Prediction method and system for energy storage frequency modulation instruction

Abstract

The invention relates to the technical field of energy storage frequency modulation, in particular to a prediction method and system for an energy storage frequency modulation instruction, and the method comprises the steps: decomposing a frequency modulation instruction sequence Pt through VMD, and obtaining a plurality of IMF subsequences; according to the total area Sii of the current IMF sub-sequence and the test group error Wi, judging whether the current IMF sub-sequence is a false sequence or not by using an activation function; and improving the IMF sub-sequence of which the judgment result is false, and performing prediction output of a frequency modulation instruction by utilizing the improved IMF sub-sequence. The method has the advantages that false sequences possibly generated in the variational mode decomposition process can be effectively recognized and improved. According to the method, the false sequence can be accurately found out and processed by calculating the area defined by the adjacent data points in the subsequences and the X axis, the prediction error is reduced, and the prediction precision is improved. In addition, a false sequence is optimized by means of generating a random sequence, performing mirror image transformation and the like, and the response speed and the frequency modulation efficiency of the system are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage frequency modulation, and in particular to a method and system for predicting energy storage frequency modulation instructions. Background Art

[0002] In the existing hybrid energy storage-assisted thermal power unit frequency regulation technology, the method usually adopted is to assign the difference between the frequency regulation command and the output of the thermal power unit to a hybrid energy storage system composed of supercapacitors and lithium-ion batteries, in which the low-frequency frequency regulation task is undertaken by the lithium battery, while the high-frequency frequency regulation task is undertaken by the supercapacitor. However, the signal transmission delay and the response time difference of the energy storage unit in this process affect the frequency regulation efficiency and economic benefits of the system. In addition, traditional frequency regulation command prediction methods, such as using VMD decomposition to decompose the original sequence into multiple subsequences for prediction, have problems such as modal aliasing, end effects, and sensitivity to initial conditions. These problems may cause useful signals to be misjudged as noise, reduce the accuracy of the prediction, and thus affect the performance of the entire frequency regulation system. Therefore, a new prediction method is urgently needed to improve the accuracy of frequency regulation command prediction and reduce the response time difference, so as to improve the performance and benefits of hybrid energy storage systems in thermal power unit frequency regulation applications. Summary of the invention

[0003] In order to solve the above technical problems, a prediction method of energy storage frequency modulation instructions is proposed, including using VMD to decompose the frequency modulation instruction sequence Pt to obtain multiple IMF subsequences; According to the total area S of the current IMF subsequence ii and the experimental group error W i , use the activation function to determine whether the current IMF subsequence is a false sequence; Improve the IMF subsequence whose judgment result is false, and use the improved IMF subsequence to predict and output the frequency modulation instruction; Among them, the improvements to the IMF subsequence whose result is false include: When there are two or more false IMF subsequences, replace the false IMF subsequences by generating a random sequence; or, When there is a single false IMF subsequence, the mean of the current false IMF subsequence is calculated, and the position of the current false IMF subsequence is mirrored in the coordinate system with the horizontal line whose Y is equal to the current mean as the symmetry axis, and the changed IMF subsequence is determined as the final IMF subsequence.

[0004] As a preferred solution of the method for predicting energy storage frequency modulation instructions of the present invention, the total area S of any IMF subsequence is determined as follows: ii include: The areas enclosed by two adjacent data points and the X-axis under the current IMF subsequence are determined in sequence, where the enclosed areas include the area value and area attributes; Sum up all the enclosed areas under the current IMF subsequence to get the total area S of the current IMF subsequence ii ; The area enclosed by two adjacent data points and the X-axis is the area of ​​the figure enclosed by the line between the two adjacent data points, the vertical lines between the two adjacent data points and the X-axis, and the line between the coordinates of the two adjacent data points on the X-axis.

[0005] As a preferred solution of a method for predicting energy storage frequency modulation instructions of the present invention, the area attribute includes positive and negative values, and when the vertical coordinates of two adjacent data points are greater than or equal to 0, the current area attribute is a positive value; When the vertical coordinates of two adjacent data points are both less than 0, the current area attribute is a negative value; When one of the vertical coordinates of two adjacent data points is greater than 0 and the other is less than 0, the current area attribute is determined by the area enclosed by the current two adjacent points and the X-axis.

[0006] As a preferred solution of a method for predicting energy storage frequency modulation instructions of the present invention, the current area attribute is determined by the area enclosed by two current adjacent points and the X-axis, including: When the area enclosed by the current two adjacent points and the X-axis is greater than or equal to 0, the current area attribute is a positive value; When the area enclosed by the current two adjacent points and the X-axis is less than 0, the current area attribute is a negative value.

[0007] As a preferred solution of a method for predicting energy storage frequency modulation instructions of the present invention, wherein: using an activation function to determine whether an IMF subsequence is a false sequence includes: exp(sigmoid(W i )*S ii ) is between [0.8 1.1], it means that the current sequence is a false IMF subsequence, where sigmoid() represents the activation function.

[0008] As a preferred solution of a method for predicting energy storage frequency modulation instructions of the present invention, the random sequence is determined by taking the random sequence with the smallest area enclosed by the X-axis as the final IMF subsequence.

[0009] A prediction system for energy storage frequency modulation instructions, comprising: a decomposition module, for decomposing a frequency modulation instruction sequence Pt by using VMD to obtain a plurality of IMF subsequences; The determination module is used to determine the total area S of the current IMF subsequence.ii and the experimental group error W i , use the activation function to determine whether the current IMF subsequence is a false sequence; The improved prediction module is used to improve the IMF subsequence whose judgment result is false, and use the improved IMF subsequence to predict and output the frequency modulation instruction.

[0010] A computer device includes a memory and a processor, wherein the memory stores a computer program and is characterized in that the processor implements the steps of a method for predicting energy storage frequency modulation instructions when executing the computer program.

[0011] A computer-readable storage medium stores a computer program, characterized in that the computer program implements the steps of a method for predicting energy storage frequency modulation instructions when the computer program is executed by a processor.

[0012] The beneficial effects of the present invention are as follows: the false sequences that may be generated in the variational mode decomposition process can be effectively identified and improved. The method can accurately find and process the false sequences by calculating the area enclosed by adjacent data points in the subsequence and the X-axis, thereby reducing the prediction error and improving the prediction accuracy. In addition, the false sequences are optimized by generating random sequences and performing mirror transformation, further improving the response speed and frequency modulation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0014] Figure 1 A flow chart of a method for predicting energy storage frequency modulation instructions.

[0015] Figure 2 The flowchart of the method for predicting energy storage frequency modulation instructions is shown in FIG.

[0016] Figure 3 This is a schematic diagram of the area attribute being a positive value.

[0017] Figure 4 This is a schematic diagram of the area attribute being a negative value.

[0018] Figure 5 This is a schematic diagram of area attribute determination in special cases.

[0019] Figure 6 Schematic diagram before and after mirroring. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0021] Example 1, reference Figure 1 to Figure 6 , which is the first embodiment of the present invention, provides a method for predicting energy storage frequency modulation instructions, including: S1. VMD is used to decompose the frequency modulation instruction sequence Pt to obtain multiple IMF subsequences, where VMD is variational mode decomposition, and the IMF subsequences are time series components with different characteristics obtained after the frequency modulation instruction sequence Pt is decomposed by VMD.

[0022] It should be noted that, assuming that the frequency modulation instruction is Pt, after VMD decomposition, it becomes IMF1, IMF2, IMF3, and IMF. i ..,IMF K (Among which: IMF i is the ith subsequence, IMF K is the Kth subsequence). It is precisely because there may be false subsequences in these subsequences that the prediction results are inaccurate.

[0023] It should be further explained that, assuming that a certain frequency modulation instruction is Pt=[X1,X2,X3,.Xi..,XN] (where: X1,X2,X3,.Xi..,XN are the values ​​of the frequency modulation instruction at different time points), the integer subsequences of the first 90% length in Pt are taken to predict the integer subsequences of the last 10% length and the subsequence values ​​of the unknown group, wherein the prediction result of the integer subsequence of the last 10% length of Pt is defined as the predicted experimental group, and since the last 10% subsequences in the current Pt are known values, the difference between the predicted experimental group and the last 10% subsequence values ​​corresponding to the current Pt (all errors (MAPE) in the following embodiment) can be used to evaluate the accuracy of the prediction. Furthermore, what this method really wants to predict is the subsequence of the unknown group.

[0024] S2, based on the total area S of the current IMF subsequence ii and the experimental group error W i , use the activation function to determine whether the current IMF subsequence is a false sequence.

[0025] Furthermore, the total area S of any IMF subsequence is determined ii include: The areas enclosed by two adjacent data points and the X-axis under the current IMF subsequence are determined in sequence, where the enclosed areas include the area value and area attributes; The area enclosed by two adjacent data points and the X-axis is the area of ​​the figure enclosed by the line between the two adjacent data points, the vertical lines between the two adjacent data points and the X-axis, and the line between the coordinates of the two adjacent data points on the X-axis.

[0026] Furthermore, the area attribute includes positive and negative values. When the vertical coordinates of two adjacent data points are greater than or equal to 0, the current area attribute is a positive value; When the vertical coordinates of two adjacent data points are both less than 0, the current area attribute is a negative value; When one of the vertical coordinates of two adjacent data points is greater than 0 and the other is less than 0, the current area attribute is determined by the area enclosed by the current two adjacent points and the X-axis.

[0027] It should be noted that, assuming a subsequence IMF of VMD decomposition i =[X i1 ,X i2 ,X i3 ,.X ii ..,X iN ] (Where: X ii is the value of the ith IMF subsequence at the ith time point, X iN is the value of the ith IMF subsequence at the Nth time point), and the experimental group error of the current sequence is W i . Calculate the area S1, S2, .., S enclosed by two adjacent data points in sequence N-1 In the further coordinate system, the ordinate corresponding to each data point represents the signal feature, and there may be positive and negative and numerical differences between the signal features. The abscissa corresponding to each data point represents the sampling number (ie, the subscript).

[0028] It should be further explained that Figure 3 is a schematic diagram of the situation where "when the vertical coordinates of two adjacent data points are greater than or equal to 0, the current area attribute is a positive value"; Figure 4 This is a schematic diagram of the situation where "when the vertical coordinates of two adjacent data points are both less than 0, the current area attribute is a negative value."

[0029] Further, the current area attribute is determined by the area enclosed by the current two adjacent points and the X-axis, including: when the area enclosed by the current two adjacent points and the X-axis is greater than or equal to 0, the current area attribute is a positive value; When the area enclosed by the current two adjacent points and the X-axis is less than 0, the current area attribute is a negative value.

[0030] It should be noted that when one of the ordinates of two adjacent data points is greater than 0 and the other is less than 0, the specific determination process is: first determine the area of ​​the two triangles enclosed by the current two adjacent data points and the X-axis (refer to the attached Figure 5 ), the current area also includes the numerical value of the area and the area attribute (positive / negative value). After calculating the area difference between the two triangles (i.e.: C1-C2, the numerical value of the current area difference is the numerical value of the area enclosed by the current group of adjacent points and the X-axis), the current area difference is finally compared with 0. If the area difference is greater than or equal to 0, the current area attribute is determined to be a positive value; if the area difference is less than 0, the current area attribute is determined to be a negative value.

[0031] Sum up all the enclosed areas under the current IMF subsequence to get the total area S of the current IMF subsequence ii ; It should be noted that the subsequence IMF i The sum of the corresponding areas is S ii =S1+S2+,..,+S N-1 . Perform the above steps until all subsequences IMF1, IMF2, IMF3, .., IMF are determined. K The sum of the corresponding areas S 11 ,S 22 ,S 33 ,...,S KK。

[0032] Using the activation function to determine whether the IMF subsequence is a false sequence includes: exp(sigmoid(W i )*S ii ) is between [0.8 1.1], it means that the current sequence is a false IMF subsequence, where sigmoid() represents the activation function.

[0033] It should be noted that for a specific subsequence IMF i The area is S ii The corresponding test group error is Wi. When exp(sigmoid(W i )*S ii ) is between [0.8,1.1], the current sequence is judged to be a false sequence and needs to be improved.

[0034] S3. Improving the IMF subsequence that is judged to be false, and using the improved IMF subsequence to predict and output the frequency modulation instruction, wherein the prediction and output of the frequency modulation instruction for the improved IMF subsequence can be achieved by conventional technical means (GRU), which will not be described in detail here.

[0035] Among them, the improvements to the IMF subsequence whose result is false include: When there are two or more false IMF subsequences, a random sequence is generated to replace the false IMF subsequence, wherein the random sequence is determined by taking the random sequence with the smallest area enclosed by the X-axis as the final IMF subsequence; or, When there is a single false IMF subsequence, the mean of the current false IMF subsequence is calculated, and the position of the current false IMF subsequence is mirrored in the coordinate system with the horizontal line whose Y is equal to the current mean as the symmetry axis, and the changed IMF subsequence is determined as the final IMF subsequence.

[0036] It should be noted that if the IMF i =[X i1 ,X i2 ,X i3 ,.X ii ..,X iN ] and the IMF j =[X j1 ,X j2 ,X j3 ,.X ji ..,X jN ] are two false sequences. At this time, a random sequence IMF is generated. ij =[X ij1 ,X ij2 ,X ij3 ,.X iji ..,X ijN ] to replace these two false subsequences, each value The random range generated is: .

[0037] In the formula, Represents a generated random value The lower limit of and Sequence IMF i and IMF j The data point value (signal characteristic value) at the corresponding position in and are the factorial factors of the two sequences, which are used to adjust the size of the data point value. and The smaller value in is used as the lower limit of the random value; Represents a generated random value The upper limit of and Used to add randomness or account for volatility in data.

[0038] Furthermore, we randomly generate an IMF ij =[X ij1 ,X ij2 ,X ij3 ,.X iji ..,X ijN ](Where: X iji is the value of the current subsequence at the i-th time point, X ijN The value of the current subsequence at the Nth time point), calculate the area enclosed by this sequence and the X-axis at this time.

[0039] Generate an IMF randomly again ij =[X ij1 ,X ij2 ,X ij3 ,.X iji ..,X ijN ], calculate the area enclosed by this sequence and the X-axis.

[0040] Repeat this process 20 times randomly.

[0041] Output the IMF corresponding to the random case with the smallest area among these 20 times. ij It is determined as the final sequence. It should be noted that the combination of false sequences is based on the principle of proximity. For example, if IMF1, IMF4, IMF7, IMF9 and IMF10 are false sequences, then IMF1 and IMF4 are combined first, IMF7 and IMF9 are combined, and IMF10 is processed as if there is a single false sequence.

[0042] Preferably, the IMF is randomly generated within a certain upper and lower limit. ij Each value of the sequence can ensure that the newly generated sequence will not completely deviate from the characteristics of the original sequence, but can also introduce a certain degree of randomness, which helps to eliminate the impact of false sequences.

[0043] It should be further explained that when there is only one false sequence in a sequence, or the above-mentioned IMF10 situation occurs, please refer to the attached Figure 6 , and processed as follows: IMF i =[X i1 ,X i2 ,X i3 ,.X ii ..,X iN ] is a false sequence that appears alone. Find the mean of this sequence avg[X i1 ,X i2 ,X i3 ,.X ii ..,X iN ], with the mean Y=avg[Xi1 ,X i2 ,X i3 ,.X ii ..,X iN ] is the horizontal line of the symmetry axis, and the position of the sequence is mirrored and changed, and the changed sequence is determined as the final sequence.

[0044] It should be noted that the mean is the average value of all data points in the sequence, reflecting the central trend of the sequence. The sequence is mirrored with the calculated mean Y as the axis of symmetry.

[0045] Preferably, through this mirror transformation, the data points in the false sequence are redistributed so that they are symmetrically distributed around the mean Y, which helps to eliminate outliers or noise in the false sequence and makes the sequence more consistent with the characteristics of the real signal, thereby improving the accuracy and robustness of the prediction model.

[0046] In summary, the effective effect of a prediction method for energy storage frequency modulation instructions is that it can effectively identify and improve the false sequences that may be generated in the variational mode decomposition process. Further, this method accurately finds and processes false sequences by calculating the area enclosed by adjacent data points in the subsequence and the X-axis, reducing the prediction error and improving the prediction accuracy. In addition, by optimizing the false sequences by generating random sequences and performing mirror transformation, the response speed and frequency modulation efficiency of the system are further improved, thereby bringing higher economic benefits and better operating performance to thermal power units.

[0047] Example 2 is the second example of the present invention. In order to verify the advantages of the present invention, the frequency modulation sequence is predicted by using the method of the present invention and the prediction method of VMD-GRU. The results are shown in Table 1: Table 1 Comparison of prediction results , It should be noted that the calculation formulas and definitions of the above four evaluation indicators are shown in Table 2.

[0048] Table 2 Calculation formula and definition of each indicator , Where N represents the sample size, and Represent the actual value and predicted value at time n respectively.

[0049] In summary, according to the experimental data, the prediction errors of this method are smaller than those of VMD-GRU, which fully demonstrates that the prediction effect of this method is better.

[0050] Embodiment 3 is the third embodiment of the present invention, which is different from the first two embodiments in that: If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the current technical solution, can be embodied in the form of a software product. The current computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0051] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0052] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0053] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0054] Embodiment 4 is the fourth embodiment of the present invention. This embodiment provides a prediction system for energy storage frequency modulation instructions, including a decomposition module, which is used to decompose the frequency modulation instruction sequence Pt by using VMD to obtain multiple IMF subsequences; The determination module is used to determine the total area S of the current IMF subsequence. ii and the experimental group error W i , use the activation function to determine whether the current IMF subsequence is a false sequence; The improved prediction module is used to improve the IMF subsequence whose judgment result is false, and use the improved IMF subsequence to predict and output the frequency modulation instruction.

[0055] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for predicting energy storage frequency modulation instructions, characterized in that: include, VMD is used to decompose the frequency modulation instruction sequence Pt to obtain multiple IMF subsequences; According to the total area S of the current IMF subsequence ii and the experimental group error W i , use the activation function to determine whether the current IMF subsequence is a false sequence; Improve the IMF subsequence whose judgment result is false, and use the improved IMF subsequence to predict and output the frequency modulation instruction; Among them, the improvements to the IMF subsequence whose result is false include: When there are two or more false IMF subsequences, replace the false IMF subsequences by generating a random sequence; or, When there is a single false IMF subsequence, the mean of the current false IMF subsequence is calculated, and the position of the current false IMF subsequence is mirrored in the coordinate system with the horizontal line whose Y is equal to the current mean as the symmetry axis, and the changed IMF subsequence is determined as the final IMF subsequence.

2. A method for predicting energy storage frequency modulation instructions as claimed in claim 1, characterized in that: Determine the total area S of any IMF subsequence ii The method comprises: determining in sequence the areas enclosed by two adjacent data points and the X-axis under the current IMF subsequence, wherein the enclosed areas include area numerical values ​​and area attributes; Sum up all the enclosed areas under the current IMF subsequence to get the total area S of the current IMF subsequence ii ; The area enclosed by the two adjacent data points and the X-axis is the area of ​​the figure enclosed by the line between the two adjacent data points, the vertical lines between the two adjacent data points and the X-axis, and the line between the coordinates of the two adjacent data points on the X-axis.

3. A method for predicting energy storage frequency modulation instructions as claimed in claim 2, characterized in that: The area attribute includes positive and negative values. When the vertical coordinates of two adjacent data points are greater than or equal to 0, the current area attribute is a positive value; When the vertical coordinates of two adjacent data points are both less than 0, the current area attribute is a negative value; When one of the vertical coordinates of two adjacent data points is greater than 0 and the other is less than 0, the current area attribute is determined by the area enclosed by the current two adjacent points and the X-axis.

4. A method for predicting energy storage frequency modulation instructions as claimed in claim 3, characterized in that: The current area attribute is determined by the area enclosed by the current two adjacent points and the X-axis and includes: When the area enclosed by the current two adjacent points and the X-axis is greater than or equal to 0, the current area attribute is a positive value; When the area enclosed by the current two adjacent points and the X-axis is less than 0, the current area attribute is a negative value.

5. A method for predicting energy storage frequency modulation instructions as claimed in claim 4, characterized in that: Using the activation function to determine whether the IMF subsequence is a false sequence includes: exp(sigmoid(W i )*S ii ) is between [0.8 1.1], it means that the current sequence is a false IMF subsequence, where sigmoid() represents the activation function.

6. A method for predicting energy storage frequency modulation instructions as claimed in claim 5, characterized in that: The random sequence is determined by taking the random sequence with the smallest area enclosed by the X-axis as the final IMF subsequence.

7. A system using a method for predicting energy storage frequency modulation instructions as claimed in any one of claims 1 to 6, characterized in that: include: A decomposition module, used for decomposing the frequency modulation instruction sequence Pt by using VMD to obtain multiple IMF subsequences; The determination module is used to determine the total area S of the current IMF subsequence. ii and the experimental group error W i , use the activation function to determine whether the current IMF subsequence is a false sequence; The improved prediction module is used to improve the IMF subsequence whose judgment result is false, and use the improved IMF subsequence to predict and output the frequency modulation instruction.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of a method for predicting energy storage frequency modulation instructions as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for predicting energy storage frequency modulation instructions according to any one of claims 1 to 6 are implemented.

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