Method, device and equipment for joint prediction of incoming wind conditions based on multi-task learning

Abstract

The present disclosure relates to a method, device and equipment for joint prediction of incoming wind conditions based on multi-task learning. An input data set and an output data set are constructed based on the measured data of the incoming wind conditions, a joint prediction model for the incoming wind conditions is built, the joint prediction model for the incoming wind conditions is trained and hyperparameters are optimized to obtain a target joint prediction model for the incoming wind conditions, and the incoming wind conditions are predicted by the target joint prediction model for the incoming wind conditions to obtain second-level incoming wind condition prediction data, which includes incoming wind speed prediction data and incoming wind direction prediction data. Compared with the prior art, the embodiment of the present disclosure can effectively capture the overall dynamics of future wind conditions by building and training a joint prediction model for incoming wind conditions, thereby achieving high-precision prediction of wind speed and wind direction at the second level. This not only helps in the control of wind turbines and wind farms, but also provides key support for increasing power generation, extending equipment life and enhancing grid stability.

Description

Technical Field

[0001] The present disclosure relates to the field of renewable energy power generation technology, and in particular to a method, device, and equipment for joint prediction of incoming wind conditions based on multi-task learning. Background Art

[0002] In the field of wind power development, high-temporal-resolution future wind condition data is crucial. It can provide key support for wind turbine control and wind farm control, thereby improving power generation and grid stability.

[0003] Among related technologies, current wind forecasting techniques primarily focus on wind speed prediction, while research on wind direction prediction is relatively scarce. Most studies focus solely on wind speed or direction prediction, failing to capture the overall dynamics of future wind conditions. Furthermore, the temporal resolution of most current forecasting techniques is typically 15 minutes or longer. However, the wind conditions inflowing from wind turbines change rapidly and with significant amplitude, making this temporal resolution insufficient for wind farm control. Consequently, these wind forecasting techniques suffer from issues such as an inability to capture the overall dynamics of future wind conditions and excessively low temporal resolution. Summary of the Invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a method, device and equipment for joint prediction of incoming wind conditions based on multi-task learning.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for joint prediction of incoming wind conditions based on multi-task learning, the method comprising:

[0006] Construct input data set and output data set based on the measured data of incoming wind conditions;

[0007] Building a joint forecasting model for incoming wind conditions, which includes forecasting tasks for incoming wind speed and direction;

[0008] Training the incoming wind condition joint prediction model and optimizing hyperparameters to obtain a target incoming wind condition joint prediction model;

[0009] The incoming wind condition is predicted by the target incoming wind condition joint prediction model to obtain second-level incoming wind condition prediction data, wherein the incoming wind condition prediction data includes incoming wind speed prediction data and incoming wind direction prediction data.

[0010] In some embodiments, constructing the input data set and the output data set based on the measured data of the incoming wind conditions includes:

[0011] Preprocessing the measured data of incoming wind conditions to obtain preprocessed measured data of incoming wind conditions;

[0012] Based on the pre-processed measured data of incoming wind conditions, the input data set and the output data set are constructed through the sliding time window algorithm;

[0013] The input data set is a historical wind condition data set at the second level, and the output data set is a future wind condition data set at the second level.

[0014] In some embodiments, the building of a joint forecasting model for incoming wind conditions includes:

[0015] Designing a model architecture for the combined forecasting model for incoming wind conditions;

[0016] A hard parameter sharing mechanism is adopted for the incoming wind speed prediction task and the incoming wind direction prediction task in the incoming wind condition joint prediction model;

[0017] Based on the weights corresponding to the incoming wind speed prediction task and the incoming wind direction prediction task, the loss function of the incoming wind condition joint prediction model is customized.

[0018] In some embodiments, the design of the model architecture of the incoming wind condition joint prediction model includes:

[0019] The long short-term memory network and the fully connected network are used as the core structure of the joint prediction model of incoming wind conditions;

[0020] The long short-term memory network is used to capture the correlation characteristics of wind speed and wind direction in the time series, and the fully connected network is used to predict the incoming wind conditions based on the correlation characteristics.

[0021] In some embodiments, the training of the incoming wind condition joint prediction model and the optimization of hyperparameters to obtain a target incoming wind condition joint prediction model includes:

[0022] Create training sets, validation sets, and test sets based on the input and output data sets;

[0023] Initializing model parameters of the incoming wind condition joint prediction model and setting a hyperparameter search space;

[0024] For each hyperparameter combination in the hyperparameter search space, the inflow wind condition joint prediction model corresponding to each hyperparameter combination is trained using the training set until the inflow wind condition joint prediction model corresponding to each hyperparameter combination converges;

[0025] Using the test set to test the incoming wind condition joint prediction model corresponding to each of the hyperparameter combinations, respectively, to obtain the prediction results of the incoming wind condition joint prediction model corresponding to each of the hyperparameter combinations;

[0026] The prediction results of the combined wind flow prediction model corresponding to each hyperparameter combination are evaluated using the model evaluation index to obtain an evaluation value of the combined wind flow prediction model corresponding to each hyperparameter combination;

[0027] Based on the model evaluation index, the best evaluation value is determined from the evaluation values ​​of the incoming wind condition joint prediction models corresponding to each hyperparameter combination, and the incoming wind condition joint prediction model corresponding to the hyperparameter combination matching the best evaluation value is determined as the target incoming wind condition joint prediction model.

[0028] In some embodiments, the step of generating a training set, a validation set, and a test set based on the input data set and the output data set includes:

[0029] Normalizing the input data set and the output data set to obtain a normalized input data set and a normalized output data set;

[0030] The normalized input data set and the normalized output data set are divided into training set, validation set and test set according to the preset ratio.

[0031] In some embodiments, the method of predicting the incoming wind condition using the target incoming wind condition joint prediction model to obtain second-level incoming wind condition prediction data includes:

[0032] Obtain real-time measurement data of incoming wind conditions;

[0033] The real-time measurement data of the incoming wind condition is input into the target incoming wind condition joint prediction model, and the target incoming wind condition joint prediction model outputs the incoming wind condition prediction data at the second level.

[0034] In a second aspect, an embodiment of the present disclosure provides a device for joint prediction of incoming wind conditions based on multi-task learning, the device comprising:

[0035] A data set construction module is used to construct an input data set and an output data set based on the measured data of the incoming wind conditions;

[0036] A model building module is used to build a joint prediction model for incoming wind conditions, wherein the joint prediction model for incoming wind conditions includes tasks for predicting incoming wind speed and incoming wind direction;

[0037] A model training module is used to train the incoming wind condition joint prediction model and optimize hyperparameters to obtain a target incoming wind condition joint prediction model;

[0038] The model application module is used to predict the incoming wind conditions through the target incoming wind condition joint prediction model to obtain the incoming wind condition prediction data at the second level.

[0039] In a third aspect, an embodiment of the present disclosure provides an electronic device, including:

[0040] Memory;

[0041] processor; and

[0042] computer programs;

[0043] The computer program is stored in the memory and is configured to be executed by the processor to implement the method as described in the first aspect.

[0044] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method as described in the first aspect.

[0045] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, which includes a computer program or instructions, and when the computer program or instructions are executed by a processor, implements the method described in the first aspect.

[0046] The embodiments of the present disclosure provide a method, device, and equipment for joint prediction of incoming wind conditions based on multi-task learning. Based on the measured data of the incoming wind conditions, an input data set and an output data set are constructed to build a joint prediction model for incoming wind conditions. The joint prediction model for incoming wind conditions includes an incoming wind speed prediction task and an incoming wind direction prediction task. The joint prediction model for incoming wind conditions is trained and hyperparameters are optimized to obtain a target joint prediction model for incoming wind conditions. The incoming wind conditions are predicted using the target joint prediction model for incoming wind conditions to obtain second-level incoming wind condition prediction data. The incoming wind condition prediction data includes incoming wind speed prediction data and incoming wind direction prediction data. Compared with the prior art, the embodiments of the present disclosure, by building and training a joint prediction model for incoming wind conditions, can effectively capture the overall dynamics of future wind conditions through the target joint prediction model for incoming wind conditions, thereby achieving high-precision prediction of wind speed and wind direction at the second level. This not only helps control wind turbines and wind farms, but also provides key support for increasing power generation, extending equipment life, and enhancing grid stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0048] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0049] Figure 1 A flowchart of a method for joint prediction of incoming wind conditions based on multi-task learning provided in an embodiment of the present disclosure;

[0050] Figure 2 A schematic diagram of the architecture of the combined forecasting model for incoming wind conditions provided in an embodiment of the present disclosure;

[0051] Figure 3 A flowchart of a method for joint prediction of incoming wind conditions based on multi-task learning provided by another embodiment of the present disclosure;

[0052] Figure 4 A flowchart of a method for joint prediction of incoming wind conditions based on multi-task learning provided by another embodiment of the present disclosure;

[0053] Figure 5 A schematic diagram of the structure of a device for joint prediction of incoming wind conditions based on multi-task learning provided in an embodiment of the present disclosure;

[0054] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0055] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features therein can be combined with each other in the absence of conflict.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0057] In the field of wind power development, high-temporal-resolution future wind data is crucial. It can provide key support for wind turbine control and wind farm control, thereby improving power generation and grid stability. Among related technologies, current wind condition prediction technologies mainly focus on wind speed prediction, while research on wind direction prediction is relatively scarce. Most of these studies only predict wind speed or wind direction separately, failing to capture the overall dynamics of future wind conditions. In addition, the temporal resolution of most current prediction technologies is usually 15 minutes or longer, while the frequency and amplitude of wind turbine inflow changes are fast, and this temporal resolution cannot meet the needs of wind farm control. Therefore, the above-mentioned wind condition prediction technologies have problems such as being unable to capture the overall dynamics of future wind conditions and having too low a temporal resolution.

[0058] To address this issue, the present disclosure provides a method for joint prediction of incoming wind conditions based on multi-task learning. The method is introduced below in conjunction with specific embodiments.

[0059] Figure 1 Flowchart of the method for joint prediction of incoming wind conditions based on multi-task learning provided in the embodiment of the present disclosure. The execution subject of the method is an electronic device, which can be a portable mobile device such as a smart phone, a tablet computer, a laptop computer, or a fixed device such as a personal computer or a server, wherein the server can be a single server or a server cluster, and the server cluster can be a distributed cluster or a centralized cluster. The method can be applied to scenarios where incoming wind conditions are predicted, and can accurately predict incoming wind conditions at the second level, meeting the actual engineering requirements of wake deflection control. It can be understood that the method for joint prediction of incoming wind conditions based on multi-task learning provided in the embodiment of the present disclosure can also be applied in other scenarios.

[0060] Below Figure 1 The following is an introduction to the joint prediction method of incoming wind conditions based on multi-task learning. The specific steps of this method are as follows:

[0061] S101. Construct an input data set and an output data set based on measured incoming wind condition data.

[0062] In this step, the electronic device obtains the measured data of the incoming wind condition, and further constructs an input data set and an output data set based on the measured data of the incoming wind condition. Optionally, the measured data of the incoming wind condition can be the measurement data of the wind turbine anemometer and wind vane, or can be the laser radar measurement data, without limitation. In some optional implementations, the input data set includes the wind speed data and wind direction data from time t-n+1 to time t, with a total of n measured data points. In some optional implementations, the output data set includes the wind speed data and wind direction data from time t+1 to time t+m, with a total of m measured data points. Optionally, as Figure 2 As shown, the input data set is a historical wind condition data set at the second level, that is, wind speed data and wind direction data before a certain moment, and the output data set is a future wind condition data set at the second level, that is, wind speed data and wind direction data after a certain moment.

[0063] S102: Building a joint prediction model for incoming wind conditions, wherein the joint prediction model for incoming wind conditions includes an incoming wind speed prediction task and an incoming wind direction prediction task.

[0064] In this step, the electronic device builds a joint forecasting model for incoming wind conditions. This model includes forecasting wind speed and wind direction. Specifically, the model is built based on multi-task learning, based on the correlation between wind speed and wind direction data.

[0065] S103 , training the incoming wind condition joint prediction model and optimizing hyper parameters to obtain a target incoming wind condition joint prediction model.

[0066] In this step, after building the combined inflow wind prediction model, the electronic device can train the combined inflow wind prediction model and optimize its hyperparameters to obtain a target combined inflow wind prediction model. The target combined inflow wind prediction model represents a trained combined inflow wind prediction model with optimized hyperparameters.

[0067] S104 , predicting the incoming wind condition by using the target incoming wind condition joint prediction model to obtain second-level incoming wind condition prediction data, wherein the incoming wind condition prediction data includes incoming wind speed prediction data and incoming wind direction prediction data.

[0068] In this step, after obtaining the target incoming wind condition joint prediction model, the electronic device predicts the incoming wind condition through the target incoming wind condition joint prediction model to obtain incoming wind condition prediction data at the second level. The incoming wind condition prediction data includes incoming wind speed prediction data and incoming wind direction prediction data, which can predict the overall dynamics of future wind speed and wind direction at the second level.

[0069] The embodiment of the present disclosure constructs an input data set and an output data set based on the measured data of the incoming wind conditions, and builds a joint prediction model for the incoming wind conditions. The joint prediction model for the incoming wind conditions includes an incoming wind speed prediction task and an incoming wind direction prediction task. The joint prediction model for the incoming wind conditions is trained and hyperparameters are optimized to obtain a target joint prediction model for the incoming wind conditions. The incoming wind conditions are predicted by the target joint prediction model for the incoming wind conditions to obtain second-level incoming wind condition prediction data. The incoming wind condition prediction data includes incoming wind speed prediction data and incoming wind direction prediction data. Compared with the prior art, the embodiment of the present disclosure builds and trains a joint prediction model for the incoming wind conditions. The target joint prediction model for the incoming wind conditions can effectively capture the overall dynamics of future wind conditions, thereby achieving high-precision prediction of wind speed and wind direction at the second level. This not only helps in the control of wind turbines and wind farms, but also provides key support for increasing power generation, extending equipment life, and enhancing grid stability.

[0070] Figure 3 This is a flow chart of a method for joint prediction of incoming wind conditions based on multi-task learning provided by another embodiment of the present disclosure, such as Figure 3 As shown, the method includes the following steps:

[0071] S201 : Preprocessing the measured data of the incoming wind condition to obtain the preprocessed measured data of the incoming wind condition.

[0072] Preprocessing may include but is not limited to reducing time resolution, filtering and eliminating invalid values, etc.

[0073] S202: construct an input data set and an output data set based on the pre-processed measured data of incoming wind conditions through a sliding time window algorithm.

[0074] Furthermore, based on the sliding time window algorithm, the pre-processed incoming wind condition measured data is used to construct the input data set and the output data set. Optionally, the input data set is a historical wind condition data set at the second level, that is, the wind speed data and wind direction data before a certain moment, and the output data set is a future wind condition data set at the second level, that is, the wind speed data and wind direction data after a certain moment. In some optional embodiments, the input data set includes wind speed data and wind direction data from time t-n+1 to time t, a total of n time points of measured data. Optionally, the input data set can be expressed as X{[Ws t-n+1 ,…,Ws t ],[Wd t-n+1 ,…,Wd t ]}, where Ws t-n+1 Indicates the wind speed data at time t-n+1, ​​Ws t Represents the wind speed data at time t, Wd t-n+1 Indicates the wind direction data at time t-n+1, ​​Wd t In some optional embodiments, the output data set includes wind speed data and wind direction data from time t+1 to time t+m, a total of m time points of measured data. Optionally, the output data set can be expressed as Y{[Ws t+1 ,…,Ws t+m ],[Wd t+1 ,…,Wd t+m ]}, where Ws t+1 Represents the wind speed data at time t+1, Ws t+m Indicates the wind speed data at time t+m, Wd t+1 Indicates the wind direction data at time t+1, Wd t+m Indicates the wind direction data at time t+m.

[0075] S203: Designing a model architecture for the incoming wind condition joint prediction model.

[0076] In some embodiments, the model architecture of the design of the joint prediction model for incoming wind conditions includes: using a long short-term memory network and a fully connected network as the core structure of the joint prediction model for incoming wind conditions; wherein the long short-term memory network is used to capture the correlation characteristics of wind speed and wind direction in the time series, and the fully connected network is used to predict the incoming wind conditions based on the correlation characteristics.

[0077] In this step, the main model architecture of the incoming wind condition joint prediction model is selected, such as Figure 2As shown, neural network structures A and B form the main model architectures of the combined incoming wind forecasting model. In some optional implementations, a long short-term memory (LSTM) network and a fully connected network can be selected as the core architecture of the model. The LSTM is used to capture long-term dependencies, or correlation features, in time series data, while the fully connected network utilizes the correlation features output by the LSTM to perform the final incoming wind forecast.

[0078] S204: For the incoming wind speed prediction task and the incoming wind direction prediction task in the incoming wind condition joint prediction model, a hard parameter sharing mechanism is adopted.

[0079] In this step, the electronic device uses a hard parameter sharing mechanism to implement the multi-task model, such as Figure 2 As shown, the input layer of the incoming wind condition joint prediction model is set as a task sharing layer, and multiple tasks, namely the incoming wind speed prediction task and the incoming wind direction prediction task, can share several hidden layers of the model. By utilizing the correlation between the incoming wind speed prediction task and the incoming wind direction prediction task, the model training can cooperate with each other to improve the prediction effect.

[0080] S205 : Customizing the loss function of the incoming wind condition joint prediction model based on the weights corresponding to the incoming wind speed prediction task and the incoming wind direction prediction task.

[0081] In this step, the electronic device assigns different weights to different prediction tasks. Customizing a loss function that combines wind speed and wind direction predictions effectively balances the model's performance across tasks. The different prediction tasks include both wind speed and wind direction.

[0082] Optionally, you can set the loss function as follows:

[0083] L=α·L speed +β·L direction

[0084] Among them, L is the loss function of the combined forecast model of incoming wind conditions; L speed is the loss function of the wind speed prediction task; L direction is the loss function for the wind direction prediction task, α is the weight coefficient for the wind speed prediction task, and β is the weight coefficient for the wind direction prediction task. The weight coefficients are used to balance the importance of the two tasks. Optionally, the loss function can be mean absolute error (MAE), root mean square error (RMSE), or other similar loss functions, without specific limitation.

[0085] S206 , training the incoming wind condition joint prediction model and optimizing hyper parameters to obtain a target incoming wind condition joint prediction model.

[0086] Specifically, the implementation process and principle of S206 and S103 are the same and will not be repeated here.

[0087] S207: Acquire real-time measurement data of incoming wind conditions.

[0088] In this step, the electronic device obtains real-time measurement data of incoming wind conditions, including real-time measurement data of wind speed and real-time measurement data of wind direction.

[0089] S208: Input the real-time measurement data of the incoming wind condition into the target incoming wind condition joint prediction model, and output the second-level incoming wind condition prediction data through the target incoming wind condition joint prediction model.

[0090] After acquiring the real-time measurement data of the incoming wind condition, the electronic device inputs the real-time measurement data of the incoming wind condition into the target incoming wind condition joint prediction model, and outputs the second-level incoming wind condition prediction data through the target incoming wind condition joint prediction model.

[0091] The embodiment of the present disclosure preprocesses the measured data of the incoming wind conditions to obtain the preprocessed measured data of the incoming wind conditions. Based on the preprocessed measured data of the incoming wind conditions, a sliding time window algorithm is used to construct an input data set and an output data set. Furthermore, a model architecture of the incoming wind condition joint prediction model is designed. A hard parameter sharing mechanism is adopted for the incoming wind speed prediction task and the incoming wind direction prediction task in the incoming wind condition joint prediction model. Based on the corresponding weights of the incoming wind speed prediction task and the incoming wind direction prediction task, the loss function of the incoming wind condition joint prediction model is customized. Then, the incoming wind condition joint prediction model is trained and hyperparameters are optimized to obtain a target incoming wind condition joint prediction model. Then, real-time measurement data of the incoming wind conditions is obtained and input into the target incoming wind condition joint prediction model. The target incoming wind condition joint prediction model outputs second-level incoming wind condition prediction data. Through this method, the overall dynamics of future wind conditions can be effectively captured, thereby achieving high-precision prediction of wind speed and wind direction at the second level. This not only facilitates the control of wind turbines and wind farms, but also provides key support for increasing power generation, extending equipment life, and enhancing grid stability. By adopting a hard parameter sharing mechanism, effective collaboration between different tasks is achieved, improving prediction accuracy and reducing the risk of overfitting.

[0092] Figure 4 This is a flow chart of a method for joint prediction of incoming wind conditions based on multi-task learning provided by another embodiment of the present disclosure, such as Figure 4 As shown, the method includes the following steps:

[0093] S301: Construct an input data set and an output data set based on the measured data of incoming wind conditions.

[0094] Specifically, the implementation process and principle of S301 and S101 are the same and will not be described in detail here.

[0095] S302: Building a joint prediction model for incoming wind conditions, wherein the joint prediction model for incoming wind conditions includes an incoming wind speed prediction task and an incoming wind direction prediction task.

[0096] Specifically, the implementation process and principle of S302 and S102 are the same and will not be described in detail here.

[0097] S303: Create a training set, a validation set, and a test set based on the input data set and the output data set.

[0098] In this step, the electronic device creates a training set, a validation set, and a test set based on the input data set and the output data set.

[0099] In some embodiments, S303 may include but is not limited to S3031 and S3032:

[0100] S3031. Normalize the input data set and the output data set to obtain a normalized input data set and a normalized output data set.

[0101] In this step, the electronic device may perform normalization processing on the input data set and the output data set to obtain a normalized input data set and a normalized output data set.

[0102] S3032. Divide the normalized input data set and the normalized output data set into a training set, a validation set, and a test set according to a preset ratio.

[0103] Furthermore, the electronic device divides the normalized input data set and the normalized output data set into a training set, a validation set, and a test set according to a preset ratio. The preset ratio can be 7:1:2, or other ratios are not limited to a specific ratio. This division must be random to prevent the model from overfitting to specific wind condition sequences, which could affect prediction performance.

[0104] S304: Initialize the model parameters of the incoming wind condition joint prediction model and set a hyperparameter search space.

[0105] In this step, the model parameters are initialized and the hyperparameter search space is set. The hyperparameter search space can be used to achieve hyperparameter optimization.

[0106] S305. For each hyperparameter combination in the hyperparameter search space, use the training set to train the incoming wind condition joint prediction model corresponding to each hyperparameter combination until the incoming wind condition joint prediction model corresponding to each hyperparameter combination converges.

[0107] In this step, for each hyperparameter combination in the hyperparameter search space, the training set is used to train the incoming wind condition joint prediction model corresponding to each hyperparameter combination. Through continuous training, the loss function loss value of the incoming wind condition joint prediction model corresponding to each hyperparameter combination tends to be stable. At this time, the incoming wind condition joint prediction model corresponding to each hyperparameter combination converges. Hyperparameter combination, taking learning rate η and regularization strength λ as an example, refers to a specific learning rate and regularization strength such as (0.01, 0.1), which means that the learning rate is set to 0.01, and the regularization strength is set to 0.1. The relationship between hyperparameters is often mutually coupled and influences each other. For example, the learning rate and regularization strength jointly determine the training process of the model and the final generalization performance.

[0108] S306 , using the test set to test the incoming wind condition joint prediction model corresponding to each of the hyperparameter combinations, and obtain prediction results of the incoming wind condition joint prediction model corresponding to each of the hyperparameter combinations.

[0109] In this step, the test set is used to test the joint prediction model of the incoming wind condition corresponding to each hyperparameter combination to obtain the prediction results of the joint prediction model of the incoming wind condition corresponding to each hyperparameter combination.

[0110] S307 , using the model evaluation index to evaluate the prediction results of the incoming wind condition joint prediction model corresponding to each hyperparameter combination, and obtain the evaluation value of the incoming wind condition joint prediction model corresponding to each hyperparameter combination.

[0111] After obtaining the prediction results of the joint prediction model for incoming wind conditions corresponding to each hyperparameter combination, the prediction results of the joint prediction model for incoming wind conditions corresponding to each hyperparameter combination are evaluated respectively using the model evaluation index. The evaluation value of the joint prediction model for incoming wind conditions corresponding to each hyperparameter combination can be obtained through the model evaluation index.

[0112] S308. Determine the best evaluation value from the evaluation values ​​of the incoming wind condition joint prediction models corresponding to the various hyperparameter combinations based on the model evaluation index, and determine the incoming wind condition joint prediction model corresponding to the hyperparameter combination that matches the best evaluation value as the target incoming wind condition joint prediction model.

[0113] In this step, the electronic device will determine the best evaluation value from the evaluation values ​​of the incoming wind condition joint prediction models corresponding to each hyperparameter combination based on the model evaluation index, and determine the incoming wind condition joint prediction model corresponding to the hyperparameter combination that matches the best evaluation value among the incoming wind condition joint prediction models corresponding to each hyperparameter combination as the target incoming wind condition joint prediction model. The best evaluation value is the evaluation value that can reflect the best model performance among the various evaluation values. Specifically, whether to select the maximum evaluation value or the minimum evaluation value is related to the type of model evaluation index selected. For example, if the model evaluation index is accuracy, etc., the larger the evaluation value, the better the model. In this case, the maximum evaluation value is selected as the best evaluation value; if the model evaluation index is RMSE, etc., the smaller the evaluation value, the better the model. In this case, the minimum evaluation value is selected as the best evaluation value.

[0114] S309 , predicting the incoming wind condition through the target incoming wind condition joint prediction model to obtain second-level incoming wind condition prediction data, wherein the incoming wind condition prediction data includes incoming wind speed prediction data and incoming wind direction prediction data.

[0115] Specifically, the implementation process and principle of S309 and S104 are the same and will not be repeated here.

[0116] The embodiment of the present disclosure constructs an input data set and an output data set based on the measured data of the incoming wind conditions, and builds a joint prediction model for the incoming wind conditions. The joint prediction model for the incoming wind conditions includes an incoming wind speed prediction task and an incoming wind direction prediction task. Further, based on the input data set and the output data set, a training set, a validation set, and a test set are produced, the model parameters of the joint prediction model for the incoming wind conditions are initialized, and a hyperparameter search space is set. For each hyperparameter combination in the hyperparameter search space, the training set is used to train the joint prediction model for the incoming wind conditions corresponding to each hyperparameter combination until the joint prediction model for the incoming wind conditions corresponding to each hyperparameter combination converges. Next, the test set is used to test the joint prediction model for the incoming wind conditions corresponding to each hyperparameter combination, and the prediction results of the joint prediction model for the incoming wind conditions corresponding to each hyperparameter combination are obtained. The model evaluation index is used to evaluate the prediction results of the joint prediction model for the incoming wind conditions corresponding to each hyperparameter combination, and the evaluation value of the joint prediction model for the incoming wind conditions corresponding to each hyperparameter combination is obtained. Then, based on the model evaluation index, the best evaluation value is determined from the evaluation values ​​of the incoming wind condition joint prediction models corresponding to each hyperparameter combination, and the incoming wind condition joint prediction model corresponding to the hyperparameter combination that matches the best evaluation value is determined as the target incoming wind condition joint prediction model. The incoming wind condition is then predicted by the target incoming wind condition joint prediction model to obtain second-level incoming wind condition prediction data, which includes incoming wind speed prediction data and incoming wind direction prediction data. Through this method, the overall dynamics of future wind conditions can be effectively captured, thereby achieving high-precision predictions of wind speed and wind direction at the second level. This not only helps in the control of wind turbines and wind farms, but also provides key support for increasing power generation, extending equipment life, and enhancing power grid stability.

[0117] Figure 5 Schematic diagram of the structure of the device for joint prediction of incoming wind conditions based on multi-task learning provided by the embodiment of the present disclosure. The device for joint prediction of incoming wind conditions based on multi-task learning can be the electronic device as described in the above embodiment, or the device for joint prediction of incoming wind conditions based on multi-task learning can be a component or assembly in the electronic device. The device for joint prediction of incoming wind conditions based on multi-task learning provided by the embodiment of the present disclosure can execute the processing flow provided by the embodiment of the method for joint prediction of incoming wind conditions based on multi-task learning, such as Figure 5As shown, the device 40 for joint prediction of incoming wind conditions based on multi-task learning includes: a data set construction module 41, a model construction module 42, a model training module 43, and a model application module 44; wherein, the data set construction module 41 is used to construct an input data set and an output data set based on the measured data of the incoming wind conditions; the model construction module 42 is used to build a joint prediction model for incoming wind conditions, and the joint prediction model for incoming wind conditions includes an incoming wind speed prediction task and an incoming wind direction prediction task; the model training module 43 is used to train the joint prediction model for incoming wind conditions and optimize hyperparameters to obtain a target joint prediction model for incoming wind conditions; the model application module 44 is used to predict the incoming wind conditions through the target joint prediction model for incoming wind conditions to obtain second-level incoming wind condition prediction data.

[0118] Optionally, when the data set construction module 41 constructs the input data set and the output data set based on the measured data of the incoming wind conditions, it is specifically used to: preprocess the measured data of the incoming wind conditions to obtain the preprocessed measured data of the incoming wind conditions; and construct the input data set and the output data set based on the preprocessed measured data of the incoming wind conditions through a sliding time window algorithm; wherein, the input data set is a historical wind condition data set, and the output data set is a future wind condition data set.

[0119] Optionally, when the model building module 42 builds the joint prediction model for incoming wind conditions, it is specifically used to: design the model architecture of the joint prediction model for incoming wind conditions; adopt a hard parameter sharing mechanism for the incoming wind speed prediction task and the incoming wind direction prediction task in the joint prediction model for incoming wind conditions; and customize the loss function of the joint prediction model for incoming wind conditions based on the corresponding weights of the incoming wind speed prediction task and the incoming wind direction prediction task.

[0120] Optionally, when the model building module 42 designs the model architecture of the incoming wind condition joint prediction model, it is specifically used to: use the long short-term memory network and the fully connected network as the core structure of the incoming wind condition joint prediction model; wherein, the long short-term memory network is used to capture the correlation characteristics of wind speed and wind direction in the time series, and the fully connected network is used to predict the incoming wind condition based on the correlation characteristics.

[0121] Optionally, the model training module 43 trains the incoming wind condition joint prediction model and optimizes the hyperparameters to obtain the target incoming wind condition joint prediction model, which is specifically used to: create a training set, a validation set and a test set based on the input data set and the output data set; initialize the model parameters of the incoming wind condition joint prediction model and set the hyperparameter search space; for each hyperparameter combination in the hyperparameter search space, use the training set to train the incoming wind condition joint prediction model corresponding to each hyperparameter combination until the incoming wind condition joint prediction model corresponding to each hyperparameter combination converges; use the test set to respectively train the incoming wind condition joint prediction model corresponding to each hyperparameter combination. The joint prediction models of incoming wind conditions corresponding to the various hyperparameter combinations are tested to obtain prediction results of the joint prediction models of incoming wind conditions corresponding to the various hyperparameter combinations; the prediction results of the joint prediction models of incoming wind conditions corresponding to the various hyperparameter combinations are evaluated using model evaluation indicators to obtain evaluation values ​​of the joint prediction models of incoming wind conditions corresponding to the various hyperparameter combinations; the best evaluation value is determined from the evaluation values ​​of the joint prediction models of incoming wind conditions corresponding to the various hyperparameter combinations based on the model evaluation indicators, and the joint prediction model of incoming wind conditions corresponding to the hyperparameter combination that matches the best evaluation value is determined as the target joint prediction model of incoming wind conditions.

[0122] Optionally, when the model training module 43 produces a training set, a validation set, and a test set based on the input data set and the output data set, it is specifically used to: normalize the input data set and the output data set to obtain a normalized input data set and a normalized output data set; and divide the normalized input data set and the normalized output data set into a training set, a validation set, and a test set according to a preset ratio.

[0123] Optionally, when the model application module 44 predicts the incoming wind conditions through the target incoming wind condition joint prediction model and obtains the incoming wind condition prediction data at the second level, it is specifically used to: obtain real-time measurement data of the incoming wind conditions; input the real-time measurement data of the incoming wind conditions into the target incoming wind condition joint prediction model, and output the incoming wind condition prediction data at the second level through the target incoming wind condition joint prediction model.

[0124] Figure 5 The device for joint prediction of incoming wind conditions based on multi-task learning in the illustrated embodiment can be used to implement the technical solution of the above-mentioned method embodiment. Its implementation principle and technical effects are similar and will not be repeated here.

[0125] Figure 6 This is a schematic diagram of the structure of an electronic device in the embodiment of the present disclosure. Figure 6 , which shows a structural diagram of an electronic device 600 suitable for implementing the embodiments of the present disclosure. Figure 6The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0126] like Figure 6 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603 to implement the method for joint prediction of incoming wind conditions based on multi-task learning according to the embodiment of the present disclosure. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0127] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0128] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart, thereby realizing the joint prediction method of incoming wind conditions based on multi-task learning as described above. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0129] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0130] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with 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"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0131] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0132] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0133] Construct input data set and output data set based on the measured data of incoming wind conditions;

[0134] Building a joint forecasting model for incoming wind conditions, which includes forecasting tasks for incoming wind speed and direction;

[0135] Training the incoming wind condition joint prediction model and optimizing hyperparameters to obtain a target incoming wind condition joint prediction model;

[0136] The incoming wind condition is predicted by the target incoming wind condition joint prediction model to obtain second-level incoming wind condition prediction data, wherein the incoming wind condition prediction data includes incoming wind speed prediction data and incoming wind direction prediction data.

[0137] Optionally, when the above one or more programs are executed by the electronic device, the electronic device may also execute other steps described in the above embodiments.

[0138] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0140] The units involved in the embodiments described in this disclosure may be implemented in software or hardware, wherein the name of a unit does not necessarily limit the unit itself.

[0141] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0142] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0143] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0144] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0145] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. A method for joint prediction of incoming wind conditions based on multi-task learning, characterized in that: The method comprises: Construct input data set and output data set based on the measured data of incoming wind conditions; Building a joint forecasting model for incoming wind conditions, which includes forecasting tasks for incoming wind speed and direction; Training the incoming wind condition joint prediction model and optimizing hyperparameters to obtain a target incoming wind condition joint prediction model; The incoming wind condition is predicted by the target incoming wind condition joint prediction model to obtain the incoming wind condition prediction data at the second level, wherein the incoming wind condition prediction data includes the incoming wind speed prediction data and the incoming wind direction prediction data; The building of the combined forecasting model for incoming wind conditions includes: Designing a model architecture for the combined forecasting model for incoming wind conditions; A hard parameter sharing mechanism is adopted for the incoming wind speed prediction task and the incoming wind direction prediction task in the incoming wind condition joint prediction model; Customizing the loss function of the combined wind condition forecasting model based on the weights corresponding to the wind speed forecasting task and the wind direction forecasting task; The model architecture of the design of the combined forecasting model for incoming wind conditions includes: The long short-term memory network and the fully connected network are used as the core structure of the joint prediction model of incoming wind conditions; The long short-term memory network is used to capture the correlation characteristics of wind speed and wind direction in the time series, and the fully connected network is used to predict the incoming wind conditions based on the correlation characteristics.

2. The method according to claim 1, characterized in that The step of constructing an input data set and an output data set based on the measured data of the incoming wind conditions includes: Preprocessing the measured data of incoming wind conditions to obtain preprocessed measured data of incoming wind conditions; Based on the pre-processed measured data of incoming wind conditions, the input data set and the output data set are constructed through the sliding time window algorithm; The input data set is a historical wind condition data set at the second level, and the output data set is a future wind condition data set at the second level.

3. The method according to claim 1, characterized in that The training of the incoming wind condition joint prediction model and the optimization of hyperparameters to obtain a target incoming wind condition joint prediction model includes: Create training sets, validation sets, and test sets based on the input and output data sets; Initializing model parameters of the incoming wind condition joint prediction model and setting a hyperparameter search space; For each hyperparameter combination in the hyperparameter search space, the inflow wind condition joint prediction model corresponding to each hyperparameter combination is trained using the training set until the inflow wind condition joint prediction model corresponding to each hyperparameter combination converges; Using the test set, the inflow wind condition joint prediction model corresponding to each of the hyperparameter combinations is tested to obtain the prediction results of the inflow wind condition joint prediction model corresponding to each of the hyperparameter combinations; The prediction results of the combined wind flow prediction model corresponding to each hyperparameter combination are evaluated using the model evaluation index to obtain an evaluation value of the combined wind flow prediction model corresponding to each hyperparameter combination; Based on the model evaluation index, the best evaluation value is determined from the evaluation values ​​of the incoming wind condition joint prediction models corresponding to each hyperparameter combination, and the incoming wind condition joint prediction model corresponding to the hyperparameter combination matching the best evaluation value is determined as the target incoming wind condition joint prediction model.

4. The method according to claim 3, characterized in that The step of creating a training set, a validation set, and a test set based on the input data set and the output data set includes: Normalizing the input data set and the output data set to obtain a normalized input data set and a normalized output data set; The normalized input data set and the normalized output data set are divided into a training set, a validation set, and a test set according to a preset ratio.

5. The method according to claim 1, wherein The method of predicting the incoming wind condition by using the target incoming wind condition joint prediction model to obtain the incoming wind condition prediction data at the second level includes: Obtain real-time measurement data of incoming wind conditions; The real-time measurement data of the incoming wind condition is input into the target incoming wind condition joint prediction model, and the target incoming wind condition joint prediction model outputs the incoming wind condition prediction data at the second level.

6. A device for joint prediction of incoming wind conditions based on multi-task learning, characterized in that: include: A data set construction module is used to construct an input data set and an output data set based on the measured data of the incoming wind conditions; A model building module is used to build a joint prediction model for incoming wind conditions, wherein the joint prediction model for incoming wind conditions includes tasks for predicting incoming wind speed and incoming wind direction; A model training module is used to train the incoming wind condition joint prediction model and optimize hyperparameters to obtain a target incoming wind condition joint prediction model; A model application module is used to predict the incoming wind condition by using the target incoming wind condition joint prediction model to obtain second-level incoming wind condition prediction data, wherein the incoming wind condition prediction data includes incoming wind speed prediction data and incoming wind direction prediction data; When the model building module builds the incoming wind condition joint prediction model, it is specifically used to: Designing a model architecture for the combined forecasting model for incoming wind conditions; A hard parameter sharing mechanism is adopted for the incoming wind speed prediction task and the incoming wind direction prediction task in the incoming wind condition joint prediction model; Customizing the loss function of the combined wind condition forecasting model based on the weights corresponding to the wind speed forecasting task and the wind direction forecasting task; When the model building module designs the model architecture of the incoming wind condition joint prediction model, it is specifically used to: The long short-term memory network and the fully connected network are used as the core structure of the joint prediction model of incoming wind conditions; The long short-term memory network is used to capture the correlation characteristics of wind speed and wind direction in the time series, and the fully connected network is used to predict the incoming wind conditions based on the correlation characteristics.

7. An electronic device, characterized in that: include: Memory; processor; as well as computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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