CN110908283B - Electric heating equipment control method, device and system - Google Patents

Electric heating equipment control method, device and system Download PDF

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CN110908283B
CN110908283B CN201911232811.3A CN201911232811A CN110908283B CN 110908283 B CN110908283 B CN 110908283B CN 201911232811 A CN201911232811 A CN 201911232811A CN 110908283 B CN110908283 B CN 110908283B
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circulating water
optimization
control period
function
temperature
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CN110908283A (en
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陈广宇
庞博
蔡瑶
乞胜静
马雨薇
于宝鑫
李文龙
张华东
李佳骥
张磊
张岩
张衡阳
黄伟光
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Yanshan University
Chengde Power Supply Co of State Grid Jibei Electric Power Co Ltd
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Yanshan University
Chengde Power Supply Co of State Grid Jibei Electric Power Co Ltd
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    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

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Abstract

The application provides a method, a device and a system for controlling electric heating equipment, wherein the method comprises the following steps: acquiring the circulating water temperature of the current control period; determining the lowest temperature of the circulating water corresponding to the current control period; and starting the heating function of the electric heating equipment under the condition that the temperature of the circulating water in the current control period is lower than the lowest temperature of the circulating water corresponding to the current control period. The invention can divide a whole day into a plurality of control periods, predetermine the lowest temperature of the circulating water corresponding to each control period, execute the starting control according to the lowest temperature of the circulating water of different control periods, and can carry out the personalized starting operation aiming at different control periods, thereby improving the energy saving performance and the user comfort of the electric heating equipment.

Description

Electric heating equipment control method, device and system
Technical Field
The invention relates to the technical field of automation, in particular to a method, a device and a system for controlling electric heating equipment.
Background
In the coal-to-electricity engineering, the traditional dispersed coal-fired boiler is replaced by electric heating equipment, so that clean and efficient heating in winter can be realized. The electric heating apparatus may include an air source heat pump, a solar heat pump, a water source heat pump, a ground source heat pump, and the like. The electric heating equipment heats circulating water through electric energy, and the circulating water circulates in the water supply pipeline, the radiator and the water return pipeline and radiates heat indoors through the radiator.
In the process of using the electric heating equipment, in order to save electric energy and simultaneously ensure user comfort, the minimum temperature and the maximum temperature of circulating water for starting and stopping control are generally set. And when the temperature of the circulating water of the electric heating equipment is lower than the lowest temperature of the circulating water, starting the heating function, and when the temperature of the circulating water of the electric heating equipment reaches the highest temperature of the circulating water, stopping the heating function. At present, the lowest temperature of circulating water in the control process of the electric heating equipment is fixed and unchangeable, namely the lowest temperature of the circulating water in different time periods in one day of the electric heating equipment is consistent. Because the external temperature is different and the user feels the temperature and also is different in different time quantum, so the electric heating equipment carries out start control based on the minimum temperature of unified circulating water, leads to the energy-conservation nature of electric heating equipment and user's travelling comfort lower.
Disclosure of Invention
In view of this, the present invention provides a method, an apparatus, and a system for controlling an electric heating device, which can divide an entire day into a plurality of time periods, one time period being a control cycle, determine the lowest temperatures of the circulating water corresponding to the plurality of control cycles one to one, and perform start control according to the lowest temperatures of the circulating water in different control cycles, thereby improving energy saving performance and user comfort of the electric heating device.
In order to achieve the above object, the present application provides the technical features described above:
an electric heating equipment control method comprises the following steps:
acquiring the circulating water temperature of the current control period;
determining the lowest temperature of circulating water corresponding to the current control period;
and starting the heating function of the electric heating equipment under the condition that the temperature of the circulating water in the current control period is lower than the lowest temperature of the circulating water corresponding to the current control period.
Optionally, the determining the lowest temperature of the circulating water corresponding to the current control cycle includes:
acquiring the lowest temperature of circulating water corresponding to each control period; the minimum temperature of the circulating water corresponding to each control period is determined by performing function optimization on a heating cost minimization objective function and a heating discomfort degree minimization objective function through a multi-objective optimization algorithm;
and searching the lowest temperature of the circulating water corresponding to the current control period from the lowest temperature of the circulating water corresponding to each control period.
Optionally, the process of performing function optimization on the heating cost minimization objective function and the heating discomfort degree minimization objective function through the multi-objective optimization algorithm for the minimum temperature of the circulating water corresponding to each control cycle includes:
constructing a heating cost minimization objective function and a heating discomfort degree minimization objective function by taking the maximum temperature difference of the circulating water temperature of each control period as a decision variable; the maximum temperature difference of the circulating water temperature is the maximum difference value of the circulating water temperature and the water supply temperature of the electric heating equipment;
performing function optimization operation on the heating cost minimization objective function and the heating discomfort degree minimization objective function by adopting a multi-objective optimization algorithm;
after the function optimization operation is finished, obtaining the optimal solution of the maximum temperature difference of the circulating water temperature in each control period;
and subtracting the optimal solution of each control period from the preset water supply temperature of each control period to obtain the lowest circulating water temperature corresponding to each control period.
Optionally, in a case that the multi-objective optimization algorithm is a multi-objective bacterial population chemotaxis algorithm, performing a function optimization operation on the heating cost minimization objective function and the heating discomfort degree minimization objective function by using the multi-objective optimization algorithm includes:
performing individual optimization for each decision variable in the decision space: executing individual optimization according to a multi-target bacterial group chemotaxis algorithm to obtain a new position of a decision variable, if a function value corresponding to the new position is superior to a function value corresponding to the original position, moving the decision variable to the new position, and if not, maintaining the original position; in the individual optimization executed by the bacterial susceptibility algorithm, the moving step length of a decision variable is in a decreasing trend along with the increase of the iteration times;
after individual optimization is executed aiming at each decision variable in the decision space, the decision variable meeting the group optimization condition is determined;
performing group optimization for each decision variable in the decision space that satisfies the group optimization condition: executing group optimization according to a multi-target bacteria group drug-trending algorithm to obtain a new position of a decision variable, if a function value corresponding to the new position is superior to a function value corresponding to an original position, moving the decision variable to the new position, and if not, maintaining the original position;
judging whether an iteration end condition is reached;
if not, re-entering the step of performing individual optimization aiming at each decision variable in the decision space;
if so, determining the positions corresponding to the current decision variables in the bacterial population as the solutions to be determined;
respectively calculating the comprehensive satisfaction corresponding to each solution to be determined based on the fuzzy membership function;
and determining the solution to be determined with the maximum comprehensive satisfaction degree in the solutions to be determined as the optimal solution of the decision variables for optimizing the heating cost minimization objective function and the heating discomfort degree minimization objective function.
Optionally, before the re-entering performs the individual optimization step for each decision variable in the decision space, the method further includes:
and (3) improving the distribution of the bacterial population by adopting a bacterial population directed mutation strategy.
Optionally, the using the bacterial population directed variation strategy to improve the distributivity of the bacterial population comprises:
calculating the crowding distance of each decision variable in the bacterial population in the space of the objective function value;
sequencing each decision variable according to the crowding distance, and dividing the bacterial population into two halves: a half decision variable set with a smaller congestion distance and a half decision variable set with a larger congestion distance;
for each decision variable in the half of the decision variable set with the smaller congestion distance:
and calculating a new position of the decision variable according to a directional variation formula, wherein the new position is positioned in a half of the decision variable set with a larger crowding distance, if the objective function value corresponding to the new position is not worse than the objective function value corresponding to the original position, the decision variable is moved to the new position, and if not, the original position is maintained.
Optionally, the individual optimizing process and the group optimizing process include determining whether the new location satisfies a constraint condition;
the constraint conditions include:
the maximum temperature difference of the circulating water is in a first preset range;
the indoor temperature is within a second preset range.
An electric heating apparatus control device comprising:
the acquisition unit is used for acquiring the circulating water temperature of the current control period;
the determining unit is used for determining the lowest temperature of the circulating water corresponding to the current control period;
and the starting unit is used for starting the heating function of the electric heating equipment under the condition that the temperature of the circulating water in the current control period is lower than the lowest temperature of the circulating water corresponding to the current control period.
An electric heating device control system comprising:
the electric heating equipment is used for acquiring the circulating water temperature of the current control period; determining the lowest temperature of circulating water corresponding to the current control period; and starting the heating function of the electric heating equipment under the condition that the temperature of the circulating water in the current control period is lower than the lowest temperature of the circulating water corresponding to the current control period.
Optionally, the determining the lowest temperature of the circulating water corresponding to the current control cycle specifically includes:
the electric heating equipment performs function optimization on a heating cost minimization target and a heating discomfort degree minimization target through a multi-objective optimization algorithm, then determines the lowest temperature of circulating water corresponding to each control period, and searches the lowest temperature of circulating water corresponding to the current control period from the lowest temperature of circulating water corresponding to each control period; or,
receiving the lowest circulating water temperature corresponding to each control period issued by a server, and searching the lowest circulating water temperature corresponding to the current control period from the lowest circulating water temperature corresponding to each control period; the server performs function optimization on the heating cost minimization target and the heating discomfort degree minimization target through a multi-objective optimization algorithm, and then determines the lowest temperature of circulating water corresponding to each control period;
receiving the lowest circulating water temperature corresponding to each control period sent by the application on the mobile terminal, and searching the lowest circulating water temperature corresponding to the current control period from the lowest circulating water temperature corresponding to each control period; the method comprises the steps that a multi-objective optimization algorithm is applied to perform function optimization on a heating cost minimization objective function and a heating discomfort degree minimization objective function, and then the lowest temperature of circulating water corresponding to each control period is determined;
receiving the lowest circulating water temperature corresponding to each control period input by a user through an interactive interface, and searching the lowest circulating water temperature corresponding to the current control period from the lowest circulating water temperature corresponding to each control period; and the processing equipment performs function optimization on the heating cost minimization target and the heating discomfort degree minimization target through a multi-objective optimization algorithm, determines the minimum temperature of the circulating water corresponding to each control period, and displays the minimum temperature of the circulating water for a user to check.
Through the technical means, the following beneficial effects can be realized:
the invention can divide a whole day (generally 24 hours) into a plurality of time periods, one time period is a control period, the lowest circulating water temperature corresponding to each control period is predetermined, after the circulating water temperature of the current control period is obtained, the lowest circulating water temperature corresponding to the current control period is found, and the heating function of the electric heating equipment is started under the condition that the circulating water temperature of the current control period is lower than the lowest circulating water temperature corresponding to the current control period.
The invention can execute the starting control according to the lowest temperature of the circulating water in different control periods and can carry out personalized starting operation aiming at different control periods, thereby improving the energy saving performance and the user comfort of the electric heating equipment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1a to 1d are schematic structural diagrams of an electric heating equipment control system according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for controlling an electric heating apparatus according to an embodiment of the present invention;
fig. 3 is a flowchart of another electric heating apparatus control method according to an embodiment of the present invention;
fig. 4 is a flowchart of another electric heating apparatus control method according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a control device for electric heating equipment according to an embodiment of the present invention.
Detailed Description
The invention provides a heating equipment control method, a heating equipment control device and a heating equipment control system, which can divide a whole day into a plurality of time periods, wherein one time period corresponds to one control cycle, the lowest temperature of circulating water corresponding to the control cycles is determined, and the starting control is executed according to the lowest temperature of the circulating water in different time periods, namely the starting control is executed according to the lowest temperature of the circulating water in different control cycles, so that the energy saving performance and the user comfort of electric heating equipment are improved.
The present invention may divide an entire day (typically 24 hours) into a plurality of control periods, and preferably, divide an entire day into a plurality of control periods on average. Taking 24 control periods as an example, 24 hours can be divided equally, and one hour is one control period.
It can be understood that the smaller the duration of one control cycle, the more accurate the function of the start control of the electric heating equipment, but at the same time, the problem of the slow operation speed due to the large calculation amount is also caused. The larger the duration of the control period is, the worse the starting control function of the electric heating equipment is, but the simple calculation amount is high in operation speed. Therefore, the number of control cycles can be set according to actual conditions.
The following embodiment is described in detail by taking 24 control periods (one control period per hour) as an example, and it is understood that other numbers of control periods may be provided and the execution is consistent.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The application provides an embodiment I of an electric heating equipment control system, in the embodiment I, the lowest temperature of circulating water corresponding to each control period is calculated and stored by electric heating equipment. Referring to fig. 1a, comprising:
an electric heating apparatus 101, a buffer water tank 200, and a radiator 300.
Referring to fig. 1a, a water supply pipe and a water return pipe are provided between an electric heating apparatus, a buffer water tank, and a radiator to circulate circulating water in the pipes.
And the electric heating equipment performs function optimization on the heating cost minimization objective function and the heating discomfort degree minimization objective function through a multi-objective optimization algorithm, determines the lowest temperature of the circulating water corresponding to each control period, and stores the lowest temperature of the circulating water corresponding to each control period.
The specific implementation process of determining the minimum temperature of the circulating water corresponding to each control cycle after performing the function optimization on the heating cost minimization objective function and the heating discomfort degree minimization objective function through the multi-objective optimization algorithm is detailed in the embodiment shown in fig. 3, and is not repeated here.
The application provides an embodiment II of the control system of the electric heating equipment, in the embodiment II, the lowest temperature of circulating water corresponding to each control period is calculated and stored by a server associated with the electric heating equipment. Referring to fig. 1b, comprising:
electric heating equipment 101, a server 102, a buffer water tank 200, and a radiator 300.
Referring to fig. 1b, a water supply pipe and a water return pipe are provided between the electric heating apparatus, the buffer water tank, and the radiator to circulate the circulating water between the buffer water tank and the radiator.
And the server performs function optimization on the heating cost minimization objective function and the heating discomfort degree minimization objective function through a multi-objective optimization algorithm, determines the lowest circulating water temperature corresponding to each control period, and then sends the lowest circulating water temperature to the electric heating equipment. The electric heating equipment receives and stores the lowest temperature of the circulating water corresponding to each control period.
The specific implementation process of determining the minimum temperature of the circulating water corresponding to each control cycle after performing the function optimization on the heating cost minimization objective function and the heating discomfort degree minimization objective function through the multi-objective optimization algorithm is detailed in the embodiment shown in fig. 3, and is not repeated here.
The application provides a third embodiment of an electric heating equipment control system, in the third embodiment, the lowest temperature of circulating water corresponding to each control period is calculated and stored by the application in a mobile terminal associated with electric heating equipment. Referring to fig. 1c, comprising:
the system comprises electric heating equipment 101, a server 102, a mobile terminal 103, a buffer water tank 200 and a radiator 300.
Referring to fig. 1c, a water supply pipe and a water return pipe are provided between the electric heating apparatus, the buffer water tank and the radiator to circulate the circulating water in the pipes.
The server determines the lowest temperature of circulating water corresponding to each control cycle after performing function optimization on the heating cost minimization objective function and the heating discomfort degree minimization objective function through a multi-objective optimization algorithm, then issues an application APP corresponding to pre-installed electric heating equipment in the mobile terminal, and the application APP is sent to the electric heating equipment in a wireless mode. The electric heating equipment receives and stores the lowest temperature of the circulating water corresponding to each control period.
The specific implementation process of determining the minimum temperature of the circulating water corresponding to each control cycle after performing the function optimization on the heating cost minimization objective function and the heating discomfort degree minimization objective function through the multi-objective optimization algorithm is detailed in the embodiment shown in fig. 3, and is not repeated here.
The application provides an embodiment four of electric heating equipment control system, in the embodiment four, the server that electric heating equipment is correlated with calculates and stores the minimum temperature of circulating water that each control cycle corresponds. Referring to fig. 1d, comprising:
electric heating equipment 101, processing equipment 104, a buffer water tank 200 and a radiator 300. The processing equipment may be local processing equipment other than electric heating equipment, and the specific implementation is not limited.
Referring to fig. 1d, a water supply pipe and a water return pipe are provided between the electric heating apparatus, the buffer tank and the radiator to circulate the circulating water in the pipes.
And the processing equipment performs function optimization on the heating cost minimization objective function and the heating discomfort degree minimization objective function through a multi-objective optimization algorithm, determines the minimum temperature of the circulating water corresponding to each control period, and displays the minimum temperature of the circulating water for a user to check. The user can input the lowest temperature of the circulating water corresponding to each control period to the electric heating equipment through the interactive interface, and the electric heating equipment receives and stores the lowest temperature of the circulating water corresponding to each control period.
The specific implementation process of determining the minimum temperature of the circulating water corresponding to each control cycle after performing the function optimization on the heating cost minimization objective function and the heating discomfort degree minimization objective function through the multi-objective optimization algorithm is shown in the embodiment shown in fig. 3, and is not repeated here.
Fig. 1a to fig. 1d are several implementation manners listed in the present invention, and it should be understood that other implementation manners may also be adopted in practical applications, and the present application is not limited to specific implementation manners of the present solution.
The application provides an electric heating equipment control method, which is shown in figure 2 and comprises the following steps:
step S101: and acquiring the circulating water temperature of the current control period.
When the electric heating equipment is in use, the circulating water temperature at a certain moment in the current control period can be extracted according to the preset sampling frequency.
Step S102: and determining the lowest temperature of the circulating water corresponding to the current control period.
As can be seen from the embodiments shown in fig. 1a to 1b, the electric heating device stores the minimum temperatures of the circulating water corresponding to each control period, and the electric heating device can search the minimum temperature of the circulating water corresponding to the current control period from the minimum temperatures of the circulating water corresponding to each control period.
Step S103: and starting the heating function of the electric heating equipment under the condition that the temperature of the circulating water in the current control period is lower than the lowest temperature of the circulating water corresponding to the current control period.
And comparing the temperature of the circulating water in the current control period with the lowest temperature of the circulating water corresponding to the current control period, and if the temperature of the circulating water in the current control period is lower than the lowest temperature of the circulating water corresponding to the current control period, indicating that the temperature of the circulating water is lower, and starting the heating function of the electric heating equipment.
It can be understood that the highest temperature of the circulating water is stored in the electric heating device, and the water supply temperature of the electric heating device is the highest temperature of the circulating water. After the heating function is initiated, the temperature of the circulating water may continue to be collected. And stopping the heating function of the electric heating equipment under the condition that the temperature of the circulating water reaches the highest temperature of the circulating water, thereby completing a heating process.
It will be appreciated that multiple start stop controls may be performed within a control cycle. For example, in one control cycle, the heating function is started when the start condition is satisfied, and the heating function is stopped when the stop condition is satisfied 20 minutes after the start. After another 10 minutes, if the starting condition is satisfied, the starting is continued, and after another 10 minutes, if the stopping condition is satisfied, the heating function is stopped.
Through the technical means, the following beneficial effects can be realized:
the invention can divide a whole day (usually 24 hours) into a plurality of time periods, one time period corresponds to one control period, the lowest temperature of the circulating water corresponding to the plurality of control periods is predetermined, after the temperature of the circulating water of the current control period is obtained, the lowest temperature of the circulating water corresponding to the current control period is found, and the heating function of the electric heating equipment is started under the condition that the temperature of the circulating water of the current control period is lower than the lowest temperature of the circulating water corresponding to the current control period.
The invention can execute the starting control according to the lowest temperature of the circulating water in different control periods, and can carry out personalized starting operation aiming at different control periods, thereby improving the energy saving performance and the user comfort of the electric heating equipment.
The specific implementation process of determining the minimum temperature of the circulating water corresponding to each control cycle after performing function optimization on the heating cost minimization objective function and the heating discomfort degree minimization objective function through the multi-objective optimization algorithm is described below.
In the case where the multi-objective optimization algorithm is a system model of a multi-objective bacterial population chemotaxis algorithm, referring to fig. 3, the following steps may be included:
step S201: and constructing a heating cost minimization objective function and a heating discomfort degree minimization objective function by taking the maximum temperature difference of the circulating water temperature of each control period as a decision variable.
Taking 24 control cycles as an example, the maximum temperature difference of 24 circulating water temperatures corresponding to the 24 control cycles can be used as a set of decision variables.
In the multi-target bacterial population chemotaxis algorithm, a decision variable continuously moves positions in each iteration process to search for an optimal position. The decision variable is moved from the original position to the new position, namely the maximum temperature difference of 24 circulating water temperatures in the decision variable is moved from the original position to the new position according to the moving direction and the moving step length. The implementation is based on the fact that each variable adopts the same moving step length in the same iteration.
Constructing a heating cost minimization objective function:
Figure BDA0002304021920000091
wherein, P k Rated power, t, of electric heating equipment in the k time period k For the operating time of the electric heating system in the k-th time period, p k The grid electricity price of the kth time period. t is t k The sum of the running times is started in a plurality of start-stop controls in the kth period.
The control of starting and stopping the electric heating equipment at each time is related to the maximum temperature difference of the circulating water temperature, so the influence of the maximum temperature difference of the circulating water temperature can be embodied in the starting and stopping time at each time.
In consideration of the comfort requirements of users of electric heating, heating discomfort is defined herein as the sum of indoor temperature discomfort and indoor temperature fluctuation discomfort within one control cycle. Constructing a heating discomfort degree minimization objective function:
Figure BDA0002304021920000101
wherein, T in k Room temperature, T, at the kth time period in k-1 Is the room temperature (k is more than or equal to 2) in the k-1 th time period set Is an ideal value of the indoor temperature. After the heating time is long, the indoor temperature rises, so the maximum temperature difference of the circulating water temperature can be reflected in the indoor temperature in the kth time periodInfluence.
Step S202: and constructing a system model of the multi-target bacterial chemotaxis function optimization algorithm.
Initializing system model parameters, and performing a system model of a multi-target bacterial chemotaxis function optimization algorithm, wherein a spherical coordinate system is usually adopted. The system model parameters include: diameter d of decision space calculated from decision variable space parameters max The number of bacteria in the bacterial population, i.e. the number of decision variables P, the total number of iterations t max . And building a system model of the multi-target bacterial chemotaxis function optimization algorithm according to the initialization parameters.
Step S203: performing function optimization operation on a system model of the bacterial chemotaxis function optimization algorithm to obtain an optimal decision variable solution for optimizing a heating cost minimization objective function and a heating discomfort degree minimization objective function; wherein the step size of the decision variable in the function optimization operation is in a decreasing trend along with the increase of the iteration number.
Optionally, referring to fig. 4, the step may include the following steps:
s0: the plurality of decision variables are randomly distributed at different locations in the decision space.
A plurality of bacteria in the bacterial population represent a plurality of decision variables, and the decision variables are randomly distributed in a decision space, so that an optimal position, namely an optimal solution, needs to be searched in the decision space.
S1: individual optimization is performed for each decision variable in the decision space: executing individual optimization according to a multi-target bacterial group chemotaxis algorithm to obtain a new position of a decision variable, if a function value corresponding to the new position is superior to a function value corresponding to the original position, moving the decision variable to the new position, and if not, maintaining the original position; wherein, in the individual optimization executed by the bacterial susceptibility algorithm, the moving step length of the decision variable is in a decreasing trend along with the increase of the iteration times.
This step is described in detail by taking as an example that the moving step size of the decision variable in the function optimization operation is linearly decreased as the number of iterations increases. It is to be understood that the moving step size of the decision variable may also be determined in an exponential decreasing or random decreasing manner.
And calculating the moving step length of the decision variable in each iteration according to the following linear decreasing formula:
Figure BDA0002304021920000111
wherein,
Figure BDA0002304021920000112
l min =0; t is the number of iterations, l t Moving step length of t-th iteration, l max Is the maximum step size in the decision space, d max As the maximum of the vector in the decision space,/ min Is the minimum step size in the decision space, t max Is the total number of iterations.
Determining the moving direction after determining the moving step length, wherein the included angle between the new direction and the original direction follows Gaussian distribution, and the included angle can be determined by a Gaussian distribution function
Figure BDA0002304021920000116
For example, a random number is generated between 0 and 180 °, or between 0 and 360 °. Determining an included angle
Figure BDA0002304021920000117
Which is well-known in the art and will not be described herein.
Under the condition that the decision variables comprise n variables, namely the decision variables have n dimensions, in the spherical coordinate system, the variables corresponding to each dimension need to be included according to the included angle
Figure BDA0002304021920000118
Step length l of movement t . The moving step length of each dimension corresponding variable in the spherical vector space is as follows:
Figure BDA0002304021920000113
Figure BDA0002304021920000114
Figure BDA0002304021920000115
wherein l t (1) 、l t (i) And l t (n) Representing the moving step length of the 1 st dimension variable, the ith dimension variable and the nth dimension variable in a spherical decision space; l t Is the moving step of the t-th iteration calculated in formula (1).
The new position of the decision variables is equal to the original position and the movement step of the corresponding dimension is superposed, and then the new position of each decision variable is represented by the following formula:
X j k ＝X j k +l t k ,j＝1,2,...,P,k＝1,2,...,n……………………………(8)
wherein the position of a decision variable is X j k Representation (new and original positions are represented by the same symbol), j =1,2 j k The position of the jth decision variable (i.e. the jth bacterium in the bacterial population) in k dimensions is indicated.
After each decision variable executes individual optimization operation, obtaining a new position of each decision variable; if the function values of the heating cost minimization objective function and the heating discomfort degree minimization objective function corresponding to the new position become good (equivalent to the concentration rise of the attractant), the decision-making variable is moved to the new position; in contrast, if the heating cost minimization objective function or the heating discomfort degree minimization objective function corresponding to the new position does not become poor in function value (corresponding to the attractant concentration being unchanged or reduced), the original position is maintained.
Optionally, the constraint condition includes: the maximum temperature difference of the circulating water is in a first preset range; the indoor temperature is within a second preset range.
S2: after individual optimization is performed for each decision variable in the decision space, the decision variables that satisfy the population optimization condition are determined.
The default bacterial individual in the bacterial population optimization has global perception capability, namely, the position information of other bacterial individuals in the bacterial population can be perceived. For a bacterial individual in a bacterial population, if at least one other bacterial individual in the bacterial population is better located than the bacterial individual, determining that the bacterial individual needs to be population-optimized in order to move the bacterial individual towards the center of the other bacterial individual that is better located than the bacterial individual, resulting in a population-optimized location.
S3: performing group optimization for each decision variable in the decision space that satisfies the group optimization condition: and executing group optimization according to the multi-target bacteria group drug-trending algorithm to obtain a new position of the decision variable, if the function value corresponding to the new position is superior to the function value corresponding to the original position, moving the decision variable to the new position, and if not, maintaining the original position. The group optimization operation is a mature technique and is not described herein again. If the function values of the heating cost minimization objective function and the heating discomfort degree minimization objective function corresponding to the new position become good (equivalent to the concentration rise of the attractant), the decision-making variable is moved to the new position; in contrast, if the heating cost minimization objective function or the heating discomfort degree minimization objective function corresponding to the new position does not become good in function value (which is equivalent to the attractant concentration being unchanged or decreased), the original position is maintained.
Optionally, the constraint condition includes: the maximum temperature difference of the circulating water is in a first preset range; the indoor temperature is within a second preset range.
After population optimization is performed for each decision variable in the decision space, a new position (a case where an objective function value corresponding to the new position becomes good) or an original position (a case where the objective function value corresponding to the new position does not become bad) of each decision variable is taken as a position of each decision variable.
S4: and judging whether an iteration ending condition is reached.
Maximum number of iterations t max If the current iteration number t is equal to t max If so, indicating that the iteration end condition is reached, and entering the step S6; if the current iteration times t is less than t max Then, watchIf the iteration end condition is not met, the process proceeds to step S5.
When the iteration end condition is not met, the process may directly enter step S1, and optionally, step S5 is executed before step S1 is entered, and then step S1 is entered.
S5: if not, improving the distributivity of the bacterial population by adopting a bacterial population directed mutation strategy, and entering S1.
After individual optimization operation and group optimization operation, local optimization is easy to fall into. In order to expand the distribution of the bacterial population, a bacterial population directed variation strategy can be adopted, and the aim of the bacterial population directed variation is to improve the distribution of the bacterial population in the space of the objective function value.
Alternatively, the following steps may be employed to improve the distribution of the bacterial population:
s51: and calculating the crowding distance of each decision variable in the bacterial population in the space of the objective function value.
And calculating function values of the heating cost minimization objective function and the heating discomfort degree minimization objective function corresponding to the positions of the decision variables, wherein a space formed by the function values of the heating cost minimization objective function and the heating discomfort degree minimization objective function is called an objective function value space. Based on the objective function value space, a crowding distance between the respective objective function values can be calculated.
The crowding distance may reflect the density of the objective function value space for each decision variable in the bacterial population, with greater crowding distances giving smaller densities and smaller crowding distances giving greater densities. The objective of the directed variation of the bacterial population is to improve the distribution of the bacterial population in the space of the objective function value.
S52: sequencing each decision variable according to the crowding distance, and dividing the bacterial population into two halves: the decision variable set of half with smaller congestion distance and the decision variable set of half with larger congestion distance.
In order to improve the distribution of the bacterial population in the target function value space, sorting operation is performed on each decision variable according to the size of the crowding distance, and the bacterial population is divided into two parts: half the set of decision variables with smaller congestion distance, and half the set of decision variables with larger congestion distance.
This was done to direct mutation to the smaller half of the crowding distance to the larger half of the crowding distance, thereby improving the distribution of the bacterial population.
S53: and (4) directionally mutating the decision variable of the half with the smaller crowding distance to the decision variable of the half with the larger crowding distance.
For each decision variable in the half of the decision variable set with the smaller congestion distance:
a) Calculating a new position of a decision variable according to a directional mutation formula, wherein the new position is positioned in a half of the decision variable set with larger crowding distance. Optionally, the present invention provides a calculation formula of directional variation, which calculates a directional variation position of each decision variable:
Figure BDA0002304021920000131
b) And if the objective function value corresponding to the directional variation position is not worse than the objective function value corresponding to the original position, executing the directional variation operation, otherwise, keeping the original position unchanged.
S6: and if so, determining the position corresponding to each decision variable in the bacterial population as each solution to be determined.
After the iteration ending condition is reached, the positions corresponding to the decision variables in the bacterial population can be obtained, and the positions corresponding to the decision variables are determined as the solutions to be determined for the convenience of the description of the subsequent steps. Taking the example that the bacterial population has P decision variables, P pending solutions can be obtained.
S7: and respectively calculating the comprehensive satisfaction corresponding to each solution to be determined based on the fuzzy membership function.
Continuing with the above example, the step is to determine the comprehensive satisfaction degrees corresponding to the P pending solutions, so as to subsequently determine the optimal solution from the respective pending solutions.
Heating cost minimization objective function is f 1 (X) and heating discomfort minimization objective function f 2 (X), the step is carried outDescription of the invention:
Figure BDA0002304021920000141
wherein, mu i Satisfaction of the ith minimization function, where i =1,2. f. of i max And f i min Respectively the maximum value and the minimum value of the ith objective function in all the obtained solutions to be determined.
Calculating the comprehensive satisfaction degree of the two objective functions, and defining the comprehensive satisfaction degree as S, wherein the calculation formula can participate in the following formula:
Figure BDA0002304021920000142
wherein, c i Is a weight of the satisfaction of the ith minimizing function on the influence of the comprehensive satisfaction, an
Figure BDA0002304021920000143
S8: and determining the solution to be determined with the maximum comprehensive satisfaction degree in the solutions to be determined as the optimal solution of the decision variables for optimizing the objective function for minimizing the heating cost and the objective function for minimizing the heating discomfort degree.
And sequencing the comprehensive satisfaction degrees of all solutions to be determined, selecting the solution to be determined with the maximum comprehensive satisfaction degree, and determining the solution to be determined as the optimal solution of the decision variable for optimizing the objective function for minimizing the heating cost and the objective function for minimizing the heating discomfort degree.
Step S203 then proceeds to step S204: and subtracting the optimal solution of each control period from the preset water supply temperature of each control period to obtain the lowest circulating water temperature corresponding to each control period.
The optimal solution of the decision variables is the optimal solution of the maximum temperature difference of the circulating water temperature in each control period. And subtracting the optimal solution of each control period from the preset water supply temperature of each control period to obtain the lowest circulating water temperature corresponding to each control period.
Since the preset water supply temperature of each control period is fixed, of course, after determining the optimal solution of the maximum temperature difference of the circulating water temperature of each control period in step S203, the minimum temperature of the circulating water of each control period is as follows: and presetting an optimal solution of subtracting the maximum temperature difference of the circulating water temperature from the water supply temperature.
According to the embodiment, the invention has the following beneficial effects:
in the individual optimization operation of function optimization, the random distribution function is not used for determining the moving step length, but the moving step length is set to be in a decreasing trend according to the increase of the iteration number. Therefore, the function optimization early stage has a relatively large moving step length, and the function optimization later stage has a relatively small moving step length; the method has the advantages that excessive time consumption in a local range is avoided in the early stage of function optimization, optimization efficiency is improved, the optimal position can be gradually approached through a relatively small moving step length in the later stage of function optimization, and convergence of a bacterial chemotaxis function optimization algorithm is improved.
In addition, in each iteration process of performing function optimization in step S203, a bacterial population directed mutation strategy is additionally adopted to improve the distributivity of the bacterial population, so that a local optimal solution can be avoided from being trapped in the function optimization process.
The minimum circulating water temperature of each control period can be obtained through multi-objective function optimization operation, and convergence, optimization rate and distribution of the minimum circulating water temperature of each control period can be improved through a variable step size strategy and a directional variation strategy in the implementation.
Referring to fig. 5, the present application provides an electric heating apparatus control device, including:
an obtaining unit 41, configured to obtain a circulating water temperature of a current control period;
a determining unit 42, configured to determine a minimum temperature of the circulating water corresponding to the current control period;
and a starting unit 43, configured to start a heating function of the electric heating device when the temperature of the circulating water in the current control period is lower than the lowest temperature of the circulating water corresponding to the current control period.
For specific implementation processes of the electric heating device control device, reference may be made to the embodiments shown in fig. 2 to fig. 3, which are not described herein again.
Through the technical means, the following beneficial effects can be realized:
the invention can divide a whole day (usually 24 hours) into a plurality of control periods, predetermine the lowest temperature of the circulating water corresponding to the control periods one by one, find the lowest temperature of the circulating water corresponding to the current control period after obtaining the temperature of the circulating water of the current control period, and start the heating function of the electric heating equipment under the condition that the temperature of the circulating water of the current control period is lower than the lowest temperature of the circulating water corresponding to the current control period.
The invention can execute the starting control according to the lowest temperature of the circulating water in different control periods, and can carry out personalized starting operation aiming at different control periods, thereby improving the energy saving performance and the user comfort of the electric heating equipment.
The functions described in the method of the present embodiment, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution of the embodiments of the present invention to the prior art or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device, a network device, or the like) to execute all or part of the steps of the method described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
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 (7)

1. A control method of electric heating equipment is characterized by comprising the following steps:
acquiring the circulating water temperature of the current control period;
determining the lowest temperature of the circulating water corresponding to the current control period, wherein the step comprises the following steps: acquiring the lowest temperature of circulating water corresponding to each control period; the minimum temperature of the circulating water corresponding to each control period is determined by performing function optimization on a heating cost minimization objective function and a heating discomfort degree minimization objective function through a multi-objective optimization algorithm; searching the lowest temperature of the circulating water corresponding to the current control period from the lowest temperature of the circulating water corresponding to each control period;
starting the heating function of the electric heating equipment under the condition that the temperature of the circulating water in the current control period is lower than the lowest temperature of the circulating water corresponding to the current control period;
the process of performing function optimization on the heating cost minimization objective function and the heating discomfort degree minimization objective function by the lowest temperature of the circulating water corresponding to each control period through a multi-objective optimization algorithm comprises the following steps:
constructing a heating cost minimization target function and a heating discomfort degree minimization target function by taking the maximum temperature difference of the circulating water temperature of each control period as a decision variable; the maximum temperature difference of the circulating water temperature is the maximum difference value of the circulating water temperature and the water supply temperature of the electric heating equipment;
performing function optimization operation on the heating cost minimization objective function and the heating discomfort degree minimization objective function by adopting a multi-objective optimization algorithm; wherein, under the condition that the multi-objective optimization algorithm is a multi-objective bacterial population chemotaxis algorithm, the multi-objective optimization algorithm is adopted to execute function optimization operation on the heating cost minimization objective function and the heating discomfort degree minimization objective function, and the function optimization operation comprises the following steps: individual optimization is performed for each decision variable in the decision space: executing individual optimization according to the multi-target bacteria group drug-trending algorithm to obtain a new position of the decision variable, if the function value corresponding to the new position is superior to the function value corresponding to the original position, moving the decision variable to the new position, otherwise, maintaining the original position; wherein, in the individual optimization executed by the bacterial chemotaxis algorithm, the moving step length of the decision variable is in a decreasing trend along with the increase of the iteration times; determining decision variables meeting group optimization conditions after performing individual optimization on each decision variable in a decision space; performing group optimization for each decision variable in the decision space that satisfies the group optimization condition: executing group optimization according to a multi-target bacteria group drug-trending algorithm to obtain a new position of a decision variable, if a function value corresponding to the new position is superior to a function value corresponding to an original position, moving the decision variable to the new position, and if not, maintaining the original position; judging whether an iteration ending condition is reached; if not, re-entering the step of performing individual optimization aiming at each decision variable in the decision space; if yes, determining the positions corresponding to the current decision variables in the bacterial population as the solutions to be determined; respectively calculating the comprehensive satisfaction corresponding to each solution to be determined based on the fuzzy membership function; determining the solution to be determined with the maximum comprehensive satisfaction degree in the solutions to be determined as the optimal solution of the decision variables for optimizing the objective function for minimizing the heating cost and the objective function for minimizing the heating discomfort degree;
after the function optimization operation is finished, obtaining the optimal solution of the maximum temperature difference of the circulating water temperature in each control period;
and subtracting the optimal solution of each control period from the preset water supply temperature of each control period to obtain the lowest circulating water temperature corresponding to each control period.
2. The method of claim 1, wherein prior to said re-entering performing individual optimization steps for each decision variable in a decision space, further comprising:
and (3) improving the distribution of the bacterial population by adopting a bacterial population directed mutation strategy.
3. The method of claim 2, wherein the improvement of the distribution of the bacterial population using a bacterial population directed variation strategy comprises:
calculating the crowding distance of each decision variable in the bacterial population in the objective function value space;
sequencing each decision variable according to the size of the crowding distance, and dividing the bacterial population into two halves: a half decision variable set with a smaller congestion distance and a half decision variable set with a larger congestion distance;
for each decision variable in the half of the decision variable set with the smaller congestion distance:
and calculating a new position of the decision variable according to a directional variation formula, wherein the new position is positioned in a half of the decision variable set with a larger crowding distance, if the objective function value corresponding to the new position is not worse than the objective function value corresponding to the original position, the decision variable is moved to the new position, and if not, the original position is maintained.
4. The method of claim 1, comprising determining whether the new location satisfies a constraint during the individual optimization process and the population optimization process;
the constraint conditions include:
the maximum temperature difference of the circulating water is in a first preset range;
the indoor temperature is within a second preset range.
5. An electric heating equipment control device, characterized by comprising:
the acquisition unit is used for acquiring the circulating water temperature of the current control period;
the determining unit is used for determining the lowest temperature of the circulating water corresponding to the current control period;
the starting unit is used for starting the heating function of the electric heating equipment under the condition that the temperature of the circulating water in the current control period is lower than the lowest temperature of the circulating water corresponding to the current control period;
the determining unit is specifically configured to obtain the lowest temperature of the circulating water corresponding to each control cycle; the minimum temperature of the circulating water corresponding to each control period is determined by performing function optimization on a heating cost minimization objective function and a heating discomfort degree minimization objective function through a multi-objective optimization algorithm; searching the lowest temperature of the circulating water corresponding to the current control period from the lowest temperature of the circulating water corresponding to each control period;
the determining unit is specifically configured to construct a heating cost minimization objective function and a heating discomfort degree minimization objective function by using the maximum temperature difference of the circulating water temperature in each control cycle as a decision variable; the maximum temperature difference of the circulating water temperature is the maximum difference value of the circulating water temperature and the water supply temperature of the electric heating equipment; performing function optimization operation on the heating cost minimization objective function and the heating discomfort degree minimization objective function by adopting a multi-objective optimization algorithm; wherein, under the condition that the multi-objective optimization algorithm is a multi-objective bacterial population chemotaxis algorithm, the multi-objective optimization algorithm is adopted to execute function optimization operation on the heating cost minimization objective function and the heating discomfort degree minimization objective function, and the function optimization operation comprises the following steps: individual optimization is performed for each decision variable in the decision space: executing individual optimization according to the multi-target bacteria group drug-trending algorithm to obtain a new position of the decision variable, if the function value corresponding to the new position is superior to the function value corresponding to the original position, moving the decision variable to the new position, otherwise, maintaining the original position; wherein, in the individual optimization executed by the bacterial chemotaxis algorithm, the moving step length of the decision variable is in a decreasing trend along with the increase of the iteration times; after individual optimization is executed aiming at each decision variable in the decision space, the decision variable meeting the group optimization condition is determined; performing group optimization for each decision variable in the decision space that satisfies the group optimization condition: executing group optimization according to a multi-target bacterium group susceptibility algorithm to obtain a new position of a decision variable, if a function value corresponding to the new position is superior to a function value corresponding to an original position, moving the decision variable to the new position, and if not, maintaining the original position; judging whether an iteration end condition is reached; if not, re-entering the step of performing individual optimization aiming at each decision variable in the decision space; if so, determining the positions corresponding to the current decision variables in the bacterial population as the solutions to be determined; respectively calculating the comprehensive satisfaction corresponding to each solution to be determined based on the fuzzy membership function; determining the solution to be determined with the maximum comprehensive satisfaction degree in the solutions to be determined as the optimal solution of the decision variables for optimizing the objective function for minimizing the heating cost and the objective function for minimizing the heating discomfort degree; after the function optimization operation is finished, obtaining the optimal solution of the maximum temperature difference of the circulating water temperature in each control period; and subtracting the optimal solution of each control period from the preset water supply temperature of each control period to obtain the lowest circulating water temperature corresponding to each control period.
6. An electric heating equipment control system, comprising:
the electric heating equipment is used for acquiring the circulating water temperature of the current control period; determining the lowest temperature of the circulating water corresponding to the current control period; starting the heating function of the electric heating equipment under the condition that the temperature of the circulating water in the current control period is lower than the lowest temperature of the circulating water corresponding to the current control period;
the electric heating equipment is specifically used for determining the lowest temperature of the circulating water corresponding to each control cycle after performing function optimization on a heating cost minimization target and a heating discomfort degree minimization target through a multi-objective optimization algorithm, and searching the lowest temperature of the circulating water corresponding to the current control cycle from the lowest temperature of the circulating water corresponding to each control cycle; the process of performing function optimization on the heating cost minimization objective function and the heating discomfort degree minimization objective function by the lowest temperature of the circulating water corresponding to each control period through a multi-objective optimization algorithm comprises the following steps: constructing a heating cost minimization target function and a heating discomfort degree minimization target function by taking the maximum temperature difference of the circulating water temperature of each control period as a decision variable; the maximum temperature difference of the circulating water temperature is the maximum difference value between the circulating water temperature and the water supply temperature of the electric heating equipment; performing function optimization operation on the heating cost minimization objective function and the heating discomfort degree minimization objective function by adopting a multi-objective optimization algorithm; wherein, under the condition that the multi-objective optimization algorithm is a multi-objective bacterial population chemotaxis algorithm, the multi-objective optimization algorithm is adopted to execute function optimization operation on the heating cost minimization objective function and the heating discomfort degree minimization objective function, and the function optimization operation comprises the following steps: individual optimization is performed for each decision variable in the decision space: executing individual optimization according to a multi-target bacterial group chemotaxis algorithm to obtain a new position of a decision variable, if a function value corresponding to the new position is superior to a function value corresponding to the original position, moving the decision variable to the new position, and if not, maintaining the original position; wherein, in the individual optimization executed by the bacterial chemotaxis algorithm, the moving step length of the decision variable is in a decreasing trend along with the increase of the iteration times; after individual optimization is executed aiming at each decision variable in the decision space, the decision variable meeting the group optimization condition is determined; performing group optimization for each decision variable in the decision space that satisfies the group optimization condition: executing group optimization according to a multi-target bacteria group drug-trending algorithm to obtain a new position of a decision variable, if a function value corresponding to the new position is superior to a function value corresponding to an original position, moving the decision variable to the new position, and if not, maintaining the original position; judging whether an iteration end condition is reached; if not, re-entering the step of performing individual optimization aiming at each decision variable in the decision space; if so, determining the positions corresponding to the current decision variables in the bacterial population as the solutions to be determined; respectively calculating the comprehensive satisfaction corresponding to each solution to be determined based on the fuzzy membership function; determining the solution to be determined with the maximum comprehensive satisfaction degree in the solutions to be determined as the optimal solution of the decision variables for optimizing the objective function for minimizing the heating cost and the objective function for minimizing the heating discomfort degree; after the function optimization operation is finished, obtaining the optimal solution of the maximum temperature difference of the circulating water temperature in each control period; and subtracting the optimal solution of each control period from the preset water supply temperature of each control period to obtain the lowest circulating water temperature corresponding to each control period.
7. The system of claim 6, wherein said determining a minimum temperature of the circulating water corresponding to said current control period further comprises:
receiving the lowest circulating water temperature corresponding to each control period issued by a server, and searching the lowest circulating water temperature corresponding to the current control period from the lowest circulating water temperature corresponding to each control period; the server performs function optimization on the heating cost minimization target and the heating discomfort degree minimization target through a multi-objective optimization algorithm, and then determines the lowest temperature of circulating water corresponding to each control period;
receiving the lowest temperature of circulating water corresponding to each control period sent by an application on a mobile terminal, and searching the lowest temperature of the circulating water corresponding to the current control period from the lowest temperature of the circulating water corresponding to each control period; the method comprises the steps that a multi-objective optimization algorithm is applied to perform function optimization on a heating cost minimization objective function and a heating discomfort degree minimization objective function, and then the lowest temperature of circulating water corresponding to each control period is determined;
receiving the lowest temperature of circulating water corresponding to each control period input by a user through an interactive interface, and searching the lowest temperature of circulating water corresponding to the current control period from the lowest temperature of circulating water corresponding to each control period; and the processing equipment performs function optimization on the heating cost minimization target and the heating discomfort degree minimization target through a multi-objective optimization algorithm, determines the lowest temperature of the circulating water corresponding to each control period, and displays the lowest temperature of the circulating water for a user to check.
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