CN117556239A

CN117556239A - Bidding clear data characteristic extraction and analysis method, system, chip and equipment

Info

Publication number: CN117556239A
Application number: CN202311488613.XA
Authority: CN
Inventors: 袁沐琛; 丁强; 蔡帜; 程雪婷; 邹鹏; 薄利明; 陈丹阳; 崔校瑞; 刘新元; 张超; 郑惠萍
Original assignee: State Grid Electric Power Research Institute Of Sepc; China Electric Power Research Institute Co Ltd CEPRI
Current assignee: State Grid Electric Power Research Institute Of Sepc; China Electric Power Research Institute Co Ltd CEPRI
Priority date: 2023-11-09
Filing date: 2023-11-09
Publication date: 2024-02-13

Abstract

The invention discloses a bid clear data feature extraction and analysis method, a bid clear data feature extraction and analysis system, a bid clear data feature extraction and analysis chip and bid clear data feature extraction and analysis equipment, which are used for preprocessing historical transaction data of an electric power market and converting the historical transaction data into an n-dimensional vector form; and extracting characteristics of n-dimensional vector form power market historical transaction data by adopting a K-Medoids clustering algorithm, calculating DB indexes corresponding to each K value to obtain the final clustered K value, and generating a quotation curve of the unit by taking the central point of the obtained K value as a typical quotation curve to realize characteristic extraction of bidding clear data.

Description

Bidding clear data characteristic extraction and analysis method, system, chip and equipment

Technical Field

The invention belongs to the technical field of electric power markets, and particularly relates to a bid clear data feature extraction and analysis method, a bid clear data feature extraction and analysis system, a bid clear data feature extraction and analysis chip and bid clear data feature extraction and analysis equipment.

Background

The K-Medoids algorithm, also called the K-center point clustering algorithm, differs from the K-means by: the K-means algorithm does not use the average value of the objects in the cluster as a reference point, but rather selects the object located at the center most in the cluster, i.e., the center point, as the reference point. Specifically:

firstly, randomly selecting a representative object for each cluster; the remaining objects are assigned to the nearest cluster according to their distance from the representative object; the representative object is then repeatedly replaced with a non-representative object to improve the quality of the clusters; the quality of the clustering result is estimated using a cost function that evaluates the average degree of dissimilarity between an object and its reference object.

The steps of the K-medoids algorithm are as follows:

1) Arbitrarily selecting k points as representative objects;

2) According to the principle of nearest to the representative object, the rest points are distributed to the current best cluster of the representative object;

3) In each class, calculating a criterion function corresponding to each member point, and selecting a point corresponding to the minimum criterion function as a new representative object; the criterion function is the sum of the distances between a certain member point and other member points in the class;

4) The process of 2) -3) is repeated until all representative objects no longer change or the set maximum number of iterations has been reached.

The unit quotation curve is taken as the basis of bidding clearing, and the accuracy of the unit quotation curve is very important to the research of the field of bidding clearing of the electric power market. The existing clustering method has limited precision and cannot adapt to the research precision requirement.

Disclosure of Invention

The invention aims to solve the technical problems of solving the technical problems that effective information of historical transaction data cannot be fully considered in the existing reinforcement learning bidding simulation method, and improving the precision of the simulation method.

The invention adopts the following technical scheme:

a bid clear data feature extraction and analysis method comprises the following steps:

converting the historical transaction data of the electric power market into an n-dimensional vector form;

extracting characteristics of n-dimensional vector form power market historical transaction data by adopting a K-Medoids clustering algorithm, calculating DB indexes corresponding to all K values to obtain final clustered K values, and generating a quotation curve of the unit by taking the central point of the obtained K values as a typical quotation curve for bidding clear simulation.

Specifically, preprocessing historical transaction data of the electric power market includes:

when the capacity of the quotation curve is lower than the maximum capacity of all data of the unitDeleting corresponding data, unifying the declared capacity, extending the quotation curve to the maximum capacity, and setting the newly added price to be k times of the declared maximum price, wherein k is more than 1;

and sampling quotation curves of all the units, wherein each vector is one sample, and an n-dimensional vector is obtained.

Further, the number of samples for cluster analysis is twice the number of clusters.

Further, converting the quotation curve into an n-dimensional vector is specifically:

will output the intervalEqually divided into n sections, each section having a length +.>And taking the price corresponding to each segment to form an n-dimensional vector.

Specifically, K is clustered once within the range of 1 to 15 in the characteristics of the n-dimensional vector form power market historical transaction data extracted by adopting a K-Medoids clustering algorithm.

Further, the K value with the smallest DB index is selected as the K value of the final cluster.

Further, the probability that a typical quotation curve is selected is the ratio of the number of sample points to the number of all sample points in the cluster in which the representative quotation curve is located, and a roulette selection method is adopted to generate the quotation curve of the unit.

In a second aspect, an embodiment of the present invention provides a bid out clear data feature extraction analysis system, including:

the preprocessing module is used for preprocessing the historical transaction data of the electric power market and converting the historical transaction data into an n-dimensional vector form;

and the extraction and analysis module is used for extracting the characteristics of the n-dimensional vector form power market historical transaction data by adopting a K-Medoids clustering algorithm, calculating DB indexes corresponding to each K value to obtain the final clustered K value, and generating a quotation curve of the unit by taking the central point of the obtained K value as a typical quotation curve for bidding clear simulation.

Specifically, in the preprocessing module, preprocessing the historical transaction data of the electric power market specifically includes:

when the capacity of the quotation curve is lower than the maximum capacity Q of all data of the unit _i ^max Deleting corresponding data, unifying the declared capacity, extending the quotation curve to the maximum capacity, and setting the newly added price to be k times of the declared maximum price, wherein k is more than 1;

Specifically, the extraction analysis module is specifically configured to:

and (3) extracting characteristics of the n-dimensional vector form power market historical transaction data by adopting a K-Medoids clustering algorithm, and clustering K once within the range of 1 to 15.

In a third aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the bid out data feature extraction analysis method described above when executing the computer program.

In a fourth aspect, an embodiment of the present invention provides an electronic device, including a computer program, where the computer program when executed by the electronic device implements the steps of the above bid out clear data feature extraction analysis method.

Compared with the prior art, the invention has at least the following beneficial effects:

firstly, preprocessing historical transaction data and converting the historical transaction data into a vector form; and then, the K-Medoids clustering algorithm is adopted to analyze and extract the historical transaction data characteristics of the power market, so that the method can be matched with the existing quotation simulation method, and the problem that effective information in the historical transaction data cannot be fully considered in the research in the existing bidding clear simulation method is solved.

Further, the K-center point algorithm (K-means) solves the problem that the traditional K-means algorithm is sensitive to noise, so that the reliability of a calculation result is improved.

It will be appreciated that the advantages of the second to fourth aspects may be found in the relevant description of the first aspect and are not repeated here.

In summary, the invention firstly adopts a clustering method to extract the characteristics of the unit bid clear data, and solves the problem that the research in the existing bid clear simulation method can not fully consider the effective information in the historical transaction data. Secondly, by improving the traditional K-means clustering method, the accuracy of the result is improved.

The technical scheme of the invention is further described in detail through the drawings and the embodiments.

Drawings

FIG. 1 is a flow chart of the method of the present invention;

FIG. 2 is the declared capacity of the unit 1 within one year;

FIG. 3 is a diagram of a unified reporting capacity;

FIG. 4 is a schematic diagram of a computer device according to an embodiment of the present invention;

fig. 5 is a block diagram of a chip according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. 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.

In the description of the present invention, it will be understood that the terms "comprises" and "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In the present invention, the character "/" generally indicates that the front and rear related objects are an or relationship.

It should be understood that although the terms first, second, third, etc. may be used to describe the preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish one preset range from another. For example, a first preset range may also be referred to as a second preset range, and similarly, a second preset range may also be referred to as a first preset range without departing from the scope of embodiments of the present invention.

Depending on the context, the word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection". Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.

Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. The figures are not drawn to scale, wherein certain details are exaggerated for clarity of presentation and may have been omitted. The shapes of the various regions, layers and their relative sizes, positional relationships shown in the drawings are merely exemplary, may in practice deviate due to manufacturing tolerances or technical limitations, and one skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions as actually required.

The data feature extraction refers to a process of extracting effective information from original data. It involves identifying various features from raw data and using those features in a machine learning algorithm to create a model with predictive or classification functionality. In the data preprocessing process, feature extraction plays a very important role, and can directly influence the performance of the model.

There are many methods for extracting data features, one of which is based on a statistical method, that is, original data is scanned by using a statistical method, and then features of interest and correlations between the features are obtained according to a statistical result; another type of feature extraction method is machine learning based feature extraction, which uses algorithms to analyze raw data to mine features with certain characteristics.

In addition, the feature extraction can also extract features from manually designed features according to business requirements. This feature extraction, also known as "feature selection" or "feature preprocessing", can reduce feature dimensions, reduce run time, and improve model performance. In summary, feature extraction is a process of extracting effective information from raw data, and further can be used for application program development based on artificial intelligence algorithms. By applying the method, the accuracy of the model can be remarkably improved.

The invention provides a bid clear data characteristic extraction and analysis method, which comprises the steps of firstly preprocessing declaration data of a unit in an electric power market, and converting the declaration data of a multi-section price combination team into a vector form; then clustering the processed quotation curves, and taking the central points of all clusters obtained by clustering as typical quotation curves of the unit, wherein the input of a clustering algorithm is an n-dimensional vector; and finally, sampling from a typical quotation curve of the unit to obtain spot market declaration data of the unit, wherein the spot market declaration data is used as input data in an electric power market bidding clearing simulation method, and the problem that effective information in historical transaction data cannot be fully considered in research in the existing bidding clearing simulation method is solved.

Referring to fig. 1, the invention discloses a bid clear data feature extraction and analysis method, which comprises the following steps:

s1, data preprocessing

S101, screening effective data

Referring to fig. 2, considering the capacity (maximum output) declared by the same unit in one year, it can be seen that the capacity (maximum output) declared by the same unit in different periods is not necessarily the same, and for maintenance or other reasons, the capacity declared by some periods is significantly lower than the normal level, which cannot represent the bidding strategy of the generator, and therefore these data need to be deleted. When the capacity of the quotation curve is lower than the maximum capacity Q of all data of the unit _i ^max The group of data is deleted. If the data volume of the unit is smaller, the unit is not clustered.

Unified reporting capacity: the declared capacities of the same unit in different time periods may be different, and for better clustering effect, the quotation curve is prolonged to the maximum capacity, and the newly added price of the section is set to be k (k is more than 1) times of the declared maximum price.

Referring to fig. 3, to prolong the quotation curve of the unit i at t,for the capacity of the quotation curve at this point, +.>For maximum price declared +.>For the historical maximum capacity, the dark part is the declaration curve at the moment, the light part is an extended section, and the corresponding price is +>

S102, sampling segmentation price: the quotation curve is converted into an n-dimensional vector by sampling the quotation curve, and the concrete steps are as follows:

The data of all the units are sampled, and each vector is one sample.

S2, self-adaptive clustering

S201, calculating Wasserstein distances between sample points, clustering the data by adopting a K-Medoids clustering algorithm, wherein K is the number of clusters obtained after clustering. Since the proper K value of each unit is not known, the K value is not directly set during clustering, and K is clustered once within the range of 1 to 15;

s202, calculating DB indexes corresponding to the K values, wherein the smaller the DB indexes are, the better the clustering effect is indicated, so that the K value with the smallest DB index is selected as the K value of the final cluster.

Taking the central points of K clusters obtained by clustering as typical quotation curves, selecting from the K typical quotation curves by adopting a roulette selection method so as to generate the quotation curves of the unit, wherein the probability of selecting the typical quotation curves is the ratio of the number of sample points to the number of all sample points in the cluster where the typical quotation curves are located.

In still another embodiment of the present invention, a bid clear data feature extraction and analysis system is provided, which can be used to implement the above bid clear data feature extraction and analysis method, and specifically, the bid clear data feature extraction and analysis system includes a preprocessing module and an extraction and analysis module.

The preprocessing module is used for preprocessing historical transaction data of the electric power market and converting the historical transaction data into an n-dimensional vector form;

the preprocessing of the historical transaction data of the electric power market comprises the following steps:

Wherein the number of samples for cluster analysis is twice the number of clusters; the conversion of the quotation curve into an n-dimensional vector is specifically:

Extracting characteristics of n-dimensional vector form power market historical transaction data by adopting a K-Medoids clustering algorithm, clustering K once within a range of 1-15, selecting a K value with the smallest DB index as a K value of a final cluster, wherein the probability of a typical quotation curve being selected is the ratio of the number of sample points to the number of all sample points in the cluster where the typical quotation curve is located, and generating the quotation curve of the unit by adopting a roulette selection method.

In yet another embodiment of the present invention, a terminal device is provided, the terminal device including a processor and a memory, the memory for storing a computer program, the computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular to load and execute one or more instructions to implement the corresponding method flow or corresponding functions; the processor of the embodiment of the invention can be used for bidding out the operation of the clear data characteristic extraction analysis method, which comprises the following steps:

preprocessing historical transaction data of the electric power market, and converting the historical transaction data into an n-dimensional vector form; extracting characteristics of n-dimensional vector form power market historical transaction data by adopting a K-Medoids clustering algorithm, calculating DB indexes corresponding to all K values to obtain final clustered K values, and generating a quotation curve of the unit by taking the central point of the obtained K values as a typical quotation curve for bidding clear simulation.

Referring to fig. 4, the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61, the computer program 63 when executed by the processor 61 implements the reservoir inversion wellbore fluid composition calculation method of the embodiment, and is not described in detail herein to avoid repetition. Alternatively, the computer program 63, when executed by the processor 61, performs the functions of the models/units in the exemplary bid clear data feature extraction analysis system, and is not described in detail herein to avoid redundancy.

The computer device 60 may be a desktop computer, a notebook computer, a palm top computer, a cloud server, or the like. Computer device 60 may include, but is not limited to, a processor 61, a memory 62. It will be appreciated by those skilled in the art that fig. 4 is merely an example of a computer device 60 and is not intended to limit the computer device 60, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., a computer device may also include an input-output device, a network access device, a bus, etc.

The processor 61 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk provided on the computer device 60, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like.

Further, the memory 62 may also include both internal storage units and external storage devices of the computer device 60. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

Referring to fig. 5, the terminal device is a chip, and the chip 600 of this embodiment includes a processor 622, which may be one or more in number, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in memory 632 may include one or more modules each corresponding to a set of instructions. Further, the processor 622 may be configured to execute the computer program to perform the bid clear data feature extraction analysis method described above.

In addition, chip 600 may further include a power supply component 626 and a communication component 650, where power supply component 626 may be configured to perform power management of chip 600, and communication component 650 may be configured to enable communication of chip 600, e.g., wired or wireless communication. In addition, the chip 600 may also include an input/output (I/O) interface 658. Chip 600 may operate based on an operating system stored in memory 632.

In a further embodiment of the present invention, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a terminal device, for storing programs and data. It will be appreciated that the computer readable storage medium herein may include both a built-in storage medium in the terminal device and an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium may be a high-speed RAM Memory or a Non-Volatile Memory (Non-Volatile Memory), such as at least one magnetic disk Memory.

One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the respective steps of the method for extracting and analyzing characteristics of bid amount data in the above-described embodiments; one or more instructions in a computer-readable storage medium are loaded by a processor and perform the steps of:

In summary, according to the bid clear data feature extraction and analysis method, the system, the chip and the device, the bid clear data feature of the unit is extracted by adopting the clustering method, the problem that effective information in historical transaction data cannot be fully considered in the research of the existing bid clear simulation method is solved, and the accuracy of the result is improved by improving the traditional K-means clustering method.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a usb disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random-Access Memory (RAM), an electrical carrier wave signal, a telecommunications signal, a software distribution medium, etc., it should be noted that the content of the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in jurisdictions, such as in some jurisdictions, according to the legislation and patent practice, the computer readable medium does not include electrical carrier wave signals and telecommunications signals.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims

1. The bid clear data characteristic extraction and analysis method is characterized by comprising the following steps of:

2. The bid clearance data feature extraction and analysis method of claim 1, wherein the power market history transaction data preprocessing is specifically:

3. The bid out of clear data feature extraction analysis method of claim 2, wherein the number of samples of the cluster analysis is twice the number of clusters.

4. The bid out data feature extraction analysis method of claim 2, wherein converting the bid curve into an n-dimensional vector is specifically:

5. The bid amount clearing data feature extraction and analysis method according to claim 1, wherein the feature of the n-dimensional vector form power market history transaction data is extracted by adopting a K-media clustering algorithm, and K is clustered once in a range of 1 to 15.

6. The bid out of clear data feature extraction analysis method of claim 5, wherein the K value with the smallest DB index is selected as the K value of the final cluster.

7. The method of claim 5, wherein the probability of a typical bid curve being selected is a ratio of the number of sample points in the cluster to the number of all sample points in the cluster, and wherein the bid curve for the group is generated using a roulette selection method.

8. A bid clearance data feature extraction analysis system, comprising:

9. The bid amount feature extraction and analysis system of claim 8, wherein in the preprocessing module, the preprocessing of the power market history transaction data is specifically:

10. The bid out of clear data feature extraction analysis system of claim 9, wherein the number of samples of the cluster analysis is twice the number of clusters.

11. The bid clearance data feature extraction analysis system of claim 9, wherein converting the bid curve into an n-dimensional vector is embodied by:

12. The bid clearance data feature extraction analysis system of claim 8, wherein the extraction analysis module is specifically configured to:

13. The bid out of clear data feature extraction analysis system of claim 12, wherein the K value with the smallest DB index is selected as the K value of the final cluster.

14. The bid out of clear data feature extraction analysis system of claim 12, wherein the probability that a typical bid curve is selected is a ratio of the number of sample points in the cluster in which it is located to the number of all sample points, and wherein a roulette selection method is used to generate the bid curve for the group.

15. A chip is characterized in that,

a memory having a computer program stored thereon;

a processor for executing the computer program in the memory to implement the steps of the method of any one of claims 1-7.

16. An electronic device, characterized in that,

comprising a chip as claimed in claim 15.