A Genetic Algorithm-Based Method and Device for Optimized Temperature Control in Carbon Fiber Electric Heating Buildings

Abstract

This application discloses a building temperature control method and device based on a genetic algorithm for carbon fiber electric heating. By dividing the building area into N rooms, with the total number of rooms N forming a population size, each room represents a chromosome. The chromosome is encoded with a five-bit binary number, and the fitness of each individual in each generation of the population is calculated. Genetic operations are performed on the chromosomes to find the optimal conditions for combined heating of the building. Through the division of the area and the rational use of peak-hour electricity, not only can normal heating of the area be guaranteed, but energy can also be saved, while bringing certain economic benefits and reducing costs for the operating company.

Description

Technical Field

[0001] This application relates to the field of carbon fiber electric heating technology, specifically to a method and device for optimizing temperature control in carbon fiber electric heating buildings based on genetic algorithms. Background Technology

[0002] Carbon fiber electric heating temperature control systems can control the temperature of each building that has been installed, thereby achieving a balance between energy consumption and temperature. However, due to various factors, such as some buildings being relatively enclosed and well-insulated while others have fewer office workers and require less heating, there is a need for combined optimization of heating systems. Genetic algorithms are computational models that simulate biological evolution and are a method for searching for optimal solutions by simulating natural evolution. They can achieve good optimization results for complex combinatorial optimization problems. Therefore, how to improve the energy consumption of carbon fiber electric heating equipment using genetic algorithms while ensuring overall campus heating, and how to optimize the energy consumption of each building according to the actual scenario to achieve reasonable temperature control and reduce unnecessary energy consumption, has become an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0003] To address this, this application provides a method and device for optimizing building temperature control based on a genetic algorithm using carbon fiber electric heating, in order to solve the problem in existing technologies of how to optimize the energy consumption combination of each building according to the actual scenario to achieve reasonable temperature control and reduce unnecessary energy consumption.

[0004] To achieve the above objectives, this application provides the following technical solution:

[0005] Firstly, a method for optimizing temperature control in carbon fiber electric heating buildings based on genetic algorithms includes:

[0006] The entire building area is divided into N rooms, with the total number of rooms N forming the population size. Each room represents chromosome S1...S2. n ;

[0007] Chromosomes are encoded using five binary digits;

[0008] The fitness of each individual in each generation of the population is calculated according to the first formula, and genetic operations are performed on their chromosomes.

[0009] The first formula is: f(S) n = [T(flat) + T(valley)] * [p(flat) + p(valley)], where T(flat) is the total duration of flat-rate heating, T(valley) is the total duration of valley-rate heating, p(flat) is the average energy consumption per unit area per unit time during flat-rate heating, and p(valley) is the average energy consumption per unit area per unit time during valley-rate heating.

[0010] Calculate the selection probability of each individual in the population;

[0011] Calculate the cumulative probability;

[0012] N probabilities R are randomly generated on the curve [0, 1]. n And compare it with the cumulative probability;

[0013] If R n If the cumulative probability is less than or equal to the chromosome s, then chromosome s i Selected;

[0014] The selected chromosomes will undergo crossover and mutation operations.

[0015] When the curve reaches its highest point during the iterations, T(flat) + T(valley) in the first formula is the optimal solution.

[0016] Preferably, the population size N ranges from 0 to 1. <N<12。

[0017] Preferably, the roulette wheel selection method is used to select the selection probability when calculating the cumulative probability.

[0018] Preferably, the crossover operation specifically involves matching the selected chromosomes pairwise and performing bitwise operations to obtain a new chromosome.

[0019] Preferably, the mutation rate bp = 0.001 during the mutation operation.

[0020] Preferably, the mutation operation uses the basic mutation method, with the last bit inverted.

[0021] Preferably, the number of iterations is 500.

[0022] Secondly, a carbon fiber electric heating building temperature optimization control device based on a genetic algorithm includes:

[0023] The population generation module is used to divide the entire building area into N rooms. The total number of rooms N forms the population size, and each room represents chromosome S1...S... n ;

[0024] The encoding module is used to encode each chromosome into a five-bit binary number;

[0025] The fitness calculation module is used to calculate the fitness of each individual in each generation of the population according to the first formula, and to perform genetic operations on their chromosomes;

[0026] The first formula is: f(S) n= [T(flat) + T(valley)] * [p(flat) + p(valley)], where T(flat) is the total duration of flat-rate heating, T(valley) is the total duration of valley-rate heating, p(flat) is the average energy consumption per unit area per unit time during flat-rate heating, and p(valley) is the average energy consumption per unit area per unit time during valley-rate heating.

[0027] The selection probability calculation module is used to calculate the selection probability of each individual in the population.

[0028] The cumulative probability calculation module is used to calculate the cumulative probability.

[0029] The selection module is used to randomly generate N probabilities R on the [0, 1] curve. n And compare it with the cumulative probability;

[0030] If R n If the cumulative probability is less than or equal to the chromosome s, then chromosome s i Selected;

[0031] The crossover module is used to perform crossover operations on the selected chromosomes;

[0032] The mutation module is used to perform mutation operations. When the generated curve reaches its highest point during the iteration, T(flat) + T(valley) in the first formula is the optimal solution.

[0033] Thirdly, a computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a carbon fiber electric heating building optimized temperature control method based on a genetic algorithm.

[0034] Fourthly, a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a carbon fiber electric heating building optimized temperature control method based on a genetic algorithm.

[0035] Compared with the prior art, this application has at least the following beneficial effects:

[0036] This application provides a method and device for optimizing temperature control in carbon fiber electric heating buildings based on genetic algorithms. By dividing the building area into N rooms, with the total number of rooms N forming the population size, each room represents a chromosome. The chromosomes are encoded with five-bit binary numbers, and the fitness of each individual in each generation of the population is calculated. Genetic operations are performed on the chromosomes to find the optimal conditions for combined heating of the building. Through the division of the area and the rational use of peak-hour electricity, not only can normal heating of the area be guaranteed, but energy can also be saved, while bringing certain economic benefits and reducing costs for the operating company. Attached Figure Description

[0037] To more intuitively illustrate the prior art and this application, several exemplary figures are provided below. It should be understood that the specific shapes and structures shown in the figures should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary figures, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0038] Figure 1 Here is a flowchart of the genetic algorithm;

[0039] Figure 2 Flowchart of the carbon fiber electric heating building temperature optimization control method based on genetic algorithm provided in this application;

[0040] Figure 3 This is a schematic diagram illustrating the search for the optimal solution provided in this application. Detailed Implementation

[0041] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] In the description of this application: unless otherwise stated, "a plurality of" means two or more. The terms "first," "second," "third," etc., in this application are intended to distinguish the objects referred to and do not have any special meaning in terms of technical connotation (e.g., they should not be construed as an emphasis on importance or order). Expressions such as "comprising," "including," and "having" also mean "not limited to" (certain units, components, materials, steps, etc.).

[0043] The terms used in this application, such as "upper," "lower," "left," "right," and "middle," are generally used to facilitate intuitive understanding by referring to the accompanying drawings, and are not absolute limitations on the positional relationships in the actual product. Changes in these relative positional relationships, without departing from the technical concept disclosed in this application, should also be considered within the scope of this application.

[0044] Example 1:

[0045] To ensure that the genetic algorithm can be integrated with the carbon fiber electric heating temperature control system and achieve optimal temperature control management in office buildings, it is necessary to analyze the total energy consumption, specifically:

[0046] S1: Divide the building area;

[0047] Specifically, in order to reasonably ensure the heating environment, the heating status needs to be classified according to category. The office area that needs to be heated is set as A1, and the equipment room, toilet, canteen and other public areas are set as A2.

[0048] S2: Determine the insulation parameters for each area;

[0049] Specifically, the insulation parameter α ranges from 0 to 1, with a larger value for thicker insulation layers and other insulation areas. In this application, the value of the insulation parameter α is mainly determined by the year.

[0050] α < 2000, taking a value of 0.1;

[0051] The value of α is 0.3 for 2000 < α < 2010.

[0052] 2010 < α < 2015 takes the value 0.5;

[0053] 2015 < α < 2020, with a value of 0.8;

[0054] S3: Determine reasonable energy consumption and derive the energy consumption calculation formula;

[0055] The total energy consumption in region A1 is: P(A1) = α A1 (T*p*sizeA1)

[0056] Where T is the total heating duration, p is the average energy consumption per unit area per unit time, and sizeA1 represents the area A1;

[0057] The total energy consumption is then P = P(A1) + P(A2) + ... + P(An).

[0058] Given the existence of peak, average, and off-peak electricity rates in the region, the principle is to avoid using peak electricity, reduce the use of average electricity, and actively use off-peak electricity. This not only saves energy consumption but also reduces heating operation costs. Therefore, the formulas for calculating T and p are:

[0059] T = T(flat) + T(valley); p = p(flat) + p(valley);

[0060] The total energy consumption formula is:

[0061]

[0062] In reality, to first control the total amount of P to achieve reasonable economic benefits, it is necessary to calculate the duration of T. The relationship between p and T is p = kT + λ, where k is the heat transfer coefficient of the equipment and λ is the heat transfer constant. Therefore, the formula for the total energy consumption for renewal is:

[0063]

[0064] According to the above formula, the solution space of T is very large. By using the GA genetic algorithm to optimize the solution of T, the most suitable combination of T(flat) and T(valley) can be found to achieve optimal energy consumption.

[0065] Please see Figure 1 and Figure 2 , based on the above analysis, this embodiment provides an optimized temperature control method for carbon fiber electric heating buildings based on the genetic algorithm, including:

[0066] S101: Set the population size to N, where 0 < N < 12, and the chromosome is: S1.....S n ;

[0067] Specifically, in this application, the total number of rooms is the population size, and each room is regarded as a chromosome S1.....S n .

[0068] S102: Encode the chromosome with a five-bit binary number. For example, 5 = (00101);

[0069] Specifically, room S1 = 00000, room S2 = 00010, room S3 = 00011, and so on.

[0070] S103: Define the fitness function, and take the fitness function: f(x) = x * x;

[0071] S104: Calculate the fitness of each individual in each generation of the population according to formula (1), and perform genetic operations on its chromosome;

[0072] f(S n ) = [T(flat) + T(valley)] * [p(flat) + p(valley)] (1)

[0073] S105: Calculate the selection probability of each individual in the population according to formula (2);

[0074]

[0075] Among them, P(s i ) is the selection probability of the i-th chromosome, that is, the probability that the i-th room is preferentially selected;

[0076] S106: Use the roulette wheel selection method to select the selection probability, that is, the cumulative selection formula for chromosome i is:

[0077]

[0078] S107: Selection operation, randomly generate N probabilities R on the [0, 1] curve n , and compare them with the cumulative selection. If R n ≤q i , then the chromosome s i is selected. Here, the number of N is the same as the number of rooms;

[0079] S108: Crossover operation, pair the selected chromosomes and perform bit operations to obtain new chromosomes;

[0080] S109: Mutation operation. Set the mutation rate bp = 0.001. There are k = 5 × n × 0.001 genes in the new chromosome that can be mutated. 5 is the number of bits in the binary code. If k > 1, perform this round of mutation; otherwise, skip. The mutation uses the basic mutation method, and the last bit is inverted.

[0081] Please see Figure 3 Using the GA genetic algorithm, with 500 iterations, when the curve reaches its highest point during the iterations, the choice is considered a relatively optimal solution. The function is: f(S) n The optimal solution is [T(flat) + T(valley)] * [p(flat) + p(valley)]. Since p(flat) + p(valley) is fixed, T(flat) + T(valley) is a better solution.

[0082] This application can, to a certain extent, find the optimal conditions for combined heating in buildings. By dividing the area and making reasonable use of electricity in low- and high-risk areas, it can not only ensure normal heating in the area, but also save energy, while bringing certain economic benefits and reducing costs for the operating company. It is feasible and achievable.

[0083] Example 2:

[0084] This embodiment provides a carbon fiber electric heating building temperature optimization control device based on a genetic algorithm, including:

[0085] The population generation module is used to divide the entire building area into N rooms. The total number of rooms N forms the population size, and each room represents chromosome S1...S... n ;

[0086] The encoding module is used to encode each chromosome into a five-bit binary number;

[0087] The fitness calculation module is used to calculate the fitness of each individual in each generation of the population according to the first formula, and to perform genetic operations on their chromosomes;

[0088] The first formula is: f(S) n = [T(flat) + T(valley)] * [p(flat) + p(valley)], where T(flat) is the total duration of flat-rate heating, T(valley) is the total duration of valley-rate heating, p(flat) is the average energy consumption per unit area per unit time during flat-rate heating, and p(valley) is the average energy consumption per unit area per unit time during valley-rate heating.

[0089] The selection probability calculation module is used to calculate the selection probability of each individual in the population.

[0090] The cumulative probability calculation module is used to calculate the cumulative probability.

[0091] The selection module is used to randomly generate N probabilities R on the [0, 1] curve. n And compare it with the cumulative probability;

[0092] If R n If the cumulative probability is less than or equal to the chromosome s, then chromosome s i Selected;

[0093] The crossover module is used to perform crossover operations on the selected chromosomes;

[0094] The mutation module is used to perform mutation operations. When the generated curve reaches its highest point during the iteration, T(flat) + T(valley) in the first formula is the optimal solution.

[0095] For specific limitations regarding the building temperature control device for carbon fiber electric heating based on genetic algorithms, please refer to the limitations of the building temperature control method for carbon fiber electric heating based on genetic algorithms mentioned above, which will not be repeated here.

[0096] Example 3:

[0097] This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of a carbon fiber electric heating building optimized temperature control method based on a genetic algorithm.

[0098] Example 4:

[0099] This embodiment provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of a carbon fiber electric heating building optimized temperature control method based on a genetic algorithm.

[0100] The technical features of the above embodiments can be combined in any way (as long as there is no contradiction in the combination of these technical features). For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; these embodiments not explicitly written should also be considered to be within the scope of this specification.

[0101] The present application has been described in a relatively specific and detailed manner above through general descriptions and specific embodiments. It should be understood that, based on the technical concept of the present application, several conventional adjustments or further innovations can be made to these specific embodiments; however, as long as they do not depart from the technical concept of the present application, the technical solutions obtained by these conventional adjustments or further innovations also fall within the protection scope of the claims of the present application.

Claims

1. A method for optimizing temperature control in carbon fiber electric heating buildings based on genetic algorithms, characterized in that, include: The entire building area is divided into N rooms, with the total number of rooms N forming the population size. Each room represents chromosome S1...S2. n The population size N ranges from 0 to 1. <N<12; Chromosomes are encoded using five binary digits; The fitness of each individual in each generation of the population is calculated according to the first formula, and genetic operations are performed on their chromosomes. The first formula is: f(S) n = [T(flat) + T(valley)] * [p(flat) + p(valley)], where T(flat) is the total duration of flat-rate heating, T(valley) is the total duration of valley-rate heating, p(flat) is the average energy consumption per unit area per unit time during flat-rate heating, and p(valley) is the average energy consumption per unit area per unit time during valley-rate heating. Calculate the selection probability of each individual in the population; Calculate the cumulative probability; the cumulative probability is calculated using a roulette wheel selection method to select the probability of selection. N probabilities R are randomly generated on the curve [0, 1]. n And compare it with the cumulative probability; If R n If the cumulative probability is less than or equal to the chromosome s, then chromosome s i Selected; The selected chromosomes are subjected to crossover and mutation operations; the crossover operation specifically involves pairwise matching of the selected chromosomes and performing bitwise operations to obtain new chromosomes; the mutation operation uses the basic mutation method, with the last bit inverted; the mutation rate during the mutation operation is bp = 0.001; When the curve reaches its highest point during the iterations, T(flat) + T(valley) in the first formula is the optimal solution; the number of iterations is 500.

2. A carbon fiber electric heating building temperature optimization control device based on genetic algorithm, characterized in that, include: The population generation module is used to divide the entire building area into N rooms. The total number of rooms N forms the population size, and each room represents chromosome S1...S... n The population size N ranges from 0 to 1. <N<12; The encoding module is used to encode each chromosome into a five-bit binary number; The fitness calculation module is used to calculate the fitness of each individual in each generation of the population according to the first formula, and to perform genetic operations on their chromosomes; The first formula is: f(S) n = [T(flat) + T(valley)] * [p(flat) + p(valley)], where T(flat) is the total duration of flat-rate heating, T(valley) is the total duration of valley-rate heating, p(flat) is the average energy consumption per unit area per unit time during flat-rate heating, and p(valley) is the average energy consumption per unit area per unit time during valley-rate heating. The selection probability calculation module is used to calculate the selection probability of each individual in the population. The cumulative probability calculation module is used to calculate the cumulative probability; the calculation of the cumulative probability uses a roulette wheel selection method to select the probability. The selection module is used to randomly generate N probabilities R on the [0, 1] curve. n And compare it with the cumulative probability; If R n If the cumulative probability is less than or equal to the chromosome s, then chromosome s i Selected; The crossover module is used to perform crossover operations on the selected chromosomes; the crossover operation specifically involves matching the selected chromosomes pairwise and performing bitwise operations to obtain new chromosomes. The mutation module is used to perform mutation operations. When the generated curve reaches its highest point during the iteration, T(flat) + T(valley) in the first formula is the optimal solution. The mutation operation adopts the basic mutation method, with the last bit inverted. The mutation rate bp = 0.001 during the mutation operation. The number of iterations is 500.

3. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method described in claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method described in claim 1.

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