Method and device for automatically generating target net rack based on saturated annual load prediction

Abstract

The invention provides an automatic generation method and device for a target network frame based on saturated annual load prediction, and the method comprises the steps: obtaining the power utilization information of a power distribution network in a region, and enabling the power utilization information to comprise the power utilization type, the annual power utilization amount and the overhead line layout in the region; determining saturated annual load prediction data of the power distribution network in the region based on the power utilization information; generating a basic target network frame of the power distribution network in the region based on the saturated annual load prediction data; constructing a deep learning model, and automatically generating a saturated year target net rack based on the learning model and the basic target net rack; the deep learning model is trained through the saturated annual load prediction data changing in the specific area, and the saturated annual target net rack is automatically generated, so that when the saturated annual load of the area changes, the generated target net rack can be synchronously scheduled in real time, the accuracy and reliability of the generated target net rack are improved, and the user experience is improved. While the waste of electric energy is avoided, it is ensured that the specific area can obtain sufficient electric quantity supply.

Description

Technical Field

[0001] The present invention relates to the field of power supply, and in particular to the field of power system planning technology, and specifically to a method and device for automatically generating a target grid based on saturated annual load forecasting. Background Art

[0002] The target grid in the power grid is a grid planning and design model, which is usually used as a reference for grid construction and provides a decision-making basis for the operation and management of the distribution network. When formulating the target grid planning of the distribution network, the development planning department of the existing power supply enterprise needs to be based on detailed planning, road information, load forecast and other multi-source information support, while considering the constraints of the planned supply area for reliability indicators, power supply capacity indicators, typical wiring, economy, etc., to complete a series of tasks such as substation site selection and capacity determination, power supply unit division, station site layout, and wiring in the planned area, so as to complete the planning of the target grid.

[0003] The target grid is a very important tool in the power grid. It is of great significance to ensure the safe and stable power supply of the power grid. When the power supply of the power grid is very sufficient, the power consumption in a specific area will be saturated when there is enough power supply. If the target grid of the power grid exceeds the load to supply power to a specific area in a saturated state, it will cause waste of electricity. Therefore, in the current relevant technical solutions, the saturated annual load of the area will be predicted first, and the target grid will be generated based on the predicted saturated annual load to avoid the situation where the target grid is overloaded to supply power to a specific area in a saturated state, thereby avoiding waste of electricity.

[0004] However, in the existing relevant technical solutions, although the target grid can be automatically generated according to the saturated annual load, if the saturated annual load of the area changes, the generated target grid cannot make synchronous real-time scheduling, and the generated target grid lacks flexibility. Therefore, it is difficult for the existing relevant technical solutions to automatically generate accurate and reliable target grids in real time.

[0005] Therefore, there is a technical problem in the prior art that it is difficult to automatically generate an accurate and reliable target grid in real time. Summary of the invention

[0006] The main purpose of the present invention is to provide a method and device for automatically generating a target grid based on saturated annual load forecasting, so as to solve the technical problem in the prior art that it is difficult to automatically generate an accurate and reliable target grid in real time.

[0007] In order to achieve the above-mentioned purpose, according to one aspect of the present invention, there is provided a method for automatically generating a target grid based on a saturated annual load forecast, comprising the following steps: obtaining electricity consumption information of a distribution network in a region, the electricity consumption information including electricity consumption type, annual electricity consumption and overhead line layout in the region; determining saturated annual load forecast data of the distribution network in the region based on the electricity consumption information; generating a basic target grid of the distribution network in the region based on the saturated annual load forecast data; constructing a deep learning model, and automatically generating a saturated annual target grid based on the learning model and the basic target grid.

[0008] In some embodiments, determining the saturated annual load forecast data of the distribution network in the region based on the electricity consumption information includes: calculating the power loss of the overhead lines in the distribution lines in the region based on the electricity consumption information; and determining the saturated annual load forecast data of the distribution network in the region based on the power loss and electricity consumption information.

[0009] In some implementations, the calculation method of the saturated annual load forecast data is as follows:

[0010]

[0011] Where P data represents the saturated annual load forecast data, T n Indicates the nth type of electricity consumption, E n represents the annual electricity consumption of the nth type of electricity consumption, γ n Represents the weight of the nth type of electricity consumption, 1≤n≤N, P f Indicates power loss.

[0012] In some embodiments, generating the basic target grid of the distribution network in the region based on the saturated annual load forecast data includes the following steps: calculating the saturated annual load forecast data of each load device in the region based on the saturated annual load forecast data; performing preliminary site selection and sizing of the substation according to the power supply range of the substation and the saturated annual load forecast data of each load device within the power supply range of the substation; and automatically generating the basic target grid based on the site selection and sizing.

[0013] In some embodiments, constructing a deep learning model and automatically generating a saturated annual target grid based on the learning model and a basic target grid includes: constructing a deep learning model and training the deep learning model; and automatically generating a saturated annual target grid based on the trained deep learning model and the basic target grid.

[0014] In some embodiments, constructing a deep learning model and training the deep learning model include: using a convolutional neural network or a recurrent neural network to construct a deep learning model; when it is detected that the saturated annual load forecast data of the distribution network in the region has changed, collecting the saturated annual load forecast data of the distribution network in the region after the change, and synthesizing all the changed saturated annual load forecast data into a training set; based on the convolutional neural network or the recurrent neural network, using the training set to train the deep learning model.

[0015] In some embodiments, based on the convolutional neural network or the recurrent neural network, training the deep learning model using the training set includes: the training method of the deep learning model includes the following expression:

[0016]

[0017] Where M1 represents the trained deep learning model, M0 represents the initial deep learning model, S represents the changed annual load forecasting dataset, and μ represents the rate of change of the saturated annual load forecasting data.

[0018] In some embodiments, before automatically generating a saturated annual target grid based on the trained deep learning model and the basic target grid, the method further includes: testing the trained deep learning model using a standard test set to obtain a test result; when the test result exceeds expectations, adjusting a training strategy for training the deep learning model.

[0019] In some embodiments, the method for determining whether the test result exceeds expectations includes the following expression:

[0020]

[0021] In the formula, ε min Indicates the expected minimum value of the test result, ε max represents the expected maximum value of the test result, D0 represents the test result obtained by testing the trained deep learning model with the standard test set, and D1 represents the prediction result of the deep learning model.

[0022] According to another aspect of the present invention, the present invention also provides an automatic generation device for a target grid based on a saturated annual load forecast, comprising: an acquisition module for acquiring electricity consumption information of a distribution network in a region, the electricity consumption information including electricity type, annual electricity consumption and overhead line layout in the region; a determination module for determining saturated annual load forecast data of the distribution network in the region based on the electricity consumption information; a first generation module for generating a basic target grid of the distribution network in the region based on the saturated annual load forecast data; and a second generation module for constructing a deep learning model, and automatically generating a saturated annual target grid based on the learning model and the basic target grid.

[0023] According to another aspect of the present invention, the present invention also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for automatically generating a target grid based on a saturated annual load forecast as described in any of the aforementioned items.

[0024] According to another aspect of the present invention, the present invention also provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for automatically generating a target grid based on a saturated annual load forecast as described in any of the aforementioned items when executing the computer program on the memory.

[0025] By applying the method and device for automatically generating a target grid based on a saturated annual load forecast provided by the present invention, a basic target grid of the distribution network in a specific area is generated based on the saturated annual load forecast data of the distribution network in the specific area, and then a deep learning model is constructed. The deep learning model is trained by the saturated annual load forecast data that has changed in the specific area, and a saturated annual target grid is automatically generated. When the saturated annual load of the area changes, the generated target grid can be synchronously dispatched in real time, thereby improving the accuracy and reliability of the generated target grid, and ensuring that the specific area can obtain sufficient electricity supply while avoiding waste of electric energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings constituting a part of the present application 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 drawings:

[0027] Figure 1 A flowchart showing the steps of the method for automatically generating a target grid based on saturated annual load forecasting provided by the present invention is shown;

[0028] Figure 2 A flowchart showing the steps of determining the saturated annual load forecast data of the regional distribution network based on the power consumption information provided by the present invention is shown;

[0029] Figure 3 It shows a flowchart of the steps of generating a basic target grid of a regional distribution network based on saturated annual load forecast data provided by the present invention;

[0030] Figure 4 A flowchart of the steps of constructing a deep learning model provided by the present invention and automatically generating a saturated annual target grid based on the learning model and the basic target grid is shown;

[0031] Figure 5 The structural block diagram of the automatic generation device of the target grid based on the saturated annual load forecast provided by the present invention is shown;

[0032] Figure 6 The figure shows a schematic diagram of the structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0033] The specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings, but are not intended to limit the present disclosure.

[0034] It should be understood that various modifications may be made to the embodiments disclosed herein. Therefore, the above description should not be considered as limiting, but merely as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0035] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0036] These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the attached drawings.

[0037] It should also be understood that, although the present disclosure has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present disclosure that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0038] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0039] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0040] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0041] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0042] The present disclosure is further described below in conjunction with the accompanying drawings and specific embodiments.

[0043] In order to solve the technical problem in the prior art that it is difficult to automatically generate an accurate and reliable target grid in real time, the present invention provides a method and device for automatically generating a target grid based on saturated annual load forecasting.

[0044] Figure 1 The flowchart of the steps of the automatic generation method of the target grid based on the saturated annual load forecast provided by the present invention is shown as follows: Figure 1 As shown, the automatic generation method of the target grid based on the saturated annual load forecast includes the following steps:

[0045] S101, obtaining electricity consumption information of a distribution network in a region, wherein the electricity consumption information includes electricity consumption type, annual electricity consumption and overhead line layout in the region.

[0046] In this step, the power consumption information of the distribution network in the area is obtained, wherein the power consumption information includes the type of power consumption, annual power consumption and overhead line layout in the area.

[0047] Specifically, in the process of automatically generating the target grid, the area to be planned is first selected to obtain the power consumption information of the distribution network in the area, which includes the power consumption type, annual power consumption and overhead line layout in the area. The power consumption type includes but is not limited to the main grid power and photovoltaic power grid used to characterize the power source, and industrial power and civil power used to characterize the power application; the annual power consumption includes the classified power consumption of each different power consumption type and the total power consumption, and the overhead line is used to characterize the distribution of overhead cables in the power transmission and distribution process.

[0048] In the application process, the electricity consumption information can be collected from multiple data sources such as a supervisory control and data acquisition system SCADA, a geographic information system platform GIS, and an enterprise resource planning system ERP.

[0049] S102: Determine the saturated annual load forecast data of the distribution network in the region based on the electricity consumption information.

[0050] After completing the above step S101, in this step, the saturated annual load forecast data of the distribution network in the region is determined based on the electricity consumption information.

[0051] The power saturation load forecast is of great significance for the city to formulate long-term power development plans, which will directly affect the development of the power industry, the development of the national economy and the overall planning of the power grid. In this step, the saturated annual load forecast data is calculated based on the power type and annual power consumption in the selected area.

[0052] Figure 2 The flowchart of the steps of determining the saturated annual load forecast data of the distribution network in the region based on the power consumption information provided by the present invention is shown as follows: Figure 2 As shown, determining the saturated annual load forecast data of the distribution network in the region based on the power consumption information includes the following steps:

[0053] S201, calculating the power loss of the overhead lines in the distribution lines in the area based on the power consumption information.

[0054] In this step, the power loss of the overhead lines in the distribution lines in the area is calculated based on the power consumption information.

[0055] Specifically, the power loss of the overhead line layout in the distribution line in the selected area is calculated by the following calculation method:

[0056] P f =P0-P r ;

[0057] Among them, P frepresents the power loss of overhead line layout in the selected area in the distribution line, P0 represents the original distribution power in the selected area, P r The sum of the powers received by all loads in the selected area; the power loss of the overhead lines in the distribution lines in the selected area can be calculated using a common algorithm in the prior art, for example, the ratio of conductor resistance loss to active power can be used for calculation; the specific algorithm is not further limited in this application.

[0058] S202: Determine the saturated annual load forecast data of the distribution network in the region based on the power loss and electricity consumption information.

[0059] After completing the above step S201, in this step, the saturated annual load forecast data of the distribution network in the region is determined based on the power loss and electricity consumption information.

[0060] Specifically, based on the power loss and electricity consumption information, the saturated annual load forecast data of the distribution network in the region is determined, and the saturated annual load forecast data is calculated by the following formula:

[0061]

[0062] Where P data represents the saturated annual load forecast data, T n Indicates the nth type of electricity consumption, E n represents the annual electricity consumption of the nth type of electricity consumption, γ n Represents the weight of the nth type of electricity consumption, 1≤n≤N, P f Indicates power loss.

[0063] S103: Generate a basic target grid of the distribution network in the region based on the saturated annual load forecast data.

[0064] After obtaining the saturated annual load forecast data, in this step, a basic target grid of the distribution network in the region is generated based on the saturated annual load forecast data.

[0065] Further, Figure 3 The flowchart of the steps of generating the basic target grid of the distribution network in the region based on the saturated annual load forecast data according to the present invention is shown as follows: Figure 3 As shown, in the process of generating the basic target grid, first, based on the saturated annual load forecast data, the saturated annual load forecast data of each load device in the area is calculated. Then, according to the power supply range of the substation and the saturated annual load forecast data of each load device in the power supply range of the substation, the preliminary site selection and capacity determination of the substation is carried out to form the prototype of the site selection and capacity determination of the substation. Finally, all substations in the selected area are connected through power grid lines to form the basic target grid.

[0066] S104, constructing a deep learning model, and automatically generating a saturated annual target grid based on the learning model and the basic target grid.

[0067] After completing the above step S103, in this step, a deep learning model is constructed, and a saturated annual target grid is automatically generated based on the learning model and the basic target grid.

[0068] Specifically, deep learning refers to machine learning based on deep neural network models and methods. In this step, a deep learning model for generating a saturated annual target grid is constructed, such as Figure 4 As shown, building a deep learning model and automatically generating a saturated annual target grid based on the learning model and the basic target grid includes the following steps:

[0069] S401, constructing a deep learning model and training the deep learning model.

[0070] In this step, a deep learning model is constructed and trained.

[0071] Specifically, a convolutional neural network or a recurrent neural network is used to construct a deep learning model. When it is detected that the saturated annual load forecast data of the distribution network in the region has changed, the saturated annual load forecast data of the distribution network in the region after the change is collected, and all the changed saturated annual load forecast data are synthesized into a training set; based on the convolutional neural network or the recurrent neural network, the deep learning model is trained using the training set.

[0072] Wherein, based on the convolutional neural network or the recurrent neural network, using the training set to train the deep learning model includes: the training method of the deep learning model includes the following expression:

[0073]

[0074] Where M1 represents the trained deep learning model, M0 represents the initial deep learning model, S represents the changed annual load forecasting dataset, and μ represents the rate of change of the saturated annual load forecasting data.

[0075] In one embodiment of the present invention, after training the deep learning model, the method further includes: testing the trained deep learning model using a standard test set to obtain a test result; when the test result exceeds expectations, adjusting the training strategy for training the deep learning model.

[0076] The method for determining whether the test result exceeds expectations includes the following expression:

[0077]

[0078] In the formula, ε min Indicates the expected minimum value of the test result, ε max represents the expected maximum value of the test result, D0 represents the test result obtained by testing the trained deep learning model with the standard test set, and D1 represents the prediction result of the deep learning model.

[0079] S402, automatically generating a saturated annual target framework based on the trained deep learning model and the basic target framework.

[0080] After obtaining the trained deep learning model, in this step, the obtained basic target grid is input into the trained deep learning model to automatically generate the saturated annual target grid.

[0081] By applying the automatic generation method of the target grid based on the saturated annual load forecast provided by the present invention, based on the saturated annual load forecast data of the distribution network in the specific area, a basic target grid of the distribution network in the specific area is generated, and then a deep learning model is constructed. The deep learning model is trained by the saturated annual load forecast data that has changed in the specific area, and the saturated annual target grid is automatically generated. When the saturated annual load of the area changes, the generated target grid can be synchronously dispatched in real time, thereby improving the accuracy and reliability of the generated target grid, and ensuring that the specific area can obtain sufficient electricity supply while avoiding waste of electric energy.

[0082] Example 2

[0083] In order to better implement the above method, the second aspect of the present disclosure further provides an automatic generation device for a target grid based on saturated annual load forecasting, and the generation device can be integrated on an electronic device.

[0084] For example, Figure 5 As shown, the generating device 200 may include: an acquisition module 210, a determination module 220, a first generating module 230 and a second generating module 240, which are specifically as follows:

[0085] (1) An acquisition module 210, which is used to acquire power consumption information of the distribution network in the region, wherein the power consumption information includes the type of power consumption, annual power consumption and overhead line layout in the region.

[0086] (2) A determination module 220, wherein the first determination module 220 is used to determine the saturated annual load forecast data of the distribution network in the region based on the power consumption information.

[0087] (3) A first generation module 230, which is used to generate a basic target grid structure of the distribution network in the region based on the saturated annual load forecast data.

[0088] (4) A second generation module 240, which is used to construct a deep learning model and automatically generate a saturated annual target grid based on the learning model and the basic target grid.

[0089] Furthermore, the determination module 220 includes a calculation unit and a determination unit, wherein the calculation unit is used to calculate the power loss of the overhead lines in the distribution lines in the area based on the power consumption information, and the determination unit determines the saturated annual load forecast data of the distribution network in the area based on the power loss and power consumption information.

[0090] Furthermore, the calculation method of the saturated annual load forecast data is as follows:

[0091]

[0092] Where P data represents the saturated annual load forecast data, T n Indicates the nth type of electricity consumption, E n represents the annual electricity consumption of the nth type of electricity consumption, γ n Represents the weight of the nth type of electricity consumption, 1≤n≤N, P f Indicates power loss.

[0093] Furthermore, the first generation module 230 includes a second calculation unit, a site selection unit and a first generation unit, wherein the second calculation unit is used to calculate the saturated annual load forecast data of each load device in the area based on the saturated annual load forecast data; the site selection unit is used to perform preliminary site selection and capacity determination of the substation according to the power supply range of the substation and the saturated annual load forecast data of each load device within the power supply range of the substation; and the first generation unit is used to automatically generate the basic target grid based on the site selection and capacity determination.

[0094] Furthermore, the second generation module 240 includes a construction unit and a second generation unit, wherein the construction unit is used to construct a deep learning model and train the deep learning model; the second generation unit is used to automatically generate a saturated annual target grid based on the trained deep learning model and the basic target grid.

[0095] Furthermore, the construction of a deep learning model and the training of the deep learning model include: using a convolutional neural network or a recurrent neural network to construct a deep learning model; when it is detected that the saturated annual load forecast data of the distribution network in the region has changed, collecting the saturated annual load forecast data of the distribution network in the region after the change, and synthesizing all the changed saturated annual load forecast data into a training set; based on the convolutional neural network or the recurrent neural network, using the training set to train the deep learning model.

[0096] Further, based on the convolutional neural network or the recurrent neural network, using the training set to train the deep learning model includes: the training method of the deep learning model includes the following expression:

[0097]

[0098] Where M1 represents the trained deep learning model, M0 represents the initial deep learning model, S represents the changed annual load forecasting dataset, and μ represents the rate of change of the saturated annual load forecasting data.

[0099] Furthermore, the method for determining whether the test result exceeds expectations includes the following expression:

[0100]

[0101] In the formula, ε min Indicates the expected minimum value of the test result, ε max represents the expected maximum value of the test result, D0 represents the test result obtained by testing the trained deep learning model with the standard test set, and D1 represents the prediction result of the deep learning model.

[0102] The automatic generation device of the target grid based on the saturated annual load forecast provided by the present invention is applied to generate the basic target grid of the distribution network in the specific area based on the saturated annual load forecast data of the distribution network in the specific area, and then construct a deep learning model. The deep learning model is trained by the saturated annual load forecast data that has changed in the specific area, and the saturated annual target grid is automatically generated. When the saturated annual load of the area changes, the generated target grid can be synchronously dispatched in real time, thereby improving the accuracy and reliability of the generated target grid, and ensuring that the specific area can obtain sufficient electricity supply while avoiding waste of electric energy.

[0103] Example 3

[0104] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0105] To this end, a third embodiment of the present disclosure provides a storage medium, which is a computer-readable medium and stores a computer program. When the computer program is executed by a processor, the method provided by the embodiment of the present disclosure is implemented, including the following steps S11 to S14:

[0106] S11, obtaining power consumption information of the distribution network in the region, wherein the power consumption information includes the type of power consumption, annual power consumption and overhead line layout in the region;

[0107] S12, determining saturated annual load forecast data of the distribution network in the region based on the electricity consumption information;

[0108] S13, generating a basic target grid of the distribution network in the region based on the saturated annual load forecast data;

[0109] S14, constructing a deep learning model, and automatically generating a saturated annual target grid based on the learning model and the basic target grid.

[0110] Furthermore, when the computer program is executed by a processor, other methods provided by any of the above embodiments of the present disclosure are implemented.

[0111] By applying the automatic generation method of the target grid based on the saturated annual load forecast provided by the present invention, based on the saturated annual load forecast data of the distribution network in the specific area, a basic target grid of the distribution network in the specific area is generated, and then a deep learning model is constructed. The deep learning model is trained by the saturated annual load forecast data that has changed in the specific area, and the saturated annual target grid is automatically generated. When the saturated annual load of the area changes, the generated target grid can be synchronously dispatched in real time, thereby improving the accuracy and reliability of the generated target grid, and ensuring that the specific area can obtain sufficient electricity supply while avoiding waste of electric energy.

[0112] Example 4

[0113] A fourth embodiment of the present disclosure provides an electronic device, such as Figure 6 As shown, the electronic device at least includes a memory 310 and a processor 320. The memory 310 stores a computer program. The processor 320 implements the method provided by any embodiment of the present disclosure when executing the computer program on the memory 310. Exemplarily, the method executed by the electronic device computer program is as follows:

[0114] S21, obtaining power consumption information of the distribution network in the region, wherein the power consumption information includes the type of power consumption, annual power consumption and overhead line layout in the region;

[0115] S22, determining saturated annual load forecast data of the distribution network in the region based on the electricity consumption information;

[0116] S23, generating a basic target grid of the distribution network in the region based on the saturated annual load forecast data;

[0117] S24, constructing a deep learning model, and automatically generating a saturated annual target grid based on the learning model and the basic target grid.

[0118] In specific implementation, the acquisition module 210, the determination module 220, the first generation module 230 and the second generation module 240 are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.

[0119] By applying the automatic generation method of the target grid based on the saturated annual load forecast provided by the present invention, based on the saturated annual load forecast data of the distribution network in the specific area, a basic target grid of the distribution network in the specific area is generated, and then a deep learning model is constructed. The deep learning model is trained by the saturated annual load forecast data that has changed in the specific area, and the saturated annual target grid is automatically generated. When the saturated annual load of the area changes, the generated target grid can be synchronously dispatched in real time, thereby improving the accuracy and reliability of the generated target grid, and ensuring that the specific area can obtain sufficient electricity supply while avoiding waste of electric energy.

[0120] The storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0121] The storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0122] Alternatively, the storage medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; and returns the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0123] Computer program code for performing operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the passenger computer, partially on the passenger computer, as a separate software package, partially on the passenger computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the passenger computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0124] It should be noted that the storage medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: 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 the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any storage medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0125] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0126] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0127] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0128] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0129] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0130] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0131] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

[0132] Multiple embodiments of the present disclosure are described in detail above, but the present disclosure is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications to the embodiments based on the concepts of the present disclosure, and these variations and modifications should all fall within the scope of protection required by the present disclosure.

Claims

1. A method for automatically generating a target grid based on saturated annual load forecasting, characterized in that: The steps include: Obtaining power consumption information of the distribution network in the region, the power consumption information including the type of power consumption, annual power consumption and overhead line layout in the region; Determine saturated annual load forecast data of the distribution network in the region based on the electricity consumption information; Generate a basic target grid of the distribution network in the region based on the saturated annual load forecast data; A deep learning model is constructed, and a saturated annual target grid is automatically generated based on the learning model and the basic target grid.

2. The automatic generation method of the target grid based on saturated annual load forecast according to claim 1 is characterized in that: The determining of the saturated annual load forecast data of the distribution network in the region based on the power consumption information comprises: Calculate the power loss of the overhead lines in the distribution lines in the area based on the power consumption information; Based on the power loss and electricity consumption information, the saturated annual load forecast data of the distribution network in the region is determined.

3. The automatic generation method of the target grid based on saturated annual load forecast according to claim 2 is characterized in that: The calculation method of the saturated annual load forecast data is as follows: Where P data represents the saturated annual load forecast data, T n Indicates the nth type of electricity consumption, E n represents the annual electricity consumption of the nth type of electricity consumption, γ n Represents the weight of the nth type of electricity consumption, 1≤n≤N, P f Indicates power loss.

4. The automatic generation method of the target grid based on saturated annual load forecast according to claim 1 is characterized in that: The generating of the basic target grid of the distribution network in the region based on the saturated annual load forecast data comprises the following steps: Based on the saturated annual load forecast data, calculating the saturated annual load forecast data of each load device in the area; Conduct preliminary site selection and capacity determination of the substation based on the power supply range of the substation and the saturated annual load forecast data of each load device within the power supply range of the substation; The basic target grid is automatically generated based on the site selection and capacity determination.

5. The automatic generation method of the target grid based on saturated annual load forecast according to claim 1 is characterized in that: The step of constructing a deep learning model and automatically generating a saturated annual target grid based on the learning model and the basic target grid includes: Building a deep learning model and training the deep learning model; Based on the trained deep learning model and the basic target grid, a saturated annual target grid is automatically generated.

6. The method for automatically generating a target grid based on saturated annual load forecast according to claim 5 is characterized in that: The step of constructing a deep learning model and training the deep learning model includes: Use convolutional neural networks or recurrent neural networks to build deep learning models; When it is detected that the saturated annual load forecast data of the distribution network in the region has changed, the saturated annual load forecast data of the distribution network in the region after the change is collected, and all the saturated annual load forecast data that have changed are combined into a training set; Based on the convolutional neural network or the recurrent neural network, the deep learning model is trained using the training set.

7. The method for automatically generating a target grid based on saturated annual load forecast according to claim 6 is characterized in that: Based on the convolutional neural network or the recurrent neural network, using the training set to train the deep learning model includes: The training method of the deep learning model includes the following expression: Where M1 represents the trained deep learning model, M0 represents the initial deep learning model, S represents the changed annual load forecasting dataset, and μ represents the rate of change of the saturated annual load forecasting data.

8. The method for automatically generating a target grid based on saturated annual load forecast according to claim 5 is characterized in that: Before automatically generating a saturated annual target framework based on the trained deep learning model and the basic target framework, the method further includes: Using a standard test set to test the trained deep learning model to obtain a test result; When the test result exceeds expectations, the training strategy for training the deep learning model is adjusted.

9. The method for automatically generating a target grid based on saturated annual load forecast according to claim 8, characterized in that: The method for judging whether the test result exceeds expectations includes the following expressions: In the formula, ε min Indicates the expected minimum value of the test result, ε max represents the expected maximum value of the test result, D0 represents the test result obtained by testing the trained deep learning model with the standard test set, and D1 represents the prediction result of the deep learning model.

10. An automatic generation device for a target grid based on saturated annual load forecast, characterized in that: include: An acquisition module, used to acquire power consumption information of the distribution network in the region, wherein the power consumption information includes the type of power consumption, annual power consumption and overhead line layout in the region; A determination module, configured to determine saturated annual load forecast data of the distribution network in the region based on the electricity consumption information; A first generating module generates a basic target grid of the distribution network in the region based on the saturated annual load forecast data; The second generation module is used to build a deep learning model and automatically generate a saturated annual target grid based on the learning model and the basic target grid.

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