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CN112257913A - Medium-voltage line load value prediction method, device, equipment and storage medium - Google Patents

Medium-voltage line load value prediction method, device, equipment and storage medium Download PDF

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CN112257913A
CN112257913A CN202011110691.2A CN202011110691A CN112257913A CN 112257913 A CN112257913 A CN 112257913A CN 202011110691 A CN202011110691 A CN 202011110691A CN 112257913 A CN112257913 A CN 112257913A
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韦园清
赖来源
李荣斌
罗云梅
谢伟东
曾丽丽
宋�莹
汤正宇
尹仕豪
陈耀廷
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Guangdong Power Grid Co Ltd
Huizhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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Huizhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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Abstract

The invention discloses a method, a device, equipment and a storage medium for predicting a load value of a medium-voltage line. The method comprises the steps of obtaining at least one piece of user information of a preset planning year and at least one piece of historical user data; wherein the user information includes: the user applying capacity and the practical coefficient of the user applying; the historical user data includes: the maximum power utilization load value of a user in a preset year from the beginning of power utilization and the maximum power utilization load value after the power utilization level of the user is stable; determining a release coefficient of the user in a load stage of a preset planning year according to the historical user data; and predicting the maximum load value of the medium-voltage line in a preset planning year according to the historical maximum load value, the natural growth rate, the user information and the load stage release coefficient of the user in the preset planning year. By adopting the technical scheme of the invention, the accuracy of the load value prediction of the medium-voltage line can be improved.

Description

Medium-voltage line load value prediction method, device, equipment and storage medium
Technical Field
The present invention relates to a medium-voltage line load value prediction technology, and in particular, to a method, an apparatus, a device, and a storage medium for medium-voltage line load value prediction.
Background
The development of social economy brings great challenges to a power grid, and in order to ensure that the power supply capacity of the power grid can provide support for economic development, the power grid construction needs to moderately advance the development of economy, so that the load value of the power grid needs to be predicted.
The result of the current power grid load prediction method is obviously higher, and the power distribution network planning project is guided by the prediction value, so that more equipment runs under light load for a longer time, the utilization efficiency of the equipment is low, and the investment benefit of enterprises is reduced.
Therefore, a method for predicting the medium voltage line load value is needed to improve the accuracy of the medium voltage line load value prediction.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for predicting a medium-voltage line load value, which aim to improve the accuracy of the prediction of the medium-voltage line load value.
In a first aspect, the present invention provides a method for predicting a medium-voltage line load value, including:
acquiring at least one piece of user information and at least one piece of historical user data of a preset planning year; wherein the user information includes: the user applying capacity and the practical coefficient of the user applying; the historical user data includes: the maximum power utilization load value of a user in a preset year from the beginning of power utilization and the maximum power utilization load value after the power utilization level of the user is stable;
determining a release coefficient of the user in a load stage of a preset planning year according to the historical user data;
and predicting the maximum load value of the medium-voltage line in a preset planning year according to the historical maximum load value, the natural growth rate, the user information and the load stage release coefficient of the user in the preset planning year. In a second aspect, the present invention further provides a medium voltage line load value prediction apparatus, including:
the acquisition module is used for acquiring at least one piece of user information and at least one piece of historical user data of a preset planning year; wherein the user information includes: the user applying capacity and the practical coefficient of the user applying; the historical user data includes: the maximum power utilization load value of a user in a preset year from the beginning of power utilization and the maximum power utilization load value after the power utilization level of the user is stable;
the load stage release coefficient determining module is used for determining the load stage release coefficient of the user in a preset planning year according to the historical user data;
and the maximum load value prediction module is used for predicting the maximum load value of the medium-voltage line in a preset planning year according to the historical maximum load value of the medium-voltage line, the natural growth rate, the user information and the release coefficient of the user in the load stage of the preset planning year.
In a third aspect, the present invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for predicting a medium voltage line load value according to any one of the embodiments of the present invention when executing the program.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the medium-voltage line load value prediction method according to any one of the embodiments of the present invention.
The method comprises the steps of acquiring at least one piece of user information of a preset planning year and at least one piece of historical user data; wherein the user information includes: the user applying capacity and the practical coefficient of the user applying; the historical user data includes: the maximum power utilization load value of a user in a preset year from the beginning of power utilization and the maximum power utilization load value after the power utilization level of the user is stable; determining a release coefficient of the user in a load stage of a preset planning year according to the historical user data; and predicting the maximum load value of the medium-voltage line in a preset planning year according to the historical maximum load value, the natural growth rate, the user information and the load stage release coefficient of the user in the preset planning year. By adopting the technical scheme of the invention, the beneficial effect of improving the accuracy of the load value prediction of the medium-voltage line can be realized.
Drawings
Fig. 1a is a schematic flowchart of a method for predicting a medium-voltage line load value according to a first embodiment of the present invention;
FIG. 1b is a power supply partition comparison table provided in the first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a medium-voltage line load value prediction apparatus according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an apparatus provided in the third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, subprograms, and the like.
Example one
Fig. 1a is a schematic flowchart of a method for predicting a load value of a medium-voltage line according to an embodiment of the present invention, where the method is applicable to a situation of predicting a load value of a medium-voltage line, and the method may be implemented by a medium-voltage line load value predicting apparatus, and the apparatus may be implemented in a software and/or hardware manner, and may be integrated in an electronic device, and specifically includes the following steps:
s110, acquiring at least one piece of user information and at least one piece of historical user data of a preset planning year; wherein the user information includes: the user applying capacity and the practical coefficient of the user applying; the historical user data includes: the maximum power utilization load value of the user in a preset year from the beginning of power utilization and the maximum power utilization load value after the power utilization level of the user is stable.
In this embodiment, the preset planning year refers to 5 years after the user starts to use electricity from the power grid, where at least one piece of user information and at least one piece of historical user data in each year of the preset planning year need to be acquired. The user information refers to data information stored in the power grid marketing system, the metering automation system and the dispatching automation system after the user accesses the power grid, specifically, the user information includes but is not limited to user installation capacity and user installation practical coefficients, and may further include: the service management method comprises the following steps of the industry property of a user, the starting power utilization time of the user, the power utilization address of the user and the power supply partition to which the user belongs. In this embodiment, the historical user data includes: the maximum power utilization load value of the user in a preset year from the beginning of power utilization and the maximum power utilization load value after the power utilization level of the user is stable. The power supply subareas belonging to the users are distinguished through the load density values, and the load stage release coefficients for measuring of the power supply subareas belonging to different users are different.
See fig. 1b for a specific power supply partition comparison table.
Wherein the preset year is different from the preset planning year. The preset year is a year used for measuring the past, and the preset planned year is a year used for prediction, but the preset year is the same as the preset planned year in time length. Illustratively, the preset year is 5 years, and the preset planning year is 5 years.
And S120, determining a release coefficient of the user in a load stage of a preset planning year according to the historical user data.
Optionally, the determining, according to the historical user data, a release coefficient of the user at a load stage of a preset planning year includes:
and taking the ratio of the maximum power load value of the user in each year of the preset year after the user starts to use power and the maximum power load value after the power utilization level of the user is stable as the load stage release coefficient of the user in the preset planning year.
In this embodiment, the maximum power load value of each year of the user when the user starts to use power in a preset year refers to the maximum power load value of each year of the user after the user is connected to the power grid to operate, and specifically, the maximum power load value of each year of the user is recorded as PNi. In this embodiment, the maximum power load value after the power consumption level of the user is stable refers to the maximum power load value when the power load value of the user is in a relatively stable state after the user is connected to the power grid, and specifically, the maximum power load value after the power consumption level of the user is stable is recorded as PMiRecording the release coefficient of the user in each year load stage of the preset year for starting power utilization as rNiThen, then
Figure BDA0002728489080000051
Further, each user corresponds to one rNi. In this embodiment, the load phase release coefficient of each year of the user, from the beginning of electricity utilization in the preset year, is used as the load phase release coefficient of the user in the preset planning year. Illustratively, the predetermined year is 5 years, and the release factor of the user during the first year of the predetermined year is rN1Then the user's release factor during the loading phase of the first year of the preset planning year is rN1. The release coefficient of the user in the second year of the preset year is rN2The release coefficient of the user in the second year of the preset planning year is rN2。
Optionally, before determining the release coefficient of the user at the load stage of the preset planning year according to the historical user data, the method further includes:
and if the historical user data belongs to invalid information, removing the historical user data.
In this embodiment, the invalid information is information that cannot be used to predict the current maximum load value of the medium-voltage line, and the user information is removed. Specifically, the invalid information may be an abnormality of the user information due to an abnormality of the economic condition of the year in which the user information is located.
Optionally, if the user information belongs to invalid information, the method includes:
and if the difference value between the current load stage release coefficient of the user and the standard user load stage release coefficient of the power supply partition to which the user belongs is larger than a threshold value, the user information of the user belongs to invalid information.
In this embodiment, the standard user load phase release coefficient refers to a common value of the load phase release coefficient of users of the same type located in the same power supply partition, and may be obtained from an empirical value. Wherein the threshold value can be set by a user or an empirical value.
Specifically, if the difference between the current load stage release coefficient of the user and the standard user load stage release coefficient of the power supply partition to which the user belongs is greater than the threshold, the information is abnormal, and the information is rejected. Specifically, the current load stage release coefficients of different users may be calculated, and then compared with the load stage release coefficients of the standard users of the power supply partition to which the user belongs, and if the difference is large, the user information is indicated to belong to invalid information.
And S130, predicting the maximum load value of the medium-voltage line in a preset planning year according to the historical maximum load value, the natural growth rate, the user information and the load stage release coefficient of the user in the preset planning year.
In this embodiment, the maximum load value of the medium-voltage line history is obtained from the scheduling automation system, and specifically, the maximum load value of the medium-voltage line history may be denoted as PN-1. In this embodiment, the natural growth rate may be calculated from the historical maximum load value of the medium-voltage line by a linear regression method, and is recorded as αN. In this embodiment, the user installation capacity is recorded as SiThe practical coefficient of the user's installation is recorded as betaiThe current maximum load value of the medium-voltage line is recorded as PN is totalThen, then
Figure BDA0002728489080000071
Where m is the number of users on the medium voltage line.
The embodiment of the invention acquires at least one piece of user information and at least one piece of historical user data of a preset planning year; wherein the user information includes: the user applying capacity and the practical coefficient of the user applying; the historical user data includes: the maximum power utilization load value of a user in a preset year from the beginning of power utilization and the maximum power utilization load value after the power utilization level of the user is stable; determining a release coefficient of the user in a load stage of a preset planning year according to the historical user data; and predicting the maximum load value of the medium-voltage line in a preset planning year according to the historical maximum load value, the natural growth rate, the user information and the load stage release coefficient of the user in the preset planning year. By adopting the technical scheme of the embodiment of the invention, the accuracy of the load value prediction of the medium-voltage line can be improved.
Example two
Fig. 2 is a schematic structural diagram of a medium-voltage line load value prediction apparatus according to a second embodiment of the present invention. The medium-voltage line load value prediction device provided by the embodiment of the invention can execute the medium-voltage line load value prediction method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. As shown in fig. 2, the apparatus includes:
an obtaining module 201, configured to obtain at least one piece of user information and at least one piece of historical user data of a preset planning year; wherein the user information includes: the user applying capacity and the practical coefficient of the user applying; the historical user data includes: the maximum power utilization load value of a user in a preset year from the beginning of power utilization and the maximum power utilization load value after the power utilization level of the user is stable;
the load stage release coefficient determining module 202 is configured to determine a load stage release coefficient of the user in a preset planning year according to the historical user data;
the maximum load value prediction module 203 is configured to predict a maximum load value of the medium-voltage line in a preset planning year according to a historical maximum load value of the medium-voltage line, a natural growth rate, the user information, and a load stage release coefficient of the user in the preset planning year.
The device further comprises:
and the historical user data removing module 204 is configured to remove the historical user data if the historical user data belongs to invalid information.
The user information removing module 204 is specifically configured to, if a difference between the current load stage release coefficient of the user and the standard user load stage release coefficient of the power supply partition to which the user belongs is greater than a threshold, determine that the user information of the user belongs to invalid information.
A load phase release coefficient determining module 202, configured to use a ratio of a maximum power load value of each year of the user since the beginning of power utilization in a preset year to the maximum power load value after the power utilization level of the user is stable as a load phase release coefficient of the user in a preset planning year.
In this embodiment, the user takes a maximum user load value in a preset year, and the number of the preset years is the number of the maximum user load values.
Optionally, the historical maximum load value of the medium-voltage line is obtained from a dispatching automation system.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the above-described apparatus may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an apparatus according to a third embodiment of the present invention, and fig. 3 is a schematic structural diagram of an exemplary apparatus suitable for implementing the embodiment of the present invention. The device 12 shown in fig. 3 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present invention.
As shown in FIG. 3, device 12 is in the form of a general purpose computing device. The components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments described herein.
Device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with device 12, and/or with any devices (e.g., network card, modem, etc.) that enable device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 20. As shown in FIG. 3, the network adapter 20 communicates with the other modules of the device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement a medium-voltage line load value prediction method provided by an embodiment of the present invention, including:
acquiring at least one piece of user information and at least one piece of historical user data of a preset planning year; wherein the user information includes: the user applying capacity and the practical coefficient of the user applying; the historical user data includes: the maximum power utilization load value of a user in a preset year from the beginning of power utilization and the maximum power utilization load value after the power utilization level of the user is stable;
determining a release coefficient of the user in a load stage of a preset planning year according to the historical user data;
and predicting the maximum load value of the medium-voltage line in a preset planning year according to the historical maximum load value, the natural growth rate, the user information and the load stage release coefficient of the user in the preset planning year.
Example four
A fourth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program (or referred to as a computer-executable instruction) is stored, where the computer program, when executed by a processor, can implement a method for predicting a medium-voltage line load value according to any of the embodiments described above, and the method includes:
acquiring at least one piece of user information and at least one piece of historical user data of a preset planning year; wherein the user information includes: the user applying capacity and the practical coefficient of the user applying; the historical user data includes: the maximum power utilization load value of a user in a preset year from the beginning of power utilization and the maximum power utilization load value after the power utilization level of the user is stable;
determining a release coefficient of the user in a load stage of a preset planning year according to the historical user data;
and predicting the maximum load value of the medium-voltage line in a preset planning year according to the historical maximum load value, the natural growth rate, the user information and the load stage release coefficient of the user in the preset planning year.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (11)

1. A method for predicting a medium voltage line load value, comprising:
acquiring at least one piece of user information and at least one piece of historical user data of a preset planning year; wherein the user information includes: the user applying capacity and the practical coefficient of the user applying; the historical user data includes: the maximum power utilization load value of a user in a preset year from the beginning of power utilization and the maximum power utilization load value after the power utilization level of the user is stable;
determining a release coefficient of the user in a load stage of a preset planning year according to the historical user data;
and predicting the maximum load value of the medium-voltage line in a preset planning year according to the historical maximum load value, the natural growth rate, the user information and the load stage release coefficient of the user in the preset planning year.
2. The method of claim 1, wherein before determining a load phase release factor of a user in a preset planning year based on the historical user data, further comprising:
and if the historical user data belongs to invalid information, removing the historical user data.
3. The method of claim 2, wherein the step of, if the user information belongs to invalid information, comprises:
and if the difference value between the current load stage release coefficient of the user and the standard user load stage release coefficient of the power supply partition to which the user belongs is larger than a threshold value, the user information of the user belongs to invalid information.
4. The method of claim 2, wherein the step of, if the user information belongs to invalid information, comprises: the invalid information refers to information which cannot be used for predicting the current maximum load value of the medium-voltage line, and the user information is removed; wherein the invalid information comprises information of user information abnormality caused by abnormality of economic condition of the year in which the user information is located.
5. The method of claim 1, wherein determining a user release factor at a load phase of a preset planning year based on the historical user data comprises:
and taking the ratio of the maximum power load value of the user in each year of the preset year after the user starts to use power and the maximum power load value after the power utilization level of the user is stable as the load stage release coefficient of the user in the preset planning year.
6. The method of claim 1, wherein the maximum load value of the medium voltage line history is obtained from a dispatch automation system.
7. A medium voltage line load value prediction apparatus, comprising:
the acquisition module is used for acquiring at least one piece of user information and at least one piece of historical user data of a preset planning year; wherein the user information includes: the user applying capacity and the practical coefficient of the user applying; the historical user data includes: the maximum power utilization load value of a user in a preset year from the beginning of power utilization and the maximum power utilization load value after the power utilization level of the user is stable;
the load stage release coefficient determining module is used for determining the load stage release coefficient of the user in a preset planning year according to the historical user data;
and the maximum load value prediction module is used for predicting the maximum load value of the medium-voltage line in a preset planning year according to the historical maximum load value of the medium-voltage line, the natural growth rate, the user information and the release coefficient of the user in the load stage of the preset planning year.
8. The apparatus of claim 7, further comprising:
and the historical user data removing module is used for removing the historical user data if the historical user data belongs to invalid information.
9. The apparatus of claim 8, wherein the historical user data culling module is configured to:
and if the difference value between the current load stage release coefficient of the user and the standard user load stage release coefficient of the power supply partition to which the user belongs is larger than a threshold value, the user information of the user belongs to invalid information.
10. Computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, implements a medium voltage line load value prediction method as claimed in any one of claims 1-6.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a medium voltage line load value prediction method according to any one of claims 1 to 6.
CN202011110691.2A 2020-10-16 2020-10-16 Medium-voltage line load value prediction method, device, equipment and storage medium Pending CN112257913A (en)

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