Wind speed prediction method based on complex numerical value forward neural network

Abstract

The invention discloses a wind speed prediction method based on a complex numerical value forward neural network, and the method comprises the steps: obtaining a training set and a prediction set for wind speed prediction, constructing the complex numerical value forward neural network, and initializing a parameter vector; introducing a GroupLasso regularization item into a target function trained by the complex numerical value forward neural network, converting training into solution of a constraint optimization problem, training the complex numerical value forward neural network by adopting a training set and a customized complex numerical value projection quasi-Newton algorithm, and ending training until a preset condition is met to obtain a trained complex numerical value forward neural network, and inputting the prediction set into the trained complex numerical value forward neural network to obtain a wind speed prediction result. According to the method, the GroupLasso regularization item is introduced, and the complex numerical value forward neural network is trained by using the customized complex numerical value projection quasi-Newton algorithm to realize optimization of the network structure and parameters, so that the network structure is compact, the generalization performance is high, and meanwhile, the accuracy of wind speed prediction is improved.

Description

technical field [0001] The invention relates to the technical field of wind speed prediction, in particular to a wind speed prediction method based on a complex-valued forward neural network. Background technique [0002] Compared with some traditional non-renewable energy sources such as petroleum, wind energy, as a green and environmentally friendly renewable energy source, has attracted more and more people's attention, and the development of wind energy has become a current trend. However, due to the randomness and intermittent nature of wind speed, the instability of wind speed will pose a threat to the security and stability of the power grid system. Therefore, accurate prediction of wind speed plays a vital role in wind energy development. [0003] At present, there are mainly two methods of wind speed prediction: the physical model prediction method based on weather forecast data and the wind speed prediction method based on historical data. However, due to the lac...

AI Technical Summary

Problems solved by technology

However, when using the artificial neural network model for wind speed prediction, it is often difficult to achieve the expected performance in wind speed prediction due to the unreasonable design of the network structure.

Therefore, for the artificial neural network method, choosing a suitable network structure is an urgent problem to be solved. The simplest method is to determine a more suitable structure through manual trial and error, but this method is time-consuming and laborious.

At the same time, in order to obtain appropriate parameters such as network weights and biases, the gradient descent method is widely used in the training process of the feedforward neural network, but the gradient descent method is prone to problems such as falling into local minima and slow convergence speed.

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