22 2015-08-19 上海电力学院 Chaos particle swarm based thermal power unit load optimal distribution method
CN106447024A (en) * 2016-08-31 2017-02-22 上海电机学院 Particle swarm improved algorithm based on chaotic backward learning
CN108168030A (en) * 2017-12-14 2018-06-15 南京师范大学 A kind of intelligent control method based on refrigeration performance curve
CN109447393A (en) * 2018-09-13 2019-03-08 西安理工大学 A kind of modified particle swarm optiziation of Power System Economic Load Dispatch
CN110110380A (en) * 2019-04-11 2019-08-09 上海电力学院 A kind of piezo actuator Hysteresis Nonlinear modeling method and application

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112347597A (en) * 2020-11-13 2021-02-09 西安工程大学 Method for improving LED illumination uniformity based on improved particle swarm optimization
CN112347597B (en) * 2020-11-13 2024-04-05 西安工程大学 Method for improving LED illumination uniformity based on improved particle swarm algorithm
CN112484257A (en) * 2020-12-04 2021-03-12 东南大学 Double-layer consistency control method for large-scale variable-frequency air conditioner group
CN112733433A (en) * 2020-12-25 2021-04-30 北京航天测控技术有限公司 Equipment testability strategy optimization method and device
CN114440404A (en) * 2022-02-09 2022-05-06 重庆美的通用制冷设备有限公司 Load distribution method and device of air conditioning system and electronic equipment
CN114440404B (en) * 2022-02-09 2023-09-19 重庆美的通用制冷设备有限公司 Load distribution method and device of air conditioning system and electronic equipment
CN116203830A (en) * 2022-11-21 2023-06-02 广东石油化工学院 Air flow uncontrolled type heating furnace temperature PID optimization control method
CN115654786A (en) * 2022-12-28 2023-01-31 北京绿建软件股份有限公司 Method and device for automatically determining operation strategy of water chilling unit

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