CN114943384A - Transformer substation load prediction method, device, equipment and storage medium - Google Patents

Transformer substation load prediction method, device, equipment and storage medium Download PDF

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CN114943384A
CN114943384A CN202210643943.0A CN202210643943A CN114943384A CN 114943384 A CN114943384 A CN 114943384A CN 202210643943 A CN202210643943 A CN 202210643943A CN 114943384 A CN114943384 A CN 114943384A
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transformer substation
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黄书健
杨茂强
施理成
蔡素雄
刘焕辉
赖咏
刘俊威
彭威望
季高炜
李海发
郑梓瑶
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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 the load of a transformer substation, wherein the method comprises the following steps: acquiring target operation data corresponding to the transformer substation, and inputting the target operation data into a pre-constructed load prediction model; the load prediction model is obtained by training according to historical operation data corresponding to the transformer substation, and the historical operation data comprises historical environment data and historical transformer substation load data; outputting a target load prediction result corresponding to the target operation data through a load prediction model; and maintaining the equipment in the transformer substation according to the target load prediction result. The technical scheme of the embodiment of the invention can improve the accuracy of the load prediction result.

Description

Transformer substation load prediction method, device, equipment and storage medium
Technical Field
The embodiment of the invention relates to the technical field of transformer substations, in particular to a transformer substation load prediction method, a device, equipment and a storage medium.
Background
One of the most common problems in a substation is equipment heating, especially when the load is high, the temperature of the equipment rises along with the equipment heating, the service life of the equipment is influenced, and a power failure accident can be caused due to the fact that the equipment breaks down. Therefore, predicting the load of the substation is an important means for prolonging the service life of equipment and reducing the power failure risk.
In the prior art, when the load of a transformer substation is predicted, the load of the transformer substation is predicted by using a self-adaptive deep belief network based on the load data of equipment in the transformer substation; or a short-term load prediction model is constructed by collecting power grid topological structure data and utilizing a factor analysis algorithm.
However, the existing substation load prediction method has single consideration factor, so that the accuracy of the load prediction result is low.
Disclosure of Invention
The embodiment of the invention provides a transformer substation load prediction method, a transformer substation load prediction device, transformer substation load prediction equipment and a storage medium, and the accuracy of a load prediction result can be improved.
In a first aspect, an embodiment of the present invention provides a substation load prediction method, where the method includes:
acquiring target operation data corresponding to a transformer substation, and inputting the target operation data into a pre-constructed load prediction model; the load prediction model is obtained by training according to historical operation data corresponding to the transformer substation, and the historical operation data comprises historical environment data and historical transformer substation load data;
outputting a target load prediction result corresponding to the target operation data through the load prediction model;
and maintaining equipment in the transformer substation according to the target load prediction result.
In a second aspect, an embodiment of the present invention further provides a substation load prediction apparatus, where the apparatus includes:
the data acquisition module is used for acquiring target operation data corresponding to the transformer substation and inputting the target operation data into a pre-constructed load prediction model; the load prediction model is obtained by training according to historical operation data corresponding to the transformer substation, wherein the historical operation data comprises historical environment data and historical transformer substation load data;
a result output module, configured to output a target load prediction result corresponding to the target operation data through the load prediction model;
and the equipment maintenance module is used for maintaining the equipment in the transformer substation according to the target load prediction result.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
storage means for storing one or more programs;
the method of substation load prediction provided by any embodiment of the present invention is implemented when the one or more programs are executed by the one or more processors such that the one or more processors execute the programs.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for predicting a load of a substation provided in any embodiment of the present invention is implemented.
In a fifth aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements the substation load prediction method provided in any embodiment of the present invention.
According to the technical scheme of the embodiment of the invention, the target operation data corresponding to the transformer substation is obtained, the target operation data is input into the pre-constructed load prediction model, the target load prediction result corresponding to the target operation data is output through the load prediction model, and the equipment in the transformer substation is maintained according to the target load prediction result, so that the accuracy of the load prediction result can be improved, and the probability of equipment failure in the transformer substation is reduced.
It should be understood that the statements in this section are not intended to identify key or critical features of the embodiments of the present invention, nor are they intended to limit the scope of the invention. Other features of the present invention will become apparent from the following description.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for predicting a load of a substation according to an embodiment of the present invention;
fig. 2 is a flowchart of a substation load prediction method according to a second embodiment of the present invention;
fig. 3 is a flowchart of a substation load prediction method according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a substation load prediction device according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the substation load prediction method according to the 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 structures related to the present invention are shown in the drawings, not all of them.
Example one
Fig. 1 is a flowchart of a method for predicting a load of a substation according to an embodiment of the present invention, where the embodiment is applicable to predicting a load of a substation, and the method may be executed by a substation load prediction device. The substation load prediction device may be implemented by software and/or hardware, and may be generally integrated in an electronic device (e.g., a terminal or a server) having a data processing function, and specifically includes the following steps:
and 110, acquiring target operation data corresponding to the transformer substation, and inputting the target operation data into a pre-constructed load prediction model.
In this step, the target operation data may be load data and environment data corresponding to the current time of the substation, and the environment data may be temperature data, seasonal data, and the like of an environment where equipment in the substation is located. After target operation data corresponding to the transformer substation are obtained, the target operation data can be input into the load prediction model.
In this embodiment, the load prediction model is used for predicting load data of the substation in a future time period according to current operation data of the substation. The load prediction model can be obtained by training according to historical operation data corresponding to the transformer substation, wherein the historical operation data comprises historical environment data and historical transformer substation load data. The historical environmental data can be temperature data, seasonal data and the like of the environment where equipment in the transformer substation is located in a historical time period. The historical substation load data can be load data corresponding to the substation in a historical time period.
In a specific embodiment, a preset machine learning model may be trained by using a plurality of historical operating data corresponding to the substation, so as to obtain the load prediction model.
And 120, outputting a target load prediction result corresponding to the target operation data through the load prediction model.
In this embodiment, after the target operation data is input to the load prediction model, the load prediction model may process the target operation data according to a pre-trained model parameter to obtain a target load prediction result corresponding to the target operation data.
And step 130, maintaining equipment in the transformer substation according to the target load prediction result.
In this step, after the target load prediction result is output through the load prediction model, if the target load prediction result value is large, that is, the equipment heating probability in the substation is high, a preset measure can be taken to maintain the equipment in the substation. Specifically, the patrol force of the transformer substation in a high-load period can be increased by transformer substation operators, and the probability of equipment failure in the transformer substation is reduced.
In this embodiment, since the environmental data in the substation may have a certain influence on the operating state (e.g., operating frequency, load, etc.) of the device, the load prediction model is obtained through co-training in combination with the historical environmental data and the historical load data of the substation, so that the target load prediction result of the substation can be effectively output.
According to the technical scheme, the target operation data corresponding to the transformer substation are obtained, the target operation data are input into the pre-constructed load prediction model, the target load prediction result corresponding to the target operation data is output through the load prediction model, and the equipment in the transformer substation is maintained according to the target load prediction result, so that the accuracy of the load prediction result can be improved, and the probability of equipment failure in the transformer substation is reduced.
Example two
This embodiment is a further refinement of the above embodiment, and the same or corresponding terms as those of the above embodiment are explained, and this embodiment is not described again. Fig. 2 is a flowchart of a substation load prediction method provided in the second embodiment, in this embodiment, the technical solution of this embodiment may be combined with one or more methods in the solutions of the foregoing embodiments, as shown in fig. 2, the method provided in this embodiment may further include:
step 210, obtaining a plurality of historical operating data corresponding to the transformer substation, using each historical operating data as model input data, and using a load prediction result corresponding to each model input data as model output data.
In this embodiment, optionally, after a plurality of historical operating data corresponding to the substation are acquired, each of the historical operating data may be used as model input data of the machine learning model, and then a preset load prediction result corresponding to each of the historical operating data may be used as model output data of the machine learning model.
And step 220, constructing and obtaining the load prediction model according to the model input data and the model output data.
In this step, model parameters of the machine learning model may be adjusted according to each of the load prediction results, and the adjusted machine learning model may be used as the load prediction model.
In the embodiment, the machine learning model is trained by using a plurality of historical operating data, so that the load prediction model can be obtained quickly, and the load prediction efficiency of the transformer substation is improved.
And 230, acquiring target operation data corresponding to the transformer substation, and inputting the target operation data into the load prediction model.
And 240, outputting a target load prediction result corresponding to the target operation data through the load prediction model.
And step 250, maintaining equipment in the transformer substation according to the target load prediction result.
According to the technical scheme of the embodiment of the invention, the accuracy of the load prediction result can be improved and the probability of equipment failure in the transformer substation can be reduced by acquiring a plurality of historical operation data corresponding to the transformer substation, taking each historical operation data as model input data, taking the load prediction result corresponding to each model input data as model output data, constructing and obtaining the load prediction model according to each model input data and the model output data, acquiring target operation data corresponding to the transformer substation, inputting the target operation data into the load prediction model, outputting the target load prediction result corresponding to the target operation data through the load prediction model, and maintaining equipment in the transformer substation according to the target load prediction result.
EXAMPLE III
This embodiment is a further refinement of the above embodiment, and the same or corresponding terms as those of the above embodiment are explained, and this embodiment is not described again. Fig. 3 is a flowchart of a substation load prediction method provided in a third embodiment, in this embodiment, the technical solution of this embodiment may be combined with one or more methods in the solutions of the foregoing embodiments, as shown in fig. 3, the method provided in this embodiment may further include:
step 310, obtaining a plurality of historical operating data corresponding to the transformer substation, and using each historical operating data as model input data.
And step 320, constructing a model input function according to each model input data and a plurality of preset model parameters.
In this embodiment, a supervised learning algorithm in machine learning may be used to construct a model input function from each model input data.
In a specific embodiment, assume that the model input data is x and the preset model parameter is θ, where x ═ x 0 ,x 1 ,…,x n Then, the constructed model input function may be:
h θ (x)＝θ T x+θ 0 x 0 +θ 1 x 1 +θ 2 x 2 +…+θ n x n
step 330, determining a parameter value corresponding to each model parameter according to the model input function and each model input data.
In this step, optionally, the model parameter θ for minimizing the function may be determined according to the model input function 0 、θ 1 ……θ n The value of (c).
In an implementation manner of this embodiment, determining, according to the model input function and each of the model input data, a parameter value corresponding to each of the model parameters includes: calculating a data average value corresponding to the model input data and a data standard deviation; and determining parameter values corresponding to the model parameters by adopting a gradient descent algorithm according to the data average value, the data standard deviation and the model input data.
In one specific embodiment, the data mean corresponding to the model input data may be calculated according to the following formula
Figure BDA0003683322030000081
And data standard deviation s:
Figure BDA0003683322030000082
Figure BDA0003683322030000083
calculating the average value of the data by the formula
Figure BDA0003683322030000084
And after the data standard deviation s, the reference output result y corresponding to each model input data can be calculated i ：
Figure BDA0003683322030000085
Calculating the reference output result y corresponding to each model input data through the formula i Thereafter, the result y may be output based on the reference i And each model input data x i The gradient descent algorithm is adopted to determine the parameter values corresponding to the model parameters, and the specific calculation mode can be shown as the following formula:
Figure BDA0003683322030000091
in this embodiment, determining the parameter value corresponding to each of the model parameters by using a gradient descent algorithm includes: and according to a preset learning rate, iteratively updating the parameter values corresponding to the model parameters, and taking the parameter values when the model input function is minimized as final parameter values of the model parameters.
In a specific embodiment, assuming that the preset learning rate is α, the value of the model parameter θ may be iteratively updated by continuously adjusting (increasing or decreasing) α, and the specific calculation formula is as follows:
Figure BDA0003683322030000092
the initial value of α may be set to 0.01, the number of iterations may be set to 500, and the specific value may be adjusted according to an actual situation, which is not limited in this embodiment.
And 340, calculating a load prediction result corresponding to each model input data according to the parameter value corresponding to each model parameter and each model input data.
In an embodiment of the present invention, calculating a load prediction result corresponding to each model input data according to a parameter value corresponding to each model parameter and each model input data includes: calculating a difference value between each model input data and the data average value, and dividing the difference value by the data standard deviation to obtain a reference output result corresponding to each model input data; and multiplying the reference output result corresponding to each model input data by the corresponding parameter value to obtain the load prediction result corresponding to each model input data.
In one specific embodiment, assume that the model input data corresponds to a reference output result of y i If the load prediction result corresponding to the model input data is Y i Wherein:
Y i ＝y i *θ
step 350, using the load prediction result corresponding to each model input data as model output data.
And step 360, constructing and obtaining the load prediction model according to the model input data and the model output data.
In this step, after obtaining the model output data, the model parameters may be saved, and a function between the model input data and the model output data may be used as the load prediction model.
And 370, acquiring target operation data corresponding to the transformer substation, and inputting the target operation data into the load prediction model.
And 380, outputting a target load prediction result corresponding to the target operation data through the load prediction model.
In this embodiment, the historical operating data may further include a date, and assuming that historical environmental data and historical load data per hour in 12 months are acquired, after the load prediction model is obtained through the data according to the above manner, if the target operating data is input to the load prediction model, the load prediction model may output a corresponding load prediction result according to a preset time length (for example, month or hour).
390, maintaining the equipment in the transformer substation according to the target load prediction result.
In this embodiment, in addition to predicting the load of the transformer substation, the method can also be applied to state prediction of other devices, for example, historical data of the high-voltage switch cabinet is trained, so that a corresponding predicted value of the high-voltage switch cabinet can be obtained, and operating personnel can observe the health state of the switch cabinet in time.
The technical scheme of the embodiment of the invention is that a plurality of historical operation data corresponding to a transformer substation are obtained, each historical operation data is used as model input data, a model input function is built according to each model input data and a plurality of model parameters, parameter values corresponding to the model parameters are determined according to the model input function and each model input data, a load prediction result corresponding to each model input data is calculated according to the parameter values corresponding to the model parameters and the model input data, the load prediction result corresponding to each model input data is used as model output data, a load prediction model is built according to each model input data and each model output data, target operation data corresponding to the transformer substation is obtained, the target operation data is input into the load prediction model, a target load prediction result is output through the load prediction model, and equipment in the transformer substation is maintained according to the target load prediction result, the accuracy of the load prediction result can be improved, and the probability of equipment failure in the transformer substation is reduced.
Example four
Fig. 4 is a schematic structural diagram of a substation load prediction apparatus according to a fourth embodiment of the present invention, and as shown in fig. 4, the apparatus includes: a data acquisition module 410, a result output module 420, and a device maintenance module 430.
The data acquisition module 410 is configured to acquire target operation data corresponding to a substation and input the target operation data into a pre-constructed load prediction model; the load prediction model is obtained by training according to historical operation data corresponding to the transformer substation, and the historical operation data comprises historical environment data and historical transformer substation load data;
a result output module 420, configured to output a target load prediction result corresponding to the target operation data through the load prediction model;
and the equipment maintenance module 430 is configured to maintain the equipment in the substation according to the target load prediction result.
According to the technical scheme provided by the embodiment of the invention, the target operation data corresponding to the transformer substation is acquired, the target operation data is input into the pre-constructed load prediction model, the target load prediction result corresponding to the target operation data is output through the load prediction model, and the equipment in the transformer substation is maintained according to the target load prediction result, so that the accuracy of the load prediction result can be improved, and the probability of equipment failure in the transformer substation is reduced.
On the basis of the above embodiment, the substation load prediction apparatus further includes:
the system comprises a historical data acquisition module, a load prediction module and a load prediction module, wherein the historical data acquisition module is used for acquiring a plurality of historical operating data corresponding to a transformer substation, using each historical operating data as model input data, and using a load prediction result corresponding to each model input data as model output data;
and the model construction module is used for constructing and obtaining the load prediction model according to the model input data and the model output data.
Wherein, historical data acquisition module includes:
the function building unit is used for building a model input function according to each model input data and a plurality of preset model parameters;
a parameter value determining unit, configured to determine a parameter value corresponding to each of the model parameters according to the model input function and each of the model input data;
a result calculation unit, configured to calculate a load prediction result corresponding to each model input data according to a parameter value corresponding to each model parameter and each model input data;
the data value calculating unit is used for calculating a data average value corresponding to the model input data and a data standard deviation;
the gradient descent processing unit is used for determining parameter values corresponding to the model parameters by adopting a gradient descent algorithm according to the data average value, the data standard deviation and the model input data;
the parameter value updating unit is used for iteratively updating the parameter values corresponding to the model parameters according to the preset learning rate, and taking the parameter values when the model input function is minimized as the final parameter values of the model parameters;
a reference result determining unit, configured to calculate a difference between each of the model input data and the data average, and divide the difference by the data standard deviation to obtain a reference output result corresponding to each of the model input data;
and the prediction result determining unit is used for multiplying the reference output result corresponding to each model input data by the corresponding parameter value to obtain the load prediction result corresponding to each model input data.
The device can execute the methods provided by all the embodiments of the invention, and has corresponding functional modules and beneficial effects for executing the methods. For technical details which are not described in detail in the embodiments of the present invention, reference may be made to the methods provided in all the aforementioned embodiments of the present invention.
EXAMPLE five
FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the substation load prediction method.
In some embodiments, the substation load prediction method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the substation load prediction method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the substation load prediction method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A transformer substation load prediction method is characterized by comprising the following steps:
acquiring target operation data corresponding to a transformer substation, and inputting the target operation data into a pre-constructed load prediction model; the load prediction model is obtained by training according to historical operation data corresponding to the transformer substation, and the historical operation data comprises historical environment data and historical transformer substation load data;
outputting a target load prediction result corresponding to the target operation data through the load prediction model;
and maintaining equipment in the transformer substation according to the target load prediction result.
2. The method of claim 1, before acquiring the target operation data corresponding to the substation, further comprising:
acquiring a plurality of historical operation data corresponding to a transformer substation, taking each historical operation data as model input data, and taking a load prediction result corresponding to each model input data as model output data;
and constructing the load prediction model according to the model input data and the model output data.
3. The method of claim 2, further comprising, prior to using the load predictions corresponding to each of the model input data as model output data:
constructing a model input function according to each model input data and a plurality of preset model parameters;
determining parameter values corresponding to the model parameters according to the model input function and the model input data;
and calculating a load prediction result corresponding to each model input data according to the parameter value corresponding to each model parameter and each model input data.
4. The method of claim 3, wherein determining a parameter value corresponding to each of the model parameters based on the model input function and each of the model input data comprises:
calculating a data average value corresponding to the model input data and a data standard deviation;
and determining parameter values corresponding to the model parameters by adopting a gradient descent algorithm according to the data average value, the data standard deviation and the model input data.
5. The method of claim 4, wherein determining the parameter values corresponding to each of the model parameters using a gradient descent algorithm comprises:
and according to a preset learning rate, iteratively updating the parameter values corresponding to the model parameters, and taking the parameter values when the model input function is minimized as final parameter values of the model parameters.
6. The method of claim 4, wherein calculating the load prediction corresponding to each model input data based on the parameter value corresponding to each model parameter and each model input data comprises:
calculating a difference value between each model input data and the data average value, and dividing the difference value by the data standard deviation to obtain a reference output result corresponding to each model input data;
and multiplying the reference output result corresponding to each model input data by the corresponding parameter value to obtain the load prediction result corresponding to each model input data.
7. A substation load prediction device, the device comprising:
the data acquisition module is used for acquiring target operation data corresponding to the transformer substation and inputting the target operation data into a pre-constructed load prediction model; the load prediction model is obtained by training according to historical operation data corresponding to the transformer substation, and the historical operation data comprises historical environment data and historical transformer substation load data;
a result output module, configured to output a target load prediction result corresponding to the target operation data through the load prediction model;
and the equipment maintenance module is used for maintaining the equipment in the transformer substation according to the target load prediction result.
8. An electronic device, the electronic device comprising:
one or more processors;
storage means for storing one or more programs;
the substation load prediction method of any one of claims 1-6 is implemented when the one or more programs are executed by the one or more processors such that the one or more processors execute the programs.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the substation load prediction method according to any one of claims 1-6.
10. A computer program product, characterized in that the computer program product comprises a computer program which, when being executed by a processor, realizes a substation load prediction method according to any one of claims 1-6.
CN202210643943.0A 2022-06-08 2022-06-08 Transformer substation load prediction method, device, equipment and storage medium Pending CN114943384A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115473232A (en) * 2022-11-02 2022-12-13 广东电网有限责任公司中山供电局 Load parameter adjusting method, system, equipment and storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115473232A (en) * 2022-11-02 2022-12-13 广东电网有限责任公司中山供电局 Load parameter adjusting method, system, equipment and storage medium
CN115473232B (en) * 2022-11-02 2023-03-24 广东电网有限责任公司中山供电局 Load parameter adjusting method, system, equipment and storage medium

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