CN114298395B - Wind power prediction method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a wind power prediction method, a device, equipment and a storage medium. The method comprises the following steps: collecting sample data, and carrying out feature extraction on the sample data to obtain a model input feature data set; performing cluster analysis on the model input characteristic data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new characteristic into the model input characteristic data set; training a preset generalized additive model according to the model input characteristic data set to obtain a wind power prediction model; and inputting the data to be tested into the wind power prediction model to obtain the data type of the data to be tested and the wind power prediction result. According to the technical scheme provided by the embodiment of the invention, the influence of each variable on wind power is interpreted through a generalized additive model, and the accuracy of wind power prediction is effectively improved by combining cluster analysis.
Description
Technical Field
The embodiment of the invention relates to the technical field of artificial intelligence, in particular to a wind power prediction method, a wind power prediction device, wind power prediction equipment and a storage medium.
Background
In recent years, with the rapid development of the wind power industry, the installed capacity of wind power is growing on a large scale. However, the randomness and volatility of wind power generation can cause great influence on the stability of a power system after grid connection. If the wind power output can be predicted in advance for a period of time, the wind power utilization rate can be effectively improved, and the scheduling personnel can be helped to reasonably schedule the scheduling plan, so that the safe operation of the power grid is ensured. Therefore, the method has great significance in the research of short-term wind power prediction algorithms.
In addition, the wind power prediction has more influence factors and a nonlinear relation is complex, and the existing deep learning model cannot explain the nonlinear relation between the wind power and a plurality of influence factors due to the number of parameters and the complex method for extracting and combining the characteristics.
Disclosure of Invention
The embodiment of the invention provides a wind power prediction method, a device, equipment and a storage medium, which are used for realizing the explanation of the influence of various variables on wind power through a generalized additive model and effectively improving the wind power prediction precision by combining cluster analysis.
In a first aspect, an embodiment of the present invention provides a wind power prediction method, including:
collecting sample data, and extracting characteristics of the sample data to obtain a model input characteristic data set;
Performing cluster analysis on the model input characteristic data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new characteristic into the model input characteristic data set;
Training a preset generalized additive model according to a model input characteristic data set to obtain a wind power prediction model;
And inputting the data to be tested into a wind power prediction model to obtain the data type of the data to be tested and a wind power prediction result.
Optionally, the model input feature dataset comprises: an x value input by the model and a y value input by the model;
Extracting features of the sample data to obtain a model input feature data set, including:
And taking the wind speed data, the wind direction data and the air temperature data in the sample data as x values input by the model, and taking the power data in the sample data as y values input by the model.
Optionally, a k-means clustering algorithm is adopted to perform cluster analysis on the model input feature data set, and the data category obtained by clustering is added to the model input feature data set as a new feature, including:
setting a clustering category as k, and randomly selecting k model input feature data from the model input feature data set to serve as an initial clustering center;
calculating the distance from each model input characteristic data to each cluster center, and dividing each model input characteristic data into clusters corresponding to the cluster centers closest to each model input characteristic data;
calculating the average value of all model input characteristic data in each cluster, and updating the cluster center of the cluster by using the average value;
Returning to execute the step of calculating the distance from each model input characteristic data to each cluster center and dividing each model input characteristic data into clusters corresponding to the cluster center closest to the model input characteristic data until the position change of each cluster center before and after updating is smaller than a specified threshold value;
and adding the obtained data category of the feature data input by each model as a new feature into the x value input by the model.
Optionally, the distance from each model input feature data to each cluster center is euclidean distance.
Alternatively, the generalized additive model is expressed as:
yLinear＝β0+β1f1(X1)+β2f2(X2)+β3f3(X3)+β4f4(X4),
Wherein X 1 denotes a wind speed, X 2 denotes a wind direction, X 3 denotes an air temperature, X 4 denotes a data type, β k (k=1, 2,3, 4) denotes a constant parameter, f i (X) is a smoothing function, and y Linear is a power value.
Optionally, before the feature extraction is performed on the sample data, the method further includes: sample data having outliers and nulls is deleted.
Optionally, the data to be measured includes: wind speed data, wind direction data and air temperature data at the moment of the required predicted wind power.
In a second aspect, an embodiment of the present invention further provides a wind power prediction apparatus, including:
The feature extraction module is used for collecting sample data and extracting features of the sample data to obtain a model input feature data set;
The cluster analysis module is used for carrying out cluster analysis on the model input characteristic data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new characteristic into the model input characteristic data set;
The model training module is used for training a preset generalized additive model according to the model input characteristic data set to obtain a wind power prediction model;
The power prediction module is used for inputting the data to be detected into the wind power prediction model to obtain the data type of the data to be detected and the wind power prediction result.
In a third aspect, an embodiment of the present invention further provides a computer apparatus, including:
one or more processors;
storage means for storing one or more programs,
The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the wind power prediction method provided by any of the embodiments of the present invention.
In a fourth aspect, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the wind power prediction method provided by any of the embodiments of the present invention.
According to the technical scheme, the model input characteristic data set is obtained by collecting sample data and extracting the characteristics of the sample data; performing cluster analysis on the model input characteristic data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new characteristic into the model input characteristic data set; training a preset generalized additive model according to a model input characteristic data set to obtain a wind power prediction model; the wind power prediction model is used for inputting the data to be detected into the wind power prediction model to obtain the data type of the data to be detected and the wind power prediction result, so that the problem that the wind power cannot be accurately and effectively predicted in the prior art is solved, the influence of variables on the wind power is interpreted through the generalized additive model, and the wind power prediction accuracy is effectively improved by combining cluster analysis.
Drawings
FIG. 1a is a flow chart of a wind power prediction method according to a first embodiment of the present invention;
FIG. 1b is a graph showing the effect of wind speed, wind direction and air temperature on wind power in accordance with a first embodiment of the present invention;
FIG. 1c is a graph showing the effect of actual prediction in accordance with the first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a wind power prediction apparatus according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device in a third embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1a is a flowchart of a wind power prediction method according to a first embodiment of the present invention, which is applicable to predicting wind power from wind energy data to be measured, and which may be performed by a wind power prediction device, which may be implemented in hardware and/or software, and which may be generally integrated in a computer device providing wind power prediction functionality. Specifically, referring to fig. 1a, the method may comprise the steps of:
And 110, collecting sample data, and extracting the characteristics of the sample data to obtain a model input characteristic data set.
In the present embodiment, the sample data includes wind speed data, wind direction data, air temperature data, and wind power. Exemplary, the sample data may be from a fan in a regional wind farm from 2019-01-0100:00 to 2020-12-3123:45, wind speed data, wind direction data, air temperature data and power data, and the resolution of data acquisition is 15min. Wherein 2019-01-0100:00 to 2020-12-2823:45 as a training set of models, 2020-12-2900:00 to 2020-12-3123: sample data of 45 is used as a test set for the model.
Optionally, before the feature extraction of the sample data, the method may further include: sample data having outliers and nulls is deleted.
In this embodiment, in order to avoid affecting the accuracy of the subsequent training model, for the sample data in the training set, firstly, abnormal sample data with values significantly deviating from the rest values and sample data with null values are screened out, the screened abnormal sample data is deleted, and then the rest sample data is subjected to feature extraction.
Optionally, the model input feature dataset comprises: an x value input by the model and a y value input by the model; feature extraction is performed on sample data to obtain a model input feature data set, which may include: and taking the wind speed data, the wind direction data and the air temperature data in the sample data as x values input by the model, and taking the power data in the sample data as y values input by the model. That is, the model inputs a feature dataset: x= { wind speed, wind direction, air temperature }, y= { power }.
And 120, performing cluster analysis on the model input characteristic data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new characteristic into the model input characteristic data set.
In this embodiment, in order to further improve the accuracy of wind power prediction, the wind speed, the wind direction and the air temperature are used as indexes of clustering, k-means clustering is performed on sample data after feature processing, and data types of each sample data obtained by clustering are added to the sample data.
Optionally, performing cluster analysis on the model input feature data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new feature into the model input feature data set, which may include: setting a clustering category as k, and randomly selecting k model input feature data from the model input feature data set to serve as an initial clustering center; calculating the distance from each model input characteristic data to each cluster center, and dividing each model input characteristic data into clusters corresponding to the cluster centers closest to each model input characteristic data; calculating the average value of all model input characteristic data in each cluster, and updating the cluster center of the cluster by using the average value; returning to execute the step of calculating the distance from each model input characteristic data to each cluster center and dividing each model input characteristic data into clusters corresponding to the cluster center closest to the model input characteristic data until the position change of each cluster center before and after updating is smaller than a specified threshold value; and adding the obtained data category of the feature data input by each model as a new feature into the x value input by the model.
The threshold may be set to 0.0001, or may be set to another value according to the requirement. The model input characteristic data set after adding the data category is updated as follows: x= { wind speed, wind direction, air temperature, data category }, y= { power }.
Optionally, the distance from each model input feature data to each cluster center is euclidean distance. In the n-dimensional space, the formula of the Euclidean distance is:
Wherein x= (x 1,x2…xn) represents a model input feature data, y= (y 1,y2…yn) represents a cluster center, and d (x, y) represents a euclidean distance between the model input feature data x and the cluster center y.
And 130, training a preset generalized additive model according to the model input characteristic data set to obtain a wind power prediction model.
Optionally, the generalized additive model has an expression ：yLinear＝β0+β1f1(X1)+β2f2(X2)+β3f3(X3)+β4f4(X4),, where X 1 represents wind speed, X 2 represents wind direction, X 3 represents air temperature, X 4 represents data category, β k (k=1, 2,3, 4) represents constant parameter, f i (X) is a smoothing function, and y Linear is a power value.
In this embodiment, the model input feature data set after cluster update is input into the generalized additive model to train the model parameters so as to optimize the prediction capability of the model, and after the model training is completed to obtain the wind power prediction model, the test set is input into the wind power prediction model to objectively evaluate the performance of the wind power prediction model.
Illustratively, as shown in fig. 1b, wherein the abscissa represents the measured value of each explanatory variable, the ordinate represents the smooth fit value of each explanatory variable to the influence of wind power, the solid line represents the smooth fit curve of wind power, and the broken line is the 95% confidence interval. The results show that the wind speed, the wind direction and the air temperature are all nonlinear with the wind power. From the relation between wind speed and power, it can be seen that the wind speed is between 0 and 2m/s at first, the power decreases with increasing wind speed, between 2 and 5m/s, the power increases with increasing wind speed, reaches the peak at 5m/s, then starts to rise again when it starts to fall to 10m/s, starts to fall again when it starts to fall to 15m/s, and finally starts to rise when it reaches 18 m/s. From the relationship between wind direction and power, it can be seen that wind direction is between 0-200 deg., power decreases with increasing wind direction, reaches a minimum at 200 deg., and then begins to increase with increasing wind direction. From the relationship between air temperature and power, it can be seen that the air temperature is between 0 and 4 ℃, the power is reduced with the rise of air temperature, the power is increased with the rise of air temperature between 4 and 20 ℃, the maximum value is reached at 20 ℃, and then the power starts to be reduced to 30 ℃ and then starts to be increased.
As shown in fig. 1c, the prediction effect of the short-term wind power prediction is shown, the solid line is the actual value, and the broken line is the predicted value. As can be seen from fig. 1c, the wind power prediction method provided by the embodiment can better predict the power of the wind farm. The accuracy of the wind power prediction model is verified by adopting an average absolute percentage error MAPE, and the calculation method comprises the following steps: Where N is the number of samples, y i is the actual value, Is a predicted value. The average absolute percentage error is 11.43% and the prediction accuracy is high.
And 140, inputting the data to be tested into a wind power prediction model to obtain the data type of the data to be tested and the wind power prediction result.
Optionally, the data to be measured includes: wind speed data, wind direction data and air temperature data at the moment of the required predicted wind power.
According to the technical scheme, the model input characteristic data set is obtained by collecting sample data and extracting the characteristics of the sample data; performing cluster analysis on the model input characteristic data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new characteristic into the model input characteristic data set; training a preset generalized additive model according to a model input characteristic data set to obtain a wind power prediction model; the wind power prediction model is used for inputting the data to be detected into the wind power prediction model to obtain the data type of the data to be detected and the wind power prediction result, so that the problem that the wind power cannot be accurately and effectively predicted in the prior art is solved, the influence of variables on the wind power is interpreted through the generalized additive model, and the wind power prediction accuracy is effectively improved by combining cluster analysis.
Example two
Fig. 2 is a schematic structural diagram of a wind power prediction apparatus according to a second embodiment of the present invention, where the present embodiment is applicable to predicting wind power according to wind energy data to be measured, and the apparatus may be implemented by hardware and/or software, and may be generally integrated into a computer device that provides a wind power prediction function. Specifically, referring to fig. 2, the apparatus may include:
The feature extraction module 210 is configured to collect sample data, and perform feature extraction on the sample data to obtain a model input feature data set;
The cluster analysis module 220 is configured to perform cluster analysis on the model input feature data set by adopting a k-means clustering algorithm, and add the data category obtained by clustering as a new feature into the model input feature data set;
The model training module 230 is configured to train a preset generalized additive model according to a model input feature data set to obtain a wind power prediction model;
the power prediction module 240 is configured to input the data to be tested into the wind power prediction model to obtain a data type of the data to be tested and a prediction result of the wind power.
According to the technical scheme, the model input characteristic data set is obtained by collecting sample data and extracting the characteristics of the sample data; performing cluster analysis on the model input characteristic data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new characteristic into the model input characteristic data set; training a preset generalized additive model according to a model input characteristic data set to obtain a wind power prediction model; the wind power prediction model is used for inputting the data to be detected into the wind power prediction model to obtain the data type of the data to be detected and the wind power prediction result, so that the problem that the wind power cannot be accurately and effectively predicted in the prior art is solved, the influence of variables on the wind power is interpreted through the generalized additive model, and the wind power prediction accuracy is effectively improved by combining cluster analysis.
Optionally, the model input feature dataset comprises: an x value input by the model and a y value input by the model; the feature extraction module 210 is configured to: and taking the wind speed data, the wind direction data and the air temperature data in the sample data as x values input by the model, and taking the power data in the sample data as y values input by the model.
Optionally, the cluster analysis module 220 is configured to:
setting a clustering category as k, and randomly selecting k model input feature data from the model input feature data set to serve as an initial clustering center;
calculating the distance from each model input characteristic data to each cluster center, and dividing each model input characteristic data into clusters corresponding to the cluster centers closest to each model input characteristic data;
calculating the average value of all model input characteristic data in each cluster, and updating the cluster center of the cluster by using the average value;
Returning to execute the step of calculating the distance from each model input characteristic data to each cluster center and dividing each model input characteristic data into clusters corresponding to the cluster center closest to the model input characteristic data until the position change of each cluster center before and after updating is smaller than a specified threshold value;
and adding the obtained data category of the feature data input by each model as a new feature into the x value input by the model.
Optionally, the distance from each model input feature data to each cluster center is euclidean distance.
Alternatively, the generalized additive model is expressed as:
yLinear＝β0+β1f1(X1)+β2f2(X2)+β3f3(X3)+β4f4(X4),
Wherein X 1 denotes a wind speed, X 2 denotes a wind direction, X 3 denotes an air temperature, X 4 denotes a data type, β k (k=1, 2,3, 4) denotes a constant parameter, f i (X) is a smoothing function, and y Linear is a power value.
Optionally, the method further comprises: and the preprocessing module is used for deleting the sample data with abnormal values and null values before the characteristic extraction of the sample data.
Optionally, the data to be measured includes: wind speed data, wind direction data and air temperature data at the moment of the required predicted wind power.
The wind power prediction device provided by the embodiment of the invention can execute the wind power prediction method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example III
Fig. 3 is a schematic structural diagram of a computer device in a third embodiment of the present invention. Fig. 3 illustrates a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 3 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 3, device 12 is in the form of a general purpose computing device. Components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, commonly referred to as a "hard disk drive"). Although not shown in fig. 3, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
Device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with device 12, and/or any devices (e.g., network card, modem, etc.) that enable device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, device 12 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, via network adapter 20. As shown, network adapter 20 communicates with other modules of device 12 over bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, to implement the wind power prediction method provided by the embodiment of the present invention.
Namely: a wind power prediction method is realized, which comprises the following steps:
collecting sample data, and extracting characteristics of the sample data to obtain a model input characteristic data set;
Performing cluster analysis on the model input characteristic data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new characteristic into the model input characteristic data set;
Training a preset generalized additive model according to a model input characteristic data set to obtain a wind power prediction model;
And inputting the data to be tested into a wind power prediction model to obtain the data type of the data to be tested and a wind power prediction result.
Example IV
The fourth embodiment of the present invention also discloses a computer storage medium having stored thereon a computer program which when executed by a processor implements a wind power prediction method comprising:
collecting sample data, and extracting characteristics of the sample data to obtain a model input characteristic data set;
Performing cluster analysis on the model input characteristic data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new characteristic into the model input characteristic data set;
Training a preset generalized additive model according to a model input characteristic data set to obtain a wind power prediction model;
And inputting the data to be tested into a wind power prediction model to obtain the data type of the data to be tested and a wind power prediction result.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (6)
1. A method of wind power prediction, comprising:
Collecting sample data, and carrying out feature extraction on the sample data to obtain a model input feature data set, wherein the sample data comprises wind speed data, wind direction data, air temperature data and wind power;
Performing cluster analysis on the model input characteristic data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new characteristic into the model input characteristic data set;
Training a preset generalized additive model according to the model input characteristic data set to obtain a wind power prediction model;
Inputting the data to be tested into the wind power prediction model to obtain the data type of the data to be tested and the wind power prediction result, wherein the data to be tested comprises: wind speed data, wind direction data and air temperature data at the moment of the wind power to be predicted;
The model input feature dataset comprising: an x value input by the model and a y value input by the model;
Extracting features of the sample data to obtain a model input feature data set, including:
taking wind speed data, wind direction data and air temperature data in the sample data as x values input by a model, and taking power data in the sample data as y values input by the model;
The method for clustering the model input feature data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new feature into the model input feature data set comprises the following steps:
setting a clustering category as k, and randomly selecting k model input feature data from the model input feature data set to serve as an initial clustering center;
calculating the distance from each model input characteristic data to each cluster center, and dividing each model input characteristic data into clusters corresponding to the cluster centers closest to each model input characteristic data;
calculating the average value of all model input characteristic data in each cluster, and updating the cluster center of the cluster by using the average value;
Returning to execute the step of calculating the distance from each model input characteristic data to each cluster center and dividing each model input characteristic data into clusters corresponding to the cluster center closest to the model input characteristic data until the position change of each cluster center before and after updating is smaller than a specified threshold value;
taking the data category of each obtained model input characteristic data as a new characteristic, and adding the new characteristic into the x value input by the model;
The generalized additive model has the expression:
yLinear＝β0+β1f1(X1)+β2f2(X2)+β3f3(X3)+β4f4(X4);
Wherein X 1 denotes a wind speed, X 2 denotes a wind direction, X 3 denotes an air temperature, X 4 denotes a data type, β k (k=1, 2,3, 4) denotes a constant parameter, f i (X is a smoothing function, and y Linear is a power value.
2. The method of claim 1, wherein the distance from each model input feature data to the respective cluster center is a euclidean distance.
3. The method of claim 1, further comprising, prior to feature extraction of the sample data: sample data having outliers and nulls is deleted.
4. A wind power prediction apparatus, comprising:
The characteristic extraction module is used for collecting sample data and extracting characteristics of the sample data to obtain a model input characteristic data set, wherein the sample data comprises wind speed data, wind direction data, air temperature data and wind power;
The clustering analysis module is used for carrying out clustering analysis on the model input characteristic data set by adopting a k-means clustering algorithm, and adding the data category obtained by clustering as a new characteristic into the model input characteristic data set;
The model training module is used for training a preset generalized additive model according to the model input characteristic data set to obtain a wind power prediction model; the generalized additive model has the expression:
yLinear＝β0+β1f1(X1)+β2f2(X2)+β3f3(X3)+β4f4(X4);
Wherein X 1 denotes a wind speed, X 2 denotes a wind direction, X 3 denotes an air temperature, X 4 denotes a data type, β k (k=1, 2,3, 4) denotes a constant parameter, f i (X is a smoothing function, y Linear is a power value;
The power prediction module is used for inputting data to be detected into the wind power prediction model to obtain the data type of the data to be detected and the prediction result of the wind power, wherein the data to be detected comprises: wind speed data, wind direction data and air temperature data at the moment of the wind power to be predicted;
The model input feature dataset comprising: an x value input by the model and a y value input by the model;
The characteristic extraction module is used for taking wind speed data, wind direction data and air temperature data in the sample data as x values input by a model, and taking power data in the sample data as y values input by the model;
The cluster analysis module is used for setting the cluster category as k, and randomly selecting k model input feature data from the model input feature data set to serve as an initial cluster center; calculating the distance from each model input characteristic data to each cluster center, and dividing each model input characteristic data into clusters corresponding to the cluster centers closest to each model input characteristic data; calculating the average value of all model input characteristic data in each cluster, and updating the cluster center of the cluster by using the average value; returning to execute the step of calculating the distance from each model input characteristic data to each cluster center and dividing each model input characteristic data into clusters corresponding to the cluster center closest to the model input characteristic data until the position change of each cluster center before and after updating is smaller than a specified threshold value; and adding the obtained data category of the feature data input by each model as a new feature into the x value input by the model.
5. A computer device, the device comprising:
one or more processors;
storage means for storing one or more programs,
The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the wind power prediction method of any of claims 1-3.
6. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements a wind power prediction method according to any one of claims 1-3.
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| CN104299044A (en) * | 2014-07-01 | 2015-01-21 | 沈阳工程学院 | Clustering-analysis-based wind power short-term prediction system and prediction method |
| CN111428181A (en) * | 2020-04-08 | 2020-07-17 | 深圳索信达数据技术有限公司 | Bank financing product recommendation method based on generalized additive model and matrix decomposition |
| CN113449920A (en) * | 2021-06-30 | 2021-09-28 | 上海电机学院 | Wind power prediction method, system and computer readable medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104299044A (en) * | 2014-07-01 | 2015-01-21 | 沈阳工程学院 | Clustering-analysis-based wind power short-term prediction system and prediction method |
| CN111428181A (en) * | 2020-04-08 | 2020-07-17 | 深圳索信达数据技术有限公司 | Bank financing product recommendation method based on generalized additive model and matrix decomposition |
| CN113449920A (en) * | 2021-06-30 | 2021-09-28 | 上海电机学院 | Wind power prediction method, system and computer readable medium |
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