An emotional neural network based approach for wind power prediction

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...

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Main Author: Guoling ZHANG
Format: Article
Language:zho
Published: Beijing Xintong Media Co., Ltd 2017-03-01
Series:Dianxin kexue
Subjects:
Online Access:http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017005/
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author Guoling ZHANG
author_facet Guoling ZHANG
author_sort Guoling ZHANG
collection DOAJ
description 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.
format Article
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institution Kabale University
issn 1000-0801
language zho
publishDate 2017-03-01
publisher Beijing Xintong Media Co., Ltd
record_format Article
series Dianxin kexue
spelling doaj-art-e6175ac26f3443d4b3e12bfbde8f82f82025-01-15T03:25:38ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012017-03-013316817259804770An emotional neural network based approach for wind power predictionGuoling ZHANGAccurate 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017005/emotional neural networkwind powerpredictiongenetic algorithm
spellingShingle Guoling ZHANG
An emotional neural network based approach for wind power prediction
Dianxin kexue
emotional neural network
wind power
prediction
genetic algorithm
title An emotional neural network based approach for wind power prediction
title_full An emotional neural network based approach for wind power prediction
title_fullStr An emotional neural network based approach for wind power prediction
title_full_unstemmed An emotional neural network based approach for wind power prediction
title_short An emotional neural network based approach for wind power prediction
title_sort emotional neural network based approach for wind power prediction
topic emotional neural network
wind power
prediction
genetic algorithm
url http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017005/
work_keys_str_mv AT guolingzhang anemotionalneuralnetworkbasedapproachforwindpowerprediction
AT guolingzhang emotionalneuralnetworkbasedapproachforwindpowerprediction