Subway Station Air Conditioning Cooling Regulation Method, Device, Equipment and Storage Medium

Abstract

The present invention discloses a method, device, equipment and storage medium for regulating the cooling supply of air conditioners in a subway station, belonging to the technical field of energy optimization. Based on a cooling load prediction model, the cooling load demand of the subway station in the period to be predicted is predicted to obtain cooling load prediction data; based on a cooling load redistribution model, the cooling load prediction data is redistributed in the time dimension to obtain a cooling load distribution result; based on an air-conditioning water system optimization model, the cooling load distribution result, air-conditioning equipment constraints and water circulation parameter fluctuation constraints, the parameters of the air-conditioning unit are solved to obtain an optimal parameter set. The present invention reduces energy waste while ensuring the cooling load demand on the demand side, thereby realizing the efficient utilization of energy.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization, and particularly to a method, device, equipment and storage medium for regulating and controlling the cooling supply of air conditioners in subway stations. Background Art

[0002] Since the equipment of the air conditioning system in the subway station and the supply of air conditioning cooling load on the demand side are both designed according to the long-term peak passenger flow, the cooling capacity is greater than the actual demand, resulting in a large amount of energy waste. However, the time-schedule mode control method adopted by the related technology for air conditioning cooling regulation has a low matching degree with the peak passenger flow, and the parameters on the equipment supply side are simply set by manual experience, and real-time energy consumption regulation cannot be achieved, further leading to energy waste. Summary of the Invention

[0003] The main purpose of the present invention is to provide a method, device, equipment and storage medium for regulating and controlling the cooling supply of air conditioners in subway stations, aiming to solve the technical problem of unreasonable supply of air conditioning cooling load on the demand side of subway stations in the related technology, thereby causing energy waste.

[0004] To achieve the above object, the present invention provides a method for regulating and controlling the cooling supply of air conditioners in subway stations, and the method for regulating and controlling the cooling supply of air conditioners in subway stations includes the following steps:

[0005] S1. Based on the cooling load prediction model, predict the cooling load demand of the subway station in the to-be-predicted period to obtain cooling load prediction data;

[0006] S2. Based on the cooling load redistribution model, redistribute the cooling load prediction data in the time dimension to obtain a cooling load distribution result;

[0007] S3. Based on the air conditioning water system optimization model, the cooling load distribution result, the air conditioning equipment constraints and the water circulation parameter fluctuation constraints, solve the air conditioning unit parameters to obtain an optimal parameter set.

[0008] In addition, to achieve the above object, the present invention also provides a device for regulating and controlling the cooling supply of air conditioners in subway stations, and the device includes:

[0009] A cooling load prediction module, configured to predict the cooling load demand of the subway station in the to-be-predicted period based on the cooling load prediction model to obtain cooling load prediction data;

[0010] A cooling load redistribution module, configured to redistribute the cooling load prediction data in the time dimension based on the cooling load redistribution model to obtain a cooling load distribution result;

[0011] A parameter solving module, configured to solve the air conditioning unit parameters based on the air conditioning water system optimization model, the cooling load distribution result, the air conditioning equipment constraints and the water circulation parameter fluctuation constraints to obtain an optimal parameter set.

[0012] Furthermore, to achieve the above object, the present invention also provides a subway station air-conditioning cooling regulation device, which includes: a memory, a processor, and a subway station air-conditioning cooling regulation program stored on the memory and executable on the processor. The subway station air-conditioning cooling regulation program is configured to implement the steps of the subway station air-conditioning cooling regulation method as described above.

[0013] Still further, to achieve the above object, the present invention also provides a computer-readable storage medium, on which a subway station air-conditioning cooling regulation program is stored. When the subway station air-conditioning cooling regulation program is executed by a processor, it implements the steps of the subway station air-conditioning cooling regulation method as described above.

[0014] The present invention accurately predicts the cooling load demand on the demand side during the to-be-predicted period through a cooling load prediction model, and obtains cooling load prediction data. On this basis, using a cooling load redistribution model, comprehensively considering factors such as passenger flow, electricity price, and human comfort, the cooling load prediction data is reasonably redistributed in the time dimension, saving electricity bills while taking into account passenger comfort. Finally, combined with an air-conditioning water system optimization model, equipment constraints, and water circulation parameter fluctuation constraints, the optimal air-conditioning unit parameters are solved to ensure that the air-conditioning equipment operates in the best state, thereby improving energy efficiency and reducing energy consumption. The present invention improves the overall operating efficiency of the air-conditioning system, saves energy costs, and reduces unnecessary energy consumption while ensuring the comfort inside the subway station and the long-term stable operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic structural diagram of a subway station air-conditioning cooling regulation device in the hardware operating environment related to the embodiment solution of the present invention;

[0016] Figure 2 is a schematic flowchart of an embodiment of the subway station air-conditioning cooling regulation method of the present invention;

[0017] Figure 3 is a schematic flowchart of the training process of the cooling load prediction model;

[0018] Figure 4 is a schematic flowchart of the construction process of the cooling load redistribution model;

[0019] Figure 5 is a schematic flowchart of the solution process of the air-conditioning unit parameters.

[0020] The realization, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] Analysis of related technologies reveals that:

[0023] Referring to Figure 1 , Figure 1 FIG. is a schematic structural diagram of a subway station air-conditioning cooling regulation device for the hardware operating environment involved in the solution of the embodiment of the present invention.

[0024] As Figure 1 shown, the subway station air-conditioning cooling regulation device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (RandomAccess Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0025] Those skilled in the art can understand that Figure 1 the structure shown in does not constitute a limitation on the subway station air-conditioning cooling regulation device, and may include more or fewer components than shown, or combine certain components, or arrange different components.

[0026] As Figure 1 shown, the memory 1005, as a computer-readable storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a subway station air-conditioning cooling regulation program.

[0027] In Figure 1 the subway station air-conditioning cooling regulation device shown, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with users; the processor 1001 and the memory 1005 in the subway station air-conditioning cooling regulation device of the present invention may be provided in the subway station air-conditioning cooling regulation device. The subway station air-conditioning cooling regulation device calls the subway station air-conditioning cooling regulation program stored in the memory 1005 through the processor 1001 and executes the subway station air-conditioning cooling regulation method provided by the embodiment of the present invention.

[0028] An embodiment of the present invention provides a method for regulating the cooling supply of a subway station air conditioner. Refer to Figure 2 , Figure 2 which is a schematic flowchart of an embodiment of a method for regulating the cooling supply of a subway station air conditioner according to the present invention.

[0029] In this embodiment, the method for regulating the cooling supply of a subway station air conditioner includes:

[0030] Step S1: Based on a cooling load prediction model, predict the cooling load demand of the subway station during the period to be predicted, and obtain cooling load prediction data.

[0031] Step S1 specifically includes the following steps:

[0032] Step S11: Extract features and perform preprocessing based on the historical operation data of the subway station to obtain a second feature set.

[0033] As Figure 3 shown, step S11 specifically includes the following steps:

[0034] Step S11-1: Obtain the meteorological data, passenger flow data, equipment energy consumption data, and cooling load data at all historical moments of the subway station as historical operation data; wherein, the meteorological data includes but is not limited to indoor and outdoor temperature and humidity and outdoor wind speed; the passenger flow data includes but is not limited to the number of people entering and leaving the subway station; the equipment energy consumption data includes but is not limited to the real-time power values, real-time voltage values, and real-time current values of various devices in the air conditioning system.

[0035] In this embodiment, a moment represents a short time interval, which can be specifically set according to different prediction accuracy requirements.

[0036] Step S1~1-2: Use the Pearson correlation calculation method to calculate the correlation between other different types of data and the cooling load data in the historical operation data, and use the multiple data with the correlation greater than the first preset correlation as the main features.

[0037] Suppose the historical operation data contains 4 types of data such as data A, data B, data C, and data D. The data values of data A corresponding to each historical moment form a data set Ra. Similarly, data B corresponds to a data set Rb, data C corresponds to a data set Rc, data D corresponds to a data set Rd, and the cooling load data corresponds to a data set Re. Using the Pearson correlation calculation method, the correlation between data A and the cooling load data can be calculated according to the data group Ra and the data group Re. Similarly, the correlations between data B, data C, and data D and the cooling load data can be calculated respectively. If the correlations between data B, data C, and data D and the cooling load data are all greater than the first preset correlation, then data B, data C, and data D are used as the main features.

[0038] Step S11-3: Use a time series prediction tool to perform time series feature mining on the cooling load data, obtain date features, and perform feature expansion on the main features to obtain a first feature set; the date features include the periodic features, seasonal features, and holiday features of the cooling load.

[0039] Step S11-4: Map the date features as inputs and convert them into a numerical form so that the model can understand; then perform annotation processing on the holiday features of the cooling load so that the model can identify holidays; in addition, perform fuzzy processing on the temperature and humidity features of the cooling load through a membership function, and map the numerical values of temperature and humidity to different fuzzy categories; finally, perform normalization processing on all data and construct a second feature set.

[0040] Step S12:: Train the TPA-TCN model based on the second feature set to obtain a cooling load prediction model;

[0041] Step S12 specifically includes:

[0042] Step S12-1: Perform K-means clustering based on passenger flow data to divide a day into four different time periods; these four different time periods are the passenger flow peak period, the period when the passenger flow decreases from high to low, the passenger flow low peak period, and the period when the passenger flow increases from low to high.

[0043] Step S12-2: Divide the daily feature data in the second feature set according to the corresponding four time periods to obtain four groups of training subsets; perform normalization processing on the four groups of training subsets obtained respectively to obtain four groups of feature vectors.

[0044] Step S12-3: Input the four groups of feature vectors into the TPA-TCN model for training respectively to obtain cooling load prediction models corresponding to the four time periods.

[0045] Among them, the network structure of the TPA-TCN model includes an input layer, a DCC dilated causal convolutional layer, a residual block, a TPA mechanism layer, and a fully connected layer connected in sequence. Each time period uses this network structure during model training.

[0046] Taking the model training in the passenger flow peak period as an example for illustration, the input layer receives the normalized feature vector. The dimension d of this feature vector is 20, that is, the input features at each time step consist of 20 numerical values, and each numerical value represents the normalized feature value of different dimensional features in the training subset (for example, temperature, humidity, passenger flow, etc.).

[0047] The DCC dilated causal convolutional layer performs convolutional processing on the feature vector received by the input layer to obtain a convolutional result Conv(x). During the convolutional process, the convolutional kernel size is set to 25, and the dilation coefficient is 2 nExponential growth. When the convolutional kernel of size 25 slides one time step, it will perform convolution on 25 consecutive time step data points in the feature vector. At the same time, the interval (dilation coefficient) in the convolutional kernel is increased by a factor of 2 n times continuously to expand the receptive field and extract the long-term temporal dependencies of the cooling load.

[0048] The residual block activates the convolution result Conv(x) of the DCC dilated causal convolutional layer through the RELU activation function, setting negative values to zero and retaining positive values. On this basis, the input feature vector x itself is superimposed to avoid the problems of vanishing gradients or exploding gradients. Furthermore, the output of the residual block is:

[0049] ;

[0050] where is the output of the residual block, is the input feature vector, represents the convolution result obtained by performing a convolution operation on the feature vector , represents the activation operation.

[0051] The TPA mechanism layer performs a convolution operation on the output result of the residual block using 10 one-dimensional convolutional kernels hour by hour on the time series to extract temporal features and output the convolution result. Then, the Softmax activation function is used to normalize the convolution result to obtain the temporal attention weight matrix:

[0052] ;

[0053] where represents the one-dimensional convolution operation, represents the normalization operation.

[0054] The obtained temporal attention weight matrix W is used to weight the input feature to generate the weighted feature:

[0055] ;

[0056] where represents the weighted feature. The weighted feature will weight the original input feature according to the temporal attention weight matrix W, emphasizing the features of important time steps and weakening the features of unimportant time steps, enabling the model to better focus on the critical moments in the time series and thus improving the performance of the model.

[0057] The fully connected layer outputs the normalized predicted cooling load values for each moment in the prediction period.

[0058] During the model training process, the Huber Loss (δ = 1.0) function is used as the loss function of the TPA-TCN model, which can combine the advantages of the mean squared error (MSE) and the mean absolute error (MAE). Specifically, when the error is small, the Huber Loss function uses MSE to measure the prediction error, so as to more finely adjust the hyperparameters of the cooling load prediction model (such as the learning rate, optimizer parameters, batch size, and δ value of the Huber Loss function); when the error is large, MAE is used to measure the prediction error to avoid the excessive influence of outliers. The Adam optimizer is used to calculate the adaptive learning rate of each weight parameter in the model, and the weight parameters are updated according to the first-order moment estimate (the average value of the gradient) and the second-order moment estimate (the average value of the square of the gradient). Among them, the initial learning rate , controlling the decay rate of the first-order moment estimate is set to 0.9, and the decay rate of the second-order moment estimate is set to 0.999. R 2 (variance), MAPE (mean absolute percentage error), and MAE (mean absolute error) are used as the evaluation indicators for the prediction results of the cooling load prediction model. The early stopping method is used to prevent the model from overfitting. When the validation loss does not decrease for 10 consecutive rounds, the training is terminated to obtain the optimal cooling load prediction model.

[0059] Step S13: Input the real-time values of the features corresponding to the second feature set within the time period to be predicted into the cooling load prediction model to obtain the cooling load prediction data.

[0060] Specifically, the cooling load prediction model is used to analyze and calculate the cooling load value corresponding to each moment within the time period to be predicted based on the input real-time values of the features. After all calculations are completed, the cooling load prediction data corresponding to the time period to be predicted is obtained.

[0061] Step S2: Based on the cooling load redistribution model, redistribute the cooling load prediction data in the time dimension to obtain the cooling load distribution result.

[0062] As Figure 4 shown, step S2 specifically includes the following steps:

[0063] Step S2-1: Obtain the time-of-use electricity price characteristics and passenger flow characteristics, and design an adaptive load regulation factor.

[0064] Feature extraction is performed based on time-of-use electricity price trends to obtain time-of-use electricity price characteristics. Feature extraction is performed on subway station passenger flow data to obtain passenger flow characteristics. Adaptive load control factors are designed based on time-of-use electricity price characteristics and passenger flow characteristics. This allows the cooling load redistribution model to dynamically adjust the cooling load shift value based on passenger flow and time-of-use electricity price trends, thereby achieving dynamic redistribution of cooling load forecast data.

[0065] First, based on passenger flow characteristics, construct passenger flow control factors :

[0066] ;

[0067] in, It represents the normalized time distance from time t to the peak passenger flow, which is between 0 and 1. When it is equal to 0, it means that time t is the peak time of passenger flow. When it is equal to 1, it means that time t is the valley moment of passenger flow; is the distance threshold, set to 0.4, indicating a critical value. Less than It is considered as the “peak passenger flow period”. Greater than When the passenger flow is at its peak, it is considered as the “non-peak passenger flow period”; k is the curve steepness coefficient, ranging from 2 to 3, which is used to adjust the slope of the passenger flow control factor and determines the speed at which the passenger flow control factor changes over time. A higher k value will lead to more drastic changes in the control factor; Represents power function calculation.

[0068] Then, based on the time-of-use electricity price characteristics, a time-of-use electricity price regulation factor is constructed. :

[0069] ;

[0070] in, is the countdown time to the peak electricity price. is the time attenuation coefficient, It is the electricity price benchmark factor (that is, the normal benchmark electricity price).

[0071] The TOU price control factor adjusts the cooling load value at each moment within the forecast period based on changes in electricity prices. Specifically, during high electricity price moments, the TOU price control factor suppresses increases in cooling load values at those moments, while during low electricity price moments, the TOU price control factor allows increases in cooling load values at those moments.

[0072] Finally, based on the passenger flow control factor and time-of-use electricity price control factors Establishing adaptive load regulation factors :

[0073] ;

[0074] Among them, is the regulation willingness factor (which can be fine-tuned by the operation staff according to the actual situation and requirements); is the dynamic weight factor, that is, the synchronization degree between the peak-valley state of time-of-use electricity price and the peak-valley state of passenger flow. The calculation formula is:

[0075] ;

[0076] Among them, is the time difference between the current moment and the nearest cold load peak, is the time difference between the current moment and the nearest electricity price peak.

[0077] Thus, it can be seen that the dynamic weight factor can measure the misalignment degree between the peak-valley state of time-of-use electricity price and the peak-valley state of passenger flow. The closer it is to 0.5, the more significant the misalignment is, which means that the cold load regulation will remain at a medium level. The closer it is to 1, the more synchronous the peak-valley of electricity price and passenger flow are. The cold load regulation is more effective, and more cold load can be allocated during low electricity price periods and the increase of cold load can be suppressed during high electricity price periods. It can adjust the cold load more flexibly according to the specific operation situation, so as to realize the dynamic redistribution of cold load prediction data and make the regulation process more in line with the actual needs of the subway station.

[0078] Step S2-2: Based on the adaptive load regulation factor, establish the human comfort constraint, the cold load transfer amount constraint and the equipment constraint, and construct the objective function with the goal of minimizing the electricity cost, so as to construct the cold load redistribution model.

[0079] (1) Establish the human comfort constraint:

[0080] First, construct the human comfort constraint index:

[0081] ;

[0082] Among them, represents the human comfort constraint index, is the water vapor partial pressure (unit: Pa), M is the human metabolic rate (unit: W / ㎡), is the ambient dry-bulb temperature at time t (unit: ℃), is the clothing thermal resistance after sweating, is the clothing external air boundary thermal resistance, and R is the average radiant heat energy per unit skin area (unit: W / ㎡). From the indoor temperature at the corresponding moment and the outdoor temperature It is determined that when calculating the indoor human comfort, the ambient dry-bulb temperature shall adopt the indoor temperature , and when calculating the outdoor human comfort, the ambient dry-bulb temperature shall adopt the outdoor temperature .

[0083] Then, according to the principle of dynamic heat balance, establish the relationship between the indoor and outdoor temperatures and the cooling load, which is used to relate the human comfort constraint index and the cooling load :

[0084] ;

[0085] wherein, represents the heat transfer coefficient of the enclosure structure (unit: W / ℃), C represents the air heat capacity of the subway concourse (unit: J / ℃), represents the indoor heat source, represents the cooling load.

[0086] The constructed human comfort constraint includes two aspects: absolute constraint and relative constraint:

[0087] Absolute constraint: , ;

[0088] , ;

[0089] Relative constraint: , ;

[0090] , ;

[0091] wherein, represents the start time, represents the end time, represents the indoor target temperature at time t, represents the human comfort constraint index at time t, represents the human comfort constraint index at time t + 1, represents the outdoor human comfort constraint index at time t, represents the indoor human comfort constraint index at time t.

[0092] (2) Establish the constraint of the cooling load transfer volume:

[0093] Total cooling load constraint: ;

[0094] Single - moment transfer amount range constraint: ;

[0095] Among them, represents the cooling load value at time t before re - distribution, represents the cooling load value at time t after re - distribution.

[0096] (3)Establish equipment constraints:

[0097] ;

[0098] Among them, represents the target cooling capacity, represents the minimum allowable cooling capacity of the air - conditioning equipment, represents the maximum allowable cooling capacity of the air - conditioning equipment.

[0099] Next, based on the above - mentioned constraints, establish the objective function of the cooling load re - distribution model with the minimum electricity cost as the goal:

[0100] ;

[0101] Among them, is the electricity price at time t (unit: yuan / kWh), is the RWI comfort penalty coefficient.

[0102] Step S2 - 3: Input the cooling load prediction data into the cooling load re - distribution model, and use the improved black - winged kite algorithm for solution to obtain the cooling load distribution result.

[0103] First, input the cooling load prediction data into the cooling load re - distribution model, and solve it based on the improved black - winged kite algorithm to obtain the cooling load re - distribution strategy.

[0104] Among them, the improvements of the black - winged kite algorithm include:

[0105] Improvement 1: Introduce chaotic mapping for initialization improvement, so that the black - winged kite algorithm has a more uniform solution distribution in the initialization stage, to increase the diversity of the initial solution and improve the solution quality.

[0106] Improvement 2: Add a reverse learning mechanism, so that the black - winged kite algorithm can search both forward and backward during the particle search process, accelerating the search efficiency.

[0107] Improvement 3: Improve the non - linear factor in the attack stage of the black - winged kite algorithm. Specifically, add Beta perturbation to the non - linear factor, so that a large - range search is carried out in the early stage of the search, and the range is gradually reduced in the later stage of the search to achieve convergence more quickly. The improved non - linear factor is:

[0108] ;

[0109] Among them, represents a random perturbation (that is, a random value sampled from a Beta distribution), represents the perturbation intensity control parameter.

[0110] Then, based on the cold load redistribution strategy, the cold load prediction data is redistributed to obtain the cold load distribution result.

[0111] Among them, the cold load redistribution strategy includes the cold load adjustment value corresponding to each moment within the period to be predicted. The cold load prediction data includes the cold load prediction value corresponding to each moment within the period to be predicted. During the cold load redistribution process, for each moment, the cold load prediction value is adjusted according to the cold load adjustment value to obtain the redistributed cold load value. Finally, the cold load distribution result corresponding to the entire period to be predicted is obtained.

[0112] In the entire Step 2, using the cold load redistribution model, combined with time-of-use electricity price and human comfort, the cold load prediction data predicted by the cold load prediction model is subjected to load shifting to achieve the effect of "pre-cooling during valley hours and using cold during peak hours", thereby reducing electricity costs.

[0113] Step S3: Based on the air-conditioning water system optimization model, the cold load distribution result, the air-conditioning equipment constraints, and the water circulation parameter fluctuation constraints, solve for the air-conditioning unit parameters to obtain the optimal parameter set.

[0114] As Figure 5 shown, the specific steps of the said Step S3 include the following steps:

[0115] Step S3-1: Based on the operation data of the internal equipment of the air-conditioning unit, with the minimum energy consumption and the maximum energy efficiency as the goals, construct the air-conditioning water system optimization model.

[0116] Among them, the internal equipment of the air-conditioning unit may include a chiller, a chilled water pump, a cooling water pump, a cooling tower fan, and an air handling unit fan. The operation data of the internal equipment of the air-conditioning unit refers to the data related to the energy consumption calculation of the internal equipment of the air-conditioning unit.

[0117] First, according to the operation data, use the least squares method to determine the calculation coefficients in the energy consumption calculation formula of the internal equipment of the air-conditioning unit, that is, the energy consumption model parameters. Then, based on the energy consumption model parameters, construct the air-conditioning water system mathematical model:

[0118] ;

[0119] Among them, represents the total energy consumption of the air-conditioning water system, represents the energy consumption of the chiller, represents the energy consumption of the chilled water pump, Indicates the energy consumption of the cooling water pump, Indicates the energy consumption of the cooling tower fan, Indicates the energy consumption of the air handling unit fan.

[0120] Then, based on the mathematical model of the air conditioning water system, with the goal of minimum energy consumption and maximum energy efficiency, an optimization model of the air conditioning water system is constructed:

[0121] ;

[0122] Among them, Indicates the total goal of the model, Indicates the goal of minimum total energy consumption, Indicates the goal of maximum energy efficiency, Indicates the minimum total energy consumption, Indicates the maximum energy efficiency, Indicates the total refrigeration capacity.

[0123] Step S3-2: Based on the water circulation parameters of the internal equipment of the air conditioning water system, establish the constraints of air conditioning equipment and the constraints of fluctuations in water circulation parameters.

[0124] Among them, the water circulation parameters of the internal equipment of the air conditioning water system can include the chilled water pump outlet temperature, the cooling water pump return water temperature, the cooling water pump flow rate, and the chilled water pump flow rate. The water circulation parameters reflect the air conditioning water heat balance and the energy transfer process of the internal equipment of the air conditioning water system.

[0125] First, establish the constraints of air conditioning equipment based on the water circulation parameters:

[0126] ;

[0127] ;

[0128] ;

[0129] ;

[0130] Among them, Indicates the chilled water pump outlet temperature, Indicates the minimum chilled water pump outlet temperature, Indicates the maximum chilled water pump outlet temperature, Indicates the cooling water pump return water temperature, Indicates the minimum cooling water pump return water temperature, Indicates the maximum cooling water pump return water temperature, Indicates the cooling water pump flow rate, Indicates the minimum cooling water pump flow rate, Indicates the maximum cooling water pump flow rate, Indicates the chilled water pump flow rate, Indicates the minimum flow rate of the chilled water pump, Indicates the maximum flow rate of the chilled water pump.

[0131] Then, based on the average value of the water circulation parameters over a preset period in the past, a water circulation parameter fluctuation constraint is established. Specifically, according to the water circulation parameters under the extreme operating conditions where the air-conditioning water system can operate stably, a parameter fluctuation threshold is determined. That is, the difference between the characteristic value of the water circulation parameters at this moment under the extreme operating conditions and the average value of the water circulation parameters of the day over a preset period in the past. The water circulation parameter fluctuation constraint is:

[0132] ;

[0133] Among them, is the difference between the characteristic value of the water circulation parameters at this moment and the average value of the water circulation parameters of the day over a preset period in the past, is the parameter fluctuation threshold. The water circulation parameters determine the operating state of the air-conditioning water system. Establishing the water circulation parameter fluctuation constraint can ensure the stable operation of the air-conditioning water system.

[0134] Step S3-3: Input the cooling load distribution result into the air-conditioning water system optimization model, and based on the air-conditioning equipment constraints and the water circulation parameter fluctuation constraints, perform multi-objective optimization on the air-conditioning unit parameters to obtain the optimal parameter set.

[0135] Specifically, first, take the cooling load distribution result obtained in step S2 as the total cooling capacity and input it into the constructed air-conditioning water system optimization model. Combining the air-conditioning equipment constraints and the water circulation parameter fluctuation constraints, use the NSGA-II algorithm to perform multi-objective optimization on the air-conditioning unit parameters to obtain a solution set containing multiple target variables, and each solution set is a parameter set of the air-conditioning unit. Then, obtain the subjective weight z1 and the objective weight z2 of the target variables (that is, the optimization targets) through expert subjective setting and grey relational analysis respectively. Then, perform weighted averaging on the subjective weight z1 and the objective weight z2 according to the preset ratio of the subjective weight z1 to the objective weight z2 (such as 4:6) to obtain the comprehensive weight z3. Finally, through the Topsis method, first use the comprehensive weight z3 to perform weighted processing on the target variables, then measure the distance between each parameter set and the ideal solution, and select the parameter set with the smallest distance as the optimal parameter set according to the measurement result.

[0136] Finally, use the obtained optimal parameter set for the operation control of the subway station air-conditioning to achieve the purpose of regulating the cooling supply of the subway station air-conditioning.

[0137] In the entire step S3, based on the optimized model of the air-conditioning water system, the cold load distribution result, the constraints of air-conditioning equipment, and the constraints of the fluctuation of water circulation parameters, the parameters of the air-conditioning unit are solved. While ensuring that the air-conditioning can still meet the water-heat balance condition under different loads, the energy consumption of the air-conditioning can be minimized, energy use can be reduced, and the fluctuation of water circulation parameters can be restricted to ensure the stable operation of the air-conditioning water system. Therefore, through the multi-generation evolution of the NSGA-II algorithm, the obtained optimal parameter group can achieve the best balance among energy efficiency, performance indicators, and stability.

[0138] In this embodiment, the cold load prediction model is used to accurately predict the cold load demand on the demand side during the to-be-predicted period, and cold load prediction data is obtained. On this basis, using the cold load redistribution model, considering factors such as passenger flow, electricity price, and human comfort comprehensively, the cold load prediction data is reasonably redistributed in the time dimension, saving electricity bills while taking into account the comfort of passengers. Finally, combined with the optimized model of the air-conditioning water system, equipment constraints, and the constraints of the fluctuation of water circulation parameters, the optimal parameters of the air-conditioning unit are solved to ensure that the air-conditioning equipment operates in the best state, thereby improving energy efficiency and reducing energy consumption. The present invention improves the operating efficiency of the air-conditioning system as a whole, saves energy costs and reduces unnecessary energy consumption while ensuring the comfort in the subway station and the long-term stable operation of the equipment.

[0139] Furthermore, to achieve the above object, the present invention also provides a device for regulating and controlling the cooling supply of the subway station air-conditioning. The device for regulating and controlling the cooling supply of the subway station air-conditioning may include:

[0140] A cold load prediction module, configured to predict the cold load demand of the subway station during the to-be-predicted period based on the cold load prediction model, and obtain cold load prediction data;

[0141] A cold load redistribution module, configured to redistribute the cold load prediction data in the time dimension based on the cold load redistribution model, and obtain a cold load distribution result;

[0142] A parameter solving module, configured to solve the parameters of the air-conditioning unit based on the optimized model of the air-conditioning water system, the cold load distribution result, the constraints of the air-conditioning equipment, and the constraints of the fluctuation of the water circulation parameters, and obtain an optimal parameter group.

[0143] It should be noted that the functions that can be realized by each module in the device for regulating and controlling the cooling supply of the subway station air-conditioning provided in this embodiment and the corresponding technical effects can be referred to the descriptions of the specific implementation manners in each embodiment of the method for regulating and controlling the cooling supply of the subway station air-conditioning of the present invention. For the sake of simplicity of the specification, they will not be elaborated here.

[0144] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a subway station air-conditioning cooling regulation program is stored. When the subway station air-conditioning cooling regulation program is executed by a processor, the steps of the subway station air-conditioning cooling regulation method as described above are implemented. Therefore, details will not be described herein again. In addition, the beneficial effects of adopting the same method will not be described again. For the technical details not disclosed in the embodiment of the computer-readable storage medium involved in the present invention, please refer to the description of the method embodiment of the present invention. By way of example, the program instructions can be deployed to be executed on one computing device, or on multiple computing devices located at one location, or on multiple computing devices distributed at multiple locations and interconnected by a communication network.

[0145] It should be noted that in this document, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or system comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, the element defined by the statement "including a subway station air-conditioning cooling regulation" does not exclude the existence of additional identical elements in the process, method, article or system including such element.

[0146] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0147] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a computer-readable storage medium as described above (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0148] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for controlling cooling supply of air conditioners in a subway station, characterized in that: The subway station air conditioning cooling control method comprises the following steps: S1, based on the cooling load prediction model, predict the cooling load demand of the ground station within the prediction period to obtain cooling load prediction data; S2, based on the cooling load redistribution model, redistributing the cooling load prediction data in the time dimension to obtain a cooling load distribution result; The S2 specifically includes: S2-1, obtain time-of-use electricity price characteristics and passenger flow characteristics, and design adaptive load control factors; The S2-1 specifically includes: S2-1-1, obtain passenger flow characteristics and construct passenger flow control factors : ； in, represents the normalized time distance from time t to the passenger flow peak; is the distance threshold; k is the curve steepness coefficient; Indicates power function calculation; S2-1-2, obtain time-of-use electricity price characteristics and construct time-of-use electricity price regulation factors : ； in, is the countdown time to the peak electricity price. is the time attenuation coefficient, is the electricity price benchmark factor; S2-1-3, based on passenger flow control factors and time-of-use electricity price control factors Establishing adaptive load regulation factors : ； in, To regulate the willingness factor; is the dynamic weight factor, and the calculation formula is: ； in, is the time difference between time t and the nearest cooling load peak, is the time difference between time t and the nearest electricity price peak; S2-2, based on the adaptive load control factor, establishing human comfort constraints, cooling load transfer constraints, and equipment constraints, constructing an objective function with the goal of minimizing electricity costs, thereby constructing a cooling load redistribution model; S2-3, inputting the cooling load prediction data into the cooling load redistribution model, solving the model using the improved Black Kite algorithm, and obtaining the cooling load distribution result; S3, based on the air conditioning water system optimization model, the cooling load distribution result, the air conditioning equipment constraints and the water cycle parameter fluctuation constraints, the air conditioning unit parameters are solved to obtain the optimal parameter group.

2. The subway station air conditioning cooling control method according to claim 1, characterized in that: Said S1 specifically includes: S11, performing feature extraction and preprocessing based on the historical operation data of the subway station to obtain a second feature set; S12, training the TPA-TCN model based on the second feature set to obtain the cooling load prediction model; S13: Inputting the real-time feature values corresponding to the second feature set within the prediction period into the cooling load prediction model to obtain the cooling load prediction data.

3. The subway station air conditioning cooling control method according to claim 1, characterized in that: The S3 specifically includes: S3-1, based on the operating data of the internal equipment of the air-conditioning unit, constructing the air-conditioning water system optimization model with the goal of minimizing energy consumption and maximizing energy efficiency; S3-2, based on the water circulation parameters of the internal equipment of the air conditioning water system, establish air conditioning equipment constraints and water circulation parameter fluctuation constraints; S3-3, inputting the cooling load distribution result into the air conditioning water system optimization model, performing a multi-objective solution on the air conditioning unit parameters based on the air conditioning equipment constraints and the water cycle parameter fluctuation constraints, and obtaining the optimal parameter group.

4. The method for controlling cooling supply of air conditioning in a subway station according to claim 3, wherein: The S3-1 specifically includes: S3-1-1, based on the operating data of the internal equipment of the air-conditioning unit, determine the energy consumption model parameters of the internal equipment of the air-conditioning unit using the least squares method; S3-1-2, based on the energy consumption model parameters, construct a mathematical model of the air conditioning water system: ； in, Indicates the total energy consumption of the air conditioning water system, represents the energy consumption of the chiller, represents the energy consumption of the chilled water pump, Indicates the energy consumption of the cooling water pump, Indicates the energy consumption of cooling tower fans, Indicates the energy consumption of the air conditioning box fan; S3-1-3, based on the mathematical model of the air conditioning water system, with the goal of minimizing energy consumption and maximizing energy efficiency, construct an optimization model for the air conditioning water system: ； in, represents the overall goal of the model, represents the minimum total energy consumption target, represents the maximum energy efficiency target, represents the minimum total energy consumption, Indicates maximum energy efficiency, Indicates the total cooling capacity.

5. The subway station air conditioning cooling control method according to claim 3, characterized in that: The S3-2 specifically includes: S3-2-1, establishing the air conditioning equipment constraints based on the water circulation parameters of the internal equipment of the air conditioning water system; S3-2-2, establishing a water cycle parameter fluctuation constraint based on the characteristic value of the water cycle parameter at this moment and the average value of the water cycle parameter in the past preset time period.

6. A subway station air conditioning cooling control device, characterized in that: The device comprises: A cooling load prediction module is used to predict the cooling load demand of the ground station within the prediction period based on the cooling load prediction model and obtain cooling load prediction data; A cooling load redistribution module, configured to redistribute the cooling load prediction data in a time dimension based on a cooling load redistribution model to obtain a cooling load distribution result; A parameter solving module is used to solve the air conditioning unit parameters based on the air conditioning water system optimization model, the cooling load distribution result, the air conditioning equipment constraints and the water cycle parameter fluctuation constraints to obtain the optimal parameter group; The cooling load redistribution module is specifically configured to obtain time-of-use electricity price characteristics and passenger flow characteristics, and design an adaptive load control factor; based on the adaptive load control factor, establish human comfort constraints, cooling load movement constraints, and equipment constraints, and construct an objective function with the goal of minimizing electricity costs, thereby constructing a cooling load redistribution model; input the cooling load prediction data into the cooling load redistribution model, and solve it using the improved Black Kite algorithm to obtain the cooling load distribution result; The steps of obtaining time-of-use electricity price characteristics and passenger flow characteristics and designing an adaptive load regulation factor specifically include: Obtain passenger flow characteristics and construct passenger flow control factors : ； in, represents the normalized time distance from time t to the passenger flow peak; is the distance threshold; k is the curve steepness coefficient; Indicates power function calculation; Obtain time-of-use electricity price characteristics and construct time-of-use electricity price regulation factors : ； in, is the countdown time to the peak electricity price. is the time attenuation coefficient, is the electricity price benchmark factor; Based on passenger flow control factors and time-of-use electricity price control factors Establishing adaptive load regulation factors : ； in, To regulate the willingness factor; is the dynamic weight factor, and the calculation formula is: ； in, is the time difference between time t and the nearest cooling load peak, is the time difference between time t and the nearest electricity price peak.

7. A subway station air conditioning cooling control device, characterized in that: The device includes: a memory, a processor, and a subway station air conditioning and cooling control program stored in the memory and executable on the processor. The subway station air conditioning and cooling control program is configured to implement the steps of the subway station air conditioning and cooling control method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a subway station air conditioning and cooling control program, which, when executed by a processor, implements the steps of the subway station air conditioning and cooling control method according to any one of claims 1 to 5.

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