A method for determining the probability density distribution of wind power output fluctuation and a storage medium thereof

Abstract

The present invention relates to a method for determining the probability density distribution of wind power output fluctuations and a storage medium, wherein the method comprises: obtaining the wind power output fluctuation of a wind farm group at the current moment, a sample of the wind power output fluctuation of the wind farm group within a preset time period, and a kernel density estimation function of the sample; dividing the interval corresponding to the sample into k subsample intervals, and determining the adaptive bandwidth corresponding to each subsample interval under an empirical method and the adaptive bandwidth corresponding to an unbiased cross-validation method; determining a hybrid adaptive kernel density estimation model corresponding to the sample; and using the model to determine the probability density distribution corresponding to the wind power output fluctuation of the wind farm group at the current moment. The technical solution provided by the present invention determines the probability density distribution corresponding to the wind power output fluctuation of the wind farm group at the current moment based on the hybrid adaptive kernel density estimation model, thereby improving the accuracy of probability density determination and having strong adaptability.

Description

Technical Field

[0001] The present invention relates to the field of probability density distribution, and in particular to a method for determining the probability density distribution of wind power output fluctuation and a storage medium. Background Art

[0002] With the depletion of global fossil fuels and the advent of the dual-carbon strategy, the utilization of renewable energy has garnered widespread attention. Wind energy, thanks to its low cost and cleanliness, has been widely adopted and developed worldwide. By the end of 2021, the cumulative grid-connected installed capacity of wind power reached approximately 350 million kilowatts, with 47.57 million kilowatts newly connected, a year-on-year increase of 13.59%. However, due to the volatility and intermittent nature of wind power, the continued growth of wind power access has exacerbated the difficulties faced by power grids in dispatching wind power, making accurate characterization of wind power output volatility particularly important.

[0003] In existing technology, statistically based probability density distribution research of wind power output fluctuations has been widely used. Methods for fitting the probability distribution of wind power output fluctuations are mainly divided into parametric and non-parametric methods, depending on whether they rely on the choice of parameter estimation model. However, existing methods have low accuracy and poor adaptability when determining probability density. Summary of the Invention

[0004] The present application provides a method for determining the probability density distribution of wind power output fluctuation and a storage medium, so as to at least solve the technical problems in the related art of low accuracy and poor adaptability when determining probability density.

[0005] The first embodiment of the present application provides a method for determining a probability density distribution of wind power output fluctuations, the method comprising:

[0006] Obtaining a wind power output fluctuation amount of a wind farm group at a current moment, a wind power output fluctuation amount sample of the wind farm group within a preset time period, and a kernel density estimation function of the sample;

[0007] The interval corresponding to the sample is divided into sub-sample intervals, and determine the adaptive bandwidth corresponding to each sub-sample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method;

[0008] Determine the hybrid adaptive kernel density estimation model corresponding to the sample according to the adaptive bandwidth corresponding to each sub-sample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method;

[0009] The hybrid adaptive kernel density estimation model is used to determine the probability density distribution corresponding to the wind power output fluctuation of the wind farm group at the current moment.

[0010] Preferably, the calculation formula of the kernel density estimation function of the wind power output fluctuation quantity is as follows:

[0011]

[0012] Where, is the kernel density estimation function of wind power output fluctuation, is the number of wind power output fluctuations in the sample, For a fixed bandwidth, is the wind power output fluctuation of the wind farm group at the current moment, is the value of the mth wind power output fluctuation in the sample.

[0013] Preferably, the determining of the adaptive bandwidth corresponding to each subsample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method includes:

[0014] Determine whether the goodness of fit of each subsample interval is greater than a preset goodness of fit threshold;

[0015] Each subsample interval whose goodness of fit is greater than the preset goodness of fit threshold is screened out, and the adaptive bandwidth corresponding to each screened subsample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method are determined.

[0016] Furthermore, before determining whether the goodness of fit of each subsample interval is greater than a preset goodness of fit threshold, the method further includes:

[0017] The fixed bandwidth corresponding to the kernel density estimation function of the wind power output fluctuation quantity under the empirical method and the fixed bandwidth corresponding to the unbiased cross-validation method are determined respectively by using the empirical method and the unbiased cross-validation method.

[0018] Furthermore, when the goodness of fit of the sub-sample interval is less than or equal to a preset goodness of fit threshold, the fixed bandwidth corresponding to the kernel density estimation function of the wind power output fluctuation under the empirical method is used as the adaptive bandwidth corresponding to the sub-sample interval under the empirical method, and the fixed bandwidth corresponding to the kernel density estimation function of the wind power output fluctuation under the unbiased cross-validation method is used as the adaptive bandwidth corresponding to the sub-sample interval under the unbiased cross-validation method.

[0019] Furthermore, the calculation formula of the adaptive bandwidth corresponding to the sub-sample interval under the empirical method is as follows:

[0020]

[0021] Where, For the The adaptive bandwidth corresponding to the sub-sample interval under the empirical method is, For the The bandwidth factor corresponding to the sub-sample interval under the empirical method is, is the fixed bandwidth corresponding to the kernel density estimation function of wind power output fluctuation under the empirical method.

[0022] Preferably, the determining of the hybrid adaptive kernel density estimation model corresponding to the sample according to the adaptive bandwidth corresponding to the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method includes:

[0023] Determine a first adaptive kernel density estimation function corresponding to the sample based on the adaptive bandwidth corresponding to the empirical method;

[0024] Determining a second adaptive kernel density estimation function corresponding to the sample based on the corresponding adaptive bandwidth under an unbiased cross-validation method;

[0025] The first adaptive kernel density estimation function and the second adaptive kernel density estimation function are combined to obtain a hybrid adaptive kernel density estimation model corresponding to the sample.

[0026] Furthermore, the calculation formula of the hybrid adaptive kernel density estimation model corresponding to the sample is as follows:

[0027]

[0028] Where, is a hybrid adaptive kernel density estimation model, is the first adaptive kernel density estimation function, is the weight coefficient of the first adaptive kernel density estimation function, is the second adaptive kernel density estimation function, is the weight coefficient of the second adaptive kernel density estimation function, where .

[0029] Furthermore, the calculation formula of the first adaptive kernel density estimation function is as follows:

[0030]

[0031] Where, is the first adaptive kernel density estimation function, For the The adaptive bandwidth corresponding to the sub-sample interval under the empirical method is, is the value of the g-th wind power output fluctuation in the subsample, is the number of wind power output fluctuations in the sample, is the fixed bandwidth corresponding to the kernel density estimation function of wind power output fluctuation under the empirical method.

[0032] The second embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the prediction method of the first aspect of the present application is implemented.

[0033] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0034] The present invention provides a method for determining the probability density distribution of wind power output fluctuation and a storage medium, wherein the method comprises: obtaining the wind power output fluctuation of a wind farm group at the current moment, a sample of the wind power output fluctuation of the wind farm group within a preset time period, and a kernel density estimation function of the sample; dividing the interval corresponding to the sample into sub-sample intervals, and determine the adaptive bandwidth corresponding to each sub-sample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method; determine the hybrid adaptive kernel density estimation model corresponding to the sample based on the adaptive bandwidth corresponding to each sub-sample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method; use the hybrid adaptive kernel density estimation model to determine the probability density distribution corresponding to the wind power output fluctuation of the wind farm group at the current moment. The technical solution provided by the present invention determines the adaptive bandwidth corresponding to each sub-sample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method, and determines the hybrid adaptive kernel density estimation model corresponding to the sample based on the adaptive bandwidth, and then determines the probability density distribution corresponding to the wind power output fluctuation of the wind farm group at the current moment, thereby improving the accuracy of probability density determination and having strong adaptability.

[0035] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0037] Figure 1 This is a flow chart of a method for determining the probability density distribution of wind power output fluctuations provided according to one embodiment of the present application;

[0038] Figure 2 A wind power output fluctuation momentum diagram of a wind farm group within a preset time period provided according to one embodiment of the present application;

[0039] Figure 3 This is a flow chart of the acquisition process of a hybrid adaptive kernel density estimation model provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0040] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0041] The present application proposes a method for determining the probability density distribution of wind power output fluctuation and a storage medium, wherein the method comprises: obtaining the wind power output fluctuation of the wind farm group at the current moment, the wind power output fluctuation sample of the wind farm group within a preset time period and the kernel density estimation function of the sample; dividing the interval corresponding to the sample into sub-sample intervals, and determine the adaptive bandwidth corresponding to each sub-sample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method; determine the hybrid adaptive kernel density estimation model corresponding to the sample based on the adaptive bandwidth corresponding to each sub-sample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method; use the hybrid adaptive kernel density estimation model to determine the probability density distribution corresponding to the wind power output fluctuation of the wind farm group at the current moment. The technical solution provided by the present invention determines the adaptive bandwidth corresponding to each sub-sample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method, and determines the hybrid adaptive kernel density estimation model corresponding to the sample based on the adaptive bandwidth, and then determines the probability density distribution corresponding to the wind power output fluctuation of the wind farm group at the current moment, thereby improving the accuracy of probability density determination and having strong adaptability.

[0042] The following describes a method for determining a probability density distribution of wind power output fluctuations and a storage medium according to an embodiment of the present application with reference to the accompanying drawings.

[0043] Example 1

[0044] Figure 1 A flow chart of a method for determining the probability density distribution of wind power output fluctuation provided by an embodiment of the present disclosure, such as Figure 1 As shown, the method includes:

[0045] Step 1: Obtain the wind power output fluctuation of the wind farm group at the current moment, the wind power output fluctuation samples of the wind farm group within a preset time period, and the kernel density estimation function of the samples;

[0046] It should be noted that the calculation formula for the wind power fluctuation amount can be: , is the wind power output fluctuation of the wind farm group corresponding to the current time t, is the wind power output of the wind farm group corresponding to the previous time t, is the wind power output of the wind farm group corresponding to time t-1.

[0047] Further, such as Figure 2 As shown in FIG, it is the wind power output fluctuation of the wind farm group within the preset time period.

[0048] Furthermore, a Gaussian function is used as a kernel function to obtain a wind power output fluctuation variable kernel density estimation function expression, wherein the calculation formula of the wind power output fluctuation variable kernel density estimation function is as follows:

[0049]

[0050] Where, is the kernel density estimation function of wind power output fluctuation, is the number of wind power output fluctuations in the sample, For a fixed bandwidth, is the wind power output fluctuation of the wind farm group at the current moment, is the value of the mth wind power output fluctuation in the sample, K (·) is the kernel function.

[0051] Step 2: Divide the interval corresponding to the sample into sub-sample intervals, and determine the adaptive bandwidth corresponding to each sub-sample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method;

[0052] In the embodiment of the present disclosure, determining the adaptive bandwidth corresponding to each subsample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method includes:

[0053] Determine whether the goodness of fit of each subsample interval is greater than the preset goodness of fit threshold, wherein, determine whether each subsample interval satisfies If it is satisfied, the fitting degree of the sub-sample interval is good and the bandwidth does not need to be adjusted. Otherwise, the bandwidth of the sub-sample interval needs to be determined as a fixed bandwidth. is the subsample interval of χ 2 Test statistic, At a significant level α The lower degree of freedom is m -1 χ 2 distribution, where Take 0.05.

[0054] Each subsample interval whose goodness of fit is greater than the preset goodness of fit threshold is screened out, and the adaptive bandwidth corresponding to each screened subsample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method are determined.

[0055] Furthermore, the calculation formula of the adaptive bandwidth corresponding to the sub-sample interval under the empirical method is as follows:

[0056]

[0057] The calculation formula of the adaptive bandwidth corresponding to the sub-sample interval under the empirical method is as follows:

[0058]

[0059] Where, For the The adaptive bandwidth corresponding to the sub-sample interval under the empirical method is, For the The bandwidth factor corresponding to the sub-sample interval under the empirical method is, For the The adaptive bandwidth corresponding to the sub-sample interval under the unbiased cross-validation method, is the fixed bandwidth corresponding to the kernel density estimation function of wind power output fluctuation under the unbiased cross-validation method, For the The bandwidth factor corresponding to the sub-sample interval under the unbiased cross-validation method; , is the kernel density estimation function of the fixed bandwidth wind power output fluctuation corresponding to the empirical method, is the i-th sample data in the subsample interval, is the number of samples in the subsample interval, and similarly , is the kernel density estimation function of the wind power output fluctuation with a fixed bandwidth corresponding to the unbiased cross-validation method.

[0060] It should be noted that, before determining whether the goodness of fit of each subsample interval is greater than a preset goodness of fit threshold, the following steps may also be performed:

[0061] The fixed bandwidth corresponding to the kernel density estimation function of the wind power output fluctuation quantity under the empirical method and the fixed bandwidth corresponding to the unbiased cross-validation method are determined respectively by using the empirical method and the unbiased cross-validation method.

[0062] The calculation formula of the fixed bandwidth corresponding to the kernel density estimation function of the wind power output fluctuation quantity under the empirical method is as follows:

[0063]

[0064] The calculation formula of the fixed bandwidth corresponding to the kernel density estimation function of the wind power output fluctuation quantity under the unbiased cross-validation method is as follows:

[0065]

[0066] Where, is the fixed bandwidth corresponding to the kernel density estimation function of wind power output fluctuation under the empirical method, is the normal distribution standard deviation of the wind power output fluctuation quantity samples of the wind farm group within the preset time period, is the number of wind power output fluctuations in the sample, is the fixed bandwidth corresponding to the kernel density estimation function of wind power output fluctuation under the unbiased cross-validation method, argmin is the independent variable for finding the minimum function value, K ( v )=∫ K ( u ) K ( v - u ) du for the reason K (·) The derived convolution kernel function, .

[0067] It should be noted that when the goodness of fit of the sub-sample interval is less than or equal to the preset goodness of fit threshold, the fixed bandwidth corresponding to the kernel density estimation function of the wind power output fluctuation under the empirical method is used as the adaptive bandwidth corresponding to the sub-sample interval under the empirical method, and the fixed bandwidth corresponding to the kernel density estimation function of the wind power output fluctuation under the unbiased cross-validation method is used as the adaptive bandwidth corresponding to the sub-sample interval under the unbiased cross-validation method.

[0068] Step 3: Determine the hybrid adaptive kernel density estimation model corresponding to the sample according to the adaptive bandwidth corresponding to each sub-sample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method;

[0069] In the embodiment of the present disclosure, step 3 includes:

[0070] Determine a first adaptive kernel density estimation function corresponding to the sample based on the adaptive bandwidth corresponding to the empirical method;

[0071] Determining a second adaptive kernel density estimation function corresponding to the sample based on the corresponding adaptive bandwidth under an unbiased cross-validation method;

[0072] The first adaptive kernel density estimation function and the second adaptive kernel density estimation function are combined to obtain a hybrid adaptive kernel density estimation model corresponding to the sample.

[0073] The calculation formula of the hybrid adaptive kernel density estimation model corresponding to the sample is as follows:

[0074]

[0075] Where, is a hybrid adaptive kernel density estimation model, is the first adaptive kernel density estimation function, is the weight coefficient of the first adaptive kernel density estimation function, is the second adaptive kernel density estimation function, is the weight coefficient of the second adaptive kernel density estimation function, where .

[0076] Furthermore, the calculation formula of the first adaptive kernel density estimation function is as follows:

[0077]

[0078] The calculation formula of the second adaptive kernel density estimation function is as follows:

[0079]

[0080] Where, is the first adaptive kernel density estimation function, For the The adaptive bandwidth corresponding to the sub-sample interval under the empirical method is, is the value of the g-th wind power output fluctuation in the subsample, is the number of wind power output fluctuations in the sample, is the fixed bandwidth corresponding to the kernel density estimation function of wind power output fluctuation under the empirical method, is the second adaptive kernel density estimation function, is the fixed bandwidth corresponding to the kernel density estimation function of wind power output fluctuation under the unbiased cross-validation method, For the The adaptive bandwidth corresponding to the sub-sample interval under the unbiased cross-validation method.

[0081] Step 4: using the hybrid adaptive kernel density estimation model to determine the probability density distribution corresponding to the wind power output fluctuation of the wind farm group at the current moment.

[0082] For example, the hybrid adaptive kernel density estimation model corresponding to the sample is used , calculate the probability density distribution corresponding to the wind power output fluctuation of the wind farm group at the current moment.

[0083] In the embodiment of the present disclosure, Figure 3 FIG. 4 shows the acquisition process of the hybrid adaptive kernel density estimation model, wherein the process includes:

[0084] Step S1: Obtain the first-order difference fluctuation of wind power output and the above-mentioned wind power output fluctuation;

[0085] Step S2: Determine the fixed bandwidth of the empirical method and the fixed bandwidth of the unbiased cross-validation method;

[0086] Step S3: Divide the interval corresponding to the sample into subsample intervals;

[0087] Step S4: Determine Is the goodness of fit of the subsample interval greater than the preset goodness of fit threshold? , judge again, otherwise, modify the fixed bandwidth to determine the adaptive bandwidth until ;

[0088] Step S5: modifying the empirical kernel density estimation model to obtain a first adaptive kernel density estimation function, and modifying the unbiased cross-validation kernel density estimation model to obtain a second adaptive kernel density estimation function;

[0089] Step S6: combining the first adaptive kernel density estimation function and the second adaptive kernel density estimation function to obtain a hybrid adaptive kernel density estimation model corresponding to the sample.

[0090] In summary, the embodiment of the present disclosure provides a method for determining the probability density distribution of wind power output fluctuations. First, the adaptive bandwidth corresponding to each sub-sample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method are determined. Then, based on the adaptive bandwidth, the hybrid adaptive kernel density estimation model corresponding to the sample is determined. Finally, the probability density distribution corresponding to the wind power output fluctuation of the wind farm group at the current moment is determined, which improves the accuracy of the probability density determination and has strong adaptability.

[0091] Example 2

[0092] In order to implement the above embodiments, the present disclosure also proposes a computer-readable storage medium.

[0093] The computer device provided in this embodiment stores a computer program, which implements the method in Example 1 when executed by a processor.

[0094] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0095] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0096] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for determining the probability density distribution of wind power output fluctuation, characterized in that: The method comprises: Obtaining a wind power output fluctuation amount of a wind farm group at a current moment, a wind power output fluctuation amount sample of the wind farm group within a preset time period, and a kernel density estimation function of the sample; Divide the interval corresponding to the sample into sub-sample intervals, and determine the adaptive bandwidth corresponding to each sub-sample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method; Determining a first adaptive kernel density estimation function corresponding to the sample based on the adaptive bandwidth corresponding to the empirical method, determining a second adaptive kernel density estimation function corresponding to the sample based on the adaptive bandwidth corresponding to the unbiased cross-validation method, and then combining the first adaptive kernel density estimation function with the second adaptive kernel density estimation function to obtain a hybrid adaptive kernel density estimation model corresponding to the sample; Determining the probability density distribution corresponding to the wind power output fluctuation of the wind farm group at the current moment by using the hybrid adaptive kernel density estimation model; The calculation formula of the first adaptive kernel density estimation function is as follows: Where, is the first adaptive kernel density estimation function, For the The adaptive bandwidth corresponding to the sub-sample interval under the empirical method is, is the value of the g-th wind power output fluctuation in the subsample, is the number of wind power output fluctuations in the sample, is the fixed bandwidth corresponding to the kernel density estimation function of wind power output fluctuation under the empirical method; The calculation formula of the second adaptive kernel density estimation function is as follows: Where, is the second adaptive kernel density estimation function, For the The adaptive bandwidth corresponding to the sub-sample interval under the unbiased cross-validation method, is the fixed bandwidth corresponding to the kernel density estimation function of wind power output fluctuation under the unbiased cross-validation method.

2. The method according to claim 1, wherein The calculation formula of the kernel density estimation function of the wind power output fluctuation is as follows: Where, is the kernel density estimation function of wind power output fluctuation, is the number of wind power output fluctuations in the sample, For a fixed bandwidth, is the wind power output fluctuation of the wind farm group at the current moment, is the value of the mth wind power output fluctuation in the sample.

3. The method according to claim 1, wherein Determining the adaptive bandwidth corresponding to each subsample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method includes: Determine whether the goodness of fit of each subsample interval is greater than a preset goodness of fit threshold; Each subsample interval whose goodness of fit is greater than the preset goodness of fit threshold is screened out, and the adaptive bandwidth corresponding to each screened subsample interval under the empirical method and the adaptive bandwidth corresponding to the unbiased cross-validation method are determined.

4. The method according to claim 3, wherein Before determining whether the goodness of fit of each subsample interval is greater than a preset goodness of fit threshold, the method further includes: The fixed bandwidth corresponding to the kernel density estimation function of the wind power output fluctuation quantity under the empirical method and the fixed bandwidth corresponding to the unbiased cross-validation method are determined respectively by using the empirical method and the unbiased cross-validation method.

5. The method according to claim 4, wherein When the goodness of fit of the sub-sample interval is less than or equal to a preset goodness of fit threshold, the fixed bandwidth corresponding to the kernel density estimation function of the wind power output fluctuation under the empirical method is used as the adaptive bandwidth corresponding to the sub-sample interval under the empirical method, and the fixed bandwidth corresponding to the kernel density estimation function of the wind power output fluctuation under the unbiased cross-validation method is used as the adaptive bandwidth corresponding to the sub-sample interval under the unbiased cross-validation method.

6. The method according to claim 3, wherein The calculation formula of the adaptive bandwidth corresponding to the sub-sample interval under the empirical method is as follows: Where, For the The adaptive bandwidth corresponding to the sub-sample interval under the empirical method is, For the The bandwidth factor corresponding to the sub-sample interval under the empirical method is, is the fixed bandwidth corresponding to the kernel density estimation function of wind power output fluctuation under the empirical method, where, , is the kernel density estimation function of the fixed bandwidth wind power output fluctuation corresponding to the empirical method, is the i-th sample data in the subsample interval, is the number of samples in the subsample interval; The calculation formula of the adaptive bandwidth corresponding to the sub-sample interval under the unbiased cross-validation method is as follows: Where, For the The adaptive bandwidth corresponding to the sub-sample interval under the unbiased cross-validation method, is the fixed bandwidth corresponding to the kernel density estimation function of wind power output fluctuation under the unbiased cross-validation method, For the The bandwidth factor corresponding to the sub-sample interval under the unbiased cross-validation method, where , is the kernel density estimation function of the wind power output fluctuation with a fixed bandwidth corresponding to the unbiased cross-validation method.

7. The method according to claim 1, wherein The calculation formula of the hybrid adaptive kernel density estimation model corresponding to the sample is as follows: Where, is a hybrid adaptive kernel density estimation model, is the first adaptive kernel density estimation function, is the weight coefficient of the first adaptive kernel density estimation function, is the second adaptive kernel density estimation function, is the weight coefficient of the second adaptive kernel density estimation function, where .

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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