基于情感神经网络的风电功率预测

doi:10.11959/j.issn.1000-0801.2017005

Abstract

Abstract:

Accurate wind power forecasting is vital for the integration of wind power into the grid. Emotional neural network (ENN)——a new type of neural network which could be used to model complex systems and patterns, was used to forecast wind power. To prevent ENN from stucking in locally optimal solution in the process of training, genetic algorithm was proposed to train ENN. The root-mean-square and the standard deviation of the forecast errors were also adopted to measure the accuracy and reliability of the forecast to test the performance of ENN. The results demonstrate that, compared with artificial neural network, ENN can improve the accuracy and reliability of the forecast by 3.8% and 46% respectively.

Key words: emotional neural network, wind power, prediction, genetic algorithm

CLC Number:

TP183

Guoling ZHANG. An emotional neural network based approach for wind power prediction[J]. Telecommunications Science, 2017, 33(3): 168-172.

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References 17

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			隐含层个数
	5	10	15	20	25
均方误差	0.596 7	0.629 7	0.673 4	0.697 3	0.661 2
误差的标准差	0.006 3	0.007 1	0.006 2	0.006 7	0.007 4

An emotional neural network based approach for wind power prediction

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