Subway station air conditioning system cooling load prediction method and system based on improved BPNN model, medium and computer program product

Abstract

The invention provides a subway station air conditioning system cooling load prediction method and system based on an improved BPNN model, a medium and a computer program product. The method comprises the following steps: based on subway station operation data, establishing a big data processing method for missing value and abnormal value restoration, and generating a database taking hour as a unit; carrying out correlation analysis on related factors influencing the cooling load of the subway station, and determining main influence factors; determining BPNN structure parameters according to the main influence factors and the cooling load, and establishing a BPNN model; and determining an optimal weight, a threshold value and a hidden layer node number of the BPNN model based on a wild goose optimization algorithm to obtain an optimized BPNN model, and predicting the air conditioner cooling load based on the optimized BPNN model. According to the method, effective data processing and input screening can be carried out based on the method for predicting the subway station cooling load through data driving, prediction is achieved in combination with the historical cooling load, and the method has the advantages of being high in precision, high in speed, high in adaptability and good in robustness.

Description

Technical Field

[0001] The present invention relates to the technical field of cooling load prediction of subway station air-conditioning systems, and in particular to a cooling load prediction method and system for subway station air-conditioning systems based on an improved BPNN model. Background Art

[0002] Cold load prediction provides a clear direction and effective support for the energy-saving regulation of air-conditioning systems. However, different buildings have different cold load characteristics and different factors affecting their cold loads. Therefore, it is necessary to clarify the cold load influence rules for different building types and establish a prediction mechanism. As a special type of building, subway stations need a load prediction method and device with strong pertinence, wide applicability and high integration to guide subsequent regulation.

[0003] Neural network models have been widely used in cooling load forecasting, but their prediction accuracy is related to the input and internal hyperparameter settings. The existing design of the subway station air conditioning system prediction method based on BP neural network and cluster optimization integrated neural network lacks input screening, and variables with low correlation with cooling load are included in the input, resulting in reduced prediction accuracy of the neural network. Regarding hyperparameter settings, the random generation and step-by-step iteration logic of the neural network will also cause problems such as unstable prediction results and slow convergence when solving problems in certain special scenarios. Summary of the invention

[0004] In view of the problems existing in the prior art, the purpose of the present invention is to provide a subway station air-conditioning system cooling load prediction method and system based on an improved BPNN model, so as to solve the problem of unstable model prediction results and slow convergence caused by low input correlation and improper hyperparameter setting of the neural network in the existing subway station air-conditioning system load prediction method.

[0005] According to the first aspect of the present invention, a method for predicting cooling load of air conditioners in subway stations based on an improved BPNN is proposed, comprising the following steps:

[0006] Based on the subway station operation data, a big data processing method for missing values ​​and outliers repair is established to generate a database in hours.

[0007] Conduct correlation analysis on the factors affecting the cooling load of the subway station to determine the main influencing factors, wherein the relevant factors include: outdoor temperature, outdoor humidity, forecast time, and hourly cooling load of the system 24 hours before the forecast time;

[0008] According to the main influencing factors and cooling load, the structural parameters of the BPNN network are determined and the BPNN model is established;

[0009] The optimal weights, thresholds and number of hidden layer nodes of the BPNN model are determined based on the Dayan optimization algorithm to obtain the optimized BPNN model. The air conditioning cooling load of the subway station at a certain moment is predicted based on the optimized BPNN model.

[0010] According to a second aspect of the present invention, a subway station air conditioning cooling load prediction system based on an improved BPNN is proposed, comprising:

[0011] A database generation module is configured to establish a big data processing method for repairing missing values ​​and outliers based on subway station operation data, and generate a database in hours;

[0012] A correlation analysis module is configured to perform correlation analysis on relevant factors affecting the cooling load of the subway station to determine the main influencing factors, wherein the relevant factors include: outdoor temperature, outdoor humidity, forecast time, and hourly cooling load of the system 24 hours before the forecast time;

[0013] The BPNN model building module is configured to determine the structural parameters of the BPNN network according to the main influencing factors and the cooling load, and to establish the BPNN model;

[0014] The BPNN model optimization module is configured to determine the optimal weight, threshold and number of hidden layer nodes of the BPNN model based on the Dayan optimization algorithm, obtain the optimized BPNN model, and predict the air conditioning cooling load of the subway station at a certain moment based on the optimized BPNN model.

[0015] According to a third aspect of the present invention, a computer system is provided, comprising:

[0016] one or more processors;

[0017] A memory stores operable instructions, wherein when the instructions are executed by the one or more processors, the one or more processors perform operations, wherein the operations include the process of executing the aforementioned subway station air conditioning cooling load prediction method based on improved BPNN.

[0018] According to a fourth aspect of the purpose of the present invention, a computer-readable medium storing software is proposed, wherein the software includes instructions that can be executed by one or more computers, and when the instructions are executed by the one or more computers, the process of the aforementioned subway station air conditioning cooling load prediction method based on improved BPNN is performed.

[0019] According to a fifth aspect of the present invention, a computer program product is proposed, comprising a computer program, which, when executed by a processor, implements the steps of the aforementioned subway station air conditioning cooling load prediction method based on improved BPNN.

[0020] Compared with the prior art, the subway station air conditioning cooling load prediction method based on improved BPNN of the present invention combines historical cooling load data as one of the inputs, and provides a complete load prediction method for subway stations including data processing, input analysis, model building and model optimization, which provides an important reference for the load prediction of subway station buildings. At the same time, based on the Dayan optimization algorithm, the optimal hyperparameter setting of BPNN is found, with higher prediction accuracy, more stable prediction results and faster convergence speed.

[0021] The method of the present invention can perform effective data processing and input screening based on a data-driven method for predicting cooling load of subway stations, and realize prediction in combination with historical cooling loads, which has the advantages of high accuracy, high speed, strong adaptability and good robustness in practical applications.

[0022] It should be understood that all combinations of the aforementioned concepts and the additional concepts described in more detail below can be considered as part of the inventive subject matter of the present disclosure as long as such concepts are not mutually inconsistent. In addition, all combinations of the claimed subject matter are considered as part of the inventive subject matter of the present disclosure.

[0023] The foregoing and other aspects, embodiments and features of the present invention can be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the present invention, such as the features and / or beneficial effects of the exemplary embodiments, will be apparent from the following description or learned from the practice of the specific embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are not intended to be drawn to scale. In the accompanying drawings, each identical or nearly identical component shown in each figure may be represented by the same reference numeral. For clarity, not every component is labeled in each figure. Embodiments of various aspects of the present invention will now be described by way of example and with reference to the accompanying drawings.

[0025] Figure 1 It is a flow chart of a subway station air conditioning cooling load prediction method based on improved BPNN according to an embodiment of the present invention.

[0026] Figure 2 4 is a schematic diagram of correlation coefficients of candidate factors according to an embodiment of the present invention.

[0027] Figure 3 is a block diagram of a BPNN structure according to an embodiment of the present invention.

[0028] Figure 4 1 is a flow chart of the Dayan optimization algorithm according to an embodiment of the present invention.

[0029] Figure 5 4 is a schematic diagram of the cooling load prediction effect on a certain day in summer according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to better understand the technical content of the present invention, specific embodiments are given and described as follows in conjunction with the accompanying drawings.

[0031] Various aspects of the present invention are described in this disclosure with reference to the accompanying drawings, in which many illustrative embodiments are shown. The embodiments of the present disclosure are not necessarily intended to include all aspects of the present invention. It should be understood that the various concepts and embodiments introduced above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed by the present invention are not limited to any implementation. In addition, some aspects disclosed by the present invention can be used alone or in any appropriate combination with other aspects disclosed by the present invention.

[0032] {Example 1}

[0033] Combination Figure 1-4 As shown, the subway station air conditioning cooling load prediction method based on improved BPNN according to this embodiment includes the following process:

[0034] Based on the subway station operation data, a big data processing method for missing values ​​and outliers repair is established to generate a database in hours.

[0035] Conduct correlation analysis on the factors affecting the cooling load of the subway station to determine the main influencing factors, wherein the relevant factors include: outdoor temperature, outdoor humidity, forecast time, and hourly cooling load of the system 24 hours before the forecast time;

[0036] According to the main influencing factors and cooling load, the structural parameters of the BPNN network are determined and the BPNN model is established;

[0037] The optimal weights, thresholds and number of hidden layer nodes of the BPNN model are determined based on the Dayan optimization algorithm to obtain the optimized BPNN model. The air conditioning cooling load of the subway station at a certain moment is predicted based on the optimized BPNN model.

[0038] As an optional implementation, the big data processing method for repairing missing values ​​and outliers is established based on subway station operation data to generate a database in hours, including:

[0039] A data traversal program is established, which is configured to automatically divide the original subway station operation data into hourly units;

[0040] An outlier diagnostic program is established, which is set to calculate the theoretical upper limit and theoretical lower limit of the operating data based on the quartile method, and find out the outliers that exceed the upper and lower limits according to the theoretical upper and lower limits;

[0041] Establish missing value and outlier repair procedures, which are set to repair missing values ​​and outliers based on weighted average;

[0042] According to the above three procedures, the repaired database in hours is obtained, and the average value of each factor is calculated and the processed database is output.

[0043] As an optional implementation, the repairing of missing values ​​and outliers based on weighted average includes:

[0044] By calculating the first five data in the normal data whose other dimensions are similar to the missing data, and calculating based on the weighted average, the weighted average is used as the corresponding missing value day or abnormal value;

[0045] Among them, the search for similar data is based on Euclidean distance, and the calculation formula of Euclidean distance is:

[0046]

[0047] Where: d(p,q) is the Euclidean distance between vectors p and q, p i and q i are the i-th components of vectors p and q respectively;

[0048] For the identified outliers and missing values, repair is performed based on the weighted average of the first five data with the closest Euclidean distance to the data to be repaired in the remaining dimensions found in the database.

[0049] As an optional implementation, the correlation analysis of the relevant factors affecting the cooling load of the subway station to determine the main influencing factors includes:

[0050] Calculate the Pearson correlation coefficient between outdoor temperature, outdoor humidity, forecast time, and hourly cooling load of the system 24 hours before the forecast time and air conditioning cooling load;

[0051] Factors with a Pearson correlation coefficient greater than the preset threshold were selected as the main influencing factors.

[0052] As an optional implementation, determining the structural parameters of the BPNN network according to the main influencing factors and the cooling load and establishing the BPNN model includes:

[0053] The BPNN model is configured as a three-layer neural network, including an input layer, a hidden layer, and an output layer;

[0054] Among them, the input layer corresponds to the main influencing factors one by one, and the output layer is the cooling load prediction value;

[0055] The value of each node in the hidden layer is calculated based on the internal weight and threshold of the grid, and the number of nodes in the hidden layer is determined based on the number of nodes in the input layer and the output layer.

[0056] As an optional implementation, the method of determining the optimal weight, threshold and number of hidden layer nodes of the BPNN model based on the Dayan optimization algorithm, obtaining the optimized BPNN model, and predicting the air conditioning cooling load of the subway station at a certain time based on the optimized BPNN model includes:

[0057] Set the parameters of the Dayan optimization algorithm, including the maximum number of iterations M gen , population size N, inertia weight w, acceleration factors c1 and c2;

[0058] Initialize the first generation of geese, calculate the fitness of each individual in the first generation of geese, and find the best individual as the leader goose P best ;

[0059] Generate random numbers and update the speed and position of each goose in the first generation population;

[0060] Update individual fitness and leader goose, when the number of iterations reaches the maximum number of iterations M gen When , the position and fitness of the final leader goose of the goose optimization algorithm are output;

[0061] Repeat the above steps to find the position and fitness of the leader goose under each possible number of hidden layer nodes of BPNN;

[0062] Based on the fitness, the best leader goose position and the corresponding number of hidden layer nodes are found, and the BPNN model is set and assigned to obtain the optimized BPNN model.

[0063] {Example 2}

[0064] In combination with the implementation process of the subway station air conditioning cooling load prediction method based on the improved BPNN in the above embodiment, we take a subway station in a certain city as an example in this example to more specifically explain the implementation of the method of the present invention, which includes the following process:

[0065] Step 101: Based on the subway station operation data, a big data processing method for repairing missing values ​​and outliers is established to generate a database in hours;

[0066] Step 102: performing correlation analysis on the relevant factors affecting the cooling load of the subway station to determine the main factors, wherein the aforementioned relevant factors are selected as: outdoor temperature, outdoor humidity, forecast time, and hourly cooling load of the system in the 24 hours before the forecast;

[0067] Step 103: Determine the structural parameters of BPNN according to the main factors and the cooling load, and establish a BPNN model; the model is a three-layer neural network, including an input layer, a hidden layer, and an output layer;

[0068] Step 104: Determine the optimal weight, threshold and number of hidden layer nodes of BPNN through the Dayan optimization algorithm, and predict the cooling load based on the optimized BPNN.

[0069] Furthermore, in step 101, based on the historical operation data of a certain subway station, a big data processing method for repairing missing values ​​and outliers is established to generate a database in hours. The specific operation process includes:

[0070] (1) Establish a data traversal program to automatically divide the data of the original data set into hourly units;

[0071] (2) Establish an outlier diagnosis procedure, calculate the theoretical upper and lower limits of the data set based on the quartile method, and find outliers based on the theoretical upper and lower limits;

[0072] (3) Establish a missing value and outlier repair procedure by calculating the top five data in the normal data whose remaining dimensions are similar to the missing data, and repair the missing and outlier values ​​based on the weighted average;

[0073] (4) Calculate the average value of the processed data within each hour and output the data for each hour to the database.

[0074] As an optional implementation method, the theoretical upper limit and the theoretical lower limit are calculated as follows:

[0075] U1=X 0.75 +1.5*(X 0.75 -X 0.25 )

[0076] L1=X 0.25 -1.5*(X 0.75 -X 0.25 )

[0077] Where: U1 and L1 are the reasonable upper and lower limits of normal data respectively; X 0.75 and X 0.25 are the upper and lower quartiles in the processed data, respectively.

[0078] As an optional implementation, the search for similar data is completed based on the Euclidean distance, and the calculation formula of the Euclidean distance is:

[0079]

[0080] Where: d(p,q) is the Euclidean distance between vectors p and q, p i and q i are the i-th components of vectors p and q respectively.

[0081] For the identified outliers and missing values, the weighted average of the first five data with the closest Euclidean distance to the data to be repaired based on the remaining dimensions found in the database is used for repair.

[0082] Furthermore, the specific operation process of step 102 includes:

[0083] (1) Calculate the Pearson correlation coefficient between each factor and cooling load;

[0084] The calculation formula of Pearson correlation coefficient is:

[0085]

[0086] Where: xy is the Pearson correlation coefficient between variables x and y; i and i are the i-th sample points of variables x and y, respectively. ave and ave are the average values ​​of variables x and y, respectively, and n is the number of data logarithms of variables x and y;

[0087] (2) Taking July 25 to October 24, 2021 as an example, a correlation analysis was conducted on the hourly cooling load of the case subway station and the outdoor temperature, outdoor humidity, forecast time, and the hourly cooling load of the system 24 hours before the forecast. The results are as follows: Figure 2 As shown in this example, the relevant factors with a Pearson correlation coefficient greater than the threshold of 0.4 are selected as the main influencing factors. Figure 3 In the example shown, a total of 9 factors, including outdoor temperature, forecast time, cooling load 1-3 hours before forecast, and cooling load 21-24 hours before forecast, are finally selected as the main factors.

[0088] Furthermore, the specific operation process of step 103 includes:

[0089] like Figure 3 As shown in the figure, the structural parameters of BPNN are determined according to the above 9 main factors and cooling load, and the BPNN model is established. The BPNN model is a three-layer neural network, including an input layer, a hidden layer, and an output layer.

[0090] Among them, the input layer corresponds to the nine main factors one by one, the output layer is the predicted value of the cooling load, the value of each node in the hidden layer is calculated based on the internal weight and threshold of the grid, and the number of nodes in the hidden layer is determined based on the number of nodes in the input layer and the output layer.

[0091] Before training the neural network, the database data can be normalized. The normalization formula is:

[0092]

[0093] Where: x is the variable to be normalized, x max and x min are the maximum and minimum values ​​of the variable x, respectively.

[0094] Among them, after obtaining the prediction results based on BPNN, the predicted cooling load needs to be denormalized. The denormalization formula is:

[0095] CL p =CL model ·(CL max -CL min )+CL min

[0096] Where: CL p is the cooling load prediction value after denormalization, CL model is the cooling load prediction value obtained based on BPNN, CL max and CL min are the maximum and minimum values ​​of cooling load before normalization.

[0097] Furthermore, if Figure 4 As shown, in step 104, the optimal weight, threshold and number of hidden layer nodes of BPNN are determined by the Dayan optimization algorithm, and the cooling load is predicted based on the optimized BPNN model. The specific operation process includes:

[0098] (1) Set the parameters of the Dayan optimization algorithm, including the maximum number of iterations M gen , population size N, inertia weight w, acceleration factors c1 and c2;

[0099] (2) Initialize the first generation of geese, calculate the fitness of each individual in the first generation of geese, and find the best individual as the leader goose P best ;

[0100] The evaluation criteria for the best individual are based on the error of the individual parameters when the weights and thresholds of BPNN are used to predict the cooling load. The cooling load prediction error is measured by MAE, and the MAE calculation formula is:

[0101]

[0102] Where: y i and i,pre are the actual value and predicted value of cooling load, n is the number of predicted sample points;

[0103] (3) Generate random numbers and update the speed and position of each goose in the first generation population;

[0104] Among them, for each goose, the speed update formula is:

[0105] V i (t+1)=w·V i (t)+c1·r1·(X leader (t)-X i (t))+c2·r2·(X pbest (t)-X i (t))

[0106] Where: V i (t) and V i (t+1) are the values ​​before and after the speed update, w is the inertia weight, c1 and c2 are acceleration factors, r1 and r2 are random numbers, X pbest (t) is the individual historical optimal value, X leader (t) is the historical optimal value of the group;

[0107] Among them, for each goose, the position update formula is:

[0108] X i (t+1)=X i (t)+V i (t+1)

[0109] Where: X i (t) and X i (t+1) are the positions before and after the update respectively;

[0110] (4) Update individual fitness and leader goose, when the number of iterations reaches the maximum number of iterations M gen When , the position and fitness of the final leader goose of the goose optimization algorithm are output;

[0111] (5) Repeat the above steps to find the position and fitness of the leader goose under each possible number of hidden layer nodes of BPNN;

[0112] Among them, the calculation formula for the possible range of node numbers of BPNN is:

[0113]

[0114] Where: h is the number of hidden layer nodes of BPNN, m and n are the number of input layer and output layer nodes respectively, and a is a positive integer between 1 and 10;

[0115] (6) Based on the fitness, the optimal leader goose position and the corresponding number of hidden layer nodes are found, and the BPNN model is set and assigned to obtain the optimized BPNN model.

[0116] {Example 3}

[0117] In combination with the above embodiments, according to the present disclosure, a subway station air conditioning cooling load prediction system based on improved BPNN is also proposed, including:

[0118] A database generation module is configured to establish a big data processing method for repairing missing values ​​and outliers based on subway station operation data, and generate a database in hours;

[0119] A correlation analysis module is configured to perform correlation analysis on relevant factors affecting the cooling load of the subway station to determine the main influencing factors, wherein the relevant factors include: outdoor temperature, outdoor humidity, forecast time, and hourly cooling load of the system 24 hours before the forecast time;

[0120] The BPNN model building module is configured to determine the structural parameters of the BPNN network according to the main influencing factors and the cooling load, and to establish the BPNN model;

[0121] The BPNN model optimization module is configured to determine the optimal weight, threshold and number of hidden layer nodes of the BPNN model based on the Dayan optimization algorithm, obtain the optimized BPNN model, and predict the air conditioning cooling load of the subway station at a certain moment based on the optimized BPNN model.

[0122] It should be understood that the functions, outputs and specific implementation processes of the above-mentioned database generation module, correlation analysis module, BPNN model construction module and BPNN model optimization module can be implemented in the same manner as the corresponding process of the subway station air-conditioning cooling load prediction method based on improved BPNN in the aforementioned embodiment.

[0123] As an optional implementation, the BPNN model construction module is configured to construct the BPNN model in the following manner:

[0124] The BPNN model is configured as a three-layer neural network, including an input layer, a hidden layer, and an output layer;

[0125] Among them, the input layer corresponds to the main influencing factors one by one, and the output layer is the cooling load prediction value;

[0126] The value of each node in the hidden layer is calculated based on the internal weight and threshold of the grid, and the number of nodes in the hidden layer is determined based on the number of nodes in the input layer and the output layer.

[0127] As an optional implementation, the BPNN model optimization module is configured to obtain an optimized BPNN model based on the following method to predict the air conditioning cooling load of a subway station at a certain moment, including:

[0128] Set the parameters of the Dayan optimization algorithm, including the maximum number of iterations M gen , population size N, inertia weight w, acceleration factors c1 and c2;

[0129] Initialize the first generation of geese, calculate the fitness of each individual in the first generation of geese, and find the best individual as the leader goose P best ;

[0130] Generate random numbers and update the speed and position of each goose in the first generation population;

[0131] Update individual fitness and leader goose, when the number of iterations reaches the maximum number of iterations M gen When , the position and fitness of the final leader goose of the goose optimization algorithm are output;

[0132] Repeat the above steps to find the position and fitness of the leader goose under each possible number of hidden layer nodes of BPNN;

[0133] Based on the fitness, the best leader goose position and the corresponding number of hidden layer nodes are found, and the BPNN model is set and assigned to obtain the optimized BPNN model.

[0134] {Example 4}

[0135] In combination with the above embodiments, according to the present disclosure, a computer system is also proposed, including:

[0136] one or more processors;

[0137] Memory stores instructions that can be operated.

[0138] Wherein, when the instruction is executed by the one or more processors, the one or more processors perform operations, and the operations include the process of executing the subway station air conditioning cooling load prediction method based on improved BPNN in any of the aforementioned embodiments.

[0139] {Example 5}

[0140] A computer-readable medium storing software, characterized in that the software includes instructions that can be executed by one or more computers, and when the instructions are executed by the one or more computers, the process of the subway station air conditioning cooling load prediction method based on improved BPNN is performed in any embodiment.

[0141] {Example 6}

[0142] A computer program product includes a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of the subway station air conditioning cooling load prediction method based on improved BPNN in any of the aforementioned embodiments.

[0143] Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. A person with ordinary knowledge in the technical field to which the present invention belongs may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the definition of the claims.

Claims

1. A subway station air conditioning cooling load prediction method based on improved BPNN, characterized in that: The process includes: Based on the subway station operation data, a big data processing method for missing values ​​and outliers repair is established to generate a database in hours. Conduct correlation analysis on the factors affecting the cooling load of the subway station to determine the main influencing factors, wherein the relevant factors include: outdoor temperature, outdoor humidity, forecast time, and hourly cooling load of the system 24 hours before the forecast time; According to the main influencing factors and cooling load, the structural parameters of the BPNN network are determined and the BPNN model is established; The optimal weights, thresholds and number of hidden layer nodes of the BPNN model are determined based on the Dayan optimization algorithm to obtain the optimized BPNN model. The air conditioning cooling load of the subway station at a certain moment is predicted based on the optimized BPNN model.

2. The subway station air conditioning cooling load prediction method based on improved BPNN according to claim 1 is characterized in that: The method of establishing a big data processing method for repairing missing values ​​and outliers based on subway station operation data and generating a database in hours includes: A data traversal program is established, which is configured to automatically divide the original subway station operation data into hourly units; An outlier diagnostic program is established, which is set to calculate the theoretical upper limit and theoretical lower limit of the operating data based on the quartile method, and find out the outliers that exceed the upper and lower limits according to the theoretical upper and lower limits; Establish missing value and outlier repair procedures, which are set to repair missing values ​​and outliers based on weighted average; According to the above three procedures, the repaired database in hours is obtained, and the average value of each factor is calculated and the processed database is output.

3. The subway station air conditioning cooling load prediction method based on improved BPNN according to claim 1 is characterized in that: The method of repairing missing values ​​and outliers based on weighted average includes: By calculating the first five data in the normal data whose other dimensions are similar to the missing data, and calculating based on the weighted average, the weighted average is used as the corresponding missing value day or abnormal value; Among them, the search for similar data is based on Euclidean distance, and the calculation formula of Euclidean distance is: Where: d(p,q) is the Euclidean distance between vectors p and q, p i and q i are the i-th components of vectors p and q respectively; For the identified outliers and missing values, repair is performed based on the weighted average of the first five data with the closest Euclidean distance to the data to be repaired in the remaining dimensions found in the database.

4. The subway station air conditioning cooling load prediction method based on improved BPNN according to claim 1 is characterized in that: The correlation analysis of the factors affecting the cooling load of the subway station is carried out to determine the main influencing factors, including: Calculate the Pearson correlation coefficient between outdoor temperature, outdoor humidity, forecast time, and hourly cooling load of the system 24 hours before the forecast time and air conditioning cooling load; Factors with a Pearson correlation coefficient greater than the preset threshold were selected as the main influencing factors.

5. The subway station air conditioning cooling load prediction method based on improved BPNN according to claim 1 is characterized in that: The method of determining the structural parameters of the BPNN network according to the main influencing factors and the cooling load and establishing the BPNN model includes: The BPNN model is configured as a three-layer neural network, including an input layer, a hidden layer, and an output layer; Among them, the input layer corresponds to the main influencing factors one by one, and the output layer is the cooling load prediction value; The value of each node in the hidden layer is calculated based on the internal weight and threshold of the grid, and the number of nodes in the hidden layer is determined based on the number of nodes in the input layer and the output layer.

6. The subway station air conditioning cooling load prediction method based on improved BPNN according to claim 5 is characterized in that: The method of determining the optimal weight, threshold and number of hidden layer nodes of the BPNN model based on the Dayan optimization algorithm, obtaining the optimized BPNN model, and predicting the air conditioning cooling load of the subway station at a certain time based on the optimized BPNN model includes: Set the parameters of the Dayan optimization algorithm, including the maximum number of iterations M gen , population size N, inertia weight w, acceleration factors c1 and c2; Initialize the first generation of geese, calculate the fitness of each individual in the first generation of geese, and find the best individual as the leader goose P best ; Generate random numbers and update the speed and position of each goose in the first generation population; Update individual fitness and leader goose, when the number of iterations reaches the maximum number of iterations M gen When , the position and fitness of the final leader goose of the goose optimization algorithm are output; Repeat the above steps to find the position and fitness of the leader goose under each possible number of hidden layer nodes of BPNN; Based on the fitness, the best leader goose position and the corresponding number of hidden layer nodes are found, and the BPNN model is set and assigned to obtain the optimized BPNN model.

7. A subway station air conditioning cooling load prediction system based on improved BPNN, characterized in that: include: A database generation module is configured to establish a big data processing method for repairing missing values ​​and outliers based on subway station operation data, and generate a database in hours; A correlation analysis module is configured to perform correlation analysis on relevant factors affecting the cooling load of the subway station to determine the main influencing factors, wherein the relevant factors include: outdoor temperature, outdoor humidity, forecast time, and hourly cooling load of the system 24 hours before the forecast time; The BPNN model building module is configured to determine the structural parameters of the BPNN network according to the main influencing factors and the cooling load, and to establish the BPNN model; The BPNN model optimization module is configured to determine the optimal weight, threshold and number of hidden layer nodes of the BPNN model based on the Dayan optimization algorithm, obtain the optimized BPNN model, and predict the air conditioning cooling load of the subway station at a certain moment based on the optimized BPNN model.

8. The subway station air conditioning cooling load prediction system based on improved BPNN according to claim 7 is characterized in that: The BPNN model construction module is configured to construct a BPNN model in the following manner: The BPNN model is configured as a three-layer neural network, including an input layer, a hidden layer, and an output layer; Among them, the input layer corresponds to the main influencing factors one by one, and the output layer is the cooling load prediction value; The value of each node in the hidden layer is calculated based on the internal weight and threshold of the grid, and the number of nodes in the hidden layer is determined based on the number of nodes in the input layer and the output layer.

9. The subway station air conditioning cooling load prediction system based on improved BPNN according to claim 7 is characterized in that: The BPNN model optimization module is configured to obtain an optimized BPNN model based on the following method to predict the air conditioning cooling load of a subway station at a certain moment, including: Set the parameters of the Dayan optimization algorithm, including the maximum number of iterations M gen , population size N, inertia weight w, acceleration factors c1 and c2; Initialize the first generation of geese, calculate the fitness of each individual in the first generation of geese, and find the best individual as the leader goose P best ; Generate random numbers and update the speed and position of each goose in the first generation population; Update individual fitness and leader goose, when the number of iterations reaches the maximum number of iterations M gen When , the position and fitness of the final leader goose of the goose optimization algorithm are output; Repeat the above steps to find the position and fitness of the leader goose under each possible number of hidden layer nodes of BPNN; Based on the fitness, the best leader goose position and the corresponding number of hidden layer nodes are found, and the BPNN model is set and assigned to obtain the optimized BPNN model.

10. A computer system, characterized in that: include: one or more processors; A memory storing operable instructions, wherein when the instructions are executed by the one or more processors, the one or more processors are caused to perform operations, wherein the operations include the process of executing the method described in any one of claims 1 to 6.

11. A computer-readable medium storing software, characterized in that: The software includes instructions that can be executed by one or more computers, and when the instructions are executed by the one or more computers, the process of the method according to any one of claims 1 to 6 is performed.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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