Air conditioning system optimization control method and device based on load point table and genetic algorithm

Abstract

The application provides an air conditioning system optimization control method and device based on a load point table and a genetic algorithm. The scheme relates to the technical field of central air conditioning control. The scheme includes constructing a central air conditioning system model and collecting a historical operation data set in the central air conditioning system model. The historical operation data set includes system performance indicators, system temperature parameters, and equipment operation parameters. Based on the system performance indicators, the system temperature parameters, and the equipment operation parameters, an initial load point table is generated, and a stability judgment threshold and system efficiency corresponding to all system total loads are obtained. The system performance indicators include system total loads at different time sequences. Based on the system total loads and a sliding window algorithm, system steady state intervals corresponding to the system total loads are obtained, and steady state load values in each system steady state interval are calculated. According to the steady state load values, the system efficiency, and optimization constraint conditions, the initial load point table is updated to obtain an updated load point table. The updated load point table and the genetic algorithm are used to perform optimization processing on operation parameters of the central air conditioning system model to obtain optimization parameters, and the system temperature parameters and the equipment operation parameters in the central air conditioning system are adjusted and corrected according to the optimization parameters. The scheme helps to improve the self-adaptive optimization performance of the central air conditioning system.

Description

Technical Field

[0001] This invention relates to the field of central air conditioning control technology, and in particular to an optimized control method and device for air conditioning systems based on a load point table and a genetic algorithm. Background Technology

[0002] With the increasing demand for environmental comfort and the development of the social economy, people have higher and higher requirements for indoor environments. Central air conditioning systems, as the main equipment for effectively regulating temperature and humidity inside buildings, play a vital role in people's daily lives and are inseparable from them. In my country, the energy consumption of central air conditioning systems accounts for more than 40% of the total energy consumption of the building industry. For traditional air conditioning systems, the design is usually based on peak load, setting various operating parameters to values ​​under full-load operation. However, in actual operation, full-load operation is rare, leading to a large amount of unnecessary energy waste.

[0003] Chinese patent CN110805997A discloses an energy-saving control method for a central air conditioning system. This method includes the following steps: acquiring equipment operation data and building load data of the air conditioning system; preprocessing the data; learning the preprocessed data through a neural network to obtain a system energy efficiency model; optimizing operating parameters using a genetic algorithm based on the system energy efficiency model; and optimizing the operation of the air conditioning system based on the optimized operating parameters. However, the optimization methods provided in the aforementioned application typically require a large amount of sample data for research and modeling, and finding the optimal solution usually involves highly complex calculations. Furthermore, these methods for energy-saving optimization control often have poor real-time performance and require a long time to search for the optimal solution. Therefore, it is essential to provide an air conditioning system optimization control method and device based on a load point table and a genetic algorithm to improve the adaptive optimization performance of central air conditioning systems. Summary of the Invention

[0004] In view of this, the present invention proposes an optimization control method and apparatus for an air conditioning system based on a load point table and a genetic algorithm. By combining a load point table and a genetic algorithm, the initial load point table provides relevant parameters as the range for the genetic algorithm to solve for individuals. The genetic algorithm is then used to solve for optimization parameters and further update the load point table, thereby improving the adaptive optimization performance of the central air conditioning system.

[0005] This invention provides an optimized control method for an air conditioning system based on a load point table and a genetic algorithm, the method comprising:

[0006] A central air conditioning system model is constructed, and historical operation datasets are collected from the central air conditioning system model. The historical operation datasets include system performance indicators, system temperature parameters, and equipment operation parameters.

[0007] Based on the system performance indicators, the system temperature parameters, and the equipment operating parameters, an initial load point table is generated, and the stability threshold and system efficiency corresponding to the total system load are obtained. The system performance indicators include the total system load under different time series.

[0008] Based on the total system load and the sliding window algorithm, the steady-state interval of the system corresponding to the total system load is obtained, and the steady-state load value in each steady-state interval is calculated.

[0009] The initial load point table is updated based on the steady-state load value, the system efficiency, and the optimization constraints to obtain an updated load point table.

[0010] The updated load point table and genetic algorithm are used to optimize the operating parameters of the central air conditioning system model to obtain optimized parameters, and the system temperature parameters and equipment operating parameters in the central air conditioning system are adjusted and corrected according to the optimized parameters.

[0011] Based on the above technical solutions, preferably, the system performance indicators include the energy consumption of the chilled water circulation system, the energy conversion efficiency of the chilled water circulation system, and the total system load; the system temperature parameters include the temperature difference between the inlet and outlet of the cooling water, the temperature difference between the supply and return of the chilled water, the outlet temperature of the cooling water, and the supply temperature of the chilled water; and the equipment operating parameters include the pump speed, the number of chillers in operation, the number of cooling pumps in operation, the number of chilled pumps in operation, and the number of cooling tower fans in operation.

[0012] Based on the above technical solutions, preferably, the step of obtaining the system steady-state interval corresponding to the total system load based on the total system load and the sliding window algorithm, and calculating the steady-state load value in each system steady-state interval, specifically includes:

[0013] Set the sliding window size and move the sliding window over the time series of the total system load;

[0014] Based on the steady-state determination criteria, determine whether the sliding window under different time series is a steady-state transition interval;

[0015] Multiple steady-state transition intervals that continuously meet the steady-state determination conditions are integrated into a single steady-state interval. The average load value within the steady-state interval is calculated, and this average load value is used as the steady-state load value of the steady-state interval.

[0016] More preferably, the step of obtaining the system steady-state interval corresponding to the total system load based on the total system load and the sliding window algorithm, and calculating the steady-state load value in each system steady-state interval, specifically includes:

[0017] Set the sliding window size and move the sliding window over the time series of the total system load;

[0018] Based on the steady-state determination criteria, determine whether the sliding window under different time series is a steady-state transition interval;

[0019] Multiple steady-state transition intervals that continuously meet the steady-state determination conditions are integrated into a single steady-state interval. The average load value within the steady-state interval is calculated, and this average load value is used as the steady-state load value of the steady-state interval.

[0020] More preferably, updating the initial load point table based on the steady-state load value, the system efficiency, and optimization constraints specifically includes:

[0021] In the steady-state data segment corresponding to the steady-state load value, the steady-state system efficiency corresponding to the steady-state data segment is calculated, wherein the steady-state interval of the system includes multiple steady-state data segments;

[0022] Select the data point corresponding to the maximum steady-state system efficiency, and subtract the steady-state load value corresponding to the data point from the steady-state load value of the operating point in the initial load point table to obtain the steady-state load difference value;

[0023] Determine whether the steady-state load difference is less than or equal to a preset load difference;

[0024] If the steady-state load difference is less than or equal to the preset load difference, then the data point is selected to replace the operating point corresponding to the steady-state load value.

[0025] More preferably, the method further includes:

[0026] If the steady-state load difference is greater than the preset load difference, the data point is directly stored in the initial load point table.

[0027] More preferably, the step of using the updated load point table and genetic algorithm to optimize the operating parameters of the central air conditioning system model to obtain optimized parameters specifically includes:

[0028] The original parameters input to the genetic algorithm are initialized, wherein the original parameters include gene encoding method, population size, number of iterations, energy consumption, and thermal comfort index;

[0029] The fitness function of the genetic algorithm is determined based on the energy consumption and the thermal comfort index, and the fitness function is used to evaluate each individual in the population, and the individuals in the population are sorted according to their fitness.

[0030] A crossover operation is performed on random individuals in the population to generate offspring individuals, and a mutation operation is performed on each offspring individual to generate new genomes and update the population.

[0031] If the number of iterations of the genetic algorithm reaches the preset number of iterations, the iteration process of the genetic algorithm ends, the individual with the highest fitness in the last generation population is selected as the optimal solution, and the optimization parameters are obtained by decoding.

[0032] A second aspect of this application provides an air conditioning system optimization control device based on a load point table and a genetic algorithm. The air conditioning system optimization control device includes a data acquisition module, a load update module, and a parameter optimization module, wherein...

[0033] The data acquisition module is used to construct a central air conditioning system model and collect historical operation datasets from the central air conditioning system model. The historical operation datasets include system performance indicators, system temperature parameters, and equipment operation parameters.

[0034] The load update module is used to generate an initial load point table based on the system performance indicators, the system temperature parameters, and the equipment operating parameters, and to obtain the stability threshold and system efficiency corresponding to the total system load. The system performance indicators include the total system load under different time series. Based on the total system load and the sliding window algorithm, the module obtains the system steady-state interval corresponding to the total system load and calculates the steady-state load value in each system steady-state interval. The module updates the initial load point table according to the steady-state load value, the system efficiency, and optimization constraints to obtain an updated load point table.

[0035] The parameter optimization module is used to optimize the operating parameters of the central air conditioning system model using the updated load point table and genetic algorithm to obtain optimized parameters, and to adjust and correct the system temperature parameters and equipment operating parameters in the central air conditioning system according to the optimized parameters.

[0036] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory.

[0037] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of an air conditioning system optimization control method based on a load point table and a genetic algorithm.

[0038] The air conditioning system optimization control method based on load point table and genetic algorithm provided by this invention has the following advantages over the prior art:

[0039] (1) By using digital twin technology to construct a central air conditioning system model, the central air conditioning system of a large building was modeled and simulated, and a high-accuracy and high-precision model was established. At the same time, the load point table and the genetic algorithm were combined. Based on the initial load point table of the historical operation dataset, the relevant parameters given by the initial load point table were used as the range of the individual solved by the genetic algorithm. Then, the genetic algorithm was used to solve the optimization parameters and further update the load point table, so as to control the central air conditioning system in real time, thereby improving the adaptive optimization performance of the central air conditioning system, the real-time performance of the optimization scheme and the convergence speed.

[0040] (2) By initializing the original parameters and customizing the fitness function, the system is accurately modeled and optimized for a specific air conditioning system, thereby achieving the best balance between energy saving and user experience. The combination of sorting selection, crossover and mutation operations greatly improves the algorithm's ability to explore the solution space. It can not only quickly converge to a high-quality solution, but also effectively avoid getting trapped in local optima, thus finding the global optimum or near-optimal solution in the complex parameter space. By setting a preset number of iterations and selecting the best individual, the system can control the consumption of computing resources while ensuring the optimization quality. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A schematic diagram illustrating the steps of an air conditioning system optimization control method based on a load point table and a genetic algorithm provided by the present invention;

[0043] Figure 2 A schematic diagram of the structure of the air conditioning system optimization control device provided by the present invention;

[0044] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0045] Explanation of reference numerals in the attached diagram: 1. Air conditioning system optimization control device; 11. Data acquisition module; 12. Load update module; 13. Parameter optimization module; 2. Electronic equipment; 21. Processor; 22. Communication bus; 23. User interface; 24. Network interface; 25. Memory. Detailed Implementation

[0046] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0047] Before introducing the embodiments of the present invention, some terms and their abbreviations involved in the embodiments of the present invention will be defined and explained.

[0048] Simstore simulation software is a simulation platform developed using the FORTRAN language. It's a design system integrating thermal and power components from classic industrial systems. Simstore includes typical component libraries, and its usage is similar to most simulation systems. Furthermore, the software can achieve data interaction between the simulation platform and the control platform via the OPC protocol. This protocol is a widely used standard in the automation industry, providing a secure and interoperable data exchange method while improving the speed and accuracy of data transmission. The software mainly consists of three parts: PowerBuilder, CtrlBuilder, and gView. PowerBuilder allows users to develop multi-component thermal system simulations. By combining mass, momentum, and gas pressure equations into a matrix, it facilitates quick and stable solutions for the pressure and flow rates of thermal systems, meeting the accuracy requirements of process industrial system simulation models. CtrlBuilder enables simulation modeling of analog and digital control during the simulation process, including basic control algorithm modules and control adjustment functions, making model development simple and convenient. gView is a screen configuration software developed for monitoring and simulation fields, enabling data visualization of the simulation system. Simstore simulation software focuses on simulating and optimizing HVAC systems in buildings. It provides a powerful and comprehensive set of tools through advanced simulation technology to optimize the design, operation and management of HVAC systems.

[0049] This invention discloses an optimized control method for an air conditioning system based on a load point table and a genetic algorithm, with reference to... Figure 1 The steps of this method include S1 to S5.

[0050] Step S1: Construct a central air conditioning system model and collect historical operation datasets from the central air conditioning system model. The historical operation datasets include system performance indicators, system temperature parameters, and equipment operation parameters.

[0051] In this step, a digital twin model is established using the Simstore simulation platform, based on the actual structure of a large building and the composition of its central air conditioning system. First, operational data obtained from the actual building is used to fit mathematical models of the energy consumption of the chiller, cooling water pump, and chilled water pump. Based on these models, the Simstore software is used to construct three parts of the air conditioning water system: the cooling water loop, the chilled water loop, and the chiller loop. Next, a comparative experiment is conducted between simulated and measured energy consumption. If the average error rate of energy consumption for each device is verified to be within 10%, this air conditioning water system experimental platform can be used as a simulation platform for energy-saving optimization experiments, i.e., a central air conditioning system model.

[0052] In this embodiment, the system performance indicators include the energy consumption of the chilled water circulation system, the energy conversion efficiency of the chilled water circulation system, and the total system load. The system temperature parameters include the temperature difference between the inlet and outlet of the cooling water, the temperature difference between the supply and return of the chilled water, the outlet temperature of the cooling water, and the supply temperature of the chilled water. The equipment operating parameters include the pump speed, the number of chillers in operation, the number of cooling pumps in operation, the number of chilled pumps in operation, and the number of cooling tower fans in operation.

[0053] Step S2: Based on system performance indicators, system temperature parameters, and equipment operating parameters, generate an initial load point table and obtain the stability threshold and system efficiency corresponding to the total system load. The system performance indicators include the total system load under different time series.

[0054] In one example, the collected historical operating data is sorted by time series. The data is divided into three categories: system performance indicators, system temperature parameters, and equipment operating parameters. System performance indicators include load values ​​and optimal system efficiency. System temperature parameters include cooling water inlet and outlet temperature differences, chilled water supply and return temperature differences, cooling water outlet temperature, chilled water supply temperature, and wet-bulb temperature. Equipment operating parameters include the number of chillers, cooling pumps, chilled pumps, and cooling tower fans in operation.

[0055] The total system load range is divided into several intervals, for example, based on a load rate of 10% or 20%. Cluster analysis is performed on the data within each load interval, using algorithms such as K-means or hierarchical clustering, to identify typical operating conditions under similar load conditions. The most representative point in each cluster is selected as the load point; representativeness can be determined based on distance to the cluster center or other correlation indicators. For each selected load point, the corresponding system temperature parameters and equipment operating parameters are extracted.

[0056] Step S3: Based on the total system load and the sliding window algorithm, obtain the system steady-state interval corresponding to the total system load, and calculate the steady-state load value in each system steady-state interval.

[0057] This step also includes steps S31 to S33.

[0058] Step S31: Set the sliding window size and move the sliding window over the time series of total system load.

[0059] In this step, based on the characteristics of the system's total load variation (rate of change and periodicity) and the system's response and settling times, a suitable time span is selected as the sliding window size, such as 30 minutes, 1 hour, or 2 hours. Ensure the system's total load data is arranged in chronological order and check the consistency of the data time intervals; if inconsistent, interpolation is performed. Place the sliding window at the beginning of the time series; the number of data points contained in the sliding window is equal to the window size divided by the data acquisition interval. Define the sliding step size, typically the time interval of one data point, and move the window according to the defined step size from the beginning of the time series. For each sliding window position, extract the system's total load data within the sliding window; stop sliding when the sliding window reaches the end of the time series.

[0060] Step S32: Based on the steady-state determination criteria, determine whether the sliding window under different time series is a steady-state transition interval.

[0061] In this embodiment, the steady-state determination conditions specifically include: obtaining the changes in chilled water supply temperature and cooling water return temperature for each adjustment cycle in the historical operation dataset; determining whether the changes in chilled water supply temperature and cooling water return temperature are both less than the temperature change threshold for 10 consecutive adjustment cycles; if the changes in chilled water supply temperature and cooling water return temperature are both less than the temperature change threshold for 10 consecutive adjustment cycles, then the sliding window is determined to be the steady-state transition interval.

[0062] Step S33: Integrate multiple steady-state transition intervals that continuously meet the steady-state determination conditions into a single steady-state interval, calculate the average load value within the steady-state interval, and use the average load value as the steady-state load value of the steady-state interval.

[0063] In this step, all sliding windows identified as steady-state transition intervals in S32 are traversed. Adjacent steady-state transition intervals are examined; if they are temporally continuous, they are grouped together. For each group of continuous steady-state transition intervals, they are merged into a larger steady-state interval, and the start and end times of each merged steady-state interval are recorded. For each merged steady-state interval, all total system load data within that time range are extracted. The arithmetic mean of the total system load data within each steady-state interval is calculated: Steady-state load value = Σ(all load values ​​within the interval) / number of data points within the interval. The calculated average load value is assigned to the corresponding steady-state interval as the representative steady-state load value for that interval.

[0064] Step S4: Update the initial load point table based on the steady-state load value, system efficiency, and optimization constraints to obtain an updated load point table.

[0065] This step also includes steps S41 to S45.

[0066] Step S41: Calculate the steady-state system efficiency corresponding to the steady-state data segment in the steady-state data segment corresponding to the steady-state load value. The steady-state interval of the system includes multiple steady-state data segments.

[0067] In this step, within each system steady-state interval, multiple steady-state data segments are divided according to predefined criteria (such as time length and data fluctuation range). Each steady-state data segment should represent the stable operating state of the system under a specific load. For each steady-state data segment, relevant operating parameters are extracted, including but not limited to cooling capacity, input power, chilled water supply and return temperatures, cooling water inlet and outlet temperatures, compressor operating parameters, and pump and fan operating parameters. If the duration of the steady-state data segments is different, a time-weighted average can be used to calculate efficiency. Weighted efficiency = Σ(efficiency of each segment × duration of that segment) / total duration. The system efficiency calculation formula is: COP = cooling capacity / (chiller power consumption + chilled water pump power consumption + cooling water pump power consumption + cooling tower fan power consumption).

[0068] Step S42: Select the data point corresponding to the maximum steady-state system efficiency, and subtract the steady-state load value corresponding to the data point from the steady-state load value of the operating point in the initial load point table to obtain the steady-state load difference value.

[0069] In this step, the system efficiency of all steady-state data segments calculated in step S41 is summarized to create a dataset containing steady-state load values, system efficiency, and related operating parameters. The compiled efficiency data is traversed to find the data point with the maximum system efficiency value. If multiple points have the same maximum efficiency, the point with the higher load or longer duration can be selected. The operating point closest to the optimal steady-state load value is found in the initial load point table. Binary search or nearest neighbor algorithms can be used to improve search efficiency.

[0070] Step S43: Determine whether the steady-state load difference is less than or equal to the preset load difference.

[0071] Step S44: If the steady-state load difference is less than or equal to the preset load difference, then select a data point to replace the operating point corresponding to the steady-state load value.

[0072] In this step, the original load point data is removed and new data point information is inserted in the corresponding positions to ensure that the sorting of the load point table (usually sorted by load value) remains unchanged after insertion.

[0073] Step S45: If the steady-state load difference is greater than the preset load difference, the data point is directly stored in the initial load point table.

[0074] Step S5: Use the updated load point table and genetic algorithm to optimize the operating parameters of the central air conditioning system model to obtain optimized parameters, and adjust and correct the system temperature parameters and equipment operating parameters in the central air conditioning system according to the optimized parameters.

[0075] This step also includes steps S51 to S54.

[0076] Step S51: Initialize the original parameters of the input genetic algorithm, including gene encoding method, population size, number of iterations, energy consumption, and thermal comfort index.

[0077] In this step, gene encoding can be performed and encoded in the following ways: binary encoding, where each gene is represented by 0 or 1; real number encoding, where genes are directly represented by real numbers; and integer encoding, where genes are represented by integers. Determine the chromosome length, i.e., the number of genes contained in each individual. For example, for an air conditioning system, genes include parameters such as temperature setpoint and wind speed. Determine the number of individuals in the initial population, typically between 50 and 200. Set the maximum number of iterations for the algorithm, typically between 100 and 1000, and set stopping conditions, such as reaching the maximum number of iterations or meeting a specific convergence criterion. Choose PMV (Predicted Average Votes) or PPD (Predicted Percentage of Dissatisfaction) as the thermal comfort assessment model. Based on the selected encoding method, randomly generate the initial population to ensure that the initial population covers different regions of the solution space, increasing diversity.

[0078] Step S52: Determine the fitness function of the genetic algorithm based on energy consumption and thermal comfort indices, evaluate each individual in the population using the fitness function, and sort the individuals in the population according to their fitness.

[0079] In this step, the multi-objective fitness function can be expressed as: f(x) = w1 × E(x) + w2 × C(x)

[0080] Where E(x) represents the energy consumption index, C(x) represents the thermal comfort index, and w1 and w2 are weighting coefficients used to balance the importance of the two objectives.

[0081] Step S53: Perform a crossover operation on random individuals in the population to generate offspring individuals, and perform a mutation operation on each offspring individual to generate new genomes and update the population.

[0082] In one example, the entire population is sorted from highest to lowest fitness value. A selection ratio is set, such as selecting the top 60% of individuals. The top N individuals are then selected from the sorted population, where N is the population size multiplied by the selection ratio. These selected individuals will serve as parents in subsequent crossover operations. The selected individuals are randomly paired, and a crossover point is randomly chosen on a chromosome. The resulting new individuals are saved. A small mutation probability (e.g., 0.01) is set, and mutation is performed on each offspring individual. This process iterates through each gene locus, generating random numbers; if the number is less than the mutation probability, the gene is modified. The constructed new population includes the offspring generated by crossover mutation and the retained elite individuals. If the number of new individuals is insufficient, some individuals can be added from the original population, ensuring that the new population size matches the initial setting.

[0083] Step S54: If the number of iterations of the genetic algorithm reaches the preset number of iterations, the iteration process of the genetic algorithm ends, the individual with the highest fitness in the last generation population is selected as the optimal solution, and the optimization parameters are obtained by decoding.

[0084] In one example, a table is consulted based on the load value. The control parameters are encoded using the range of control parameters given in the load point table to obtain the genotypes of the parameters. Different genotypes represent different individuals, generating the initial population. After the initial population is generated, a fitness function is designed based on energy consumption and thermal comfort. Each individual's fitness function is calculated, and individuals are selected based on their fitness, ensuring that the genes of individuals with high fitness are more likely to be retained. This selection process is then implemented. Next, the selected parent chromosomes are combined and crossoverd in pairs to produce offspring individuals. Offspring have a certain probability of gene mutation. Finally, through selection, crossover, and mutation, a new population is generated. In this way, a population with increasingly higher overall fitness evolves generation by generation. During the selection process, the environment is more likely to retain individuals with higher fitness, and the genes on the individual's chromosomes are more likely to be passed on to offspring. Therefore, the offspring population has a high probability of being more adapted to the environment than their parents. After the population has iterated to a specified number of times, the optimal chromosome in the last generation is decoded and considered the optimal solution, thus obtaining the latest control parameters.

[0085] By employing digital twin technology to construct a central air conditioning system model, the central air conditioning system of a large building was modeled and simulated, establishing a highly accurate and precise model. Simultaneously, a load point table and a genetic algorithm were combined. Based on an initial load point table derived from historical operating datasets, the parameters provided by the initial load point table served as the range for the genetic algorithm to solve for individuals. The genetic algorithm then solved for optimization parameters and further updated the load point table, thereby enabling real-time control of the central air conditioning system. This improved the adaptive optimization performance, real-time performance of the optimization scheme, and convergence speed of the central air conditioning system.

[0086] Based on the above method, this application discloses a data processing apparatus that combines enhancement and mosaic enhancement, with reference to... Figure 2 The air conditioning system optimization control device 1 includes a data acquisition module 11, a load update module 12, and a parameter optimization module 13, wherein...

[0087] The data acquisition module 11 is used to build a central air conditioning system model and collect historical operating datasets from the central air conditioning system model. The historical operating datasets include system performance indicators, system temperature parameters, and equipment operating parameters.

[0088] The load update module 12 is used to generate an initial load point table based on system performance indicators, system temperature parameters, and equipment operating parameters, and to obtain the stability threshold and system efficiency corresponding to the total system load. The system performance indicators include the total system load under different time series. Based on the total system load and the sliding window algorithm, the module obtains the system steady-state interval corresponding to the total system load and calculates the steady-state load value in each system steady-state interval. The module updates the initial load point table according to the steady-state load value, system efficiency, and optimization constraints to obtain an updated load point table.

[0089] The parameter optimization module 13 is used to optimize the operating parameters of the central air conditioning system model by updating the load point table and using a genetic algorithm to obtain optimized parameters, and to adjust and correct the system temperature parameters and equipment operating parameters in the central air conditioning system according to the optimized parameters.

[0090] In one example, system performance indicators include the energy consumption and energy conversion efficiency of the chilled water circulation system and the total system load. System temperature parameters include the temperature difference between the inlet and outlet of the cooling water, the temperature difference between the supply and return of chilled water, the outlet temperature of the cooling water, and the supply temperature of the chilled water. Equipment operating parameters include the pump speed, the number of chillers in operation, the number of cooling pumps in operation, the number of chilled pumps in operation, and the number of cooling tower fans in operation.

[0091] In one example, the load update module 12 is used to set the sliding window size and move the sliding window over the time series of the total system load; according to the steady-state determination condition, it determines whether the sliding window under different time series is a steady-state transition interval; it integrates multiple steady-state transition intervals that continuously meet the steady-state determination condition into a steady-state interval, calculates the average load value in the steady-state interval, and uses the average load value as the steady-state load value of the steady-state interval.

[0092] In one example, the steady-state determination criteria specifically include:

[0093] Obtain the changes in chilled water supply temperature and cooling water return temperature for each adjustment cycle in the historical operation dataset;

[0094] Determine whether the changes in chilled water supply temperature and cooling water return temperature are both less than the temperature change threshold within 10 consecutive adjustment cycles;

[0095] If the changes in chilled water supply temperature and cooling water return temperature are both less than the temperature change threshold for 10 consecutive adjustment cycles, then the sliding window is determined to be the steady-state transition range.

[0096] In one example, the load update module 12 is used to calculate the steady-state system efficiency corresponding to the steady-state data segment in the steady-state data segment corresponding to the steady-state load value, wherein the steady-state interval of the system includes multiple steady-state data segments; select the data point corresponding to the maximum steady-state system efficiency, and subtract the steady-state load value corresponding to the data point from the steady-state load value of the operating point in the initial load point table to obtain the steady-state load difference; determine whether the steady-state load difference is less than or equal to the preset load difference; if the steady-state load difference is less than or equal to the preset load difference, then select the data point to replace the operating point corresponding to the steady-state load value.

[0097] In one example, the load update module 12 is used to directly store the data points into the initial load point table if the steady-state load difference is greater than the preset load difference.

[0098] In one example, the parameter optimization module 13 is used to initialize the original parameters of the input genetic algorithm, including gene encoding method, population size, number of iterations, energy consumption, and thermal comfort index. The fitness function of the genetic algorithm is determined based on the energy consumption and thermal comfort index, and each individual in the population is evaluated using the fitness function. The individuals in the population are then sorted according to their fitness. Random individuals in the population are paired and crossoverdone to generate offspring individuals, and each offspring individual undergoes mutation to generate new genomes and update the population. If the number of iterations of the genetic algorithm reaches the preset number of iterations, the iteration process of the genetic algorithm ends, the individual with the highest fitness in the last generation of the population is selected as the optimal solution, and the optimized parameters are obtained through decoding.

[0099] Please see Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 2 may include: at least one processor 21, at least one network interface 24, user interface 23, memory 25, and at least one communication bus 22.

[0100] The communication bus 22 is used to enable communication between these components.

[0101] The user interface 23 may include a display screen and a camera. Optionally, the user interface 23 may also include a standard wired interface and a wireless interface.

[0102] The network interface 24 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0103] The processor 21 may include one or more processing cores. The processor 21 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 25, and by calling data stored in the memory 25. Optionally, the processor 21 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 21.

[0104] The memory 25 may include random access memory (RAM) or read-only memory. Optionally, the memory 25 may include non-transitory computer-readable storage medium. The memory 25 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 25 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 25 may also be at least one storage device located remotely from the aforementioned processor 21. Figure 3 As shown, the memory 25, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for an air conditioning system optimization control method based on a load point table and a genetic algorithm.

[0105] exist Figure 3 In the electronic device 2 shown, the user interface 23 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 21 can be used to call the application program stored in the memory 25, which is based on the load point table and the genetic algorithm for optimizing the control of the air conditioning system. When executed by one or more processors, the electronic device executes one or more methods as described in the above embodiments.

[0106] A computer-readable storage medium storing instructions that, when executed by one or more processors, cause a computer to perform one or more methods as described in the embodiments above.

[0107] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0108] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0110] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0112] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0113] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truths. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure.

[0114] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing control of an air conditioning system based on a load point table and a genetic algorithm, characterized in that, The method includes: A central air conditioning system model is constructed, and historical operation datasets are collected from the central air conditioning system model. The historical operation datasets include system performance indicators, system temperature parameters, and equipment operation parameters. Based on the system performance indicators, the system temperature parameters, and the equipment operating parameters, an initial load point table is generated, and the stability threshold and system efficiency corresponding to the total system load are obtained. The system performance indicators include the total system load under different time series. Based on the total system load and the sliding window algorithm, the steady-state interval of the system corresponding to the total system load is obtained, and the steady-state load value in each steady-state interval is calculated. The initial load point table is updated based on the steady-state load value, the system efficiency, and the optimization constraints to obtain an updated load point table. The step of updating the initial load point table based on the steady-state load value, the system efficiency, and optimization constraints specifically includes: In the steady-state data segment corresponding to the steady-state load value, the steady-state system efficiency corresponding to the steady-state data segment is calculated, wherein the steady-state interval of the system includes multiple steady-state data segments; Select the data point corresponding to the maximum steady-state system efficiency, and subtract the steady-state load value corresponding to the data point from the steady-state load value of the operating point in the initial load point table to obtain the steady-state load difference value; Determine whether the steady-state load difference is less than or equal to a preset load difference; If the steady-state load difference is less than or equal to the preset load difference, then the data point is selected to replace the operating point corresponding to the steady-state load value; If the steady-state load difference is greater than the preset load difference, the data point is directly stored in the initial load point table. The updated load point table and genetic algorithm are used to optimize the operating parameters of the central air conditioning system model to obtain optimized parameters, and the system temperature parameters and equipment operating parameters in the central air conditioning system are adjusted and corrected according to the optimized parameters.

2. The method as described in claim 1, characterized in that, The system performance indicators include the energy consumption and energy conversion efficiency of the chilled water circulation system, as well as the total system load. The system temperature parameters include the temperature difference between the inlet and outlet of the cooling water, the temperature difference between the supply and return of chilled water, the outlet temperature of the cooling water, and the supply temperature of the chilled water. The equipment operating parameters include the pump speed, the number of chillers in operation, the number of cooling pumps in operation, the number of chilled pumps in operation, and the number of cooling tower fans in operation.

3. The method as described in claim 1, characterized in that, The step of obtaining the system steady-state interval corresponding to the total system load based on the total system load and the sliding window algorithm, and calculating the steady-state load value in each system steady-state interval, specifically includes: Set the sliding window size and move the sliding window over the time series of the total system load; Based on the steady-state determination criteria, determine whether the sliding window under different time series is a steady-state transition interval; Multiple steady-state transition intervals that continuously meet the steady-state determination conditions are integrated into a single steady-state interval. The average load value within the steady-state interval is calculated, and this average load value is used as the steady-state load value of the steady-state interval.

4. The method as described in claim 3, characterized in that, The steady-state determination conditions specifically include: Obtain the changes in chilled water supply temperature and cooling water return temperature for each adjustment cycle in the historical operation dataset; Determine whether the temperature change of the chilled water supply temperature and the temperature change of the cooling water return temperature are both less than the temperature change threshold within 10 consecutive adjustment cycles; If the change in the chilled water supply temperature and the change in the cooling water return temperature are both less than the temperature change threshold for 10 consecutive adjustment cycles, then the sliding window is determined to be a steady-state transition range.

5. The method as described in claim 1, characterized in that, The process of optimizing the operating parameters of the central air conditioning system model using the updated load point table and genetic algorithm to obtain optimized parameters specifically includes: The original parameters input to the genetic algorithm are initialized, wherein the original parameters include gene encoding method, population size, number of iterations, energy consumption, and thermal comfort index; The fitness function of the genetic algorithm is determined based on the energy consumption and the thermal comfort index, and the fitness function is used to evaluate each individual in the population, and the individuals in the population are sorted according to their fitness. A crossover operation is performed on random individuals in the population to generate offspring individuals, and a mutation operation is performed on each offspring individual to generate new genomes and update the population. If the number of iterations of the genetic algorithm reaches the preset number of iterations, the iteration process of the genetic algorithm ends, the individual with the highest fitness in the last generation population is selected as the optimal solution, and the optimization parameters are obtained by decoding.

6. An air conditioning system optimization control device based on load point table and genetic algorithm, characterized in that, The air conditioning system optimization control device (1) includes a data acquisition module (11), a load update module (12), and a parameter optimization module (13), wherein, The data acquisition module (11) is used to construct a central air conditioning system model and collect historical operation datasets from the central air conditioning system model. The historical operation datasets include system performance indicators, system temperature parameters, and equipment operation parameters. The load update module (12) is used to generate an initial load point table based on the system performance indicators, the system temperature parameters, and the equipment operating parameters, and to obtain the stability threshold and system efficiency corresponding to the total system load. The system performance indicators include the total system load under different time series. Based on the total system load and the sliding window algorithm, the module obtains the system steady-state interval corresponding to the total system load and calculates the steady-state load value in each system steady-state interval. The module updates the initial load point table according to the steady-state load value, the system efficiency, and the optimization constraints to obtain an updated load point table. The step of updating the initial load point table based on the steady-state load value, the system efficiency, and optimization constraints specifically includes: In the steady-state data segment corresponding to the steady-state load value, the steady-state system efficiency corresponding to the steady-state data segment is calculated, wherein the steady-state interval of the system includes multiple steady-state data segments; Select the data point corresponding to the maximum steady-state system efficiency, and subtract the steady-state load value corresponding to the data point from the steady-state load value of the operating point in the initial load point table to obtain the steady-state load difference value; Determine whether the steady-state load difference is less than or equal to a preset load difference; If the steady-state load difference is less than or equal to the preset load difference, then the data point is selected to replace the operating point corresponding to the steady-state load value; If the steady-state load difference is greater than the preset load difference, the data point is directly stored in the initial load point table. The parameter optimization module (13) is used to optimize the operating parameters of the central air conditioning system model by using the updated load point table and genetic algorithm to obtain optimized parameters, and to adjust and correct the system temperature parameters and equipment operating parameters in the central air conditioning system according to the optimized parameters.

7. An electronic device, characterized in that, The device includes a processor (21), a memory (25), a user interface (23), and a network interface (24). The memory (25) is used to store instructions. The user interface (23) and the network interface (24) are used to communicate with other devices. The processor (21) is used to execute the instructions stored in the memory (25) to cause the electronic device (2) to perform the method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-5.

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