Indirect cooling tower temperature prediction method, system, equipment and medium
By constructing an intercooling tower temperature prediction model including CEEMDAN decomposition module, sample entropy merging module, full connection layer and bidirectional long and short-term memory neural network, the problem of traditional methods being difficult to predict the temperature of the intercooling tower return pipe is solved, and more accurate and reliable temperature prediction is achieved.
Patent Information
- Application Number
- CN202510042001.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-10
- Publication Date
- 2025-05-09
AI Technical Summary
Traditional intercooling tower return pipe temperature prediction methods are difficult to capture the complex relationships and timing information between multi-dimensional data, resulting in limited prediction accuracy and robustness.
The intercooling tower temperature prediction model including CEEMDAN decomposition module, sample entropy merging module, full connection layer and bidirectional long and short-term memory neural network is adopted. By obtaining the historical operation data and current information of the power plant, the model is trained using genetic algorithms to predict future temperatures.
Effectively capture the interaction between data features, achieve more accurate and reliable temperature prediction, and provide better temperature control and operation management solutions for thermal power generation systems.
Smart Images
Figure CN119962737A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of thermal power generation, and relates to a method, system, equipment and medium for predicting the temperature of an intercooler. Background Art
[0002] In thermal power generation systems, accurate prediction of the return water pipe temperature of intercooler towers is crucial for the stable operation and performance optimization of the system. Traditional temperature prediction methods are usually based on only a single feature and are difficult to capture the complex relationship and time series information between multidimensional data, resulting in limited prediction accuracy and robustness.
[0003] In the prior art, commonly used temperature prediction methods include those based on statistical models, machine learning, and deep learning. However, these methods have certain limitations in their ability to process multidimensional data features and time series information, and it is difficult to fully explore the hidden correlations and regularities in the data, making it difficult to accurately predict the temperature of the intercooler return pipe. Summary of the invention
[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method, system, device and medium for predicting the temperature of an intercooler tower. The method, system, device and medium can accurately predict the temperature of a return pipe of an intercooler tower.
[0005] To achieve the above object, the present invention discloses a method for predicting the temperature of an intercooling tower, comprising:
[0006] Obtain the current intercooler return pipe temperature, unit load, and unit back pressure information;
[0007] The current return water pipe temperature of the intercooler, unit load and unit back pressure information are input into the trained intercooler temperature prediction model to predict the temperature information of the return water pipe of the intercooler in the future. The intercooler temperature prediction model includes a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer and a bidirectional long short-term memory neural network.
[0008] The further improvement of the indirect cooling tower temperature prediction method of the present invention is:
[0009] Furthermore, before inputting the return water pipe temperature, unit load and unit back pressure information of the current intercooling tower into the trained intercooling tower temperature prediction model, the method further includes:
[0010] Acquire historical operation data of the power plant, the historical operation data of the power plant including date, intercooler return pipe temperature, unit load and unit back pressure, and construct a data set based on the historical operation data of the power plant;
[0011] Constructing an intercooling tower temperature prediction model, the intercooling tower temperature prediction model includes a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer and a bidirectional long short-term memory neural network, wherein the CEEMDAN decomposition module is sequentially connected to the bidirectional long short-term memory neural network via the sample entropy merging module and the fully connected layer;
[0012] The intercooling tower temperature prediction model is trained based on the data set to obtain a trained intercooling tower temperature prediction model.
[0013] Furthermore, the output result h of the fully connected layer t for:
[0014] h t =σ(Wreturn xreturn,t+Wload xload,t+Wbackpressure xbackpressure,t+b)
[0015] Among them, W 回 , W 负荷 and W 背压 is the weight matrix, b is the bias term, and σ is the Sigmoid activation function.
[0016] Furthermore, based on the data set, a genetic algorithm is used to train the intercooling tower temperature prediction model to obtain a trained intercooling tower temperature prediction model.
[0017] The invention discloses a temperature prediction system for an indirect cooling tower, comprising:
[0018] The acquisition module is used to obtain the return pipe temperature, unit load and unit back pressure information of the current intercooler;
[0019] The prediction module is used to input the current return pipe temperature of the intercooler, the unit load and the unit back pressure information into the trained intercooler temperature prediction model to predict the temperature information of the return pipe of the intercooler in the future. The intercooler temperature prediction model includes a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer and a bidirectional long short-term memory neural network.
[0020] The further improvement of the intercooling tower temperature prediction system of the present invention is:
[0021] Furthermore, it also includes:
[0022] The first construction module is used to obtain historical operation data of the power plant, wherein the historical operation data of the power plant includes date, temperature of the return water pipe of the intercooler tower, unit load and unit back pressure, and construct a data set based on the historical operation data of the power plant;
[0023] The second construction module is used to construct an intercooling tower temperature prediction model, wherein the intercooling tower temperature prediction model includes a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer, and a bidirectional long short-term memory neural network, wherein the CEEMDAN decomposition module is sequentially connected to the bidirectional long short-term memory neural network via the sample entropy merging module and the fully connected layer;
[0024] The training module is used to train the intercooling tower temperature prediction model based on the data set to obtain the trained intercooling tower temperature prediction model.
[0025] Furthermore, the output result h of the fully connected layer t for:
[0026] h t =σ(Wreturn xreturn,t+Wload xload,t+Wbackpressure xbackpressure,t+b)
[0027] Among them, W 回 , W 负荷 and W 背压 is the weight matrix, b is the bias term, and σ is the Sigmoid activation function.
[0028] Furthermore, based on the data set, a genetic algorithm is used to train the intercooling tower temperature prediction model to obtain a trained intercooling tower temperature prediction model.
[0029] The present invention discloses a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the intercooling tower temperature prediction method are implemented.
[0030] The present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the intercooling tower temperature prediction method are implemented.
[0031] The present invention has the following beneficial effects:
[0032] During specific operation, the intercooling tower temperature prediction method, system, device and medium of the present invention input the return pipe temperature of the current intercooling tower, unit load and unit back pressure information into the trained intercooling tower temperature prediction model, and predict the temperature information of the return pipe of the intercooling tower in the future period of time. The intercooling tower temperature prediction model includes a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer and a bidirectional long short-term memory neural network, wherein a multidimensional hybrid prediction model is adopted, combined with a variety of feature information and complex relationships, to effectively capture the interaction between data features, thereby achieving more accurate and reliable temperature prediction, and providing a better temperature control and operation management solution for the thermal power generation system. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:
[0034] Figure 1 is a flow chart of the method of the present invention;
[0035] Figure 2 It is a flow chart of the genetic algorithm in the present invention. DETAILED DESCRIPTION
[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0037] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.
[0038] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.
[0039] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.
[0040] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.
[0041] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.
[0042] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.
[0043] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.
[0044] Embodiment 1
[0045] refer to Figure 1 and Figure 2 The method for predicting the temperature of an intercooling tower of the present invention comprises the following steps:
[0046] 1) Build a dataset;
[0047] Acquire historical operation data of the power plant, the historical operation data of the power plant including date, intercooler return pipe temperature, unit load and unit back pressure, and construct a data set based on the historical operation data of the power plant;
[0048] 2) constructing an intercooling tower temperature prediction model, wherein the intercooling tower temperature prediction model includes a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer, and a bidirectional long short-term memory neural network;
[0049] The working process of the CEEMDAN decomposition module is as follows:
[0050] The CEEMDAN decomposition module decomposes the input raw data into multiple IMFs, where each IMF represents a partial signal of different frequencies. The CEEMDAN decomposition process can be expressed as:
[0051]
[0052] Among them, x(t) is the original signal, h k (t) is the kth EMD decomposition result, and n(t) is random noise.
[0053] The operation process of the sample entropy merging module is as follows:
[0054] For IMFs, the sample entropy of each IMF is calculated to measure the complexity and information content of the IMF. The sample entropy of each IMF is weighted averaged to combine the information of different frequency components to more comprehensively capture the correlation and information interaction between different features. The calculation formula of sample entropy is:
[0055]
[0056] Among them, p i is the probability that all identical subsequences of length m in the sequence are similar to x(t).
[0057] The weighted average of the return pipe temperature, unit load and unit back pressure is x 回 , x 负荷 , x 背压 Aligned at time step t, and then input into the fully connected layer, the fully connected layer outputs a new feature vector h t , and then input into the bidirectional long short-term memory neural network, and the bidirectional long short-term memory neural network is used to predict the temperature of the intercooler return pipe in the future, wherein the new feature vector h t for:
[0058] h t =σ(Wreturn xreturn,t+Wload xload,t+Wbackpressure xbackpressure,t+b)
[0059] Among them, W 回 , W 负荷 and W 背压 is the weight matrix, b is the bias term, and σ is the Sigmoid activation function.
[0060] 3) using the data set to train the intercooling tower temperature prediction model to obtain a trained intercooling tower temperature prediction model;
[0061] Wherein, according to a genetic algorithm, the data set is used to train the intercooling tower temperature prediction model.
[0062] 4) Obtain the return pipe temperature, unit load and unit back pressure information of the current intercooler, input the return pipe temperature, unit load and unit back pressure of the current intercooler into the trained intercooler temperature prediction model, and predict the temperature information of the return pipe of the intercooler in the future.
[0063] Embodiment 2
[0064] The temperature prediction system for an intercooling tower of the present invention comprises:
[0065] The acquisition module is used to obtain the return pipe temperature, unit load and unit back pressure information of the current intercooler;
[0066] The prediction module is used to input the current return pipe temperature of the intercooler, the unit load and the unit back pressure information into the trained intercooler temperature prediction model to predict the temperature information of the return pipe of the intercooler in the future. The intercooler temperature prediction model includes a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer and a bidirectional long short-term memory neural network.
[0067] As an embodiment of the present invention, this embodiment also includes:
[0068] The first construction module is used to obtain historical operation data of the power plant, wherein the historical operation data of the power plant includes date, temperature of the return water pipe of the intercooler tower, unit load and unit back pressure, and construct a data set based on the historical operation data of the power plant;
[0069] The second construction module is used to construct an intercooling tower temperature prediction model, wherein the intercooling tower temperature prediction model includes a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer, and a bidirectional long short-term memory neural network, wherein the CEEMDAN decomposition module is sequentially connected to the bidirectional long short-term memory neural network via the sample entropy merging module and the fully connected layer;
[0070] The training module is used to train the intercooling tower temperature prediction model based on the data set to obtain the trained intercooling tower temperature prediction model.
[0071] As an embodiment of the present invention, the output result h of the fully connected layer t for:
[0072] h t =σ(Wreturn xreturn,t+Wload xload,t+Wbackpressure xbackpressure,t+b)
[0073] Among them, W 回 , W 负荷and W 背压 is the weight matrix, b is the bias term, and σ is the Sigmoid activation function.
[0074] As an implementation scheme of the present invention, a temperature prediction model for an intercooling tower is trained using a genetic algorithm based on the data set to obtain a trained temperature prediction model for an intercooling tower.
[0075] The division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.
[0076] Embodiment 3
[0077] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of implementing the intercooling tower temperature prediction method are, for example, comprising: obtaining historical operation data of a power plant, the historical operation data of the power plant comprising date, intercooling tower return pipe temperature, unit load and unit back pressure, and constructing a data set based on the historical operation data of the power plant; constructing an intercooling tower temperature prediction model, the intercooling tower temperature prediction model comprising a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer and a bidirectional long short-term memory neural network, wherein the CEEMDAN decomposition module is sequentially connected to the bidirectional long short-term memory neural network via the sample entropy merging module and the fully connected layer; training the intercooling tower temperature prediction model based on the data set to obtain a trained intercooling tower temperature prediction model; obtaining the return pipe temperature, unit load and unit back pressure information of the current intercooling tower; inputting the return pipe temperature, unit load and unit back pressure information of the current intercooling tower into the trained intercooling tower temperature prediction model to predict the temperature information of the intercooling tower return pipe in the future. The memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc. The processor, network interface, and memory are interconnected through an internal bus, and the internal bus may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard architecture bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include a program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.
[0078] Embodiment 4
[0079] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of implementing the intercooling tower temperature prediction method are, for example, including: obtaining historical operation data of a power plant, the historical operation data of the power plant including date, intercooling tower return pipe temperature, unit load and unit back pressure, and constructing a data set based on the historical operation data of the power plant; constructing an intercooling tower temperature prediction model, the intercooling tower temperature prediction model including a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer and a bidirectional long short-term memory neural network, wherein the CEEMDAN decomposition module is sequentially connected to the bidirectional long short-term memory neural network via the sample entropy merging module and the fully connected layer; training the intercooling tower temperature prediction model based on the data set to obtain a trained intercooling tower temperature prediction model; obtaining the return pipe temperature, unit load and unit back pressure information of the current intercooling tower; inputting the return pipe temperature, unit load and unit back pressure information of the current intercooling tower into the trained intercooling tower temperature prediction model to predict the temperature information of the intercooling tower return pipe in the future. Specifically, the computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.
[0080] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.
[0081] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0082] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0084] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and disclosure of the invention. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.
[0085] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.
[0086] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.
Claims
1. A method for predicting the temperature of an indirect cooling tower, characterized in that: include: Obtain the current intercooler return pipe temperature, unit load, and unit back pressure information; The current return water pipe temperature of the intercooler, unit load and unit back pressure information are input into the trained intercooler temperature prediction model to predict the temperature information of the return water pipe of the intercooler in the future. The intercooler temperature prediction model includes a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer and a bidirectional long short-term memory neural network.
2. The method for predicting the temperature of an intercooling tower according to claim 1, characterized in that: Before inputting the current intercooling tower return pipe temperature, unit load and unit back pressure information into the trained intercooling tower temperature prediction model, the method further includes: Acquire historical operation data of the power plant, the historical operation data of the power plant including date, intercooler return pipe temperature, unit load and unit back pressure, and construct a data set based on the historical operation data of the power plant; Constructing an intercooling tower temperature prediction model, the intercooling tower temperature prediction model includes a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer and a bidirectional long short-term memory neural network, wherein the CEEMDAN decomposition module is sequentially connected to the bidirectional long short-term memory neural network via the sample entropy merging module and the fully connected layer; The intercooling tower temperature prediction model is trained based on the data set to obtain a trained intercooling tower temperature prediction model.
3. The method for predicting the temperature of an intercooling tower according to claim 2, characterized in that: The output of the fully connected layer h t for: h t =σ(Wreturn xreturn,t+Wload xload,t+Wbackpressure xbackpressure,t+b) Among them, W 回 , W 负荷 and W 背压 is the weight matrix, b is the bias term, and σ is the Sigmoid activation function.
4. The method for predicting the temperature of an intercooling tower according to claim 2, characterized in that: Based on the data set, a genetic algorithm is used to train the intercooling tower temperature prediction model to obtain a trained intercooling tower temperature prediction model.
5. A temperature prediction system for an indirect cooling tower, characterized in that: include: The acquisition module is used to obtain the return water pipe temperature, unit load and unit back pressure information of the current intercooler; The prediction module is used to input the current return pipe temperature of the intercooler, the unit load and the unit back pressure information into the trained intercooler temperature prediction model to predict the temperature information of the return pipe of the intercooler in the future. The intercooler temperature prediction model includes a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer and a bidirectional long short-term memory neural network.
6. The temperature prediction system for intercooling tower according to claim 5, characterized in that: Also includes: The first construction module is used to obtain historical operation data of the power plant, wherein the historical operation data of the power plant includes date, temperature of the return water pipe of the intercooler tower, unit load and unit back pressure, and construct a data set based on the historical operation data of the power plant; The second construction module is used to construct an intercooling tower temperature prediction model, wherein the intercooling tower temperature prediction model includes a CEEMDAN decomposition module, a sample entropy merging module, a fully connected layer, and a bidirectional long short-term memory neural network, wherein the CEEMDAN decomposition module is sequentially connected to the bidirectional long short-term memory neural network via the sample entropy merging module and the fully connected layer; The training module is used to train the intercooling tower temperature prediction model based on the data set to obtain the trained intercooling tower temperature prediction model.
7. The temperature prediction system for an intercooling tower according to claim 6, characterized in that: The output of the fully connected layer h t for: h t =σ(Wreturn xreturn,t+Wload xload,t+Wbackpressure xbackpressure,t+b) Among them, W 回 , W 负荷 and W 背压 is the weight matrix, b is the bias term, and σ is the Sigmoid activation function.
8. The temperature prediction system for an intercooling tower according to claim 6, characterized in that: Based on the data set, a genetic algorithm is used to train the intercooling tower temperature prediction model to obtain a trained intercooling tower temperature prediction model.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the intercooler temperature prediction method according to any one of claims 1 to 4 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the intercooler temperature prediction method according to any one of claims 1 to 4 are implemented.