Water chilling unit group control method and system based on ESC

Abstract

The invention relates to a water chilling unit group control method and system based on an ESC. The method comprises the steps that meteorological parameters, air conditioner area equipment heat dissipation, personnel loads and the like are obtained in real time for load prediction; establishing a water chilling unit energy efficiency model based on the mechanism; a model self-updating device is designed, operation data can be processed and stored, the characteristics can be changed due to aging and other reasons when a host operates for a period of time, a model updating strategy verifies the model precision according to the actual operation data, and model parameters are corrected if the deviation is too large; the group control strategy establishes an objective function and constraint conditions, collects cooling water parameters, outdoor environment wet bulb temperature and other parameters based on a load prediction result, performs optimization through an escape optimization algorithm ESC, and outputs the number of on-off hosts, the load rate of each host and the like.

Description

Technical Field

[0001] The present invention relates to the field of refrigeration and air conditioning, and in particular to a chiller group control method and system based on an escape optimization ESC algorithm. Background Art

[0002] As global energy issues and environmental pressures become increasingly severe, building energy consumption has become an important area for reducing carbon emissions and energy waste. In modern buildings, the energy consumption of central air-conditioning systems usually accounts for more than 40% of the total building energy consumption, and is one of the key objects in the field of building energy conservation. As the core equipment of the air-conditioning system, the chiller bears the task of cooling load, and the efficient operation of the chiller is crucial to energy conservation.

[0003] Traditional chiller load control methods often rely on experience settings or fixed modes, lack flexibility and adaptability, and cannot cope well with load changes and the impact of external environmental factors, resulting in serious energy waste. In recent years, with the development of information technology, intelligent control technology has begun to be applied to the management of air-conditioning systems in order to achieve a more refined and intelligent control effect.

[0004] Most of the existing chiller group control methods use fixed modes or empirical settings for load distribution, which have the following technical defects:

[0005] Low prediction accuracy: Traditional methods usually use static models (such as linear regression) to predict load demand, but do not consider the dynamic changes of the external environment (such as meteorological factors, personnel activities, etc.) and the operating status of the unit. Therefore, the prediction accuracy is low and excessive or insufficient cooling load supply may occur.

[0006] Model fixation: Many control strategies make decisions based on preset models. These models usually ignore the performance degradation of the unit after long-term operation or changes in the external environment, resulting in inaccurate models and inability to adapt to the complexity of long-term operation of the chiller.

[0007] Limitations of optimization algorithms: Existing optimization algorithms, such as genetic algorithms and particle swarm optimization, may fall into local optimal solutions when solving multi-unit load balancing, and cannot quickly converge to the global optimal solution. They are also difficult to cope with load fluctuations and environmental changes in actual operations. Summary of the invention

[0008] The purpose of the present invention is to solve the problems in the prior art of inaccurate load prediction and the optimization algorithm being prone to falling into local optimal solutions, and to provide an ESC-based chiller group control method and system that maximizes the overall energy efficiency of the chiller group.

[0009] In order to solve the above technical problems, the technical method adopted by the present invention is as follows: The present invention discloses a chiller group control method based on ESC, comprising the following steps:

[0010] S1. Load forecasting: The system collects relevant data affecting the cooling load in real time through sensors, including but not limited to meteorological parameters, heat dissipation of equipment in the air-conditioning area, personnel load and fresh air load;

[0011] Load forecasting algorithm: Based on the neural network model combining the long short-term memory network LSTM and the attention mechanism KAN layer, load forecasting is carried out in combination with real-time collected meteorological data, equipment heating conditions and personnel activities; the model can capture the timing and dynamic changes of cooling load and improve the accuracy of forecasting;

[0012] S2. According to the working characteristics of the chiller, an energy efficiency model is constructed to describe the relationship between load rate and power consumption;

[0013] The model takes into account the working status of the unit, including the energy efficiency performance under different conditions such as partial load and full load;

[0014] The introduction of a model self-updating device can adjust the parameters of the energy efficiency model in real time according to the operating data of the chiller to ensure that the load forecast matches the actual energy efficiency requirements;

[0015] If the model prediction deviation is large, the model parameters will be automatically corrected to ensure long-term stable operation;

[0016] S3. Based on the load forecast results, design the objective function of the chiller group control, usually to minimize the total energy consumption of the system;

[0017] The constraints include that the load factor of each chiller is within a reasonable range to ensure efficient operation of the system;

[0018] The partial load rate (PLR) of the chiller is used as the key variable for optimization. The ESC algorithm is used to optimize the number of chillers on and off and the load rate.

[0019] The goal is to optimize the energy efficiency of the entire chiller fleet and reduce energy waste;

[0020] S4. Application of ESC algorithm

[0021] S41. Initialization: First, initialize the load rate PLR ​​of each chiller and randomly assign it through the individual initialization mechanism of the ESC algorithm; the load rate of each chiller is between 0 and 1, reflecting the load state in the initial stage;

[0022] S42. Fitness calculation: Calculate the fitness of each individual. The fitness function evaluates the pros and cons of the load distribution scheme by calculating the total energy consumption of the system. The goal is to minimize the total energy consumption of the system while ensuring that the load demand of each chiller is met.

[0023] S43. According to the ESC algorithm, the load distribution problem is transformed into an individual update problem; individuals are divided into three groups: calm, gathered and panic:

[0024] Calm individuals: Individuals rationally move closer to the system load center and update the load distribution plan;

[0025] Aggregate individuals: Individuals are influenced by calm individuals and panic individuals to update the load distribution plan;

[0026] Panic individuals: individuals make random adjustments when load fluctuates drastically, simulating irrational responses in load distribution;

[0027] S44. Panic index and convergence: As the number of iterations increases, the panic index gradually decreases, individual behaviors gradually become stable, and finally converge to the global optimal solution;

[0028] S45. Output the optimal solution: After multiple iterations, the final load distribution plan is obtained, and the optimal number of chillers on and off, load rate and other information are output.

[0029] Furthermore, in step S3, the partial load ratio of the chiller is:

[0030] Among them, PLR i is the partial load rate of the i-th chiller, is the actual load of the chiller, is the design load of the chiller;

[0031]

[0032] Where: P i is the power consumption of the i-th chiller, a, b, c, d are the fitting coefficients of the host-part load rate curve.

[0033] Furthermore, in the parallel chiller system, sufficient cooling load should be provided to meet the system cooling load demand; to ensure efficient operation of the chiller, the PLR ​​value must be between 0.3 and 1; when the chiller is turned off, the PLR ​​is 0; the objective function and constraints are as follows:

[0034]

[0035] In the formula, Q need is the total cooling load demand of the system, and Obj is the minimum target power consumption of the parallel chiller system.

[0036] Furthermore, in step S4, the following inputs are set: the number of chillers N; the initial load factor of each chiller PLR0 = [PLR1, PLR2, ..., PLR N ], and the initial value satisfies 0.3≤PLR i ≤1 or 0;

[0037] The COP value of each unit COP=[COP1, COP2, ..., COP N ], maximum number of iterations T max , elite pool size E size ;

[0038] Set output: Optimal load distribution scheme PLR opt , to ensure ∑Q i PLR i ≥Q need , which corresponds to the load distribution with the lowest fitness.

[0039] Furthermore, in the step S41, the following steps are also included:

[0040] S411. Randomly generate the load rate PLR0 of the chiller, and the initial value satisfies 0.3≤PLR i ≤1 or 0;

[0041] S412. Initialize the population, each individual in the population represents a load distribution plan for a chiller;

[0042] S413. Set the maximum number of iterations T max .

[0043] Furthermore, in the step S42, the following steps are also included:

[0044] S421. For each individual PLR i , calculate its power consumption P i , and calculate the total power consumption of the system;

[0045] S422. Calculate the degree to which the load distribution meets the cooling load demand and ensure ∑Q i PLR i ≥Q need ;

[0046] S423. Comprehensive fitness:

[0047] Among them, λ is a penalty factor. When the cooling load demand is not met, the fitness will increase the penalty term.

[0048] Furthermore, in step S43, the following steps are also included:

[0049] S431. Calculate the fitness of each individual and sort them in ascending order of fitness, and select the best individual to enter the elite pool Elite Pool , size is E size ;

[0050] S4322. Iteration process: For each individual, perform the following updates:

[0051] Calm group: Individuals rationally move toward the average of the individuals in the elite pool:

[0052]

[0053] Among them, center is the average value of individual load rates in the elite pool;

[0054] Aggregate Groups: Individuals affected by calm and panic groups Update:

[0055]

[0056] Among them, PLR p is an individual randomly selected from the panic group, v h is the velocity vector of the gathering group;

[0057] Panic herd: individuals randomly choose new load distribution:

[0058]

[0059] Among them, E j For individuals randomly selected from the elite pool, PLR rand is a randomly selected individual, v p is the velocity vector of the panic group.

[0060] Furthermore, when the number of iterations exceeds T max / 2All individuals tend to the optimal solution in the elite pool, and all individuals are updated to elite pool members or randomly selected individuals; as the number of iterations t increases, the panic index P(t) gradually decreases, and the behavior of the simulated individuals gradually stabilizes: P(t) = P max ·(1-t / T max ).

[0061] The present invention also discloses a chiller group control system based on ESC, which includes the steps of the chiller group control method described above, and is optimized and adjusted based on the following modules to ensure efficient operation of the chiller group control system and achieve optimal energy efficiency;

[0062] The modules include:

[0063] Data acquisition and monitoring module: used to collect data such as meteorological parameters, heat dissipation of air-conditioning area equipment, and personnel load in real time, and transmit the real-time data to the data storage module to provide support for load forecasting and energy efficiency models;

[0064] Load forecasting module: receives real-time data from the data acquisition module, uses a neural network model that combines the long short-term memory network LSTM with the attention mechanism KAN layer to forecast load and output future load demand;

[0065] Energy efficiency model and self-update mechanism module: Based on the working characteristics of the chiller, an energy efficiency model is established to describe the relationship between load rate and power consumption; when there is a large deviation between the actual operating data and the model prediction, the model parameters are automatically adjusted to ensure long-term stable operation;

[0066] Group control strategy module: Based on the load forecast results, the group control strategy is designed. The objective function is to minimize the total energy consumption of the system, and the constraint condition is the reasonable range of the chiller load rate (for example, 30%-100%). This module uses the ESC algorithm to optimize the load distribution and the number of on / off units of the chiller to achieve optimal system energy efficiency.

[0067] ESC algorithm module: responsible for executing load distribution optimization tasks, simulating individual behaviors through the ESC algorithm, and optimizing the load distribution of the chiller; this module adjusts the PLR ​​and the number of on / off units of each unit according to the adaptability of the chiller load distribution, and outputs the optimal solution;

[0068] Output module: Output the load rate and number of on / off units of each chiller according to the optimization results, and apply the solution to the chiller group control system to ensure the energy efficiency of the system is optimized;

[0069] Real-time monitoring and accuracy assessment module: used to monitor the operating status of the chiller in real time, detect the accuracy of the load prediction model and energy efficiency model, ensure that the load distribution plan matches the actual operating requirements, and make corrections based on real-time data.

[0070] Furthermore, it also includes:

[0071] A data processing module, used for preprocessing the chiller operation data;

[0072] A data storage module, used for storing pre-processed operation data;

[0073] The model correction module is used to correct the parameters of the chiller energy efficiency model according to the preprocessed operating data.

[0074] Beneficial effects:

[0075] The present invention provides a chiller group control method and system based on the ESC algorithm, which solves the problems of inaccurate load prediction and optimization algorithm limitations in the prior art. By combining the LSTM and KAN neural networks for load prediction and using the ESC algorithm for load distribution optimization, the operation of the chiller group can be continuously optimized under dynamically changing load demands and environmental conditions, ensuring efficient use of energy, reducing energy consumption and carbon emissions, and improving building energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 Provides an overall control logic diagram of the chiller group control strategy for the embodiment of the present invention;

[0077] Figure 2 It is the overall flow chart of load forecasting in the present invention;

[0078] Figure 3 The schematic diagram is used to establish and update the energy efficiency model in the present invention. DETAILED DESCRIPTION

[0079] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0080] Example 1. Chiller group control method based on ESC

[0081] like Figure 1-3 As shown, a chiller group control method based on ESC includes the following steps:

[0082] Based on real-time meteorological parameters, heat dissipation of equipment in air-conditioned areas, and personnel load, a prediction algorithm is used to perform load forecasting to obtain future load demand.

[0083] The neural network structure includes LSTM layer, KAN layer and output layer:

[0084] 1) The LSTM layer is responsible for processing the input time series data and capturing the long-term dependencies in the data through its internal memory units and gating mechanisms.

[0085] 2) The attention layer (KAN) receives the output of the LSTM layer and assigns different weights to the outputs of different time steps through the attention mechanism, highlighting the impact of important information on the prediction results.

[0086] 3) The output layer is responsible for converting the features processed by the attention layer into the final prediction results.

[0087] Model training process:

[0088] 1) Data preprocessing

[0089] The original load data is cleaned to remove outliers and missing values.

[0090] Normalize the data to eliminate the impact of different dimensions on model training.

[0091] Divide the training set and test set for model training and evaluation.

[0092] 2) Model training

[0093] The LSTM-KAN model is trained using the training set data, and the model parameters are updated through the back-propagation algorithm and optimizer (such as Adam).

[0094] During the training process, strategies such as Early Stopping can be used to prevent overfitting.

[0095] 3) Model evaluation

[0096] Use the test set data to evaluate the trained LSTM-KAN model and calculate indicators such as prediction error (such as mean square error MSE, mean absolute error MAE, etc.) and accuracy.

[0097] A comparative analysis is conducted with other load forecasting models (such as traditional RNN, LSTM, ARIMA, etc.) to evaluate the performance advantages of the LSTM-KAN model.

[0098] Based on real-time meteorological parameters, heat dissipation of equipment in air-conditioned areas, and personnel load, a prediction algorithm is used to perform load forecasting to obtain future load demand.

[0099] like Figure 2 As shown in Figure 2, the overall load forecast includes:

[0100] 1. Input parameters

[0101] The left side of the figure lists four input parameters: meteorological parameters, equipment heat dissipation, personnel load and fresh air load. These parameters will be used as input data for the neural network to predict the load.

[0102] 2. Data processing and data collection: First, data collection is carried out to collect various load-related data.

[0103] Data processing: Processing the collected data, which may include data cleaning, normalization and other operations to facilitate subsequent analysis.

[0104] Load data true value: The processed data will be used as the load data true value for training and validation.

[0105] 3. Sample division Training samples: A portion of the data is divided into training samples for training the neural network.

[0106] Validation samples: Another part of the data is divided into validation samples to verify the performance of the trained neural network.

[0107] 4. Neural Network Training Neural Network Training: Use training samples to train the neural network. The figure shows the structure of a multi-layer neural network, including input layer, hidden layer and output layer.

[0108] RMSE compliance: During the training process, the performance of the model is evaluated by calculating the root mean square error (RMSE) and mean absolute error (MAE). The formula is as follows: When the RMSE reaches a preset standard, the training can be stopped.

[0109] 5. Verify and complete error analysis: Use verification samples to verify the trained model to ensure that the model error is within an acceptable range.

[0110] Training completed: If the error analysis meets the requirements, the training process is completed and the trained model can be used for load forecasting.

[0111] The neural network structure includes LSTM layer, KAN layer and output layer:

[0112] 1) The LSTM layer is responsible for processing the input time series data and capturing the long-term dependencies in the data through its internal memory units and gating mechanisms.

[0113] 2) The attention layer (KAN) receives the output of the LSTM layer and assigns different weights to the outputs of different time steps through the attention mechanism, highlighting the impact of important information on the prediction results.

[0114] 3) The output layer is responsible for converting the features processed by the attention layer into the final prediction results.

[0115] Model training process:

[0116] 1) Data preprocessing

[0117] The original load data is cleaned to remove outliers and missing values.

[0118] Normalize the data to eliminate the impact of different dimensions on model training.

[0119] Divide the training set and test set for model training and evaluation.

[0120] 2) Model training

[0121] The LSTM-KAN model is trained using the training set data, and the model parameters are updated through the back-propagation algorithm and optimizer (such as Adam).

[0122] During the training process, strategies such as Early Stopping can be used to prevent overfitting.

[0123] 3) Model evaluation

[0124] Use the test set data to evaluate the trained LSTM-KAN model and calculate indicators such as prediction error (such as mean square error MSE, mean absolute error MAE, etc.) and accuracy.

[0125] A comparative analysis is conducted with other load forecasting models (such as traditional RNN, LSTM, ARIMA, etc.) to evaluate the performance advantages of the LSTM-KAN model.

[0126] like Figure 1 The logic shown here assumes a chiller cluster consisting of five parallel chillers. Each chiller has different performance, and the load distribution needs to be adjusted based on real-time meteorological data, load forecasts, cooling water temperature, and outdoor wet-bulb temperature. The goal is to reduce overall energy consumption while ensuring smooth operation of the chiller during load fluctuations.

[0127] initialization:

[0128] The load allocation problem of the chiller group is transformed into an N-dimensional vector problem. Assume that there are 5 chillers, and the load factor (PLR) of each chiller is initialized to a random value, for example: [0.5, 0.3, 0.7, 0.6, 0.4].

[0129] Algorithm description:

[0130] enter:

[0131] The number of chillers N; the initial load factor of each chiller PLR0 = [PLR1, PLR2, ..., PLR N ], and the initial value satisfies 0.3≤PLR i ≤1 or 0; COP value of each unit COP=[COP1,COP2,...,COP N ], maximum number of iterations T max , elite pool size E size .

[0132] Output:

[0133] Optimal load distribution scheme PLR opt , to ensure ∑Q i PLR i ≥Q need , which corresponds to the load distribution with the lowest fitness.

[0134] step:

[0135] initialization:

[0136] Randomly generate the load rate PLR0 of the chiller, and the initial value satisfies 0.3≤PLR i ≤1 or 0.

[0137] Initialize the population, each individual in the population represents a load allocation plan for a chiller.

[0138] Set the maximum number of iterations T max .

[0139] Fitness calculation:

[0140] For each individual PLR i , calculate its power consumption P i , and calculate the total system power consumption.

[0141] Calculate the degree to which the load distribution meets the cooling load demand and ensure ∑Q i PLR i ≥Q need .

[0142] Comprehensive fitness:

[0143]

[0144] Among them, λ is a penalty factor. When the cooling load demand is not met, the fitness will increase the penalty term.

[0145] Population updating and behavior simulation:

[0146] Calculate the fitness of each individual and sort them in ascending order of fitness, and select the best individual to enter the elite pool Elite Pool (Size is E size ).

[0147] Iteration process: For each individual, the following updates are performed:

[0148] Calm group: Individuals rationally move toward the average of the individuals in the elite pool:

[0149] Among them, center is the average value of individual load rates in the elite pool.

[0150] Aggregate Groups: Individuals affected by calm and panic groups Update:

[0151] Among them, PLR p is an individual randomly selected from the panic group, v h is the velocity vector of the cluster.

[0152] Panic herd: individuals randomly choose new load distribution:

[0153]

[0154] Among them, E j For individuals randomly selected from the elite pool, PLR rand is a randomly selected individual, v p is the velocity vector of the panic group.

[0155] Convergence phase:

[0156] When the number of iterations exceeds T max / 2All individuals tend to the optimal solution in the elite pool.

[0157] All individuals are updated to elite pool members or randomly selected individuals.

[0158] Fear Index Adjustment:

[0159] As the number of iterations t increases, the panic index P(t) gradually decreases, and the behavior of the simulated individuals gradually stabilizes: P(t) = P max ·(1-t / T max )

[0160] Output the optimal solution:

[0161] Output optimal load distribution scheme PLR opt , which corresponds to the load distribution with the lowest fitness.

[0162] 3) Actual application results:

[0163] Assume that after several rounds of iterations, the load distribution scheme finally determined by the ESC algorithm may be as follows:

[0164] Unit 1: Load factor 0.6

[0165] Unit 2: Load factor 0.3

[0166] Unit 3: Load factor 0.7

[0167] Unit 4: Load factor 0.5

[0168] Unit 5: Load factor 0.4

[0169] At this time, the total energy consumption of the system is minimized and the chilled water temperature is within the set range. Through the optimization of the ESC algorithm, the chiller group can be dynamically adjusted according to the external environment, load demand and internal unit performance, thereby achieving energy efficiency optimization while meeting the chilled water temperature and load requirements.

[0170] When the chiller is in the initial operation stage and there is no historical data for modeling the objective function and constraints, in the absence of a partial load rate-energy consumption model for the chiller, the empirical P=f(PLR) formula can be used to calculate, based on experience and a large amount of literature research, the safe operating range of the chiller is 30% to 100%. The units are sorted and numbered according to their rated capacity, and the required number of units and unit numbers are calculated in sequence according to the current required load (which can be the load prediction result or the actual measurement result). According to the empirical formula P=f(PLR), the load is distributed to the high-efficiency range of each turned-on unit, and the startup plan, unit number, number of units and each load rate are output.

[0171] During the initial operation stage of the chiller, an initial model is established, and system operation data is collected and stored in real time based on the data acquisition platform. The status of the chiller system is monitored regularly, the accuracy of the load prediction model and the load distribution model of the chiller are tested, and a reasonable accuracy range is set. When the accuracy exceeds the allowable range, the load prediction model and the load distribution model of the chiller are corrected to ensure the stability and efficient operation of the system.

[0172] Example 2. Chiller group control system based on ESC

[0173] This system is based on the above-mentioned ESC chiller group control method and realizes the group control optimization of chillers. The system combines load forecasting, energy efficiency optimization and dynamic adjustment functions to minimize the total energy consumption of the chiller group and improve operating efficiency. Specific modules include data acquisition, load forecasting, energy efficiency optimization, ESC algorithm implementation, accuracy evaluation, etc., and continuously optimizes the load distribution and operating status of the chiller through a closed-loop mechanism.

[0174] 1. Data acquisition and monitoring module

[0175] This module collects data related to the operation of the chiller in real time through sensors and monitoring equipment. Specific data include but are not limited to the following:

[0176] Meteorological parameters: environmental data such as temperature, humidity, wind speed, etc., which affect the load demand of the chiller.

[0177] Heat dissipation from equipment in the air-conditioning area: The heat dissipation from equipment in the air-conditioning area affects the cooling load of the chiller.

[0178] Personnel load situation: The amount of personnel activities directly affects the load demand of the air-conditioned area.

[0179] These real-time data will be transmitted to other modules of the system for subsequent load forecasting and energy efficiency model correction.

[0180] 2. Data processing module

[0181] This module is mainly responsible for preprocessing the chiller operation data to ensure the accuracy and completeness of the data. Specific functions include:

[0182] Data cleaning: remove outliers, missing values ​​and noise data to ensure data quality.

[0183] Data standardization: Normalize different types of data to eliminate the impact of different dimensions and facilitate the calculation of subsequent models.

[0184] Feature extraction: Extract important features related to load forecasting and energy efficiency optimization from raw data, such as equipment power, load fluctuation, etc.

[0185] The preprocessed data will be passed to the downstream load forecasting and energy efficiency model modules.

[0186] 3. Data storage module

[0187] The data storage module is used to store processed data. It saves preprocessed real-time data and historical data in the database for subsequent analysis and model training. This module has the following features:

[0188] Data storage: Stores sensor data, equipment operating status, load demand forecasts, etc. collected and processed in real time.

[0189] Long-term tracking: It can record the long-term operating data of the chiller and provide data support for the correction of the energy efficiency model.

[0190] Efficient access: Adopt efficient data access mechanism to ensure that real-time data can be quickly transmitted to other modules for processing.

[0191] 4. Load forecasting module

[0192] The load forecasting module is one of the core parts of the entire system. By receiving data from the data acquisition and monitoring module, the load forecasting is performed using a neural network model based on the combination of LSTM (Long Short-Term Memory Network) and KAN (Attention Mechanism) layers. The specific steps include:

[0193] Input data: data from real-time collection of meteorological parameters, equipment heating, personnel activities, etc.

[0194] LSTM layer: processes time series data, learns and captures long-term dependencies of load changes.

[0195] KAN layer: The attention mechanism is used to assign weights to different time steps, highlighting the impact of important information on the prediction results.

[0196] Output results: Output future load demand forecast as the basis for subsequent group control strategy and energy efficiency optimization.

[0197] This module can effectively capture the timing and dynamic changes of load and provide accurate load forecasting.

[0198] 5. Energy efficiency model and self-update mechanism module

[0199] This module establishes a chiller energy efficiency model that describes the relationship between load rate (PLR) and power consumption. The energy efficiency model takes into account the unit energy efficiency performance under different working conditions (such as partial load and full load). The system calibrates the model through real-time operation data to ensure that the load forecast matches the actual energy efficiency requirements.

[0200] Model self-update mechanism: When there is a large deviation between the operating data of the chiller and the predicted results, this mechanism can automatically adjust the model parameters to ensure the continued accuracy of the model.

[0201] Performance optimization: Dynamically adjust according to data deviations in actual operation to maintain system stability and efficiency.

[0202] 6. Group control strategy module

[0203] The group control strategy module is responsible for designing the system's group control strategy based on load forecast results and energy efficiency optimization goals. The main work content of this module includes:

[0204] Optimization objective function: Design and implement an optimization objective function, usually to minimize the total energy consumption of the system:

[0205]

[0206] In the formula, Q need is the total cooling load demand of the system, and Obj is the minimum target power consumption of the parallel chiller system.

[0207] Where, the partial load ratio of the chiller is:

[0208]

[0209] Among them, PLR i is the partial load rate of the i-th chiller, is the actual load of the chiller, is the design load of the chiller.

[0210]

[0211] Where: P i is the power consumption of the i-th chiller, a, b, c, d are the fitting coefficients of the host-part load rate curve.

[0212] In a parallel chiller system, sufficient cooling load should be provided to meet the system cooling load requirements. To ensure efficient operation of the chiller, the PLR ​​value must be between 0.3-1; when the chiller is turned off, the PLR ​​is 0.

[0213] Load distribution plan: According to the group control objective function, adjust the load rate and the number of on / off units of each chiller to achieve optimal energy efficiency.

[0214] 7.ESC algorithm module

[0215] The ESC algorithm module is used to perform chiller group control optimization. This module uses the ESC (escape optimization) algorithm to simulate the behavior of the crowd and adjust the load distribution of the chiller through mechanisms such as exploration phase, calm group, gathering group, panic group, etc.:

[0216] Individual initialization: Randomly initialize the load rate PLR ​​of each chiller, and the load rate ranges from 0 to 1.

[0217] Fitness calculation: The fitness function is used to evaluate the pros and cons of the load distribution scheme for each chiller, with the goal of minimizing system energy consumption.

[0218] Group behavior simulation: Based on different individual behaviors (calm, gathering, panic), the load distribution plan is updated and eventually converges to the global optimal solution.

[0219] This module optimizes the load distribution of the chiller through multiple iterations to ensure the optimal energy efficiency of the chiller group.

[0220] 8. Output Module

[0221] This module outputs the final chiller load distribution plan based on the optimization results. The output includes:

[0222] Load factor of each chiller (PLR)

[0223] Number of switch machines

[0224] Other necessary operating parameters

[0225] The output solution will be applied to the chiller group control system in real time to ensure that the system always operates efficiently.

[0226] 9. Real-time monitoring and accuracy assessment module

[0227] This module is used to monitor the operating status of the chiller in real time and evaluate the accuracy of load forecasting and energy efficiency models. Its functions include:

[0228] Accuracy assessment: Regularly assess the gap between load forecasts and actual operations to ensure that the model's forecast results match actual operating requirements.

[0229] Dynamic correction: Based on the evaluation results, the load forecasting model and energy efficiency model are corrected to ensure that the system can adjust its operating strategy in a timely manner according to environmental changes.

[0230] This module ensures the stability and accuracy of the entire system during long-term operation.

[0231] The overall workflow of the system

[0232] Data collection: Real-time collection of environmental data, equipment data, personnel activities and other information.

[0233] Data processing: Cleaning, standardization and feature extraction of collected data.

[0234] Load forecasting: Use a neural network combining LSTM and KAN to forecast load.

[0235] Energy efficiency model calibration and optimization: Calibrate and optimize the energy efficiency model based on real-time data to ensure that the prediction matches the actual operation.

[0236] Group control strategy design: Adjust the load distribution plan of the chiller according to the load forecast results and optimization objectives.

[0237] ESC algorithm execution: The ESC algorithm simulates individual behavior, optimizes load distribution and ensures optimal energy efficiency.

[0238] Output and application: Apply the optimization results to the chiller group control system to ensure efficient operation of the system.

[0239] The chiller group control system based on the ESC algorithm provided by the present invention can effectively solve the problems of low load prediction accuracy and the optimization algorithm being prone to fall into local optimal solutions in the prior art. Through the collaborative work of multiple modules such as real-time data collection, load prediction, energy efficiency model adjustment, and ESC algorithm optimization, the system can achieve efficient operation of the chiller group, minimize total energy consumption, and ensure dynamic satisfaction of cooling load demand, which has important application prospects and market value.

[0240] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A chiller group control method based on ESC, characterized in that: The steps include: S1. Load forecasting: The system collects relevant data affecting the cooling load in real time through sensors, including but not limited to meteorological parameters, heat dissipation of equipment in the air-conditioning area, personnel load and fresh air load; Load forecasting algorithm: Based on the neural network model combining the long short-term memory network LSTM and the attention mechanism KAN layer, load forecasting is carried out in combination with real-time collected meteorological data, equipment heating conditions and personnel activities; the model can capture the timing and dynamic changes of cooling load and improve the accuracy of forecasting; S2. According to the working characteristics of the chiller, an energy efficiency model is constructed to describe the relationship between load rate and power consumption; The introduction of a model self-updating device can adjust the parameters of the energy efficiency model in real time according to the operating data of the chiller to ensure that the load forecast matches the actual energy efficiency requirements; If the model prediction deviation is large, the model parameters will be automatically corrected to ensure long-term stable operation; S3. Based on the load forecast results, design the objective function of the chiller group control, usually to minimize the total energy consumption of the system; The constraints include that the load factor of each chiller is within a reasonable range to ensure efficient operation of the system; The partial load rate (PLR) of the chiller is used as the key variable for optimization. The ESC algorithm is used to optimize the number of chillers on and off and the load rate. The goal is to optimize the energy efficiency of the entire chiller fleet and reduce energy waste; S4. Application of ESC algorithm S41. Initialization: First, initialize the load rate PLR ​​of each chiller and randomly assign it through the individual initialization mechanism of the ESC algorithm; the load rate of each chiller is between 0 and 1, reflecting the load state in the initial stage; S42. Fitness calculation: Calculate the fitness of each individual. The fitness function evaluates the quality of the load distribution scheme by calculating the total energy consumption of the system. The goal is to minimize the total energy consumption of the system while ensuring that the load requirements of each chiller are met; S43. According to the ESC algorithm, the load distribution problem is transformed into an individual update problem; Individuals are divided into three groups: calm, gathered, and panicked: Calm individuals: Individuals rationally move closer to the system load center and update the load distribution plan; Aggregate individuals: Individuals are influenced by calm individuals and panic individuals to update the load distribution plan; Panic individuals: individuals make random adjustments when load fluctuates drastically, simulating irrational responses in load distribution; S44. Panic index and convergence: As the number of iterations increases, the panic index gradually decreases, individual behaviors gradually become stable, and finally converge to the global optimal solution; S45. Output the optimal solution: After multiple iterations, the final load distribution plan is obtained, and the optimal number of chillers on and off and load rate information is output.

2. The chiller group control method based on ESC according to claim 1, characterized in that: In step S3, the partial load ratio of the chiller is: Among them, PLR i is the partial load rate of the i-th chiller, is the actual load of the chiller, is the design load of the chiller; Where: P i is the power consumption of the i-th chiller, a, b, c, d are the fitting coefficients of the host-part load rate curve.

3. The chiller group control method based on ESC according to claim 2, characterized in that: In a parallel chiller system, sufficient cooling load should be provided to meet the system cooling load requirements; to ensure efficient operation of the chiller, the PLR ​​value must be between 0.3 and 1; when the chiller is turned off, the PLR ​​is 0; the objective function and constraints are as follows: In the formula, Q need is the total cooling load demand of the system, and Obj is the minimum target power consumption of the parallel chiller system.

4. The chiller group control method based on ESC according to claim 1, characterized in that: In step S4, the following inputs are set: the number of chillers N; the initial load factor of each chiller PLR0 = [PLR1, PLR2, ..., PLR N ], and the initial value satisfies 0.3≤PLR i ≤1 or 0; The COP value of each unit COP=[COP1, COP2, ..., COP N ], maximum number of iterations T max , elite pool size E size ; Set output: Optimal load distribution scheme PLR opt , to ensure ∑Q i PLR i ≥Q need , which corresponds to the load distribution with the lowest fitness.

5. The chiller group control method based on ESC according to claim 4, characterized in that: In the step S41, the following steps are also included: S411. Randomly generate the load rate PLR0 of the chiller, and the initial value satisfies 0.3≤PLR i ≤1 or 0; S412. Initialize the population, each individual in the population represents a load distribution plan for a chiller; S413. Set the maximum number of iterations T max .

6. The chiller group control method based on ESC according to claim 5, characterized in that: In the step S42, the following steps are also included: S421. For each individual PLR i , calculate its power consumption P i , and calculate the total power consumption of the system; S422. Calculate the degree to which load distribution meets cooling load demand and ensure vQ i PLR i ≥Q need ; S423. Comprehensive fitness: Among them, λ is a penalty factor. When the cooling load demand is not met, the fitness will increase the penalty term.

7. The chiller group control method based on ESC according to claim 6, characterized in that: In the step S43, the following steps are also included: S431. Calculate the fitness of each individual and sort them in ascending order of fitness, and select the best individual to enter the elite pool Elite Pool , size is E size ; S4322. Iteration process: For each individual, perform the following updates: Calm group: Individuals rationally move toward the average of the individuals in the elite pool: Among them, center is the average value of individual load rates in the elite pool; Aggregate Groups: Individuals affected by calm and panic groups Update: Among them, PLR p is an individual randomly selected from the panic group, v h is the velocity vector of the gathering group; Panic herd: individuals randomly choose new load distribution: Among them, E j For individuals randomly selected from the elite pool, PLR rand is a randomly selected individual, v p is the velocity vector of the panic group.

8. The chiller group control method based on ESC according to claim 7, characterized in that: When the number of iterations exceeds T max / 2All individuals tend to the optimal solution in the elite pool, and all individuals are updated to members of the elite pool or randomly selected individuals; as the number of iterations t increases, the panic index P(t) gradually decreases, and the behavior of the simulated individuals gradually stabilizes: P(t)=P max ·(1-t / T max )。 9. A chiller group control system based on ESC, characterized in that: The method comprises the steps of the chiller group control method as described in any one of claims 1 to 8, and is optimized and adjusted based on the following modules to ensure efficient operation of the chiller group control system and achieve optimal energy efficiency; The modules include: Data acquisition and monitoring module: used to collect meteorological parameters, heat dissipation of air-conditioning area equipment, and personnel load data in real time, and transmit the real-time data to the data storage module to provide support for load forecasting and energy efficiency models; Load forecasting module: receives real-time data from the data acquisition module, uses a neural network model that combines the long short-term memory network LSTM with the attention mechanism KAN layer to forecast load and output future load demand; Energy efficiency model and self-update mechanism module: Based on the working characteristics of the chiller, an energy efficiency model is established to describe the relationship between load rate and power consumption; when there is a large deviation between the actual operating data and the model prediction, the model parameters are automatically adjusted to ensure long-term stable operation; Group control strategy module: Based on the load forecast results, the group control strategy is designed. The objective function is to minimize the total energy consumption of the system, and the constraint condition is the reasonable range of the chiller load rate (for example, 30%-100%). This module uses the ESC algorithm to optimize the load distribution and the number of on / off units of the chiller to achieve optimal system energy efficiency. ESC algorithm module: responsible for executing load distribution optimization tasks, simulating individual behaviors through the ESC algorithm, and optimizing the load distribution of the chiller; this module adjusts the PLR ​​and the number of on / off units of each unit according to the adaptability of the chiller load distribution, and outputs the optimal solution; Output module: Output the load rate and number of on / off units of each chiller according to the optimization results, and apply the solution to the chiller group control system to ensure the energy efficiency of the system is optimized; Real-time monitoring and accuracy assessment module: used to monitor the operating status of the chiller in real time, detect the accuracy of the load prediction model and energy efficiency model, ensure that the load distribution plan matches the actual operating requirements, and make corrections based on real-time data.

10. A chiller group control system based on ESC, characterized in that: Also includes: A data processing module, used for preprocessing the chiller operation data; A data storage module, used for storing pre-processed operation data; The model correction module is used to correct the parameters of the chiller energy efficiency model according to the preprocessed operating data.

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