Methods, devices, media, equipment and products for determining unit load
By acquiring and analyzing a variety of data from cogeneration units, and determining the target load value using pre-trained models and optimization algorithms, the problem that the existing technology is difficult to take into account multiple goals, and the stable and efficient operation of the unit load is achieved.
Patent Information
- Application Number
- CN202411594158.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-08
- Publication Date
- 2025-04-01
AI Technical Summary
The existing cogeneration unit load distribution methods are difficult to balance economical, efficient and environmentally friendly goals. They are usually optimized based on a single goal and cannot effectively balance multiple needs.
By obtaining unit data of cogeneration units, including operation data, market data and standard specification data, the pre-trained load determination model calculates thermal load value and power load value, and combining market data and standard specification data, the target load value is determined through a preset optimization algorithm, and the unit operation is finally controlled.
It achieves the continuity and stability of unit load operation, balances economic benefits and work efficiency, and meets various requirements such as environmental protection.
Smart Images
Figure CN119740730A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of equipment control technology, and in particular, to a method, device, medium, equipment and product for determining unit load. Background Art
[0002] A combined heat and power unit is an energy supply device that can provide both electricity and heat. In actual operation, how to reasonably distribute the load of each unit to achieve economical, efficient and environmentally friendly operation is an important issue facing the combined heat and power field. Existing load distribution methods are often optimized based on a single goal, which makes it difficult to take into account the needs of multiple aspects. Summary of the invention
[0003] The purpose of the present disclosure is to provide a method, device, medium, equipment and product for determining unit load.
[0004] In order to achieve the above objectives, in a first aspect, the present disclosure provides a method for determining a unit load, the method comprising: Acquire unit data of a cogeneration unit; the unit data includes operation data, market data and standard specification data of the cogeneration unit; According to the operation data, a thermal load value and an electric load value of the cogeneration unit are obtained by using a pre-trained load determination model; According to the thermal load value and the power load value, in combination with the market data and the standard specification data, a target thermal load value and a target power load value are determined by a preset optimization algorithm; The operation of the cogeneration unit is controlled according to the target thermal load value and the target power load value.
[0005] Optionally, the operation data includes power load data and thermal load data; and obtaining the thermal load value and power load value of the cogeneration unit through a pre-trained load determination model based on the operation data includes: Preprocessing the power load data and the thermal load data to obtain target data; The target data is used as an input of the pre-trained load determination model to obtain the thermal load value and the electric load value output by the pre-trained load determination model.
[0006] Optionally, the preprocessing the power load data and the thermal load data to obtain target data includes: Preprocessing the power load data and the thermal load data to obtain candidate data; Feature selection is performed on the candidate data to obtain the target data.
[0007] Optionally, the determining the target thermal load value and the target electrical load value according to the thermal load value and the electrical load value, in combination with the market data and the standard specification data, by using a preset optimization algorithm comprises: Update the market data and the standard specification data according to a preset time range; According to the thermal load value and the electric power load value, in combination with the updated market data and the standard specification data, the target thermal load value and the target electric power load value are determined through the preset optimization algorithm.
[0008] Optionally, the preset optimization algorithm includes one or more of a cost optimization algorithm, an efficiency optimization algorithm, and an emission optimization algorithm.
[0009] In a second aspect, the present disclosure provides a device for determining a unit load, the device comprising: An acquisition module, used to acquire unit data of a cogeneration unit; the unit data includes operation data, market data and standard specification data of the cogeneration unit; A first determination module, configured to obtain a thermal load value and an electric load value of the cogeneration unit according to the operation data through a pre-trained load determination model; A second determination module is used to determine a target thermal load value and a target electrical load value according to the thermal load value and the electrical load value, in combination with the market data and the standard specification data, through a preset optimization algorithm; The control module is used to control the operation of the cogeneration unit according to the target thermal load value and the target power load value.
[0010] Optionally, the operating data includes power load data and thermal load data; the first determination module is used to preprocess the power load data and the thermal load data to obtain target data; the target data is used as the input of the pre-trained load determination model to obtain the thermal load value and power load value output by the pre-trained load determination model.
[0011] Optionally, the first determination module is used to preprocess the power load data and the thermal load data to obtain candidate data; and perform feature selection on the candidate data to obtain the target data.
[0012] Optionally, the second determination module is used to update the market data and the standard specification data within a preset time range; based on the thermal load value and the power load value, combined with the updated market data and the standard specification data, determine the target thermal load value and the target power load value through the preset optimization algorithm.
[0013] Optionally, the preset optimization algorithm includes one or more of a cost optimization algorithm, an efficiency optimization algorithm, and an emission optimization algorithm.
[0014] In a third aspect, the present disclosure provides a non-temporary computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for determining the unit load described in the first aspect above.
[0015] In a fourth aspect, the present disclosure provides an electronic device, including: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the method for determining the unit load described in the first aspect above.
[0016] In a fifth aspect, the present disclosure provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method for determining the unit load described in the first aspect above.
[0017] Through the above technical solution, the unit load can be controlled in combination with the current operating status of the unit, changes in market data and standard specification data, thereby maintaining the continuity and stability of the unit load operation and achieving a balance between the unit's economic benefits and work efficiency.
[0018] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. The accompanying drawings are as follows.
[0020] Figure 1 The present invention is a flow chart showing a method for determining a unit load according to an exemplary embodiment.
[0021] Figure 2 is based on Figure 1 A flowchart of a method for determining a unit load is shown in an exemplary embodiment.
[0022] Figure 3 is based on Figure 2 A flowchart of a method for determining a unit load is shown in an exemplary embodiment.
[0023] Figure 4 The figure is a flow chart showing another method for determining the load of a unit according to an exemplary embodiment.
[0024] Figure 5 The present invention is a block diagram of a device for determining a unit load according to an exemplary embodiment.
[0025] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION
[0026] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.
[0027] First, the application scenario of the present disclosure is introduced. The present disclosure is applied in the scenario of load adjustment of cogeneration units. With the continuous growth of energy demand and the strengthening of environmental protection awareness, combined heat and power (CHP) has attracted widespread attention as an efficient and environmentally friendly energy supply method. Cogeneration units can generate electricity and heat at the same time to meet the various needs of social and economic development.
[0028] However, in actual operation, how to reasonably distribute the load of each unit to achieve economical, efficient and environmentally friendly operation is an important issue facing the cogeneration field. Existing load distribution methods are often optimized based on a single goal and it is difficult to take into account the needs of multiple aspects.
[0029] In order to solve the above problems, the present disclosure provides a method, device, medium, equipment and product for determining the load of a unit; obtaining unit data of a cogeneration unit; the unit data includes operating data, market data and standard specification data of the cogeneration unit; according to the operating data, a thermal load value and a power load value of the cogeneration unit are obtained through a pre-trained load determination model; according to the thermal load value and the power load value, in combination with the market data and the standard specification data, a target thermal load value and a target power load value are determined through a preset optimization algorithm; according to the target thermal load value and the target power load value, the operation of the cogeneration unit is controlled; through the above technical scheme, the load of the unit can be controlled in combination with the current operating status of the unit, changes in market data and standard specification data, thereby maintaining the continuity and stability of the load operation of the unit and achieving a balance between the economic benefit and work efficiency of the unit.
[0030] Figure 1 FIG. 1 is a flow chart showing a method for determining a unit load according to an exemplary embodiment. Figure 1 As shown, the method may include the following steps.
[0031] S101. Obtain unit data of a cogeneration unit.
[0032] Among them, the unit data includes the operating data, market data and standard specification data of the cogeneration unit.
[0033] For example, the operation data may include one or more of the thermal load, power load, coal consumption, heat consumption, etc. of the unit. The market data may include the value of power, thermal value, fuel value, etc. within the current time range. The standard specification data may include energy specification data and environmental protection specification data.
[0034] For example, the market data may be directly obtained from market-related documents, and the standard specification data may be directly obtained from energy-related documents and environmental protection-related documents.
[0035] S102: According to the operation data, a thermal load value and an electric load value of the cogeneration unit are obtained by using a pre-trained load determination model.
[0036] For example, the operating data may be used as the input of the load determination model to obtain the thermal load value and the electric load value output by the load determination model.
[0037] In some embodiments, the load determination model can be trained in the following manner: sample data can be obtained; the sample data includes sample operation data and sample results corresponding to the sample operation data, the sample results include sample thermal load values and sample power load values, and the preset network model is trained according to the sample data to obtain the load determination model. In this way, the network model can be trained in advance to obtain the load determination model, so that the thermal load value and the power load value can be quickly obtained through the load determination model, thereby improving work efficiency.
[0038] S103: According to the thermal load value and the power load value, in combination with the market data and the standard specification data, a target thermal load value and a target power load value are determined through a preset optimization algorithm.
[0039] S104: Control the operation of the cogeneration unit according to the target thermal load value and the target power load value.
[0040] For example, the current thermal load value of the cogeneration unit can be adjusted to the target thermal load value, and the current power load value of the cogeneration unit can be adjusted to the target power load value to control the operation of the cogeneration unit and achieve economical, efficient and environmentally friendly operation of the cogeneration unit.
[0041] Through the above technical solution, the unit load can be controlled in combination with the current operating status of the unit, changes in market data and standard specification data, thereby maintaining the continuity and stability of the unit load operation and achieving a balance between the unit's economic benefits and work efficiency.
[0042] Figure 2 is based on Figure 1 A flow chart of a method for determining a unit load is shown in an exemplary embodiment of FIG. Figure 2 As shown, the operating data may include power load data and thermal load data; the above S102 may include the following steps.
[0043] S1021. Preprocess the electric power load data and the thermal load data to obtain target data.
[0044] Since the acquired data is often incomplete, inaccurate or inconsistent, it is necessary to preprocess the power load data and the thermal load data.
[0045] In some embodiments, the power load data and the thermal load data may be cleaned to obtain first candidate data. By way of example, missing values, abnormal values, and duplicate values in the power load data and the thermal load data may be processed to complete data cleaning and ensure the accuracy and reliability of the data. Next, the first candidate data may be converted to obtain second candidate data. By way of example, the power load data and the thermal load data may be converted into a preset format; the preset format may be a format suitable for model input, such as a feature vector. Then, the second candidate data may be standardized to obtain target data. By way of example, the power load data and the thermal load data may be standardized by preset standard coefficients to eliminate the dimensional differences between the power load data and the thermal load data.
[0046] S1022. Use the target data as input of the pre-trained load determination model to obtain the thermal load value and the power load value output by the pre-trained load determination model.
[0047] In some embodiments, the load determination model can be trained in the following manner: sample target data can be obtained; the sample target data can include sample power load data and sample thermal load data, as well as sample power load values corresponding to the sample power load data and sample thermal load values corresponding to the sample thermal load data; according to the sample target data, the preset network model is trained to obtain the load determination model. In this way, the accuracy of determining the load value can be improved by determining the thermal load value and the power load value through the pre-trained model; and the load distribution between the units can be adjusted in real time according to the output results of the model to achieve economic, efficient and environmentally friendly operation of the cogeneration unit.
[0048] In some embodiments, the above S1021 may include: preprocessing the power load data and the thermal load data to obtain candidate data; and performing feature selection on the candidate data to obtain the target data.
[0049] For example, the power load data and the thermal load data may be cleaned, converted, and standardized to obtain the candidate data, and feature selection may be performed on the candidate data to obtain the target data.
[0050] For example, feature selection can be performed on the candidate data based on the power load data, the first weight coefficient corresponding to the power load data, the thermal load data, and the second weight coefficient corresponding to the thermal load data to obtain the target data. The first weight coefficient represents the weight of the power load data in the unit load, and the second weight coefficient represents the weight of the thermal load data in the unit load. In this way, the operating data can be further processed through feature selection, so as to facilitate subsequent calculations through the model.
[0051] Figure 3 is based on Figure 2 A flow chart of a method for determining a unit load is shown in an exemplary embodiment of FIG. Figure 3 As shown, the above S103 may include the following steps.
[0052] S1031. Update the market data and the standard specification data according to a preset time range.
[0053] For example, market data and standard specification data may be acquired periodically, and current market data and current standard specification data acquired in the current period may be used as new market data and new standard specification data to update the market data and standard specification data.
[0054] For example, the preset time range can be set by the user and is not limited here. In this way, the market data and the standard specification data can be updated regularly to ensure that the market data and the standard specification data are the latest data, so that the unit operation can meet market demand.
[0055] S1032. According to the thermal load value and the electric load value, in combination with the updated market data and the standard specification data, the target thermal load value and the target electric load value are determined through the preset optimization algorithm.
[0056] For example, the preset optimization algorithm may include one or more of a cost optimization algorithm, an efficiency optimization algorithm, and an emission optimization algorithm. For example, the cost optimization algorithm may include a genetic algorithm, and the efficiency optimization algorithm may include a particle swarm algorithm, which are not limited here.
[0057] For example, the thermal load value and the electric load value can be corrected by using the updated market data and the standard specification data through a preset optimization algorithm, and multiple groups of pending data can be obtained; each group of pending data includes a thermal load value and an electric load value; and according to a preset threshold, the target data is determined from the multiple groups of pending data; the target data includes a target thermal load value and a target electric load value. Among them, the range of the preset threshold can be set by the user, and the preset threshold can be the sum of the preset thermal load value and the preset electric load value, or the preset threshold can be the preset thermal load value and the preset electric load value, which is not limited here. In this way, the unit load can be further optimized based on the preset optimization algorithm, so as to achieve coordinated operation and load balance between units, so that the unit can simultaneously meet the requirements of economy, efficiency and environmental protection during operation.
[0058] Figure 4 FIG. 1 is a flow chart showing another method for determining unit load according to an exemplary embodiment. Figure 4 As shown, the method may include the following steps.
[0059] S401, obtaining unit data of the cogeneration unit.
[0060] Among them, the unit data includes the operating data, market data and standard specification data of the cogeneration unit.
[0061] S402: pre-process the electric load data and the thermal load data to obtain target data.
[0062] S403: Using the target data as input of the pre-trained load determination model to obtain the thermal load value and the electric load value output by the pre-trained load determination model.
[0063] S404: Update the market data and the standard specification data according to a preset time range.
[0064] S405: According to the thermal load value and the electric load value, in combination with the updated market data and the standard specification data, the target thermal load value and the target electric load value are determined through the preset optimization algorithm.
[0065] S406: Control the operation of the cogeneration unit according to the target thermal load value and the target power load value.
[0066] Through the above technical solution, the unit load can be controlled in combination with the current operating status of the unit, changes in market data and standard specification data, thereby maintaining the continuity and stability of the unit load operation and achieving a balance between the unit's economic benefits and work efficiency.
[0067] Figure 5 FIG. 1 is a block diagram of a device for determining a unit load according to an exemplary embodiment. Figure 5 As shown, the apparatus 500 may include an acquisition module 510, a first determination module 520, a second determination module 530 and a control module 540; The acquisition module 510 is used to acquire the unit data of the cogeneration unit; the unit data includes the operation data, market data and standard specification data of the cogeneration unit; The first determination module 520 is used to obtain the thermal load value and the electric load value of the cogeneration unit according to the operation data through a pre-trained load determination model; The second determination module 530 is used to determine a target thermal load value and a target electrical load value according to the thermal load value and the electrical load value, in combination with the market data and the standard specification data, through a preset optimization algorithm; The control module 540 is used to control the operation of the cogeneration unit according to the target thermal load value and the target power load value.
[0068] Through the above technical solution, the unit load can be controlled in combination with the current operating status of the unit, changes in market data and standard specification data, thereby maintaining the continuity and stability of the unit load operation and achieving a balance between the unit's economic benefits and work efficiency.
[0069] Optionally, the operating data includes power load data and thermal load data; the first determination module 520 is used to preprocess the power load data and the thermal load data to obtain target data; the target data is used as the input of the pre-trained load determination model to obtain the thermal load value and the power load value output by the pre-trained load determination model.
[0070] Optionally, the first determination module 520 is used to preprocess the power load data and the thermal load data to obtain candidate data; and perform feature selection on the candidate data to obtain the target data.
[0071] Optionally, the second determination module 530 is used to update the market data and the standard specification data according to a preset time range; based on the thermal load value and the power load value, combined with the updated market data and the standard specification data, the target thermal load value and the target power load value are determined through the preset optimization algorithm.
[0072] Optionally, the preset optimization algorithm includes one or more of a cost optimization algorithm, an efficiency optimization algorithm, and an emission optimization algorithm.
[0073] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.
[0074] In summary, the present disclosure provides a method, device, medium, equipment and product for determining the load of a unit; obtaining unit data of a cogeneration unit; the unit data includes operating data, market data and standard specification data of the cogeneration unit; according to the operating data, a thermal load value and a power load value of the cogeneration unit are obtained through a pre-trained load determination model; according to the thermal load value and the power load value, in combination with the market data and the standard specification data, a target thermal load value and a target power load value are determined through a preset optimization algorithm; according to the target thermal load value and the target power load value, the operation of the cogeneration unit is controlled; through the above technical scheme, the load of the unit can be controlled in combination with the current operating status of the unit, changes in market data and standard specification data, thereby maintaining the continuity and stability of the load operation of the unit and achieving a balance between the economic benefit and work efficiency of the unit.
[0075] Figure 6 6 is a block diagram of an electronic device 600 according to an exemplary embodiment. For example, the electronic device 600 may be provided as a server. Figure 6 The electronic device 600 includes a processor 622, which may be one or more, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 622 may be configured to execute the computer program to perform the above-mentioned method for determining the unit load.
[0076] In addition, the electronic device 600 may further include a power supply component 626 and a communication component 660, wherein the power supply component 626 may be configured to perform power management of the electronic device 600, and the communication component 660 may be configured to implement communication, for example, wired or wireless communication, of the electronic device 600. In addition, the electronic device 600 may further include an input / output interface 658. The electronic device 600 may operate based on an operating system stored in the memory 632.
[0077] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned method for determining the unit load are implemented. For example, the non-transitory computer-readable storage medium can be the above-mentioned memory 632 including program instructions, and the above-mentioned program instructions can be executed by the processor 622 of the electronic device 600 to complete the above-mentioned method for determining the unit load.
[0078] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for executing the above-mentioned unit load determination method when executed by the programmable device.
[0079] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.
[0080] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.
[0081] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.
Claims
1. A method for determining unit load, characterized in that: The method comprises: Acquire unit data of a cogeneration unit; the unit data includes operation data, market data and standard specification data of the cogeneration unit; According to the operation data, a thermal load value and an electric load value of the cogeneration unit are obtained by using a pre-trained load determination model; According to the thermal load value and the power load value, in combination with the market data and the standard specification data, a target thermal load value and a target power load value are determined by a preset optimization algorithm; The operation of the cogeneration unit is controlled according to the target thermal load value and the target power load value.
2. The method according to claim 1, characterized in that The operation data includes power load data and thermal load data; the thermal load value and power load value of the cogeneration unit are obtained by using a pre-trained load determination model based on the operation data, including: Preprocessing the power load data and the thermal load data to obtain target data; The target data is used as an input of the pre-trained load determination model to obtain the thermal load value and the electric load value output by the pre-trained load determination model.
3. The method according to claim 2, characterized in that The preprocessing of the power load data and the thermal load data to obtain target data includes: Preprocessing the power load data and the thermal load data to obtain candidate data; Feature selection is performed on the candidate data to obtain the target data.
4. The method according to claim 1, characterized in that The determining of the target thermal load value and the target electrical load value according to the thermal load value and the electrical load value, in combination with the market data and the standard specification data, through a preset optimization algorithm comprises: Update the market data and the standard specification data according to a preset time range; According to the thermal load value and the electric power load value, in combination with the updated market data and the standard specification data, the target thermal load value and the target electric power load value are determined through the preset optimization algorithm.
5. The method according to claim 4, characterized in that The preset optimization algorithm includes one or more of a cost optimization algorithm, an efficiency optimization algorithm, and an emission optimization algorithm.
6. A device for determining unit load, characterized in that: The device comprises: An acquisition module, used to acquire unit data of a cogeneration unit; the unit data includes operation data, market data and standard specification data of the cogeneration unit; A first determination module, configured to obtain a thermal load value and an electric load value of the cogeneration unit according to the operation data by using a pre-trained load determination model; A second determination module is used to determine a target thermal load value and a target electrical load value according to the thermal load value and the electrical load value, in combination with the market data and the standard specification data, through a preset optimization algorithm; The control module is used to control the operation of the cogeneration unit according to the target thermal load value and the target power load value.
7. The device according to claim 6, characterized in that The operation data includes power load data and thermal load data; the first determination module is used to pre-process the power load data and the thermal load data to obtain target data; The target data is used as an input of the pre-trained load determination model to obtain the thermal load value and the electric load value output by the pre-trained load determination model.
8. The device according to claim 7, characterized in that The first determination module is used to pre-process the power load data and the thermal load data to obtain candidate data; and perform feature selection on the candidate data to obtain the target data.
9. The device according to claim 6, characterized in that The second determination module is used to update the market data and the standard specification data according to a preset time range; based on the thermal load value and the power load value, combined with the updated market data and the standard specification data, determine the target thermal load value and the target power load value through the preset optimization algorithm.
10. The device according to claim 9, characterized in that The preset optimization algorithm includes one or more of a cost optimization algorithm, an efficiency optimization algorithm, and an emission optimization algorithm.
11. A non-transitory computer-readable storage medium having a computer program stored thereon, 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 5 are implemented.
12. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 5.
13. 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 5 are implemented.