An air conditioning unit energy consumption optimization regulation method and device based on artificial intelligence, equipment and medium

Abstract

The application provides an air conditioning unit energy consumption optimization regulation method and device based on artificial intelligence, equipment and medium, belonging to the air conditioning unit energy consumption control technical field, using cosine similarity to evaluate the similarity of external environment parameters and current environment parameters; determining whether there is similar environment parameters; determining whether there is prior optimization record, if there is, exporting the optimization data in the early stage; based on swarm intelligence algorithm, step-by-step optimization is carried out to obtain the optimal control parameters under the current external constraint environment; the air conditioning unit operates according to the optimal control parameters under the current external constraint environment, and the air conditioning unit operation data is read in real time; determining whether the air conditioning unit operation data meets the end self-optimization condition; if it meets, the current self-optimization strategy is terminated, and the optimization record under the current environment is saved. The application effectively improves the efficiency of equipment operation, and effectively reduces the energy consumption of the equipment under the condition of meeting the user comfort.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of air conditioning unit energy consumption control, and particularly relates to an air conditioning unit energy consumption optimization control method, device, equipment and medium based on artificial intelligence. BACKGROUND

[0002] In the field of air conditioning systems, traditional control methods are often based on fixed set values and simple logical judgments, which are difficult to adapt to complex and variable external environmental conditions and user needs.

[0003] Traditional air conditioning systems often have difficulty adapting to complex and variable external environmental conditions, resulting in low energy efficiency. Air conditioning systems often overlook the use of historical data and operating data. The use of historical data resources cannot provide reference and basis for optimization.

[0004] With the rapid development of artificial intelligence technology, intelligent control can be realized. How to introduce artificial intelligence algorithms, and the air conditioning unit energy consumption optimization control method based on artificial intelligence introduces artificial intelligence algorithms and intelligent optimization mechanisms to realize effective management and optimization of air conditioning unit energy consumption is a technical problem to be solved at present. SUMMARY

[0005] The application provides an air conditioning unit energy consumption optimization control method based on artificial intelligence, which fully utilizes the advantages of swarm intelligence algorithms in multi-parameter optimization by fusing real-time detection data of multiple sensors, and finally realizes low-energy consumption operation of central air conditioning core units.

[0006] The method comprises:

[0007] S101: Evaluate the similarity of external environment parameters and current environment parameters using cosine similarity;

[0008] S102: Determine whether there are similar environment parameters, if there are similar environment parameters, jump to step S103;

[0009] If there are no similar environment parameters, jump to step S105;

[0010] S103: Determine whether there are prior optimization records, if there are prior optimization records, jump to step S104; otherwise, determine that there are no prior optimization records, and jump to step S105;

[0011] S104: Derive prior optimization data, and the derived prior optimization data includes the swarm intelligence algorithm used in the prior period and the device configuration parameters searched in the prior period;

[0012] S105: Initialize the swarm intelligence algorithm and the optimization limit condition;

[0013] S106: Optimize in a step-by-step manner based on the swarm intelligence algorithm to obtain optimal control parameters under the current external constraint environment;

[0014] S107: The air conditioning unit operates according to the optimal control parameters under the current external constraint environment, and real-time air conditioning unit operation data is read;

[0015] S108: Determine whether the air conditioning unit operation data meets the end self-optimization condition;

[0016] If yes, terminate the current self-optimization strategy and save the optimization record under the current environment.

[0017] It should be further explained that the method further comprises obtaining environment data , air conditioning unit operation process data , power consumption data , and air conditioning unit setting parameter range ;

[0018] i represents a certain data type, t represents the data type at a certain time point.

[0019] It should be further explained that the energy consumption sample pair is constructed based on the environment data, air conditioning unit operation process data, and power consumption data, and is represented as:

[0020] .

[0021] It should be further explained that in the method, the heat released by a certain instantaneous liquid is calculated based on the following formula and the temperature difference between the current temperature of the liquid and the contact environment temperature .

[0022]

[0023]

[0024] wherein, represents the specific heat capacity of water, represents the mass of the liquid;

[0025] The instantaneous heat is integrated over a preset time period to obtain the heat exchanged by the air conditioning unit under a certain flow rate within the preset time period , and the calculation method is:

[0026]

[0027] wherein, represents a certain calculation time point, represents the corresponding time interval.

[0028] Further need to explain, in step S106, based on the following formula to obtain the optimal control parameters under the current external constraint environment ;

[0029] (5)

[0030] opt represents the state of the current air conditioning unit; represents a certain set of air conditioning control parameters for running from t time to a time interval T; A represents the parameter range of the air conditioning unit self-regulation; S represents the constraint relationship between the variables of the air conditioning unit.

[0031] Further need to explain, swarm intelligence algorithm includes: genetic algorithm, particle swarm optimization algorithm, ant colony optimization algorithm, artificial fish school algorithm and artificial bee colony algorithm.

[0032] Further need to explain, the optimization limit condition in step S105 includes:

[0033] Configure the start order of each host in the air conditioning unit;

[0034] Configure the running time of each cooling tower of the air conditioning unit;

[0035] Configure the water inlet and return water temperature threshold of the air conditioning unit host, and only adjust one of the water inlet temperature or return water temperature.

[0036] The application also provides an air conditioning unit energy consumption optimization control device based on artificial intelligence, the device comprises:

[0037] Similarity evaluation module, for using cosine similarity to evaluate the similarity of external environment parameters and current environment parameters;

[0038] Parameter judgment module, for judging whether there is a similar environment parameter;

[0039] Optimization record searching module, for judging whether there is a prior optimization record;

[0040] Optimization data export module, for exporting the optimization data of the early stage, the exported early optimization data includes the swarm intelligence algorithm used in the early stage and the device configuration parameter searched in the early stage;

[0041] Algorithm processing module, for initializing the swarm intelligence algorithm and the optimization limit condition;

[0042] Optimization control module, based on the swarm intelligence algorithm to step by step optimization, get the optimal control parameters under the current external constraint environment;

[0043] A device control module is configured to control the air conditioning unit to operate according to optimal control parameters under a current external constraint environment, and to read air conditioning unit operation data in real time.

[0044] An optimization state judgment module is configured to judge whether the air conditioning unit operation data satisfies an end self-optimization condition, and if yes, to terminate the current self-optimization strategy and save the optimization record under the current environment.

[0045] According to another embodiment of the present application, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the air conditioning unit energy consumption optimization control method based on artificial intelligence when executing the program.

[0046] According to still another embodiment of the present application, a storage medium is also provided, which stores a computer program, and the computer program implements the steps of the air conditioning unit energy consumption optimization control method based on artificial intelligence when executed by a processor.

[0047] As can be seen from the above technical solutions, the present application has the following advantages:

[0048] The air conditioning unit energy consumption optimization control method provided by the present application uses a swarm intelligence algorithm to search for an optimal solution, and realizes the goal of minimizing the energy consumption of a building air conditioning unit under the premise of meeting user comfort based on real-time monitoring data of multiple sensors. The present application uses a swarm intelligence algorithm to perform self-optimization within the normal operating parameter range of various devices, and searches for an optimal energy-saving solution for large buildings under various complex conditions.

[0049] The present application determines whether the self-optimization process needs to be ended by judging whether the air conditioning unit has reached the expected control effect, thereby ensuring that the air conditioning unit minimizes energy consumption while meeting comfort requirements, and avoiding unnecessary control operations and energy waste. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the present application, the drawings required to be used in the description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0051] Figure 1 A flowchart of the air conditioning unit energy consumption optimization control method;

[0052] Figure 2 An architectural diagram of the air conditioning unit energy consumption optimization control method;

[0053] Figure 3 A flowchart of an embodiment of the air conditioning unit energy consumption optimization control method;

[0054] Figure 4 Figure 1 is a schematic diagram of an air conditioning unit energy consumption optimization and regulation device based on artificial intelligence. DETAILED DESCRIPTION

[0055] The air conditioning unit energy consumption optimization and regulation method based on artificial intelligence provided in the present application fuses real-time detection data of multiple sensors, analyzes the implicit and nonlinear relationship between different working environments such as indoor and outdoor temperature and humidity, illumination, and personnel density and air conditioning unit sensor data and energy consumption data, and finally realizes the lowest energy consumption operation strategy of the air conditioning unit under different working environments by using the advantages of swarm intelligence algorithm in multi-parameter optimization, thereby reducing the energy consumption of the air conditioning unit equipment.

[0056] The steps of the air conditioning unit energy consumption optimization and regulation method based on artificial intelligence will be described in detail below. In order to illustrate but not to limit, specific details such as specific system structures, technologies, etc. are proposed to thoroughly understand the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details.

[0057] The term "comprising" indicates the presence of described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or sets thereof. The terms "comprising", "including", "having" and their variants mean "including but not limited to", unless otherwise specifically emphasized.

[0058] The phrase "one embodiment" or "some embodiments" appearing in the present application means that the specific feature, structure or characteristic described in the embodiment is included in one or more embodiments of the present application. Therefore, the phrases "in one embodiment", "in some embodiments", "in other some embodiments", "in further some embodiments" appearing in different places in the present application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized.

[0059] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0060] Please refer to Figure 1A flow chart of an air conditioning unit energy consumption optimization control method in an embodiment is shown, and the method comprises:

[0061] S101: Evaluate the similarity between the external environment parameters and the current environment parameters using the cosine similarity.

[0062] In some embodiments, the similarity between the two environment parameters is evaluated based on the cosine similarity, that is, the distance between the external environment parameter vector and the current environment parameter vector is measured by calculating the cosine of the angle between them.

[0063] In the air conditioning unit energy consumption optimization control of the present embodiment, temperature, humidity, and light can be regarded as two vectors of external environment parameters and current environment parameters, and the cosine similarity formula is used to calculate the similarity between them.

[0064] Specifically, the external environment parameters and the current environment parameters to be evaluated are determined, which can include temperature, humidity, light intensity, wind speed, etc.

[0065] Since the value range and unit of the external environment parameters and the current environment parameters may be different, the external environment parameters and the current environment parameters are standardized to ensure the same weight when calculating the cosine similarity.

[0066] The external environment parameters and the current environment parameters are represented in vector form.

[0067] For example, based on temperature and humidity parameters, the external environment parameters can be represented as temperature and humidity vectors, and the current environment parameters can be represented as temperature and humidity vectors. The cosine similarity formula is used for calculation. That is, the external environment parameter vector and the current environment parameter vector are substituted into the cosine similarity formula to calculate the similarity value. The closer the similarity value is to 1, the more similar the two environment parameters are; the closer the similarity value is to -1, the less similar the two environment parameters are.

[0068] As can be seen, the cosine similarity evaluation of the environment parameters is intuitive and can effectively measure the similarity between the two environment parameters.

[0069] According to the calculated cosine similarity value, the similarity between the external environment parameters and the current environment parameters can be determined. If the similarity value exceeds a certain preset threshold, it can be considered that the two environment parameters are similar. If it is determined that the environment is similar, the historical control strategy can be referred to to develop the control strategy for the current environment to optimize the energy consumption of the air conditioning unit.

[0070] S102: Determine whether there are similar environment parameters. If there are similar environment parameters, go to step S103. If there are no similar environment parameters, go to step S105.

[0071] In some embodiments, the definition of similar environmental parameters is that the current environmental parameters have a high similarity with the external environmental parameters under the cosine similarity evaluation. The parameters include but are not limited to temperature, humidity, light intensity, etc., depending on the actual needs of air conditioning unit energy consumption optimization control.

[0072] In this embodiment, it is determined whether the current environment is similar to the historical environment, so as to decide whether to refer to the historical control strategy. If there are similar environmental parameters, the historical control strategy can be used to optimize the current control strategy, thereby improving the control efficiency and accuracy.

[0073] S103: It is determined whether there is a prior optimization record. If there is a prior optimization record, go to step S104; otherwise, it is determined that there is no prior optimization record, and go to step S105.

[0074] In some embodiments, the prior air conditioning unit energy consumption optimization record can include historical environmental parameters, historical control strategies, historical energy consumption data, etc. The data records the control strategies and energy consumption of the air conditioning unit under different environmental parameters, providing a reference for the subsequent formulation of the control strategy.

[0075] It should be noted that this embodiment determines whether there is a historical control record similar to the current environment, so as to decide whether to use historical experience to optimize the current control strategy. If there is a prior optimization record, the records can be used to guide the formulation of the current control strategy, thereby improving the control efficiency and accuracy.

[0076] S104: Prior optimization data is derived. The derived prior optimization data includes the swarm intelligence algorithm used in the prior period and the device configuration parameters searched in the prior period.

[0077] In some embodiments, the derived prior optimization data can include the swarm intelligence algorithm used in the prior period, the device configuration parameters such as fan speed and compressor power searched in the prior period, and corresponding energy consumption data, etc.

[0078] S105: The swarm intelligence algorithm and the optimization limit condition are initialized.

[0079] In some embodiments, the swarm intelligence algorithm plays an important role in the air conditioning unit energy consumption optimization control based on artificial intelligence. It can simulate the behavior of biological groups in nature, and find the optimal solution or approximate optimal solution through iteration and search. In the air conditioning unit energy consumption optimization control, the swarm intelligence algorithm can be used to search for the optimal device configuration parameters and control strategies to minimize energy consumption. The beneficial effects include improving the control efficiency and accuracy, and reducing the energy consumption and operating cost of the air conditioning unit.

[0080] S106: Step-by-step optimization is performed based on the swarm intelligence algorithm, and the optimal control parameters under the current external constraint environment are obtained.

[0081] In some embodiments, the process of step-by-step optimization based on swarm intelligence algorithm includes the steps of initializing a population, evaluating the fitness of the population, selecting excellent individuals, generating a new population, and iterative updating.

[0082] The swarm intelligence algorithm of the present embodiment generates a new population according to the current population and the fitness function, and iteratively updates until the end condition is met, that is, the maximum number of iterations is reached or the optimal solution is found.

[0083] The obtained optimal control parameters under the current external constraint environment include fan speed, compressor power, refrigerant flow rate, and other operating parameters of the air conditioning unit. The optimal solution or approximate optimal solution obtained by searching under the current external constraint environment through the swarm intelligence algorithm.

[0084] In this way, the present embodiment uses a swarm intelligence algorithm to search for optimal control parameters under the current external constraint environment to minimize the energy consumption of the air conditioning unit.

[0085] Optionally, the specific process of the present embodiment can include:

[0086] S1061: Initialize the population: generate a set of random air conditioning unit control parameters as the initial population.

[0087] S1062: Evaluate the fitness of the population: calculate the fitness value of each individual according to the current external constraint environment and the energy consumption model of the air conditioning unit.

[0088] S1063: Select excellent individuals: select a part of excellent individuals as parents according to the fitness value.

[0089] S1064: Generate a new population: generate a new population through operations such as crossover and mutation.

[0090] Iterative updating: repeat steps S1062-S1064 until the maximum number of iterations is reached or the optimal solution is found.

[0091] S107: The air conditioning unit operates according to the optimal control parameters under the current external constraint environment, and real-time reading of the air conditioning unit operating data is performed.

[0092] In some embodiments, the air conditioning unit operates according to the optimal control parameters of fan speed and compressor power searched in step S106. Through the control system, the execution mechanism of the air conditioning unit is delivered, thereby realizing accurate control of the operation of the air conditioning unit.

[0093] The optimal control parameters in the current external constraint environment include fan speed, compressor power, refrigerant flow, etc. The optimal solution or approximate optimal solution obtained by searching through the swarm intelligence algorithm in the current external constraint environment can ensure that the air conditioning unit minimizes energy consumption while meeting comfort requirements.

[0094] The embodiment can enable the air conditioning unit to operate according to the optimal control parameters to minimize energy consumption and maximize operating efficiency. The beneficial effects include improving the energy efficiency ratio and operating stability of the air conditioning unit, reducing energy consumption and operating costs, and improving user comfort and satisfaction.

[0095] For the embodiment, reading the optimal control parameters is to obtain the optimal control parameters from step S106. The optimal control parameters are sent to the actuators of the air conditioning unit through the control system. The actuators of the air conditioning unit adjust the operation according to the received control instructions. The operating state and energy consumption of the air conditioning unit are monitored in real time through the sensors.

[0096] S108: Determine whether the air conditioning unit operating data meets the end self-optimization condition. If it does, terminate the current self-optimization strategy and save the optimization record under the current environment.

[0097] In some embodiments, determining whether the air conditioning unit operating data meets the end self-optimization condition can include multiple aspects, such as whether the energy consumption reaches the preset target, whether the operating stability meets the requirements, whether the user comfort is guaranteed, etc.

[0098] The embodiment can specifically include the following steps:

[0099] S1081: Collect operating data.

[0100] The operating data of the air conditioning unit is collected through sensors and a control system.

[0101] S1082: Analyze operating data.

[0102] The collected operating data is processed and analyzed to evaluate indicators such as energy consumption, operating stability, and user comfort of the air conditioning unit.

[0103] S1083: Determine whether the conditions are met.

[0104] According to the preset end self-optimization condition, determine whether the current operating data meets the requirements.

[0105] The embodiment determines whether the air conditioning unit has achieved the expected control effect to decide whether to end the self-optimization process. This ensures that the air conditioning unit minimizes energy consumption while meeting comfort requirements, and avoids unnecessary control operations and energy waste.

[0106] Furthermore, as a refinement and extension of the specific implementation methods of the above embodiments, in order to fully explain the specific implementation process of this embodiment and realize the operation of the air conditioning unit according to the lowest energy consumption strategy under different working environments, a more specific implementation process of the air conditioning unit energy consumption optimization control method is provided below. For example... Figure 2 As shown.

[0107] In some embodiments, the environmental parameters that are aggregated, processed, and analyzed are environmental data ( ), air conditioning unit operation data ( Electricity consumption data () ) and the range of setting parameters for air conditioning units ( ),in i This indicates a specific data type within this major category of data. t This indicates the data type at a specific point in time within this major category of data.

[0108] Specifically, environmental data refers to the environmental parameters inside and outside the building during the operation of the air conditioning unit, including but not limited to outdoor temperature and humidity sensor data, floor infrared radiation data, and camera-based human monitoring data.

[0109] Air conditioning unit operation data refers to the operation data generated by various devices during the operation of the air conditioning unit, including but not limited to ultrasonic flow meter sensor data, pressure transmitter sensor data, cooling water inlet and outlet water temperature sensor data, chilled water inlet and outlet water temperature sensor data, etc.

[0110] Electricity consumption data refers to the data of the main power-consuming equipment during the operation of the air conditioning unit, specifically the data generated by the three-phase electricity meters of each power-consuming device.

[0111] The settings parameters of an air conditioning unit refer to the reasonable controllable operating range of the adjustable parameters of each piece of equipment in a central air conditioning unit, including the operating combination of the air conditioning unit, the operating combination of the cooling tower, the parameter setting range of the chilled water supply and return water temperature, the parameter setting range of the chilled water pump frequency, the parameter setting range of the cooling water supply and return water temperature, and the parameter setting range of the cooling pump frequency.

[0112] An energy consumption sample can be constructed by combining environmental data, air conditioning unit operation data, and electricity consumption data, which can be expressed using formula (1):

[0113] (1)

[0114] The swarm intelligence algorithm in this embodiment may include three parts: a swarm intelligence algorithm library, a physical model constraint module, and a parameter autonomous optimization module.

[0115] Among them, the swarm intelligence algorithm library contains swarm intelligence algorithms.

[0116] Swarm intelligence algorithm is a kind of calculation model imitating the behavior of group creatures in nature, which is used to solve complex problems, especially non-convex optimization problems. Swarm intelligence algorithm can simulate the intelligent behavior of biological groups, and solve complex problems through simple interaction rules between individuals.

[0117] The embodiment realizes the lowest energy consumption on the basis of meeting user comfort through intelligent adjustment of each parameter of the air conditioning unit. However, considering that air conditioning intelligent adjustment involves comprehensive changes of the internal and external environment of the building, such optimization problem is difficult to express through explicit logical relationship, and belongs to NP-hard problem.

[0118] The embodiment uses swarm intelligence algorithm to find the optimal solution within the adjustable parameter range. The swarm intelligence algorithm used in the embodiment includes genetic algorithm, particle swarm algorithm, ant colony optimization algorithm, artificial fish swarm algorithm and artificial bee colony algorithm.

[0119] The swarm intelligence algorithm library is shown in Table 1:

[0120] Table 1 Swarm intelligence algorithm library

[0121]

[0122] Further, when performing parameter initialization, the embodiment can include the size of the population, the initial position, the learning rate and some control parameters of the algorithm itself (such as learning factor, pheromone concentration, etc.). Therefore, in order to exclude the influence of algorithm and parameter selection as much as possible, search for the lowest energy consumption of the central air conditioning unit under the current environment, the embodiment uses the above-mentioned five kinds of swarm intelligence algorithms, randomly initializes two groups of parameters for each swarm intelligence algorithm to search for the optimal solution under the current parameter environment. And compare the search results of 10 groups, select the optimal operating parameter combination, through the above-mentioned way, try to get the global optimal solution, avoid falling into local optimal solution.

[0123] The air conditioning unit of the embodiment is a thermodynamic system based on liquid to realize heat exchange, which conforms to the thermodynamic law in the field of physics, as shown in formula (2):

[0124] (2)

[0125] (3)

[0126] Wherein, represents the heat released by the liquid at a certain moment, represents the specific heat capacity of water, represents the mass of the liquid, represents the temperature difference between the current temperature of the liquid and the temperature of the contacted environment.

[0127] Integrating the instantaneous heat over a period of time, the heat exchanged by the air conditioning system at a certain flow rate within a certain period of time can be obtained, as shown in equation (4):

[0128] (4)

[0129] wherein, represents the heat exchanged by the liquid at a certain flow rate within a certain calculation period, represents a certain calculation time point, represents the corresponding time interval.

[0130] The embodiment can obtain the restriction conditions of the swarm intelligence algorithm in global optimization according to the above physical law, prevent the occurrence of safety production hidden dangers and damage to equipment caused by violation of physical laws, and the related restriction conditions are shown in Table 2:

[0131] Table 2 Swarm intelligence algorithm optimization restriction conditions

[0132]

[0133] The embodiment clearly defines the swarm intelligence algorithm and the constraint variables in the optimization process. Subsequently, AI autonomous optimization is performed on the basis of the current external environment and parameter settings, which can be represented by equation (5):

[0134] (5)

[0135] wherein, represents the optimal control parameter obtained under the current external constraint environment; opt represents the current entire real central air conditioning system; represents the operation of a certain set of air conditioning control parameters within a time period of T starting from t time interval; A represents the parameter range that the central air conditioning system can autonomously regulate and control; S represents the constraint relationship between the variables of the air conditioning unit.

[0136] The further process of the air conditioning unit energy consumption optimization and control method is shown in Figure 3 .

[0137] S301: Calculate the similarity of the current environment and the external environment.

[0138] The embodiment uses cosine similarity to evaluate the similarity of the calculated external environment parameter vector and the current environment parameter vector.

[0139] S302: Determine whether there is a similar environment parameter.

[0140] When the similarity is greater than 0.8, it is determined that there is a similar environment parameter, and the process jumps to step S303; otherwise, it is determined that there is no similar environment parameter, and the process jumps to step S305.

[0141] S303: Determine whether there is a previous optimization record.

[0142] If there is a previous optimization record, the process jumps to step S304; otherwise, it is determined that there is no previous optimization record, and the process jumps to step S305.

[0143] S304: Import previous data.

[0144] This embodiment imports the previous optimization data, and the imported data includes the swarm intelligence algorithm used previously and the device configuration parameters searched previously.

[0145] S305: Initialize parameters, including initializing the swarm intelligence algorithm and the corresponding parameters.

[0146] S306: Swarm intelligence algorithm optimization.

[0147] In this embodiment, each swarm intelligence algorithm is used to perform step-by-step optimization according to its search algorithm, and the next device configuration parameter is calculated.

[0148] S307: Strategy execution, the air conditioning unit executes the strategy searched by the current swarm intelligence algorithm, and evaluates whether the strategy is effectively executed by reading the data of the device and the sensor, and the effective execution time of the air conditioning unit is not less than 1 hour.

[0149] S308: Determine whether the current running data meets the requirements according to the preset end autonomous optimization condition.

[0150] This embodiment realizes effective management and optimization of air conditioning unit energy consumption. The cosine similarity is used to evaluate the similarity of environmental parameters, and the step-by-step optimization is performed in combination with the swarm intelligence algorithm, so that the air conditioning unit can run with optimal control parameters under the current external constraint environment.

[0151] The following is an embodiment of an air conditioning unit energy consumption optimization and control device based on artificial intelligence provided by the present disclosure. The device and the above-mentioned air conditioning unit energy consumption optimization and control method based on artificial intelligence belong to the same inventive concept. Details not described in the embodiment of the air conditioning unit energy consumption optimization and control device based on artificial intelligence can be referred to the above-mentioned embodiment of the air conditioning unit energy consumption optimization and control method based on artificial intelligence.

[0152] The device comprises a similarity evaluation module for evaluating the similarity of external environment parameters and current environment parameters using cosine similarity.

[0153] A parameter judgment module is configured to determine whether there is a similar environment parameter.

[0154] An optimization record searching module is configured to determine whether a previous optimization record exists.

[0155] An optimization data exporting module is configured to export previous optimization data, and the exported previous optimization data includes a previously used swarm intelligence algorithm and previously searched device configuration parameters.

[0156] An algorithm processing module is configured to initialize the swarm intelligence algorithm and optimization limiting conditions.

[0157] An optimization control module is configured to perform step-by-step optimization based on the swarm intelligence algorithm to obtain optimal control parameters under a current external constraint environment.

[0158] A device control module is configured to cause the air conditioning unit to operate according to the optimal control parameters under the current external constraint environment and to read air conditioning unit operation data in real time.

[0159] An optimization state determining module is configured to determine whether the air conditioning unit operation data satisfies an end self-optimization condition, and if so, terminate the current self-optimization strategy and save the optimization record under the current environment.

[0160] In an embodiment of the present application, a device for optimizing and regulating air conditioning unit energy consumption is provided, and a possible embodiment thereof will be described below.

[0161] In an exemplary embodiment, the device for optimizing and regulating air conditioning unit energy consumption is established, and a reinforcement learning algorithm is introduced into the device, so that the device can perceive and analyze the external environment and make reasonable decisions based on the swarm intelligence optimization results.

[0162] The device for optimizing and regulating air conditioning unit energy consumption can realize a dynamic optimization process, that is, a process of selecting an optimal or most satisfactory solution from a plurality of candidate air conditioning control schemes according to a plurality of index data of each scheme or reusing a swarm intelligence algorithm to perform optimization.

[0163] The device for optimizing and regulating air conditioning unit energy consumption further includes a swarm intelligence model algorithm library, an external environment library, a control strategy library, and a device mapping library.

[0164] The swarm intelligence model algorithm library includes artificial bee colony algorithms, genetic algorithms, and other swarm intelligence algorithms, and the swarm intelligence algorithm library stores code data.

[0165] The external environment library includes external temperature, humidity, and illumination collected by sensors in different months, as well as data information collected by video cameras and infrared sensors on each floor of the office building.

[0166] The control strategy library includes air conditioner control parameters searched by the current swarm intelligence algorithm under different environmental parameters. The external environment library and the control strategy library store data through a relational database.

[0167] The intelligent control operation process of the air conditioner unit energy consumption optimization and regulation device is as follows:

[0168] Step 401: Obtain environmental information through internal and external sensors;

[0169] Step 402: Read the external environment library to determine whether similar environmental parameters exist at present; if yes, jump to step 403; if no, jump to step 406

[0170] Step 403: Match the control strategy through the external environment library, and determine whether the current control strategy is mature; if yes, jump to step 404; if no, jump to step 405;

[0171] Step 404: Push the control strategy to the air conditioner unit, and establish a virtual point to realize the issuance of the device control strategy;

[0172] Step 405: Determine whether the issued mature strategy is effective, and count the running time according to the device running for 1 hour, and monitor the change of the external environment at the running time;

[0173] Step 406: Call the running strategy with the highest similarity to the current environment, jump to step 404 for execution, and start step 407 at the same time;

[0174] Step 407: Select the optimal control strategy matched under the current similar external environment; if no, randomly generate a control strategy, and further optimize the current control strategy based on the swarm intelligence algorithm.

[0175] The embodiment searches for an optimal solution by using a swarm intelligence algorithm, realizes the target of the lowest energy consumption of the building air conditioner unit device running under the premise of meeting the user comfort degree based on real-time monitoring data of multiple sensors, and searches for the energy-saving optimal solution of large buildings under various complex conditions (including air temperature, illumination, personnel flow, etc.) by using the swarm intelligence algorithm to independently optimize in the normal working parameter range of various devices. The efficiency of the device running is effectively improved, and the energy consumption of the device is effectively reduced under the condition of meeting the user comfort degree.

[0176] The application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the air conditioner unit energy consumption optimization and regulation method based on artificial intelligence when executing the program.

[0177] In embodiments of the application, electronic devices include, but are not limited to, laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components, their connections, and relationships, and functions, as described herein, are meant to be examples only, and are not intended to limit the implementations of the application described and / or claimed in this document.

[0178] The embodiments also provide a storage medium, in which a program product capable of realizing the air conditioning unit energy consumption optimization control method based on artificial intelligence is stored. In some possible implementation manners, various aspects of the disclosure can also be implemented in the form of a program product, which includes program codes for causing terminal equipment to perform the steps according to various exemplary embodiments of the disclosure described in the above “Exemplary Method” section of the specification when the program product runs on the terminal equipment.

[0179] The storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0180] The above description of disclosed embodiments allows a person skilled in the art to implement or use the application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing energy consumption control of air conditioning units based on artificial intelligence, characterized in that, The methods include: S101: Use cosine similarity to evaluate the similarity between external environmental parameters and current environmental parameters; S102: Determine whether there are similar environmental parameters. If there are similar environmental parameters, proceed to step S103. If no similar environmental parameters are found, proceed to step S105; S103: Determine if there is a prior optimization record. If there is a prior optimization record, proceed to step S104. Otherwise, it is determined that there is no previous optimization record, and the process proceeds to step S105; S104: Export the optimization data from the previous stage. The exported optimization data includes the swarm intelligence algorithm used in the previous stage and the device configuration parameters searched in the previous stage. S105: Initialize the swarm intelligence algorithm and optimization constraints; S106: Optimize the stepping method based on swarm intelligence algorithm to obtain the optimal control parameters under the current external constraints; In step S106, the optimal control parameters under the current external constraints are obtained based on the following formula. ; (5) Among them, opt Indicates the current status of the air conditioning unit; This indicates that a certain set of air conditioning control parameters is used for operation during a time interval of T, starting from time t; A represents the parameter range for autonomous adjustment by the air conditioning unit; S represents the constraint relationship between the variables of the air conditioning unit. S107: The air conditioning unit operates according to the optimal control parameters under the current external constraints and reads the air conditioning unit's operating data in real time; S108: Determine whether the air conditioning unit's operating data meets the conditions for ending the autonomous optimization process; If the conditions are met, the current autonomous optimization strategy is terminated, and the optimization record under the current environment is saved.

2. The method for optimizing and controlling the energy consumption of air conditioning units based on artificial intelligence according to claim 1, characterized in that, The method also includes: acquiring environmental data Air conditioning unit operation data Electricity consumption data Air conditioning unit setting parameter range ; i Represents a certain data type, t A data type representing a specific point in time.

3. The energy consumption optimization and control method for air conditioning units based on artificial intelligence according to claim 2, characterized in that, Energy consumption sample pairs are constructed based on environmental data, air conditioning unit operation data, and electricity consumption data, and are represented as follows: 。 4. The method for optimizing and controlling the energy consumption of air conditioning units based on artificial intelligence according to claim 1, characterized in that, The method calculates the heat released by the liquid at a given instant based on the following formula. The temperature difference between the current temperature of the liquid and the ambient temperature. ; in, This indicates the specific heat capacity of water. Indicates the mass of the liquid; By integrating the heat at a given instant over a preset time period, the heat exchanged by the air conditioning unit at a certain flow rate within that preset time period can be obtained. The calculation method is as follows: in, This represents a specific point in time during calculation. This indicates the corresponding time interval.

5. The energy consumption optimization and control method for air conditioning units based on artificial intelligence according to claim 1, characterized in that, Swarm intelligence algorithms include: genetic algorithm, particle swarm algorithm, ant colony optimization algorithm, artificial fish swarm algorithm, and artificial bee colony algorithm.

6. The energy consumption optimization and control method for air conditioning units based on artificial intelligence according to claim 1, characterized in that, The optimization constraints in step S105 include: Configure the startup sequence of each main unit in the air conditioning unit; Configure the operating timing of each cooling tower of the air conditioning unit; Configure the inlet and return water temperature thresholds for the air conditioning unit, and adjust only one of the inlet or return water temperatures.

7. An artificial intelligence-based energy consumption optimization and control device for air conditioning units, characterized in that, The apparatus is used to implement the artificial intelligence-based energy consumption optimization and control method for air conditioning units as described in any one of claims 1 to 6; the apparatus includes: The similarity evaluation module is used to evaluate the similarity between external environmental parameters and current environmental parameters using cosine similarity. The parameter determination module is used to determine whether similar environmental parameters exist. The optimization record lookup module is used to determine whether a prior optimization record exists. The optimization data export module is used to export the optimization data from the previous stage. The exported optimization data includes the swarm intelligence algorithm used in the previous stage and the device configuration parameters searched in the previous stage. The algorithm processing module is used to initialize the swarm intelligence algorithm and optimization constraints; The optimization control module uses a swarm intelligence algorithm to perform step-by-step optimization to obtain the optimal control parameters under the current external constraints. The equipment control module is used to enable the air conditioning unit to operate according to the optimal control parameters under the current external constraints and to read the air conditioning unit's operating data in real time. The optimization status judgment module is used to determine whether the air conditioning unit's operating data meets the conditions for ending autonomous optimization; if it does, the current autonomous optimization strategy is terminated and the optimization record under the current environment is saved.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the artificial intelligence-based air conditioning unit energy consumption optimization and control method as described in any one of claims 1 to 6.

9. A 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 artificial intelligence-based air conditioning unit energy consumption optimization and control method as described in any one of claims 1 to 6.

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