Wind speed prediction method based on hybrid neural network model

A hybrid neural network and neural network model technology, applied in the field of data analysis and wind energy, can solve the problems of large prediction error and insufficient prediction ability, and achieve the effect of reducing the difficulty of prediction and improving the accuracy of prediction

Active Publication Date: 2020-09-22
ZHEJIANG UNIV
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However, the current short-term wind speed data prediction m...

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  • Wind speed prediction method based on hybrid neural network model
  • Wind speed prediction method based on hybrid neural network model
  • Wind speed prediction method based on hybrid neural network model

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[0055] The present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments, and the purpose and effect of the present invention will become clearer. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

[0056] In order to improve the predictability of the random non-stationary time series, the method of the present invention adopts the EEMD method to decompose the original time series into multiple components (each component is a single-frequency stationary signal) and a residual signal. The decomposed signal is used to train the neural network, and the BO algorithm is used to adjust and optimize the hyperparameters. On the basis of the trained neural network, the subsequent time series data are forecasted by summing all the forecast values ​​of the decomposed signal.

[0057] The short-term wind speed pr...

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Abstract

The invention discloses a wind speed prediction method based on a hybrid neural network model. The method comprises the following steps: carrying out integrated empirical mode decomposition on wind speed original time series data; and establishing a long-short-term memory neural network to predict a component signal obtained by integrated empirical mode decomposition, adjusting and optimizing hyper-parameters of the long-short-term memory neural network through a Bayesian optimization algorithm, and synthesizing a prediction result of the component signal into a final prediction result. According to the invention, a random unsteady original short-term wind speed time sequence is decomposed into stably changing time sequence data; and the hyper-parameters of the long-term and short-term memory neural network are automatically adjusted and optimized to obtain a prediction result, so that the prediction error is greatly reduced, the prediction precision is improved, the method can be applied to prediction of short-term wind speed, and a powerful tool is provided for intelligent operation and maintenance of a wind power generation network.

Description

technical field [0001] The present invention relates to the fields of data analysis and wind energy, in particular to a method based on integrated empirical mode decomposition (Ensemble empirical mode decomposition, EEMD), long short-term memory neural network (Long short-term memory, LSTM) and Bayesian optimization (Bayesian optimization) , BO) wind speed prediction method based on hybrid neural network model. Background technique [0002] In wind power generation, the power system is a complex dynamic system, which needs to maintain the power balance among power generation, transmission and consumption. In the traditional power system without wind power, the power grid dispatching organization makes a power generation plan according to the daily load curve to meet the power demand of the next day. Wind power generation depends on climatic conditions, and the intermittent nature of the wind leads to great fluctuations in the output power of wind farms. Therefore, the large...

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Application Information

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IPC IPC(8): G06Q10/04G06F17/15G06N3/04G06N3/08G06Q50/06
CPCG06Q10/04G06Q50/06G06F17/15G06N3/049G06N3/08G06N3/044G06N3/045Y04S10/50Y02A30/00
Inventor 胡伟飞何亦菡程锦刘振宇谭建荣
Owner ZHEJIANG UNIV
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