Wind speed prediction method, system and device and storage medium

Abstract

The invention discloses a wind speed prediction method, system and device and a storage medium. The method comprises the following steps: acquiring a first historical observation wind speed data set and a second historical observation wind speed data set of a fan head; based on the background field data and the first historical observation wind speed data set, determining first prediction data of the current day of the fan head in the background field, and based on the background field data and the second historical observation wind speed data set, determining second prediction data of the fan head after the current day in the background field; inputting the first prediction data and the second historical observation wind speed data set into a convolutional neural network to obtain third prediction data; adjusting parameters of the convolutional neural network according to the second prediction data and the third prediction data to obtain a trained convolutional neural network; and inputting the observed wind speed of the fan head before the prediction day into the trained convolutional neural network to obtain a target wind speed. The method can be widely applied to the technical field of artificial intelligence.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a wind speed prediction method, system, device and storage medium. Background Art

[0002] With many countries incorporating the utilization of wind energy into their national energy policies, wind energy prediction has become increasingly important. For power systems, integrating wind energy requires changes in the scheduling and operation of other generators to handle unpredictable deviations between supply and demand. Wind speed prediction is crucial in wind energy prediction because wind speed directly determines the volatility and predictability of wind power generation. Accurate wind speed prediction can help power companies optimize buying and selling strategies in the power market and avoid economic losses caused by insufficient or excessive power generation. In addition, accurate prediction can also reduce the pressure on grid scheduling, reduce the demand for backup power, thereby reducing operating costs and enhancing the market competitiveness of wind energy. This is of great significance for promoting the popularization of renewable energy and the stability of the power market.

[0003] In related technologies, the NWP model for simulation is the main method for wind speed prediction. Such models predict weather conditions by simulating atmospheric flow and physical processes. Commonly used numerical weather prediction models include the Global Forecast System (GFS), the European Centre for Medium-Range Weather Forecasts (ECMWF), etc. Such models use partial differential equations to describe the behavior of atmospheric fluids, but the computational load is large, resulting in relatively low accuracy of wind speed prediction. Therefore, there are still technical problems to be solved in related technologies. Summary of the Invention

[0004] The purpose of this application is to solve at least to some extent one of the technical problems existing in the prior art.

[0005] To this end, an object of an embodiment of this application is to provide a wind speed prediction method, system, device and storage medium, and this solution can improve the accuracy of wind speed prediction.

[0006] To achieve the above technical objectives, the technical solutions adopted in the embodiments of the present application include: A wind speed prediction method, comprising the following steps: obtaining a first historical observed wind speed data set and a second historical observed wind speed data set of the wind turbine nacelle; wherein the first historical observed wind speed data set is a set of historical observed wind speed data before the current day of the wind turbine nacelle; the second historical observed wind speed data set is a set of wind speed data before the current day and the wind speed data of the current day of the wind turbine nacelle; based on the background field data and the first historical observed wind speed data set, determining the first prediction data of the current day of the wind turbine nacelle under the background field, and based on the background field data and the second historical observed wind speed data set, determining the second prediction data after the current day of the wind turbine nacelle under the background field; inputting the first prediction data and the second historical observed wind speed data set into a convolutional neural network to obtain the third prediction data after the current day; adjusting the parameters of the convolutional neural network according to the second prediction data and the third prediction data to obtain a trained convolutional neural network; inputting the observed wind speed of the wind turbine nacelle before the prediction day into the trained convolutional neural network to obtain the target wind speed of the prediction day.

[0007] The present application can obtain a first historical observed wind speed data set and a second historical observed wind speed data set of the wind turbine nacelle; wherein the first historical observed wind speed data set is a set of historical observed wind speed data before the current day of the wind turbine nacelle; the second historical observed wind speed data set is a set of wind speed data before the current day and the wind speed data of the current day of the wind turbine nacelle; based on the background field data and the first historical observed wind speed data set, determining the first prediction data of the current day of the wind turbine nacelle under the background field, and based on the background field data and the second historical observed wind speed data set, determining the second prediction data after the current day of the wind turbine nacelle under the background field; inputting the first prediction data and the second historical observed wind speed data set into a convolutional neural network to obtain the third prediction data after the current day; adjusting the parameters of the convolutional neural network according to the second prediction data and the third prediction data to obtain a trained convolutional neural network; inputting the observed wind speed of the wind turbine nacelle before the prediction day into the trained convolutional neural network to obtain the target wind speed of the prediction day. The present application proposes a new prediction method, inputting the observed wind speed of the wind turbine nacelle before the prediction day into an artificial intelligence model to obtain the wind speed of the prediction day. This method fully considers the historical observed wind speed data before the current day and the wind speed data of the current day to predict the wind speed of the prediction day. The present application can improve the prediction accuracy.

[0008] In addition, according to a wind speed prediction method in the above embodiment of the present invention, the following additional technical features may also be provided:

[0009] Further, in the embodiment of the present application, determining the first prediction data of the current day of the wind turbine nacelle under the background field based on the background field data and the first historical observed wind speed data set specifically includes:

[0010] Configuring the configuration parameters of the Weather Research and Forecasting (WRF) model and sending the background field data and physical boundary conditions to the WRF model to obtain a simulation model;

[0011] Running the simulation model and inputting the first historical observed wind speed data set into the simulation model to obtain the first prediction data.

[0012] Further, in the embodiment of the present application, determining the second prediction data after the current day of the wind turbine nacelle under the background field based on the background field data and the second historical observed wind speed data set specifically includes:

[0013] Configuring the configuration parameters of the WRF model and sending the background field data and physical boundary conditions to the WRF model to obtain a simulation model;

[0014] Running the simulation model and inputting the second historical observed wind speed data set into the simulation model to obtain the second prediction data.

[0015] Further, in the embodiment of the present application, inputting the first prediction data and the second historical observed wind speed data set into a convolutional neural network to obtain the third prediction data after the current day specifically includes:

[0016] Performing data preprocessing on the first prediction data and the second historical observed wind speed data set to obtain training data;

[0017] Inputting the training data into the convolutional neural network to obtain the third prediction data.

[0018] Further, in the embodiment of the present application, performing data preprocessing on the first prediction data and the second historical observed wind speed data set to obtain training data specifically includes:

[0019] Performing data alignment processing on the first prediction data and the second historical observed wind speed data set,

[0020] and performing data format adjustment processing on the first prediction data and the second historical observed wind speed data set to obtain training data.

[0021] Further, in the embodiment of the present application, adjusting the parameters of the convolutional neural network according to the second prediction data and the third prediction data to obtain a trained convolutional neural network specifically includes:

[0022] Determine a training error according to the second prediction data and the third prediction data;

[0023] When the training error is greater than a preset error, adjust the parameters of the convolutional neural network and retrain the convolutional neural network until the training error is less than or equal to the preset error, so as to obtain the trained convolutional neural network.

[0024] Further, in the embodiment of the present application, the determining a training error according to the second prediction data and the third prediction data specifically includes:

[0025] Take the difference between the second prediction data and the third prediction data to obtain a first difference;

[0026] Use the first difference as the training error.

[0027] On the other hand, the embodiment of the present application further provides a wind speed prediction system, including:

[0028] A first processing unit, configured to obtain a first set of historical observed wind speed data and a second set of historical observed wind speed data of the wind turbine head; wherein the first set of historical observed wind speed data is a set of historical observed wind speed data before the current day of the wind turbine head; the second set of historical observed wind speed data is a set of wind speed data before the current day of the wind turbine head and the wind speed data of the current day;

[0029] A second processing unit, configured to determine first prediction data of the current day of the wind turbine head under the background field based on the background field data and the first set of historical observed wind speed data, and determine second prediction data after the current day of the wind turbine head under the background field based on the background field data and the second set of historical observed wind speed data;

[0030] A third processing unit, configured to input the first prediction data and the second set of historical observed wind speed data into a convolutional neural network to obtain third prediction data after the current day;

[0031] A fourth processing unit, configured to adjust the parameters of the convolutional neural network according to the second prediction data and the third prediction data to obtain a trained convolutional neural network;

[0032] A fifth processing unit, configured to input the observed wind speed of the wind turbine head before the prediction day into the trained convolutional neural network to obtain the target wind speed of the prediction day.

[0033] On the other hand, the present application further provides a wind speed prediction device, including:

[0034] At least one processor;

[0035] At least one memory for storing at least one program;

[0036] When the at least one program is executed by the at least one processor, the at least one processor implements a wind speed prediction method as described in any one of the invention contents.

[0037] In addition, the present application also provides a computer-readable storage medium storing instructions executable by a processor, and the instructions executable by the processor are used to execute a wind speed prediction method as described in any one of the above when executed by the processor.

[0038] The advantages and beneficial effects of the present application will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present application:

[0039] The present application can obtain a first historical observed wind speed data set and a second historical observed wind speed data set of the wind turbine nacelle; wherein the first historical observed wind speed data set is a set of historical observed wind speed data before the current day of the wind turbine nacelle; the second historical observed wind speed data set is a set of wind speed data before the current day and the wind speed data of the current day of the wind turbine nacelle; based on the background field data and the first historical observed wind speed data set, determine the first prediction data of the current day of the wind turbine nacelle under the background field, and based on the background field data and the second historical observed wind speed data set, determine the second prediction data after the current day of the wind turbine nacelle under the background field; input the first prediction data and the second historical observed wind speed data set into a convolutional neural network to obtain the third prediction data after the current day; according to the second prediction data and the third prediction data, adjust the parameters of the convolutional neural network to obtain a trained convolutional neural network; input the observed wind speed of the wind turbine nacelle before the prediction day into the trained convolutional neural network to obtain the target wind speed of the prediction day. The present application proposes a new prediction method, which inputs the observed wind speed of the wind turbine nacelle before the prediction day into an artificial intelligence model to obtain the wind speed of the prediction day. This method fully considers the historical observed wind speed data before the current day and the wind speed data of the current day to predict the wind speed of the prediction day, and the present application can improve the prediction accuracy. Description of the Drawings

[0040] Figure 1 It is a schematic diagram of the steps of the wind speed prediction method in a specific embodiment of the present invention;

[0041] Figure 2 It is a schematic flowchart of the wind speed prediction method in a specific embodiment of the present invention

[0042] Figure 3 It is a schematic structural diagram of the wind speed prediction system in another specific embodiment of the present invention;

[0043] Figure 4 This is a schematic structural diagram of a wind speed prediction device according to a specific embodiment of the present invention. Specific Embodiment

[0044] The following describes in detail the embodiments of the present invention with reference to the accompanying drawings. The principles and processes of the wind speed prediction method, system, device, and storage medium in the embodiments of the present invention are described as follows.

[0045] First, the terms in this application are explained:

[0046] 1. NWP (Numerical Weather Prediction): A numerical weather prediction model, which is a general term for weather prediction methods based on physics, mathematics, and computer science. It simulates the future changes in the atmospheric state by solving the basic physical equations describing atmospheric motion (such as the hydrodynamic equation and the thermodynamic equation). In wind energy prediction, the NWP model predicts future wind speeds by simulating physical processes.

[0047] 2. RMSE (Root Mean Square Error): The root mean square error, a statistical indicator that measures the deviation between the predicted value and the actual observed value. The calculation method is to take the square root of the average of the squares of the prediction errors.

[0048] 3. WRF (Weather Research and Forecasting Model): A specific model in the NWP model, which is dedicated to weather research and actual weather forecasting tasks. DA: Data Assimilation, a method that combines observed data and numerical models to optimize the initial conditions of the model and improve prediction accuracy.

[0049] 4. WRFDA (Weather Research and Forecasting Data Assimilation): The data assimilation system of the Weather Research and Forecasting Model (WRF). It is used to combine observed data (such as meteorological stations, satellites, radars, etc.) with the numerical weather prediction model (WRF model) to optimize the initial conditions of the model, thereby improving the accuracy and reliability of weather forecasting.

[0050] 5. ECMWF:. The European Centre for Medium-Range Weather Forecasts. It is an international organization headquartered in Reading, UK, which is dedicated to the research, development, and operation of medium-range weather forecasts (i.e., forecasts for the next 3 - 10 days). ECMWF is widely regarded as one of the most advanced numerical weather forecasting agencies in the world.

[0051] 6. Machine Learning: Machine learning is a technology that enables computers to automatically learn patterns from data and make predictions or decisions without explicit programming.

[0052] 7. Deep Learning: Deep learning is a machine learning method that automatically learns features and patterns in data through multi-layer neural networks for classification, regression, or generation tasks.

[0053] 8. CNN (Convolutional Neural Network): Convolutional neural network, a deep learning model, is particularly good at processing images and spatio-temporal data.

[0054] 9. 3DVAR, 4DVAR: Two algorithms in data assimilation that perform data assimilation in a variational manner and are used in numerical weather prediction and other applications involving complex dynamic systems to combine observational data and background fields and optimize initial conditions.

[0055] In the 3DVAR algorithm, WRFDA performs data assimilation by minimizing a scalar objective function to produce an analysis:

[0056]

[0057] While in the 4DVAR algorithm, WRFDA performs data assimilation by minimizing a scalar objective function J(x):

[0058]

[0059] The difference between 3DVAR and 4DVAR is that 4DVAR additionally considers the factor of time, so the results obtained are more accurate, but at the same time, it also brings an increase in computational cost.

[0060] As many countries incorporate the utilization of wind energy into their national energy policies, wind energy prediction has become increasingly important. For power systems, integrating wind energy requires changing the scheduling and operation of other generators to handle unpredictable deviations between supply and demand. Wind speed prediction is crucial in wind energy prediction because wind speed directly determines the volatility and predictability of wind power generation. Accurate wind speed prediction can help power companies optimize buying and selling strategies in the power market and avoid economic losses caused by insufficient or excessive power generation. In addition, accurate prediction can also reduce the pressure on grid scheduling, reduce the demand for backup power, thereby reducing operating costs and enhancing the market competitiveness of wind energy. This is of great significance for promoting the popularization of renewable energy and the stability of the power market.

[0061] In the related art, the NWP model is the main method for wind speed prediction. Such models predict weather conditions by simulating atmospheric flow and physical processes. Commonly used numerical weather prediction models include the Global Forecast System (GFS), the European Centre for Medium-Range Weather Forecasts (ECMWF), etc. These models use partial differential equations to describe the behavior of atmospheric fluids, but the computational load is large, resulting in relatively low accuracy of wind speed prediction. Therefore, there are still technical problems to be solved in the related art.

[0062] In view of the above-mentioned defects of the prior art, referring to Figure 1 , the present application provides a wind speed prediction method. In Figure 1 , the method may include the following steps S101 - S105.

[0063] S101. Obtain a first historical observed wind speed data set and a second historical observed wind speed data set of the wind turbine nacelle; wherein the first historical observed wind speed data set is a set of historical observed wind speed data before the current day of the wind turbine nacelle; the second historical observed wind speed data set is a set of wind speed data before the current day and the wind speed data of the current day of the wind turbine nacelle.

[0064] S102. Based on the background field data and the first historical observed wind speed data set, determine the first prediction data of the current day of the wind turbine nacelle under the background field, and based on the background field data and the second historical observed wind speed data set, determine the second prediction data after the current day of the wind turbine nacelle under the background field.

[0065] S103. Input the first prediction data and the second historical observed wind speed data set into a convolutional neural network to obtain the third prediction data after the current day.

[0066] S104. Adjust the parameters of the convolutional neural network according to the second prediction data and the third prediction data to obtain a trained convolutional neural network.

[0067] S105. Input the observed wind speed of the wind turbine nacelle before the prediction day into the trained convolutional neural network to obtain the target wind speed of the prediction day.

[0068] It can be understood that the prediction day is the day after the current day, and the prediction day and the current day are two adjacent days. The current day can be any day.

[0069] In some feasible embodiments of the present application, the processor can be connected to the acquisition unit through a wired or wireless connection. After establishing the connection, the processor can obtain the first historical observed wind speed data set and the second historical observed wind speed data set of the wind turbine head from the acquisition unit. The first historical observed wind speed data set is a set of historical observed wind speed data before the current day of the wind turbine head, and the second historical observed wind speed data set is a set of wind speed data before the current day and the wind speed data of the current day of the wind turbine head. Based on the background field data and the first historical observed wind speed data set, the processor can determine the first prediction data of the current day of the wind turbine head under the background field, and based on the background field data and the second historical observed wind speed data set, the processor can determine the second prediction data after the current day of the wind turbine head under the background field. By inputting the first prediction data and the second historical observed wind speed data set into the convolutional neural network, the processor can obtain the third prediction data after the current day. According to the second prediction data and the third prediction data, the processor can adjust the parameters of the convolutional neural network to obtain a trained convolutional neural network. By inputting the observed wind speed of the wind turbine head before the prediction day into the trained convolutional neural network, the processor can obtain the target wind speed of the prediction day.

[0070] It should be noted that the above-mentioned wired connection method can include the connection between the mobile device and the processing module, and can also include the connection between the processing module and the hardware device, as well as the wired connection between other currently known or future-developed devices and the processing module. The above-mentioned wireless connection method can include, but is not limited to, 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (Ultra Wide Band) connection, and other currently known or future-developed wireless connection methods.

[0071] This application can obtain the first historical observed wind speed data set and the second historical observed wind speed data set of the wind turbine nacelle; wherein the first historical observed wind speed data set is the set of historical observed wind speed data before the current day of the wind turbine nacelle; the second historical observed wind speed data set is the set of wind speed data before the current day and the wind speed data of the current day of the wind turbine nacelle; based on the background field data and the first historical observed wind speed data set, determine the first prediction data of the current day of the wind turbine nacelle under the background field, and based on the background field data and the second historical observed wind speed data set, determine the second prediction data after the current day of the wind turbine nacelle under the background field; input the first prediction data and the second historical observed wind speed data set into a convolutional neural network to obtain the third prediction data after the current day; adjust the parameters of the convolutional neural network according to the second prediction data and the third prediction data to obtain a trained convolutional neural network; input the observed wind speed of the wind turbine nacelle before the prediction day into the trained convolutional neural network to obtain the target wind speed of the prediction day. This application proposes a new prediction method, which inputs the observed wind speed of the wind turbine nacelle before the prediction day into an artificial intelligence model to obtain the wind speed of the prediction day. This method fully considers the historical observed wind speed data before the current day and the wind speed data of the current day to predict the wind speed of the prediction day. This application can improve the prediction accuracy.

[0072] Further, in the embodiment of this application, the process of determining the first prediction data of the current day of the wind turbine nacelle under the background field based on the background field data and the first historical observed wind speed data set may include step S201-step S202.

[0073] S201. Configure the configuration parameters of the Weather Research and Forecasting model and send the background field data and physical boundary conditions to the Weather Research and Forecasting model to obtain a simulation model.

[0074] S202. Run the simulation model and input the first historical observed wind speed data set into the simulation model to obtain the first prediction data.

[0075] It can be understood that the simulation model may be the Weather Research and Forecasting model, and the first prediction data may be the wind speed data of the current day obtained by running the Weather Research and Forecasting model.

[0076] Further, in the embodiment of this application, the process of determining the second prediction data after the current day of the wind turbine nacelle under the background field based on the background field data and the second historical observed wind speed data set may include step S301-step S302.

[0077] S301. Configure the configuration parameters of the Weather Research and Forecasting model and send the background field data and physical boundary conditions to the Weather Research and Forecasting model to obtain a simulation model.

[0078] S302. Run the simulation model and input the second historical observed wind speed data set into the simulation model to obtain second prediction data.

[0079] It can be understood that the simulation model can be a weather research and forecasting model, and the second prediction data can be the wind speed data of the predicted day obtained by running the weather research and forecasting model.

[0080] Further, in the embodiment of the present application, the process of inputting the first prediction data and the second historical observed wind speed data set into the convolutional neural network to obtain the third prediction data after the current day may include steps S401 - S402.

[0081] S401. Perform data preprocessing on the first prediction data and the second historical observed wind speed data set to obtain training data.

[0082] S402. Input the training data into the convolutional neural network to obtain third prediction data.

[0083] Further, in the embodiment of the present application, the process of performing data preprocessing on the first prediction data and the second historical observed wind speed data set to obtain training data may include step S501.

[0084] S501. Perform data alignment processing on the first prediction data and the second historical observed wind speed data set, and perform data format adjustment processing on the first prediction data and the second historical observed wind speed data set to obtain training data.

[0085] Further, in the embodiment of the present application, the process of adjusting the parameters of the convolutional neural network according to the second prediction data and the third prediction data to obtain a trained convolutional neural network may include steps S601 - S602.

[0086] S601. Determine the training error according to the second prediction data and the third prediction data.

[0087] S602. When the training error is greater than the preset error, adjust the parameters of the convolutional neural network and retrain the convolutional neural network until the training error is less than or equal to the preset error to obtain a trained convolutional neural network.

[0088] Further, in the embodiment of the present application, the process of determining the training error according to the second prediction data and the third prediction data may include steps S701 - S702.

[0089] S701. Subtract the third prediction data from the second prediction data to obtain a first difference.

[0090] S702. Use the first difference as the training error.

[0091] The principles of the present application will be described below with reference to the accompanying drawings. Figure 2 The principles of the present application will be described below with reference to the accompanying drawings.

[0092] Refer to Figure 2 The specific steps of the solution of this embodiment include S11 - S16.

[0093] S11. Data preparation: Use the fan to collect the wind speed data of the fan head in each area, and download the background field data from the ECMWF database. If necessary, perform time and space interpolation on the data to ensure coverage of the research area and time range, and remove outliers.

[0094] S12. Configure the WRF model: Determine the scope and grid resolution of the research area, and set the model parameters, including dynamics, physical schemes, operating environment, etc.

[0095] S13. Data assimilation (innovation point): Use the WRFDA module, select 3DVAR and / or 4DVAR, and input the observed wind speed and background field data to assimilate the initial wind field.

[0096] S14. Run the WRF model: Configure the simulation time period, set the boundary conditions and the initial conditions obtained from data assimilation, and define the output frequency. After setting the relevant parameters, perform numerical simulation on the supercomputer platform based on the assimilated data.

[0097] S15. Build and train the CNN model (innovation point):

[0098] ① Data preparation: Align the output data of WRF with the observed wind speed as the input and label; and convert the data into a format supported by the CNN model.

[0099] ② Build the model architecture: The CNN model of this technical solution consists of five convolutional layers, one fully connected layer, and one output layer. Each convolutional layer contains 32 filters and uses ReLu as the activation function. The input of the first layer consists of various physical parameters extracted from the WRF model and the grid resolution. In the convolution operation, a randomly initialized convolution kernel acts on the input features. Feature maps are obtained through the output of the first layer, and then these feature maps are used as the input of the second layer. In subsequent layers, this process is repeated. The output of the fifth convolutional layer is passed to the fully connected layer. The last layer generates the predicted result of the wind speed and outputs it.

[0100] ③ Model training: Divide the data into a training set, a validation set, and a test set. Hyperparameters (such as learning rate, batch size, and number of iterations) need to be set during the training process, and the validation set is used to prevent overfitting.

[0101] S16. Result evaluation: Use the data in the validation set for prediction, and compare it with the observed data. Evaluate the effect gap and improvement between this technical solution and the traditional method through evaluation metrics such as Bias and RMSE.

[0102] In addition, with reference to Figure 3 , corresponding to the method of Figure 1 , an embodiment of the present application also provides a wind speed prediction system. The system may include a first processing unit 1001, a second processing unit 1002, a third processing unit 1003, a fourth processing unit 1004, and a fifth processing unit 1005. Among them, the first processing unit 1001 may be used to obtain a first set of historical observed wind speed data and a second set of historical observed wind speed data of the wind turbine nacelle; wherein the first set of historical observed wind speed data is a set of historical observed wind speed data before the current day of the wind turbine nacelle; the second set of historical observed wind speed data is a set of wind speed data before the current day and the wind speed data of the current day of the wind turbine nacelle. The second processing unit 1002 may determine the first prediction data of the current day of the wind turbine nacelle under the background field based on the background field data and the first set of historical observed wind speed data, and determine the second prediction data after the current day of the wind turbine nacelle under the background field based on the background field data and the second set of historical observed wind speed data. The third processing unit 1003 may be used to input the first prediction data and the second set of historical observed wind speed data into a convolutional neural network to obtain the third prediction data after the current day; the fourth processing unit 1004 may be used to adjust the parameters of the convolutional neural network according to the second prediction data and the third prediction data to obtain a trained convolutional neural network. The fifth processing unit 1005 may be used to input the observed wind speed of the wind turbine nacelle before the prediction day into the trained convolutional neural network to obtain the target wind speed of the prediction day.

[0103] It should be noted that the first processing unit may be any integrated circuit unit or microprocessor unit obtained by integrating a processing function chip and its peripheral circuits through existing integration technologies. The first processing unit and the second processing unit may also be any integrated circuit module or microprocessor module obtained by integrating a processing function chip and its peripheral circuits through existing integration technologies. The first processing unit and the second processing unit may further include one or more memories.

[0104] It should be noted that the content in the above embodiments of the wind speed prediction method is applicable to the embodiments of this wind speed prediction system. The functions specifically implemented by the embodiments of this wind speed prediction system are the same as those of the above embodiments of the wind speed prediction method, and the beneficial effects achieved are also the same as those of the above embodiments of the wind speed prediction method.

[0105] With Figure 1Corresponding to the method, an embodiment of the present application also provides a wind speed prediction device, and its specific structure can be referred to Figure 4 , including:

[0106] At least one processor 1011;

[0107] At least one memory 1012, configured to store at least one program;

[0108] When the at least one program is executed by the at least one processor, the at least one processor implements the wind speed prediction method.

[0109] The content in the above method embodiments is applicable to the device embodiments of the present application. The functions specifically implemented by the device embodiments of the present application are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0110] Corresponding to Figure 1 the method, an embodiment of the present application also provides a computer-readable storage medium, in which instructions executable by a processor are stored, and the instructions executable by the processor are used to execute the wind speed prediction method when executed by the processor.

[0111] The content in the above wind speed prediction method embodiments is applicable to the storage medium embodiments of the present application. The functions specifically implemented by the storage medium embodiments of the present application are the same as those of the above wind speed prediction method embodiments, and the beneficial effects achieved are also the same as those of the above wind speed prediction method embodiments.

[0112] In some alternative embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two consecutive blocks shown may actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present application are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated, in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0113] In addition, although the present application has been described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present application. Rather, considering the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Thus, those skilled in the art can implement the present application as set forth in the claims without undue experimentation. It should also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the appended claims and their equivalents.

[0114] If the described 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 application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several programs for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0115] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable programs for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by a program execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can retrieve and execute programs from the program execution system, apparatus, or device), or in conjunction with these program execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with a program execution system, apparatus, or device.

[0116] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0117] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0118] In the foregoing description of the present specification, the descriptions referring to the terms "one embodiment / example", "another embodiment / example", or "certain embodiments / examples", etc. mean that the specific features, structures, materials, or characteristics described in connection 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.

[0119] Although the embodiments of the present application have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the claims and their equivalents.

[0120] The above has specifically described the preferred embodiments of the present application, but the present application is not limited to the described embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present application, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present application.

Claims

1. A wind speed prediction method, characterized in that: The following steps are involved: Acquire a first historical observed wind speed data set and a second historical observed wind speed data set for a wind turbine head; wherein the first historical observed wind speed data set is a set of historical observed wind speed data for the wind turbine head before the current day; and the second historical observed wind speed data set is a set of wind speed data for the wind turbine head before the current day and wind speed data for the current day; Determine the first forecast data of the wind turbine head under the background field on the current day based on the background field data and the first historical observed wind speed data set, and determine the second forecast data of the wind turbine head under the background field after the current day based on the background field data and the second historical observed wind speed data set; Inputting the first forecast data and the second historical observed wind speed data set into a convolutional neural network to obtain third forecast data after the current day; According to the second prediction data and the third prediction data, adjusting the parameters of the convolutional neural network to obtain a trained convolutional neural network; The wind speed observed at the wind turbine head before the forecast day is input into the trained convolutional neural network to obtain the target wind speed for the forecast day.

2. A wind speed prediction method according to claim 1, characterized in that: The determining of the first forecast data of the wind turbine head under the background field on the current day based on the background field data and the first historical observed wind speed data set specifically includes: configuring the configuration parameters of the weather research and forecasting model and sending the background field data and the physical boundary conditions to the weather research and forecasting model to obtain a simulation model; The simulation model is run and the first historical observed wind speed data set is input into the simulation model to obtain the first prediction data.

3. A wind speed prediction method according to claim 1, characterized in that: The determining, based on the background field data and the second historical observed wind speed data set, second forecast data of the wind turbine head under the background field after the current day specifically includes: configuring the configuration parameters of the weather research and forecasting model and sending the background field data and the physical boundary conditions to the weather research and forecasting model to obtain a simulation model; The simulation model is run and the second historical observed wind speed data set is input into the simulation model to obtain the second prediction data.

4. A wind speed prediction method according to claim 1, characterized in that: The first prediction data and the second historical observation wind speed data set are input into a convolutional neural network to obtain third prediction data after the current day, specifically including: Performing data preprocessing on the first prediction data and the second historical observation wind speed data set to obtain training data; The training data is input into the convolutional neural network to obtain the third prediction data.

5. A wind speed prediction method according to claim 4, characterized in that: The step of preprocessing the first prediction data and the second historical observation wind speed data set to obtain training data specifically includes: Performing data alignment processing on the first prediction data and the second historical observation wind speed data set, Furthermore, the first prediction data and the second historical observation wind speed data set are processed for data format adjustment to obtain training data.

6. A wind speed prediction method according to claim 1, characterized in that: The step of adjusting the parameters of the convolutional neural network according to the second prediction data and the third prediction data to obtain a trained convolutional neural network specifically includes: Determining a training error according to the second prediction data and the third prediction data; When the training error is greater than a preset error, the parameters of the convolutional neural network are adjusted and the convolutional neural network is retrained until the training error is less than or equal to the preset error, thereby obtaining the trained convolutional neural network.

7. A wind speed prediction method according to claim 1, characterized in that: The determining of the training error according to the second prediction data and the third prediction data specifically includes: Subtracting the second prediction data from the third prediction data to obtain a first difference; The first difference is used as the training error.

8. A wind speed prediction system, characterized in that: include: A first processing unit is used to obtain a first historical observed wind speed data set and a second historical observed wind speed data set of a wind turbine head; The first historical observed wind speed data set is a set of historical observed wind speed data of the wind turbine head before the current day; the second historical observed wind speed data set is a set of wind speed data of the wind turbine head before the current day and wind speed data of the current day; A second processing unit is used to determine the first forecast data of the wind turbine head under the background field on the current day based on the background field data and the first historical observed wind speed data set, and to determine the second forecast data of the wind turbine head under the background field after the current day based on the background field data and the second historical observed wind speed data set; A third processing unit, configured to input the first forecast data and the second historical observed wind speed data set into a convolutional neural network to obtain third forecast data after the current day; a fourth processing unit, configured to adjust the parameters of the convolutional neural network according to the second prediction data and the third prediction data to obtain a trained convolutional neural network; The fifth processing unit is used to input the wind speed observed at the wind turbine head before the forecast day into the trained convolutional neural network to obtain the target wind speed on the forecast day.

9. A wind speed prediction device, characterized in that include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a wind speed prediction method as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions executable by a processor, characterized in that: The processor-executable instructions are used to execute a wind speed prediction method as described in any one of claims 1-7 when executed by the processor.

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