Solar wind speed prediction method based on multi-task deep learning neural network

A neural network and speed prediction technology, which is applied in neural learning methods, biological neural network models, neural architectures, etc., to achieve the effects of improving prediction accuracy, improving prediction performance, and improving shallow feature extraction

Pending Publication Date: 2022-05-13
TIANJIN UNIV
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Problems solved by technology

As far as we know, there is no solar wind speed prediction method based on multi-step prediction.
In addition, how to effectively construct a multi-step solar wind speed prediction model is a challenge

Method used

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  • Solar wind speed prediction method based on multi-task deep learning neural network
  • Solar wind speed prediction method based on multi-task deep learning neural network
  • Solar wind speed prediction method based on multi-task deep learning neural network

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Embodiment

[0086] Example: such as figure 1 As shown, a 5-step multi-task model for solar wind speed prediction is designed in this embodiment. Specifically, based on historical time series [X 1 ,...,X n ], the embodiment of the present invention has 5 tasks of predicting the solar wind speed in the future, and each task predicts a specific point in time, that is, output v respectively n+t-2 , v n+t-1 , v n+t , v n+t+1 , v n+t+2 . Among them, the prediction task v at the intermediate time point n+t is the main task, t represents the step size of the main task prediction time point, 24 and 96 for 24-hour and 96-hour prediction respectively; other tasks are auxiliary tasks, and the purpose of setting auxiliary tasks is to improve the prediction performance of the main task. The model mainly consists of three modules, namely the shared module, the main LSTM module and the autoregressive layer (AR) module. The workflow of the above model is as follows: First, the multivariate time s...

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Abstract

The invention discloses a solar wind speed prediction method based on a multi-task deep learning neural network. A used model comprises a sharing module composed of a one-dimensional convolutional neural network CNN and a long and short term memory neural network LSTM, a main LSTM module and an autoregression layer AR module. The sharing module is used for extracting shallow space and time features shared by a plurality of tasks, the main LSTM module is used for independently extracting features of a main task, and the autoregression module is used for properly correcting a neural network model in a linear mode. A multi-task learning mechanism is adopted to realize multi-step prediction, and a model is constructed. In the field of solar wind speed prediction, introduction of a multi-task learning mechanism is a new attempt. The method has the advantages that a plurality of independent prediction tasks are combined for simultaneous parallel learning, data information and shared public information are fully utilized to help to improve shallow feature extraction, and an independent structure is designed for a main task to improve prediction performance.

Description

technical field [0001] The invention belongs to the technical field of neural network model design and the field of solar wind speed prediction, and in particular relates to a multi-task deep learning-based solar wind speed prediction model and a modeling method thereof. Background technique [0002] At present, there are three types of methods in the field of solar wind speed prediction: (1) physics-based prediction models, which construct physical models according to solar physical parameters; (2) empirical or semi-empirical models, which are based on manual observations and expert experience. The state of the sun is judged; (3) machine learning model, using SVM, artificial neural network and other input data to train and predict. With the increasing abundance of observational data, the complexity and catastrophe of solar activities, and the urgent need for timeliness of solar wind speed prediction, people need to develop prediction methods different from traditional physi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06F30/27G06N3/08G06N3/044G06N3/045
Inventor 谢宗霞毛凯舟孙彦茹
Owner TIANJIN UNIV
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