Energy-saving control method and system for ship refrigeration system
Through the combination of real-time monitoring and load prediction models, combined with real-time adjustment of PID algorithm, the shortcomings of existing ship refrigeration systems in energy management are solved, and more efficient energy use and more flexible refrigeration management are achieved.
Patent Information
- Application Number
- CN202411871766.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-18
- Publication Date
- 2025-04-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing ship refrigeration systems rely on manual settings or simple threshold control, and lack intelligent decision-making based on real-time data, resulting in insufficient energy potential being fully tapped and difficult to cope with complex and changing operating environments.
By monitoring the working parameters and cargo type tags of the ship's refrigeration system in real time, importing the load prediction model for accurate load prediction, and using the PID algorithm to adjust the refrigeration chamber temperature in real time and dynamically adjusting the cooling power.
Improve the accuracy and flexibility of load prediction, avoid unnecessary energy waste, ensure efficient energy use, and be able to dynamically adjust the cooling power according to load changes.
Smart Images
Figure CN119805915A_ABST
Abstract
Description
Background Art
[0002] In existing ship refrigeration systems, many energy-saving control methods rely on manual settings or simple threshold controls, lacking intelligent decision-making based on real-time data. For example, traditional systems may rely on fixed cooling strategies or adjust the temperature of the refrigerated compartment based on experience, without considering factors such as changes in the external environment, cargo type, or ship status. This results in the inability to fully tap the energy-saving potential and difficulty in coping with complex and changing operating environments; many traditional ship refrigeration systems rely on simplified models or judgments based on historical experience in load forecasting, rather than efficient prediction algorithms. These methods are usually unable to accurately predict system loads based on a variety of factors (such as ambient temperature, ship load, cargo type, etc.), which can easily lead to excessive or insufficient cooling, thereby wasting energy or affecting cargo quality. Summary of the invention
[0003] The main purpose of the present invention is to provide an energy-saving control method for a ship refrigeration system, and further to provide an energy-saving control system for a ship refrigeration system that can run and implement the above method, so as to effectively solve the above problems mentioned in the background technology.
[0004] The technical solution of the present invention is as follows:
[0005] In a first aspect, an energy-saving control method for a ship refrigeration system is proposed, the method comprising the following steps:
[0006] S1. Real-time monitoring of the working parameters of the ship's refrigeration system and extraction of cargo type labels stored in the ship's refrigeration system;
[0007] S2. Import various working parameters of the ship's refrigeration system and cargo type labels into the constructed load prediction model to predict the load demand of the refrigeration system;
[0008] S3. Based on the predicted load demand of the refrigeration system, energy-saving control is performed on the refrigeration system.
[0009] A further improvement of the present invention is that the various operating parameters of the ship refrigeration system in S1 include: refrigeration compartment temperature, external environment temperature, refrigerant flow rate, and compressor operating pressure.
[0010] A further improvement of the present invention is that the construction of the load forecasting model in S2 includes the following specific steps:
[0011] S21, obtaining at least one set of historical operating parameters of the ship refrigeration system, historically stored cargo type labels and corresponding historical load data, wherein the historical operating parameters include historical refrigeration compartment temperature, historical external environment temperature, historical refrigerant flow rate, and historical compressor operating pressure;
[0012] S22, combining various historical working parameters of the ship refrigeration system and historically stored cargo type labels into a feature vector form, and using the set of all feature vectors as input to a load forecasting model, wherein the load forecasting model uses load data predicted by each set of feature vectors as output, uses actual load data corresponding to each set of feature vectors as a forecasting target, and uses minimizing the sum of the forecasting accuracies of all predicted load data as a training target;
[0013] S23. The calculation formula for prediction accuracy is: p i =(m i -M i ) 2 , where the subscript i is the number of feature vectors, m is the predicted load data, M is the actual load data, and p i The load forecasting model is trained based on the prediction accuracy between the load data predicted by the i-th group of feature vectors and the actual load data, and the training is stopped when the sum of the prediction accuracies reaches convergence.
[0014] A further improvement of the present invention is that the load forecasting model in S2 is any one of a regression model, a support vector machine or a random forest model.
[0015] A further improvement of the present invention is that the specific content of S3 is: using a PID algorithm to adjust the temperature in the refrigerated compartment in real time, and the control formula of the PID algorithm is: Where u(t) is the control output, e(t) is the difference between the set temperature and the actual temperature of the refrigeration system, and k p , k i , k d They are the proportional, integral and differential coefficients of the PID controller respectively.
[0016] In the second aspect, an energy-saving control system for a ship refrigeration system is proposed, the system comprising: an information collection module, a load prediction module, and an energy-saving control module;
[0017] The information collection module is used to monitor various working parameters of the ship's refrigeration system in real time and extract cargo type labels stored in the ship's refrigeration system;
[0018] The load prediction module is used to import various operating parameters of the ship refrigeration system and cargo type labels into the constructed load prediction model to predict the load demand of the refrigeration system;
[0019] The energy-saving control module is used to perform energy-saving control on the refrigeration system based on the predicted load demand of the refrigeration system.
[0020] The technical effects of the present invention are as follows:
[0021] An energy-saving control method for a ship refrigeration system has been constructed. The present invention is based on real-time data (such as refrigeration compartment temperature, external ambient temperature, refrigerant flow, etc.) combined with intelligent prediction models (such as regression models, support vector machines, random forests, etc.) to accurately predict the load of the refrigeration system. This method not only takes into account traditional factors such as temperature and humidity, but also incorporates other influencing factors such as cargo type, thereby improving the accuracy and flexibility of load prediction; the refrigeration compartment temperature is adjusted in real time through the PID control algorithm to keep the temperature within the set range, and the cooling power can be dynamically adjusted according to changes in load. The PID algorithm can not only stably control the temperature, but also respond to load changes, avoid unnecessary energy waste, and ensure efficient energy use. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:
[0023] Figure 1 This is a flow chart of an energy-saving control method for a ship refrigeration system according to Embodiment 1 of the present invention;
[0024] Figure 2 This is a schematic structural diagram of an energy-saving control system for a ship refrigeration system according to Embodiment 2 of the present invention. DETAILED DESCRIPTION
[0025] The present invention aims to propose an energy-saving control method for a ship refrigeration system, which accurately predicts the load of the refrigeration system based on real-time data (such as refrigeration compartment temperature, external ambient temperature, refrigerant flow, etc.) combined with intelligent prediction models (such as regression models, support vector machines, random forests, etc.). This method not only takes into account traditional factors such as temperature and humidity, but also incorporates other influencing factors such as cargo type, thereby improving the accuracy and flexibility of load prediction; the refrigeration compartment temperature is adjusted in real time through the PID control algorithm to keep the temperature within the set range, and the cooling power can be dynamically adjusted according to changes in load. The PID algorithm can not only stably control the temperature, but also respond to load changes, avoid unnecessary energy waste, and ensure efficient energy use.
[0026] Embodiment 1:
[0027] This embodiment proposes an energy-saving control method for a ship refrigeration system. Figure 1 As shown, the specific steps include:
[0028] S1. Real-time monitoring of the working parameters of the ship's refrigeration system and extraction of cargo type labels stored in the ship's refrigeration system;
[0029] S2. Import various working parameters of the ship's refrigeration system and cargo type labels into the constructed load prediction model to predict the load demand of the refrigeration system;
[0030] S3. Based on the predicted load demand of the refrigeration system, energy-saving control is performed on the refrigeration system.
[0031] In this embodiment, the various operating parameters of the ship refrigeration system in S1 include: refrigeration compartment temperature, external environment temperature, refrigerant flow rate, and compressor operating pressure.
[0032] In this embodiment, the construction of the load forecasting model in S2 includes the following specific steps:
[0033] S21, obtaining at least one set of historical operating parameters of the ship refrigeration system, historically stored cargo type labels and corresponding historical load data, wherein the historical operating parameters include historical refrigeration compartment temperature, historical external environment temperature, historical refrigerant flow rate, and historical compressor operating pressure;
[0034] S22, combining various historical working parameters of the ship refrigeration system and historically stored cargo type labels into a feature vector form, and using the set of all feature vectors as input to a load forecasting model, wherein the load forecasting model uses load data predicted by each set of feature vectors as output, uses actual load data corresponding to each set of feature vectors as a forecasting target, and uses minimizing the sum of the forecasting accuracies of all predicted load data as a training target;
[0035] S23. The calculation formula for prediction accuracy is: p i =(m i -M i ) 2 , where the subscript i is the number of feature vectors, m is the predicted load data, M is the actual load data, and p i The load forecasting model is trained based on the prediction accuracy between the load data predicted by the i-th group of feature vectors and the actual load data, and the training is stopped when the sum of the prediction accuracies reaches convergence.
[0036] In this embodiment, the load forecasting model in S2 is any one of a regression model, a support vector machine or a random forest model.
[0037] In this embodiment, the specific content of S3 is: using the PID algorithm to adjust the temperature in the refrigerated compartment in real time, and the control formula of the PID algorithm is: Where u(t) is the control output, e(t) is the difference between the set temperature and the actual temperature of the refrigeration system, and k p , k i , k dThey are the proportional, integral and differential coefficients of the PID controller respectively.
[0038] Embodiment 2:
[0039] This embodiment proposes an energy-saving control system for a ship refrigeration system. Figure 2 As shown, the system includes: an information collection module, a load prediction module, and an energy-saving control module;
[0040] The information collection module is used to monitor various working parameters of the ship's refrigeration system in real time and extract cargo type labels stored in the ship's refrigeration system;
[0041] The load prediction module is used to import various operating parameters of the ship refrigeration system and cargo type labels into the constructed load prediction model to predict the load demand of the refrigeration system;
[0042] The energy-saving control module is used to perform energy-saving control on the refrigeration system based on the predicted load demand of the refrigeration system.
[0043] Embodiment 3:
[0044] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned energy-saving control method for a ship refrigeration system by calling the computer program stored in the memory.
[0045] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement an energy-saving control method for a ship refrigeration system provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.
[0046] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), and it can also be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.
[0047] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.
[0048] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowchart and block diagram, as well as the combination of processes and blocks in the flowchart 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 produce 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 flowcharts. Figure 1 Process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0049] 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 Process or multiple processes and boxes Figure 1 A step that specifies a function in one or more boxes.
[0050] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.
Claims
1. An energy-saving control method for a ship refrigeration system, characterized in that: The specific steps include: S1. Real-time monitoring of the working parameters of the ship's refrigeration system and extraction of cargo type labels stored in the ship's refrigeration system; S2. Import various working parameters of the ship's refrigeration system and cargo type labels into the constructed load prediction model to predict the load demand of the refrigeration system; S3. Based on the predicted load demand of the refrigeration system, energy-saving control is performed on the refrigeration system.
2. The energy-saving control method for a ship refrigeration system according to claim 1, characterized in that: The various operating parameters of the ship refrigeration system in S1 include: refrigeration compartment temperature, external environment temperature, refrigerant flow rate, and compressor operating pressure.
3. The energy-saving control method for a ship refrigeration system according to claim 2, characterized in that: The construction of the load forecasting model in S2 includes the following specific steps: S21, obtaining at least one set of historical operating parameters of the ship refrigeration system, historically stored cargo type labels and corresponding historical load data, wherein the historical operating parameters include historical refrigeration compartment temperature, historical external environment temperature, historical refrigerant flow rate, and historical compressor operating pressure; S22, combining various historical working parameters of the ship refrigeration system and historically stored cargo type labels into a feature vector form, and using the set of all feature vectors as input to a load forecasting model, wherein the load forecasting model uses load data predicted by each set of feature vectors as output, uses actual load data corresponding to each set of feature vectors as a forecasting target, and uses minimizing the sum of the forecasting accuracies of all predicted load data as a training target; S23. The calculation formula for prediction accuracy is: p i =(m i -M i ) 2 , where the subscript i is the number of feature vectors, m is the predicted load data, M is the actual load data, and p i The load forecasting model is trained based on the prediction accuracy between the load data predicted by the i-th group of feature vectors and the actual load data, and the training is stopped when the sum of the prediction accuracies reaches convergence.
4. The energy-saving control method for a ship refrigeration system according to claim 3, characterized in that: The load forecasting model in S2 is any one of a regression model, a support vector machine or a random forest model.
5. The energy-saving control method for a ship refrigeration system according to claim 4, characterized in that: The specific content of S3 is: using the PID algorithm to adjust the temperature in the refrigerated compartment in real time. The control formula of the PID algorithm is: Where u(t) is the control output, e(t) is the difference between the set temperature and the actual temperature of the refrigeration system, k p , k i , k d They are the proportional, integral and differential coefficients of the PID controller respectively.
6. An energy-saving control system for a ship refrigeration system, which is implemented based on an energy-saving control method for a ship refrigeration system according to any one of claims 1 to 5, characterized in that: The system includes: an information collection module, a load prediction module, and an energy-saving control module; The information collection module is used to monitor various working parameters of the ship's refrigeration system in real time and extract cargo type labels stored in the ship's refrigeration system; The load prediction module is used to import various operating parameters of the ship refrigeration system and cargo type labels into the constructed load prediction model to predict the load demand of the refrigeration system; The energy-saving control module is used to perform energy-saving control on the refrigeration system based on the predicted load demand of the refrigeration system.