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基于情感神经网络的风电功率预测
An emotional neural network based approach for wind power prediction
- 电信科学 2017年33卷第3期 页码：168-172
- 作者机构：
- 作者简介：
[ "张国玲（1975-），女，玉林师范学院教育技术中心工程师，主要研究方向为计算机网络、智能控制。" ]
- 基金信息：
DOI：10.11959/j.issn.1000-0801.2017005
中图分类号： TP183-
网络出版日期：2017-03，
纸质出版日期：2017-03-20
- 稿件说明：
移动端阅览
张国玲. 基于情感神经网络的风电功率预测[J]. 电信科学, 2017,33(3):168-172.
Guoling ZHANG. An emotional neural network based approach for wind power prediction[J]. Telecommunications science, 2017, 33(3): 168-172.
张国玲. 基于情感神经网络的风电功率预测[J]. 电信科学, 2017,33(3):168-172. DOI： 10.11959/j.issn.1000-0801.2017005.
Guoling ZHANG. An emotional neural network based approach for wind power prediction[J]. Telecommunications science, 2017, 33(3): 168-172. DOI： 10.11959/j.issn.1000-0801.2017005.
摘要
风力发电功率预测对于风能并网具有重要意义。采用一种可用于复杂系统和模式建模的新型神经网络——情感神经网络，对风力发电功率进行预测。为防止ENN在训练时陷入局部最优解，提出采用遗传算法对其进行训练。采用预测误差的均方根和标准差衡量预测准确性、稳定性，对ENN性能进行了检验。结果表明，相比于人工神经网络、支持向量机和自滑动回归模型，ENN能够获得更高的预测准确率和预测可靠性。
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.
关键词
Keywords
references
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