CN117350485A - Power market control method and system based on data mining model - Google Patents
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Abstract
The invention discloses a method and a system for controlling an electric power market based on a data mining model, wherein the method comprises the following steps: acquiring historical electricity utilization data of a plurality of setting subareas, and recording the historical electricity utilization data as first historical electricity utilization data; clustering the plurality of set subareas according to the first historical electricity consumption data to obtain a plurality of clustering areas, and recording the historical electricity consumption data of each clustering area as second historical electricity consumption data; splicing the second historical electricity consumption data of each clustering area to obtain a spliced image; and inputting the spliced images into a prediction model, and predicting the electricity consumption condition of the clustering area so as to realize regional fine management and control. According to the embodiment of the invention, the data of each adjacent month are correlated in an image stitching mode, so that a more accurate prediction result can be obtained.
Description
Technical Field
The invention relates to the technical field of data mining, in particular to an electric power market control method and system based on a data mining model.
Background
With the continuous development of economy and the opening of the power market, the requirements of the power industry are continuously increasing, and the analysis and prediction of the power market demands are increasingly important. In order to ensure the economical efficiency and the safety of the operation of the power system and improve the benefit of power enterprises, the requirements of the power market need to be effectively analyzed and predicted, and guidance and reference are provided for the development of the power industry.
The data mining technology mainly refers to a process of extracting some implicit data from a large amount of random, fuzzy and incomplete data, has certain representativeness and regularity, and has knowledge and information with potential utilization value. The data mining can traverse and inquire past data, predict future behaviors and development trends, automatically explore modes which do not appear before, and further provide support for behaviors and decisions of people. The mined information may be used in information management, processing queries, supporting decision-making and control processes, and the like.
In the existing power demand mode for the power market, related data are mostly determined through a certain clustering algorithm such as k-means and the like, and power demand prediction is performed based on the related data through a prediction model such as a neural network model, so that a rough prediction result can be obtained to a certain extent, but the mode obviously does not consider the correlation among all data, so that the prediction result is not very accurate, and further the refinement of power market management and control cannot be realized.
Disclosure of Invention
In order to overcome the defects of the prior art, the embodiment of the invention aims to provide a power market control method and system based on a data mining model, which correlate data of adjacent months in an image stitching mode, so that a more accurate prediction result can be obtained.
To solve the above problems, a first aspect of the embodiments of the present invention discloses a method for controlling an electric power market based on a data mining model, which includes the following steps:
acquiring historical electricity utilization data of a plurality of setting subareas, and recording the historical electricity utilization data as first historical electricity utilization data;
clustering the plurality of set subareas according to the first historical electricity consumption data to obtain a plurality of clustering areas, and recording the historical electricity consumption data of each clustering area as second historical electricity consumption data;
splicing the second historical electricity consumption data of each clustering area to obtain a spliced image;
and inputting the spliced images into a prediction model, and predicting the electricity consumption condition of the clustering area so as to realize regional fine management and control.
In a first aspect of the embodiment of the present invention, obtaining historical electricity data of a plurality of setting sub-areas, recorded as first historical electricity data, includes:
wherein X is i A first historical electricity usage data for the ith set sub-region,setting first historical electricity utilization data of the jth month of the sub-region for the ith, wherein i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n, m is the total number of the set sub-regions, and n is the total month number of the historical electricity utilization data.
In a first aspect of the embodiment of the present invention, clustering the plurality of set sub-areas according to the first historical electricity consumption data to obtain a plurality of clustered areas, and recording the historical electricity consumption data of each clustered area as second historical electricity consumption data, where the clustering method includes:
clustering the plurality of set subregions based on the first historical electricity utilization data by using a k-means clustering algorithm or an improved k-means clustering algorithm to obtain P clustering regions, wherein P is less than or equal to m;
recording the historical electricity consumption data of each clustering area as second historical electricity consumption data:
wherein Y is k For the second historical electricity usage data for the kth cluster region,setting the first historical electricity consumption data of the sub-region for the r-th of the kth cluster region,/->Setting first historical electricity consumption data of the jth month of the sub-region for the (r) th clustering region, wherein r is more than or equal to 1 and less than or equal to q, k is more than or equal to 1 and less than or equal to P, and q is the total number of the set sub-regions of the kth clustering region.
In a first aspect of the embodiment of the present invention, the stitching the second historical electricity consumption data of each cluster area to obtain a stitched image includes:
normalizing the second historical electricity consumption data of each clustering region to 0-255;
any adjacent three of the normalized second historical electricity consumption data in each clustering area are spliced into an array, and the normalized second historical electricity consumption data in each clustering area can repeatedly appear in different arrays;
and sequentially taking all the numerical values in each array as an R value, a G value and a B value according to the sequence, and constructing a spliced image of each clustering area.
In a first aspect of the embodiment of the present invention, inputting the stitched image into a prediction model, and predicting the electricity consumption of the clustered region to implement regional fine management and control includes:
and inputting the target spliced image into a pre-trained prediction model, and predicting the electricity consumption condition of the target clustering region for one month in the future to obtain an electricity consumption prediction result.
The second aspect of the embodiment of the invention discloses an electric power market management and control system based on a data mining model, which comprises the following components:
the acquisition module is used for acquiring historical electricity utilization data of a plurality of setting subareas and recording the historical electricity utilization data as first historical electricity utilization data;
the clustering module is used for clustering the plurality of set subareas according to the first historical electricity utilization data to obtain a plurality of clustering areas, and recording the historical electricity utilization data of each clustering area as second historical electricity utilization data;
the splicing module is used for splicing the second historical electricity utilization data of each clustering area to obtain spliced images;
and the prediction module is used for inputting the spliced image into a prediction model and predicting the electricity consumption condition of the clustering area so as to realize regional fine management and control.
In a second aspect of the embodiment of the present invention, the obtaining module includes:
wherein X is i A first historical electricity usage data for the ith set sub-region,setting first historical electricity utilization data of the jth month of the sub-region for the ith, wherein i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n, m is the total number of the set sub-regions, and n is the total month number of the historical electricity utilization data.
The clustering module comprises:
clustering the plurality of set subregions based on the first historical electricity utilization data by using a k-means clustering algorithm or an improved k-means clustering algorithm to obtain P clustering regions, wherein P is less than or equal to m;
recording the historical electricity consumption data of each clustering area as second historical electricity consumption data:
wherein Y is k For the second historical electricity usage data for the kth cluster region,setting the first historical electricity consumption data of the sub-region for the r-th of the kth cluster region,/->Setting first historical electricity consumption data of the jth month of the sub-region for the (r) th clustering region, wherein r is more than or equal to 1 and less than or equal to q, k is more than or equal to 1 and less than or equal to P, and q is the total number of the set sub-regions of the kth clustering region.
In a second aspect of the embodiment of the present invention, the splicing module includes:
normalizing the second historical electricity consumption data of each clustering region to 0-255;
any adjacent three of the normalized second historical electricity consumption data in each clustering area are spliced into an array, and the normalized second historical electricity consumption data in each clustering area can repeatedly appear in different arrays;
and sequentially taking all the numerical values in each array as an R value, a G value and a B value according to the sequence, and constructing a spliced image of each clustering area.
A third aspect of an embodiment of the present invention discloses an electronic device, including: a memory storing executable program code; a processor coupled to the memory; the processor invokes the executable program code stored in the memory to perform a data mining model-based power market management and control method disclosed in the first aspect of the embodiment of the present invention.
A fourth aspect of the embodiment of the present invention discloses a computer-readable storage medium storing a computer program, where the computer program causes a computer to execute a method for controlling an electric power market based on a data mining model disclosed in the first aspect of the embodiment of the present invention.
A fifth aspect of the embodiments of the present invention discloses a computer program product, which when run on a computer causes the computer to perform a method for controlling a power market based on a data mining model disclosed in the first aspect of the embodiments of the present invention.
A sixth aspect of the embodiment of the present invention discloses an application publishing platform, where the application publishing platform is configured to publish a computer program product, where the computer program product when run on a computer causes the computer to execute a method for controlling an electric power market based on a data mining model disclosed in the first aspect of the embodiment of the present invention.
Compared with the prior art, the invention has the beneficial effects that:
after clustering, the embodiment of the invention associates the data of adjacent months in each cluster in a splicing way to form a spliced image, thereby obtaining a more accurate prediction result and providing guidance and reference for the fine management in the area.
Drawings
FIG. 1 is a schematic flow diagram of a method for controlling an electric power market based on a data mining model according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an electric power market management and control system based on a data mining model according to a fifth embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to a sixth embodiment of the present invention.
Detailed Description
The present embodiment is only for explanation of the present invention and is not to be construed as limiting the present invention, and those skilled in the art can make modifications to the present embodiment without creative contribution as required after reading the present specification, but are protected by patent laws within the scope of claims of the present invention.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
In embodiments of the invention, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
After clustering, the embodiment of the invention associates the data of adjacent months in each cluster in a splicing way to form a spliced image, thereby obtaining a more accurate prediction result, providing guidance and reference for the fine management in the area, and being described in detail below with reference to the accompanying drawings.
Example 1
Referring to fig. 1, a method for controlling an electric power market based on a data mining model may include the following steps:
s110, acquiring historical electricity utilization data of a plurality of setting subareas, and recording the historical electricity utilization data as first historical electricity utilization data.
The embodiment of the invention is used for predicting the monthly electricity consumption condition, so that the first historical electricity consumption data can be determined according to the monthly statistics data, the first historical electricity consumption data needs to count the month before the target month (the month of which the electricity consumption condition needs to be predicted), and the month number of the historical electricity consumption data of each set subarea can be set according to the needs, for example, 50-100 months before the target month. Of course, in other embodiments, the first historical electricity usage data may also be daily historical electricity usage data.
The setting subareas are set according to the control requirements of the electric power market, for example, the setting subareas can be set in streets, villages, parks, communities and the like, and the setting subareas can be formed independently or by dividing according to the coverage areas of different power supply lines.
The first historical electricity usage data may be represented by an array:
wherein X is i A first historical electricity usage data for the ith set sub-region,setting first historical electricity utilization data of the jth month of the sub-region for the ith, wherein i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n, m is the total number of the set sub-regions, and n is the total month number of the historical electricity utilization data.
The total first history data may be expressed as:
X＝[X 1 ,X 2 ,…X m ]
s120, clustering the plurality of set subareas according to the first historical electricity consumption data; and obtaining a plurality of clustering areas, and recording the historical electricity utilization data of each clustering area as second historical electricity utilization data.
The clustering algorithm for setting the subareas can be realized by adopting any one of the conventional k-means clustering algorithm, the improved k-means clustering algorithm and the like. Illustratively, when a k-means clustering algorithm is employed, euclidean distance may be used to calculate the similarity of two set subregions.
Clustering the plurality of set subregions based on the first historical electricity consumption data by using a clustering algorithm to obtain P clustering regions, wherein P is less than or equal to m; the number P of cluster areas at the time of clustering can be set as needed, and in general, the cluster areas are set to be optimal by 1/4 to 1/6 of the set sub-areas.
The second historical electricity consumption data corresponding to the historical electricity consumption data of each cluster region may be characterized as:
wherein Y is k For the second historical electricity consumption data of the kth clustering region, each subset in the clustering region represents the first historical data of the corresponding set subarea, namely the second historical electricity consumption data of the kth clustering region is composed of the first historical data of a plurality of set subareas:
wherein Y is k For the second historical electricity usage data for the kth cluster region,setting the first historical electricity consumption data of the sub-region for the r-th of the kth cluster region,/->Setting first historical electricity consumption data of the jth month of the sub-region for the (r) th clustering region, wherein r is more than or equal to 1 and less than or equal to q, k is more than or equal to 1 and less than or equal to P, and q is the total number of the set sub-regions of the kth clustering region.
And S130, splicing the second historical electricity consumption data of each clustering area to obtain a spliced image.
In the prior art, the clustered data set is directly input into a prediction model, such as a linear regression model and a neural network model, so as to obtain the electricity consumption prediction condition of the target month.
The method has the obvious problems that the historical electricity consumption data of each month has certain correlation, and the historical electricity consumption data of different set subareas in the same cluster also has certain approximation, so that in the preferred embodiment of the invention, the adjacent multiple data are combined in a data splicing mode, so that the correlation among the data is reflected, and the prediction accuracy is further improved.
Specifically, all second historical electricity consumption data of each clustering area are normalized, wherein the normalization is to normalize the second historical electricity consumption data to be between 0 and 255, the normalization is used for converting the normalized second historical electricity consumption data into RGB values of pixels, and the range of the RGB values is 0 to 255.
The normalization may be performed in various ways, for example, by means of dispersion normalization or z-score normalization, and the like, and is not limited thereto.
All the second historical electricity data normalized data of each cluster region can be represented by an array:
wherein Z is k Representing an array obtained by normalizing all second historical electricity utilization data of the kth clustering region,representing Z k The s-th data in (1), L is Z k Is the total number of data in the database.
Any adjacent three of the normalized second historical electricity consumption data in each clustering area are spliced into an array, and the normalized second historical electricity consumption data in each clustering area can repeatedly appear in different arrays.
Illustratively, in Z as described above k For example, the spliced arrays are respectively as follows:
and sequentially taking each numerical value in each array as an R value, a G value and a B value, and constructing a spliced image of each clustering region, namely, each array formed by splicing forms a point of the spliced image, and the pixel value of the changed point is the R value, the G value and the B value of the array.
S140, inputting the spliced images into a prediction model, and predicting the electricity consumption condition of the clustering area to realize regional fine control.
The prediction model may be any machine learning model with a prediction function, for example, may be a neural network model. When training the prediction model, the adopted sample also needs to be formed into a spliced image through the process of the step S130, and the prediction model is trained through the spliced image.
The training process of the prediction model is similar to the existing model training process except that the spliced image is used for training, and the description is omitted here.
The target spliced image is input into a trained prediction model, so that a prediction result of a target month (generally the next month of the latest historical electricity consumption data) of a target clustering area can be obtained.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of an electric power market management and control system based on a data mining model according to an embodiment of the present invention. As shown in fig. 2, the data mining model-based power market management and control system may include:
an obtaining module 210, configured to obtain historical electricity data of a plurality of setting sub-areas, and record the historical electricity data as first historical electricity data;
the clustering module 220 is configured to cluster the plurality of set sub-areas according to the first historical electricity consumption data to obtain a plurality of clustered areas, and record the historical electricity consumption data of each clustered area as second historical electricity consumption data;
the splicing module 230 is configured to splice the second historical electricity consumption data of each clustering area to obtain a spliced image;
and the prediction module 240 is configured to input the stitched image into a prediction model, and predict the electricity consumption condition of the clustered region, so as to implement regional fine management and control.
Preferably, the obtaining module 210 may include:
wherein X is i A first historical electricity usage data for the ith set sub-region,setting first historical electricity utilization data of the jth month of the sub-region for the ith, wherein i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n, m is the total number of the set sub-regions, and n is the total month number of the historical electricity utilization data.
The clustering module 220 may include:
clustering the plurality of set subregions based on the first historical electricity utilization data by using a k-means clustering algorithm or an improved k-means clustering algorithm to obtain P clustering regions, wherein P is less than or equal to m;
recording the historical electricity consumption data of each clustering area as second historical electricity consumption data:
wherein Y is k For the second historical electricity usage data for the kth cluster region,setting the first historical electricity consumption data of the sub-region for the r-th of the kth cluster region,/->Setting first historical electricity consumption data of the jth month of the sub-region for the (r) th clustering region, wherein r is more than or equal to 1 and less than or equal to q, k is more than or equal to 1 and less than or equal to P, and q is the total number of the set sub-regions of the kth clustering region.
Preferably, the splicing module 230 may include:
normalizing the second historical electricity consumption data of each clustering region to 0-255;
any adjacent three of the normalized second historical electricity consumption data in each clustering area are spliced into an array, and the normalized second historical electricity consumption data in each clustering area can repeatedly appear in different arrays;
sequentially taking all the numerical values in each array as R value, G value and B value according to the sequence to construct a spliced image of each clustering area
Preferably, the prediction module 240 may include:
and inputting the target spliced image into a pre-trained prediction model, and predicting the electricity consumption condition of the target clustering region for one month in the future to obtain an electricity consumption prediction result.
Example III
Referring to fig. 3, fig. 3 is a schematic diagram of an electronic device that may be used to implement an embodiment of the present 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. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, 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 embodiments of the invention described and/or claimed herein.
As shown in fig. 3, the electronic device includes at least one processor 310, and a memory, such as a ROM (read only memory) 320, a RAM (random access memory) 330, etc., communicatively connected to the at least one processor 310, wherein the memory stores computer programs executable by the at least one processor, and the processor 310 can perform various suitable actions and processes according to the computer programs stored in the ROM 320 or the computer programs loaded from the storage unit 380 into the random access memory RAM 330. In the RAM 330, various programs and data required for the operation of the electronic device may also be stored. The processor 310, ROM 320, and RAM 330 are connected to each other by a bus 340. An I/O (input/output) interface 350 is also connected to bus 340.
A number of components in the electronic device are connected to the I/O interface 350, including: an input unit 360 such as a keyboard, a mouse, etc.; an output unit 370 such as various types of displays, speakers, and the like; a storage unit 380 such as a magnetic disk, an optical disk, or the like; and a communication unit 390, such as a network card, modem, wireless communication transceiver, etc. The communication unit 390 allows the electronic device to exchange information/data with other devices via a computer network such as the internet or/and various telecommunications networks.
Processor 310 may be a variety of general-purpose or/and special-purpose processing components having processing and computing capabilities. Some examples of processor 310 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, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 310 performs one or more steps of a data mining model-based power market management method as described in embodiment one above.
In some embodiments, a data mining model-based power market management method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 380. In some embodiments, part or all of the computer program may be loaded onto or/and installed onto the electronic device via ROM 320 or/and communication unit 390. When the computer program is loaded into RAM 330 and executed by processor 310, one or more steps of a data mining model-based power market management method described in embodiment one above may be performed. Alternatively, in other embodiments, processor 310 may be configured to perform a data mining model-based power market management method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, or/and combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed or/and interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit 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 embodiments 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 implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of embodiments 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. The 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) through 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 may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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. The client and server are typically 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 hosts and VPS service are overcome.
The above describes in detail a method and a system for controlling an electric power market based on a data mining model, and specific examples are applied to illustrate the principles and embodiments of the present invention, and the above examples are only used to help understand the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (10)
1. The power market control method based on the data mining model is characterized by comprising the following steps of:
acquiring historical electricity utilization data of a plurality of setting subareas, and recording the historical electricity utilization data as first historical electricity utilization data;
clustering the plurality of set subareas according to the first historical electricity consumption data to obtain a plurality of clustering areas, and recording the historical electricity consumption data of each clustering area as second historical electricity consumption data;
splicing the second historical electricity consumption data of each clustering area to obtain a spliced image;
and inputting the spliced images into a prediction model, and predicting the electricity consumption condition of the clustering area so as to realize regional fine management and control.
2. The method for controlling an electric power market based on a data mining model according to claim 1, wherein obtaining historical electricity consumption data of a plurality of setting sub-areas, recorded as first historical electricity consumption data, comprises:
wherein X is i A first historical electricity usage data for the ith set sub-region,setting first historical electricity utilization data of the jth month of the sub-region for the ith, wherein i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n, m is the total number of the set sub-regions, and n is the total month number of the historical electricity utilization data.
3. The method for controlling an electric power market based on a data mining model according to claim 1, wherein clustering the plurality of set subregions according to the first historical electricity consumption data to obtain a plurality of clustered regions, and recording the historical electricity consumption data of each clustered region as second historical electricity consumption data, comprises:
clustering the plurality of set subregions based on the first historical electricity utilization data by using a k-means clustering algorithm or an improved k-means clustering algorithm to obtain P clustering regions, wherein P is less than or equal to m;
recording the historical electricity consumption data of each clustering area as second historical electricity consumption data:
wherein Y is k For the second historical electricity usage data for the kth cluster region,setting the first historical electricity consumption data of the sub-region for the r-th of the kth cluster region,/->Setting first historical electricity consumption data of the jth month of the sub-region for the (r) th clustering region, wherein r is more than or equal to 1 and less than or equal to q, k is more than or equal to 1 and less than or equal to P, and q is the total number of the set sub-regions of the kth clustering region.
4. The method for controlling an electric power market based on a data mining model according to claim 3, wherein the step of stitching the second historical electricity consumption data of each clustered region to obtain a stitched image includes:
normalizing the second historical electricity consumption data of each clustering region to 0-255;
any adjacent three of the normalized second historical electricity consumption data in each clustering area are spliced into an array, and the normalized second historical electricity consumption data in each clustering area can repeatedly appear in different arrays;
and sequentially taking all the numerical values in each array as an R value, a G value and a B value according to the sequence, and constructing a spliced image of each clustering area.
5. The method for controlling an electric power market based on a data mining model according to any one of claims 1 to 4, wherein inputting the spliced image into a prediction model predicts the electricity consumption of the clustered region to realize regional fine control, comprising:
and inputting the target spliced image into a pre-trained prediction model, and predicting the electricity consumption condition of the target clustering region for one month in the future to obtain an electricity consumption prediction result.
6. An electric power market management and control system based on a data mining model, characterized in that it comprises:
the acquisition module is used for acquiring historical electricity utilization data of a plurality of setting subareas and recording the historical electricity utilization data as first historical electricity utilization data;
the clustering module is used for clustering the plurality of set subareas according to the first historical electricity utilization data to obtain a plurality of clustering areas, and recording the historical electricity utilization data of each clustering area as second historical electricity utilization data;
the splicing module is used for splicing the second historical electricity utilization data of each clustering area to obtain spliced images;
and the prediction module is used for inputting the spliced image into a prediction model and predicting the electricity consumption condition of the clustering area so as to realize regional fine management and control.
7. The data mining model-based power market management and control system of claim 6, wherein the acquisition module comprises:
wherein X is i A first historical electricity usage data for the ith set sub-region,setting first historical electricity utilization data of the jth month of the sub-region for the ith, wherein i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n, m is the total number of the set sub-regions, and n is the total month number of the historical electricity utilization data.
The clustering module comprises:
clustering the plurality of set subregions based on the first historical electricity utilization data by using a k-means clustering algorithm or an improved k-means clustering algorithm to obtain P clustering regions, wherein P is less than or equal to m;
recording the historical electricity consumption data of each clustering area as second historical electricity consumption data:
wherein Y is k For the second historical electricity usage data for the kth cluster region,setting the first historical electricity consumption data of the sub-region for the r-th of the kth cluster region,/->Setting first historical electricity consumption data of the jth month of the sub-region for the (r) th clustering region, wherein r is more than or equal to 1 and less than or equal to q, k is more than or equal to 1 and less than or equal to P, and q is the total number of the set sub-regions of the kth clustering region.
8. The data mining model-based power market management and control system of claim 7, wherein the stitching module comprises:
normalizing the second historical electricity consumption data of each clustering region to 0-255;
any adjacent three of the normalized second historical electricity consumption data in each clustering area are spliced into an array, and the normalized second historical electricity consumption data in each clustering area can repeatedly appear in different arrays;
and sequentially taking all the numerical values in each array as an R value, a G value and a B value according to the sequence, and constructing a spliced image of each clustering area.
9. An electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor invokes the executable program code stored in the memory for performing the data mining model-based power market management method of any one of claims 1-5.
10. A computer-readable storage medium, characterized in that it stores a computer program, wherein the computer program causes a computer to execute the data mining model-based power market management method according to any one of claims 1 to 5.
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