A power prediction method, device, equipment and medium of a new energy device

Abstract

The embodiment of the application discloses a new energy equipment power prediction method, device, equipment and medium, the method comprises: acquiring a preset fitting sequence, wherein the preset fitting sequence is generated by fitting curve serialization, and the fitting curve represents the power change trend of the new energy equipment corresponding to the set environment factor; generating a historical operation sequence according to the historical power sequence and the historical environment sequence of the new energy equipment, wherein the historical environment sequence comprises an environment factor associated with the power of the new energy equipment; determining the correlation of the historical operation sequence and the preset fitting sequence, and predicting the power of the new energy equipment according to the correlation. The technical scheme provided by the application can improve the accuracy of power prediction when predicting the power of the new energy equipment in combination with the environment factor of the new energy equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of deep learning, and in particular to a power prediction method and device for new energy equipment, and a new energy equipment power prediction apparatus and medium. BACKGROUND

[0002] With the increasing global energy demand and the growing awareness of environmental protection, distributed new energy has gradually become one of the important energy supply methods. At the same time, the power of new energy equipment is one of the main technical parameters of new energy equipment. Therefore, in response to the current use demand of new energy equipment, power supervision and prediction of new energy equipment are extremely important.

[0003] However, the power of new energy equipment has significant volatility and uncertainty, mainly affected by meteorological conditions and time factors and other factors. The current power prediction method cannot combine the environmental factors of new energy equipment, thereby resulting in low power prediction accuracy. SUMMARY

[0004] The present application provides a new energy equipment power prediction method, device, equipment and medium. Through the technical solution of the present application, the accuracy of power prediction can be improved by combining the environmental factors of new energy equipment when predicting the power of new energy equipment.

[0005] In a first aspect, the present application provides a new energy equipment power prediction method, comprising:

[0006] obtaining a preset fitting sequence, wherein the preset fitting sequence is generated by serializing a fitting curve, and the fitting curve represents the power change trend of the new energy equipment corresponding to a set environmental factor;

[0007] generating a historical operation sequence according to the historical power sequence and the historical environmental sequence of the new energy equipment, wherein the historical environmental sequence includes environmental factors associated with the power of the new energy equipment;

[0008] determining the correlation of the historical operation sequence and the preset fitting sequence, and predicting the power of the new energy equipment according to the correlation.

[0009] In a second aspect, the present application provides a new energy equipment power prediction device, comprising:

[0010] an acquisition module for obtaining a preset fitting sequence, wherein the preset fitting sequence is generated by serializing a fitting curve, and the fitting curve represents the power change trend of the new energy equipment corresponding to a set environmental factor;

[0011] The splicing module is configured to generate a historical operation sequence according to a historical power sequence and a historical environment sequence of the new energy equipment, wherein the historical environment sequence comprises an environmental factor associated with the power of the new energy equipment.

[0012] The prediction module is configured to determine a correlation between the historical operation sequence and a preset fitting sequence, and predict the power of the new energy equipment according to the correlation.

[0013] In a third aspect, an electronic device is provided, and the electronic device comprises:

[0014] at least one processor; and

[0015] a memory in communication with the at least one processor; wherein

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the power prediction method of the new energy equipment according to any one of the embodiments of the present application.

[0017] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores computer instructions for enabling a processor to implement the power prediction method of the new energy equipment according to any one of the embodiments of the present application.

[0018] The embodiments of the present application provide a power prediction method, device, equipment and medium of a new energy equipment, and the method comprises: acquiring a preset fitting sequence, wherein the preset fitting sequence is generated by sequence of fitting curves, and the fitting curve represents a power change trend of the new energy equipment corresponding to an environmental factor; generating a historical operation sequence according to a historical power sequence and a historical environment sequence of the new energy equipment, wherein the historical environment sequence comprises an environmental factor associated with the power of the new energy equipment; determining a correlation between the historical operation sequence and the preset fitting sequence, and predicting the power of the new energy equipment according to the correlation. Specifically, the preset fitting sequence can represent the power change trend of the new energy equipment corresponding to the environmental factor, and then by determining the correlation between the historical operation sequence and the preset fitting sequence, the power of the new energy equipment is predicted, so that the purpose of combining the environmental factor of the new energy equipment in power prediction can be achieved, and then the error caused by the environmental factor can be reduced, and the accuracy of power prediction can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to make the technical solution in the embodiments of the present application clearer, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0020] Figure 1 A flow chart of a power prediction method of a new energy equipment provided by the embodiment one of the present application is shown in FIG. 1.

[0021] Figure 2 A flow chart of a power prediction method of a new energy equipment provided by the embodiment two of the present application is shown in FIG. 2.

[0022] Figure 3 A power prediction schematic diagram of a new energy equipment provided by the embodiment of the present application is shown in FIG. 3.

[0023] Figure 4 A structure schematic diagram of a power prediction device of a new energy equipment provided by the embodiment three of the present application is shown in FIG. 4.

[0024] Figure 5 A structure schematic diagram of an electronic device provided by the embodiment four of the present application is shown in FIG. 5. DETAILED DESCRIPTION

[0025] In order to make the technical solution in the embodiments of the present application clearer, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0026] It should be noted that the terms "first", "second", and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to include all the steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] It should be noted that the collection, storage, use, processing, transmission, provision and disclosure of user personal information in the technical solutions of the present disclosure comply with relevant laws and regulations and do not violate public order and good customs.

[0028] Embodiment one

[0029] Figure 1 A flowchart of a power prediction method of a new energy equipment provided by the embodiment one of the present application is provided, which can be applied to the case of predicting the power of the new energy equipment in combination with the environmental data and power data of the new energy equipment, and can be executed by a power prediction device of the new energy equipment, which can be composed of software and / or hardware and configured in a computer or a server.

[0030] As shown in Figure 1 , it comprises:

[0031] In step 110, a preset fitting sequence is acquired, wherein the preset fitting sequence is generated by sequence of fitting curves, and the fitting curve represents the power change trend of the new energy equipment corresponding to a set environmental factor.

[0032] The preset fitting sequence is a time sequence generated by sequence of fitting curves, and the fitting curve represents the power change trend of the new energy equipment corresponding to a set environmental factor.

[0033] Specifically, different environmental factors may have an impact on the input / output power of the new energy equipment, such as strong wind weather that may increase the input power of the power fan, and rainy weather that may reduce the input power of the power fan. Therefore, for the power prediction of the new energy equipment, not only the power data of the new energy equipment itself should be referred to, but also the specific environmental factors such as weather factors should be combined to improve the accuracy of the power prediction of the new energy equipment.

[0034] Optionally, the determination method of the fitting curve comprises:

[0035] The power data set and the environmental data set of the new energy equipment are acquired, the preset curve is fitted according to the power data set and the environmental data set, the candidate fitting parameter is determined, and if the candidate fitting curve corresponding to the candidate fitting parameter meets the preset fitting error, the candidate fitting curve is determined as the fitting curve.

[0036] The power dataset of the new energy equipment can be historical working power data of the new energy equipment, such as input power, output power, and rated power, and the like. The environmental dataset includes environmental change data in the historical running process of the new energy equipment, such as weather changes, temperature and humidity changes, and the like. Specifically, the power dataset and the environmental dataset of the new energy equipment can be a time sequence, representing the power change and environmental change of the new energy equipment at different times.

[0037] The candidate fitting parameter is a curve parameter of the preset curve in the fitting process, such as a value of each point of the curve, a slope, and an intercept, and the like. Specifically, different candidate fitting parameters will directly affect the accuracy of the fitting curve, and therefore, for the candidate fitting curve corresponding to the candidate fitting parameter, the fitting error needs to be determined in combination with the real power data. If the fitting error is less than the preset fitting error, it indicates that the fitting is successful, and the corresponding candidate fitting curve can be determined as the fitting curve. Exemplarily, the fitting algorithm can be a Prophet algorithm.

[0038] Optionally, fitting the preset curve according to the power dataset and the environmental dataset to determine the candidate fitting parameter includes:

[0039] fitting the first curve, the second curve, and the third curve according to the power dataset and the environmental dataset to determine the candidate curve parameters of the first curve, the second curve, and the third curve;

[0040] The candidate curve parameter of the first curve is used to represent the characteristic of the non-periodic power change of the new energy equipment caused by the environmental change.

[0041] The candidate curve parameter of the second curve is used to represent the characteristic of the periodic power change of the new energy equipment caused by the environmental change.

[0042] The candidate curve parameter of the third curve is used to represent the characteristic of the irregular power change of the new energy equipment caused by the environmental change.

[0043] Specifically, the candidate curve parameter of the first curve can represent the characteristic of the non-periodic power change of the new energy equipment caused by the environmental change, such as the overall change trend of the power in a preset time period, such as an overall increasing trend or a decreasing trend.

[0044] Specifically, the candidate curve parameter of the second curve can represent the characteristic of the periodic power change of the new energy equipment caused by the environmental change. It can be understood that, for example, the periodic change trend of the power can be the periodic fluctuation of the power caused by seasonal changes. The power demand increases in winter and summer every year, and the output power of the new energy equipment increases.

[0045] Specifically, the candidate curve parameter of the third curve represents the characteristics of irregular changes in the power of the new energy equipment caused by environmental changes. Specifically, the environment may change suddenly, thereby affecting the power input and output of the new energy equipment. For example, sudden weather changes may affect the power input of the new energy equipment.

[0046] Further, the first curve, the second curve, and the third curve can represent the power influence of the new energy equipment caused by environmental changes from multiple dimensions, thereby reducing the error caused by the environment during power prediction and improving the accuracy of the prediction.

[0047] For example, the fitted curve can be y(t)=g(t)+s(t)+h(t)+∈(t), where g(t) describes the long-term trend in the time series data, capturing the influence of non-periodic factors on the value and the first curve; s(t) represents periodic fluctuations, seasonal changes every week or year, that is, the second curve; h(t) represents the influence of irregular schedules of holidays that may occur within one day or more days, that is, the third curve; and the error term ∈(t) represents random unpredictable fluctuations.

[0048] Step 120, generating a historical operation sequence according to a historical power sequence of the new energy equipment and a historical environment sequence, wherein the historical environment sequence includes environmental factors associated with the power of the new energy equipment.

[0049] The historical power sequence and the historical environment sequence can be sequence data of the new energy equipment in a historical time period.

[0050] Specifically, since the power prediction of the present application needs to combine the power data and the environmental data of the new energy equipment, the historical power sequence and the historical environment sequence obtained need to be preprocessed to generate a historical operation sequence, so as to achieve the goal of improving the prediction accuracy. The specific method is described below.

[0051] Step 130, determining the correlation of the historical operation sequence and a preset fitted sequence, and predicting the power of the new energy equipment according to the correlation.

[0052] Specifically, the correlation can represent the association between the historical operation sequence and the preset fitted sequence. Through the correlation, the power change trend of the new energy equipment corresponding to the historical operation sequence can be reflected, thereby achieving the purpose of predicting the power of the new energy equipment through the power change trend.

[0053] Optionally, if the predicted power represented by the power prediction result is greater than the rated power of the new energy equipment, an alarm is given.

[0054] The embodiment of the present application provides a power prediction method of a new energy equipment, the method comprises the following steps: acquiring a preset fitting sequence, wherein the preset fitting sequence is generated by sequence generation of a fitting curve, and the fitting curve represents a power change trend of the new energy equipment corresponding to a set environment factor; generating a historical operation sequence according to a historical power sequence and a historical environment sequence of the new energy equipment, wherein the historical environment sequence comprises an environment factor associated with the power of the new energy equipment; determining the correlation of the historical operation sequence and the preset fitting sequence, and predicting the power of the new energy equipment according to the correlation. Specifically, the preset fitting sequence can represent the power change trend of the new energy equipment corresponding to the environment factor, and then by determining the correlation of the historical operation sequence and the preset fitting sequence, the power of the new energy equipment can be predicted, so that the purpose of combining the environment factor of the new energy equipment in power prediction can be achieved, and the error caused by the environment factor in power prediction can be reduced, and the accuracy of power prediction can be improved.

[0055] Embodiment two

[0056] Figure 2 A flowchart of a power prediction method of a new energy equipment provided by the second embodiment of the present application, the present embodiment is based on the above-mentioned embodiments, and further limits the method of generating a historical operation sequence according to a historical power sequence and a historical environment sequence of the new energy equipment.

[0057] As shown in Figure 2 , comprising:

[0058] Step 210, acquiring a preset fitting sequence, wherein the preset fitting sequence is generated by sequence generation of a fitting curve, and the fitting curve represents a power change trend of the new energy equipment corresponding to a set environment factor.

[0059] Step 220, preprocessing the historical power sequence and the historical environment sequence to obtain a preprocessed historical power sequence and a preprocessed historical environment sequence, wherein the preprocessed historical power sequence and the preprocessed historical environment sequence have the same dimension.

[0060] Specifically, before power prediction, the historical power sequence and the historical environment sequence need to be preprocessed to have the same format and correspond to each other. Further, the historical power sequence and the historical environment sequence of the same period can be determined as the sequence with the corresponding relationship. Further, to ensure the accuracy of subsequent operation, it is necessary to ensure that the historical power sequence and the historical environment sequence have the same dimension.

[0061] Optionally, step 220 comprises:

[0062] acquiring a preset sequence element time interval and a preset sequence length;

[0063] Specifically, since different elements of the historical power sequence and the historical environment sequence represent power data and environment data at different times, in order to ensure the accuracy of subsequent calculation, the time intervals between the elements of the historical power sequence and the historical environment sequence need to be unified, i.e., the time intervals between the elements of the sequence are determined as a preset sequence element time interval, such as the time interval of each element in the historical power sequence and the historical environment sequence being one hour. Further, due to the deviation in data collection itself, the lengths of different sequences may be different, such as the A sequence representing data from 3-5 o'clock every day and the B sequence representing data from 3-6 o'clock, therefore, the lengths of the sequences need to be unified, i.e., the length of the sequence is unified as a preset sequence length.

[0064] adjusting the sequence elements of the historical power sequence and / or the historical environment sequence, so that the time intervals of the adjusted sequence elements of the historical power sequence and / or the historical environment sequence are the preset sequence element time intervals;

[0065] Optionally, the least common multiple of the initial time intervals of the sequence elements of the historical power sequence and / or the historical environment sequence can be determined as the preset sequence element time interval, and then by adjusting the sequence elements of the historical power sequence and / or the historical environment sequence, only the elements with the preset sequence element time interval are retained in the sequence. For example, the A sequence is (0, 1, 2, 3, 4, 5, 6) and the B sequence is (0, 2, 4, 6, 8), therefore, the least common multiple of the initial time intervals of the A sequence and the B sequence is 2, then the preset sequence element time interval can be set to 2, and the elements of the A sequence and the B sequence are adjusted, and after adjustment, the A sequence is (0, 2, 4, 6) and the B sequence is (0, 2, 4, 6, 8).

[0066] If the lengths of the historical power sequence and the historical environment sequence are greater than the preset sequence length, the sequence elements exceeding the length are deleted;

[0067] If the lengths of the historical power sequence and the historical environment sequence are less than the preset sequence length, the last element of the historical power sequence and the historical environment sequence is determined as a supplement element, and the historical power sequence and the historical environment sequence are supplemented to the preset sequence length by the supplement element.

[0068] Specifically, to ensure that the length of the historical power sequence and the historical environment sequence is the same as the preset sequence length, the overlong sequence can be deleted, or the too short sequence can be supplemented. If the length of the historical power sequence and the historical environment sequence is greater than the preset sequence length, the sequence elements exceeding the length are deleted. If the length of the historical power sequence and the historical environment sequence is less than the preset sequence length, the last element of the historical power sequence and the historical environment sequence is determined as a supplement element, and the historical power sequence and the historical environment sequence are supplemented to the preset sequence length through the supplement element.

[0069] For example, the A sequence is (0, 2, 4, 6), the B sequence is (0, 2, 4, 6, 8), and the preset sequence length is 4. The 8 in the B sequence can be deleted. If the preset sequence length is 5, the 6 in the A sequence is determined as a supplement element, and the A sequence is supplemented to (0, 2, 4, 6, 6).

[0070] In the above manner, the historical power sequence and the historical environment sequence have the same dimension, which facilitates subsequent calculation and improves the prediction accuracy.

[0071] In step 230, the preprocessed historical power sequence and the historical environment sequence are spliced to generate a historical operation sequence.

[0072] Specifically, the splicing manner can be that the preprocessed historical power sequence and the historical environment sequence are spliced at the head and tail, or corresponding elements of the historical power sequence and the historical environment sequence are grouped into a vector group, which is not limited here.

[0073] In step 240, the historical operation sequence and the preset fitting sequence are spliced to generate a feature sequence, wherein the dimension of the feature sequence is determined by the number of environment parameters in the historical operation sequence.

[0074] Specifically, the splicing manner of the historical operation sequence and the preset fitting sequence can be that the historical power sequence and the historical environment sequence are spliced at the head and tail, or corresponding elements of the historical power sequence and the historical environment sequence are grouped into a vector group, which is not limited here.

[0075] For example, the feature sequence can have the following form: X = {X LMD ,X P} ∈ R T*(N+1) , wherein X LMD is the historical operation sequence, X P is the preset fitting sequence, T is a time parameter, and N is the number of parameter variables. Further, the feature sequence can contain information of values of multiple variables at the same time step. Further, the feature sequence can be mapped to a high dimension through the following formula: To improve the generalization ability of the model.

[0076] Step 250, input the feature sequence into the pre-trained prediction model, determine the query matrix, key matrix and value matrix based on the weight matrix and the feature sequence through the prediction model, wherein the prediction model includes a sparse self-attention layer, the sparse self-attention layer is used to determine the relevance of the query matrix, key matrix and value matrix, and the matrix elements in the weight matrix are updated in the training process of the sparse self-attention layer.

[0077] Wherein, the attention mechanism optimized by the sparse self-attention layer is used to calculate the query matrix, key matrix and value matrix corresponding to the feature sequence through the weight matrix.

[0078] Specifically, in the training process, the first weight matrix can be an identity matrix, and the elements of the weight matrix are updated through the sparse self-attention layer in the training process, and then the weight matrix satisfying the preset training condition is determined after the training of the prediction model is successful. Optionally, the weight matrix can include a first matrix, a second matrix and a third matrix, which are used for dot product operation with the feature sequence to generate the query matrix, the key matrix and the value matrix corresponding to the feature sequence, respectively.

[0079] Specifically, according to inputting the feature sequence into the pre-trained prediction model, the training model will calculate based on the feature sequence and the weight matrix to determine the query matrix, the key matrix and the value matrix.

[0080] Optionally, the query matrix, the key matrix and the value matrix can be determined by the following formula.

[0081] A=X*W_A

[0082] B=X*W_B

[0083] C=X*W_C

[0084] Wherein, W_A, W_B, W_C are the first matrix, the second matrix and the third matrix trained successfully, and A, B, C are the query matrix, the key matrix and the value matrix, respectively.

[0085] Step 260, determine the dependence relationship between power and environmental factors according to the relevance between the query matrix, the key matrix and the value matrix, and predict the power of the new energy equipment according to the dependence relationship between power and environmental factors.

[0086] Optionally, the correlation determination formula can be

[0087]

[0088] Wherein, A, B, C are the query matrix, the key matrix and the value matrix, respectively, and d BThe dimension of the key matrix vector, and the Softmax is an activation function. Specifically, by the formula, the dot product range can be reduced, the gradient stability of the softmax is guaranteed, and the accuracy of the correlation calculation is improved.

[0089] Specifically, the correlation can represent the dependence relationship between the power and the environmental factors. If the correlation is larger, it represents that the current power is seriously affected by the environment, otherwise, the current power is not seriously affected by the environment.

[0090] Further, by determining the dependence relationship between the power and the environmental factors, the power of the new energy equipment can be predicted. The specific manner is not limited here.

[0091] Exemplarily, Figure 3 A power prediction diagram of a new energy equipment is provided in an embodiment of the present application.

[0092] Specifically, Figure 3 It mainly includes two parts. The first part is the fitting of the prophet model, and the second part is the prediction of the power of the new energy equipment through the sparse self-attention mechanism.

[0093] Specifically, in the first part, the power data and the meteorological data of the new energy equipment need to be input, and then the power data and the meteorological data are preprocessed to unify the format, generate the power dataset and the environmental dataset, and further, the samples in the dataset can be decomposed by the prophet model to determine the trend item (the first curve), the seasonal item (the second curve), the holiday item (the third curve) and the residual item, that is, to determine the fitting result (the fitting curve).

[0094] In the second part, the meteorological data and the power data of the new energy equipment need to be obtained first, and then normalized to generate the historical operation sequence. Further, the historical operation sequence and the fitting result are input into the inverted embedding layer. The inverted embedding layer can learn the feature representation of the input sequence, and aggregate the global features of each input sequence independently to improve the performance of the model. Further, by inputting the sequence into the sparse self-attention layer, the query matrix, the key matrix and the value matrix of the input sequence are determined, and then the correlation is determined according to the query matrix, the key matrix and the value matrix, and the power prediction is completed through the correlation.

[0095] The embodiment of the present application provides a power prediction method of a new energy equipment. By inputting the feature sequence into the pre-trained prediction model, the query matrix, the key matrix and the value matrix corresponding to the feature sequence are determined, and then the correlation between the query matrix, the key matrix and the value matrix can be used to predict the power. This method can effectively combine the environmental factors of the new energy equipment when predicting the power, reduce the error caused by the environmental factors when predicting the power, and improve the accuracy of the power prediction.

[0096] Embodiment three

[0097] Figure 4 A structural schematic diagram of a power prediction device of a new energy equipment is provided for embodiment three of the present application, and the device can be used to execute any power prediction method of the new energy equipment described in the embodiments of the present application

[0098] As shown in Figure 4 , the device comprises:

[0099] The acquisition module 310 is configured to acquire a preset fitting sequence, wherein the preset fitting sequence is generated by sequence generation of a fitting curve, and the fitting curve represents a power change trend of the new energy equipment corresponding to a set environment factor.

[0100] The splicing module 320 is configured to generate a historical operation sequence according to a historical power sequence and a historical environment sequence of the new energy equipment, wherein the historical environment sequence comprises an environment factor associated with the power of the new energy equipment.

[0101] The prediction module 330 is configured to determine a correlation between the historical operation sequence and the preset fitting sequence, and predict the power of the new energy equipment according to the correlation.

[0102] The embodiment of the present application provides a power prediction device of a new energy equipment, which acquires a preset fitting sequence, wherein the preset fitting sequence is generated by sequence generation of a fitting curve, and the fitting curve represents a power change trend of the new energy equipment corresponding to a set environment factor; generates a historical operation sequence according to a historical power sequence and a historical environment sequence of the new energy equipment, wherein the historical environment sequence comprises an environment factor associated with the power of the new energy equipment; determines a correlation between the historical operation sequence and the preset fitting sequence, and predicts the power of the new energy equipment according to the correlation. Specifically, the preset fitting sequence can represent the power change trend of the new energy equipment corresponding to the environment factor, and then by determining the correlation between the historical operation sequence of the new energy equipment and the preset fitting sequence, the power of the new energy equipment can be predicted, which can achieve the purpose of combining the environment factor of the new energy equipment when power prediction is performed, and then reduce the error caused by the environment factor when power prediction is performed, and improve the accuracy of power prediction.

[0103] Optionally, the device further comprises a fitting module configured to determine the fitting curve.

[0104] The fitting module comprises:

[0105] The acquisition unit is configured to acquire a power data set and an environment data set of the new energy equipment

[0106] a fitting unit, configured to fit a preset curve according to the power dataset and the environment dataset, to determine candidate fitting parameters

[0107] a determining unit, configured to determine the candidate fitting curve as the fitting curve if the candidate fitting curve corresponding to the candidate fitting parameters meets a preset fitting error.

[0108] The fitting unit is specifically configured to fit a first curve, a second curve and a third curve according to the power dataset and the environment dataset, to determine candidate curve parameters of the first curve, the second curve and the third curve, the candidate curve parameters of the first curve being used to represent characteristics of non-periodic power changes of the new energy equipment caused by environmental changes.

[0109] The candidate curve parameters of the second curve are used to represent characteristics of periodic power changes of the new energy equipment caused by environmental changes.

[0110] The candidate curve parameters of the third curve are used to represent characteristics of irregular power changes of the new energy equipment caused by environmental changes.

[0111] Optionally, the splicing module 320 includes:

[0112] a preprocessing unit, configured to preprocess the historical power sequence and the historical environment sequence, to obtain a preprocessed historical power sequence and a preprocessed historical environment sequence, wherein the preprocessed historical power sequence and the preprocessed historical environment sequence have the same dimension.

[0113] a splicing unit, configured to splice the preprocessed historical power sequence and the preprocessed historical environment sequence, to generate a historical running sequence.

[0114] Optionally, the preprocessing unit includes:

[0115] an obtaining subunit, configured to obtain a preset sequence element time interval and a preset sequence length;

[0116] an adjusting unit, configured to adjust sequence elements of the historical power sequence and / or the historical environment sequence, so that the time interval of the adjusted sequence elements of the historical power sequence and / or the historical environment sequence is the preset sequence element time interval.

[0117] a judging unit, configured to delete sequence elements exceeding the length if the length of the historical power sequence and the historical environment sequence is greater than the preset sequence length, and to determine the last element of the historical power sequence and the historical environment sequence as a make-up element if the length of the historical power sequence and the historical environment sequence is less than the preset sequence length, and to make up the historical power sequence and the historical environment sequence to the preset sequence length through the make-up element.

[0118] Optionally, the prediction module 330 comprises:

[0119] a splicing unit configured to splice the historical operation sequence and the preset fitting sequence to generate a feature sequence, wherein a dimension of the feature sequence is determined by a number of environment parameters in the historical operation sequence;

[0120] a matrix determination unit configured to input the feature sequence into a pre-trained prediction model to determine a query matrix, a key matrix and a value matrix based on a weight matrix and the feature sequence by using the prediction model, wherein the prediction model comprises a sparse self-attention layer configured to determine a correlation of the query matrix, the key matrix and the value matrix, and matrix elements in the weight matrix are updated in a training process of the sparse self-attention layer;

[0121] a prediction unit configured to determine a power-environment factor dependency relationship according to the correlation between the query matrix, the key matrix and the value matrix, and to predict the power of the new energy equipment according to the power-environment factor dependency relationship.

[0122] Optionally, the power prediction device of the new energy equipment further comprises an alarm unit configured to perform alarm reminding if a predicted power represented by the power prediction result is greater than a rated power of the new energy equipment.

[0123] The power prediction device of the new energy equipment provided by the embodiment of the present application can execute the power prediction method of the new energy equipment provided by any embodiment of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0124] Embodiment four

[0125] Figure 5 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0126] As Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., connected to the at least one processor 11 in communication. The memory stores a computer program executable by the at least one processor 11, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0127] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0128] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the power prediction method of the new energy device.

[0129] In some embodiments, the power prediction method of the new energy device can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the power prediction method of the new energy device described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the power prediction method of the new energy device by any other appropriate means, such as by means of firmware.

[0130] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0131] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program

[0132] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, 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.

[0133] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0134] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0135] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0136] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in a different order, as long as the desired results of the present disclosure are achieved, and the present disclosure is not limited herein.

[0137] The specific embodiments described above are not intended to be limiting, and persons skilled in the art will appreciate that various modifications, combinations, sub-combinations and alternatives can be made to the specific embodiments without departing from the spirit and principles of the disclosure. Accordingly, the disclosure is not limited to the specific embodiments described above, but only by the scope of the appended claims.

Claims

1. A power prediction method of a new energy device, characterized by, The method comprises the following steps: obtaining a preset fitting sequence, wherein the preset fitting sequence is generated by sequenceizing a fitting curve, and the fitting curve represents a power change trend of the new energy equipment corresponding to a set environment factor; generating a historical operation sequence according to a historical power sequence and a historical environment sequence of the new energy equipment, wherein the historical environment sequence comprises an environment factor associated with the power of the new energy equipment; determining a correlation between the historical operation sequence and the preset fitting sequence, and predicting the power of the new energy equipment according to the correlation; The method for determining the fitting curve comprises the following steps: obtaining a power dataset and an environment dataset of the new energy equipment; fitting a preset curve according to the power dataset and the environment dataset to determine candidate fitting parameters; if a candidate fitting curve corresponding to the candidate fitting parameters meets a preset fitting error, the candidate fitting curve is determined as the fitting curve; The method for fitting the preset curve according to the power dataset and the environment dataset to determine the candidate fitting parameters comprises the following steps: fitting a first curve, a second curve and a third curve according to the power dataset and the environment dataset to determine candidate curve parameters of the first curve, the second curve and the third curve; the candidate curve parameters of the first curve are used to represent characteristics of non-periodic power changes of the new energy equipment caused by environmental changes; the candidate curve parameters of the second curve are used to represent characteristics of periodic power changes of the new energy equipment caused by environmental changes; the candidate curve parameters of the third curve are used to represent characteristics of irregular power changes of the new energy equipment caused by environmental changes.

2. The method of claim 1, wherein, The method for generating the historical operation sequence according to the historical power sequence and the historical environment sequence of the new energy equipment comprises the following steps: preprocessing the historical power sequence and the historical environment sequence to obtain preprocessed historical power sequence and historical environment sequence, wherein the preprocessed historical power sequence and the historical environment sequence have the same dimension; splicing the preprocessed historical power sequence and the historical environment sequence to generate the historical operation sequence.

3. The method of claim 2, wherein, The method for preprocessing the historical power sequence and the historical environment sequence comprises the following steps: obtaining a preset sequence element time interval and a preset sequence length; adjusting sequence elements of the historical power sequence and / or the historical environment sequence, so that the time interval of the adjusted sequence elements of the historical power sequence and / or the historical environment sequence is the preset sequence element time interval; if the length of the historical power sequence and the historical environment sequence is greater than the preset sequence length, deleting sequence elements exceeding the length; if the length of the historical power sequence and the historical environment sequence is less than the preset sequence length, then the last element of the historical power sequence and the historical environment sequence is determined as a complementary element, and the historical power sequence and the historical environment sequence are supplemented to the preset sequence length through the complementary element.

4. The method of claim 1, wherein, The method for determining the correlation between the historical operation sequence and the preset fitting sequence and predicting the power of the new energy equipment according to the correlation comprises the following steps: The historical operation sequence and the preset fitting sequence are spliced to generate a feature sequence, wherein a dimension of the feature sequence is determined by a number of environment parameters in the historical operation sequence; The feature sequence is input into a pre-trained prediction model, and the prediction model determines a query matrix, a key matrix and a value matrix based on a weight matrix and the feature sequence, wherein the prediction model includes a sparse self-attention layer, the sparse self-attention layer is used to determine a correlation of the query matrix, the key matrix and the value matrix, and a matrix element in the weight matrix is updated in a training process of the sparse self-attention layer; A dependence relationship between power and environment factors is determined according to the correlation between the query matrix, the key matrix and the value matrix, and the power of the new energy equipment is predicted according to the dependence relationship between the power and the environment factors.

5. The method of claim 1, wherein, Further comprising: If a predicted power represented by the power prediction result is greater than a rated power of the new energy equipment, an alarm is given.

6. A power prediction device for a new energy device, for executing the power prediction method for a new energy device according to any one of claims 1 to 5, characterized by Comprising: An acquisition module is configured to acquire a preset fitting sequence, wherein the preset fitting sequence is generated by sequence of a fitting curve, and the fitting curve represents a power change trend of the new energy equipment corresponding to a set environment factor; A splicing module is configured to generate a historical operation sequence according to a historical power sequence and a historical environment sequence of the new energy equipment, wherein the historical environment sequence includes an environment factor associated with the power of the new energy equipment; A prediction module is configured to determine a correlation of the historical operation sequence and the preset fitting sequence, and predict the power of the new energy equipment according to the correlation.

7. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the power prediction method of the new energy equipment in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the power prediction method of the new energy equipment in any one of claims 1-5 when executed.

Images