[1]朱俊丞,杨之乐,郭媛君,等.深度学习在电力负荷预测中的应用综述[J].郑州大学学报(工学版),2019,40(05):12-21.
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深度学习在电力负荷预测中的应用综述()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年05期
页码:
12-21
栏目:
出版日期:
2019-09-20

文章信息/Info

作者:
朱俊丞杨之乐郭媛君于坤杰张建康穆晓敏
郑州大学产业技术研究院,河南郑州450001 ; 中国科学院深圳先进技术研究院,广东深圳 518000;郑州大学电气工程学院,河南郑州450001 ;郑州大学信息工程学院,河南郑州450001
关键词:
深度学习电力系统负荷预测人工神经网络LSTM
Keywords:
Deep learning power system load forecasting artificial neural network LSTM
文献标志码:
A
摘要:
摘要:在综合能源系统和能源互联网的高速发展中,电力负荷预测对电力系统的经济安全运行具有 重要的作用.传统的负荷预测模型方法已在电力系统中取得了广泛应用,传统方法的简单计算模型对于 高随机性、大数据背景下的动态负荷预测精度无法保证.近年来,在计算工具不断升级和训练数据量大 规模提升的背景下,深度学习方法在电力负荷预测领域的应用得到了广泛重视.对多种深度学习方法在 负荷预测领域中的应用进行了叙述分析,回顾了循环神经网络(RNN)、长短期记忆网络(LSTM)、深度 置信网络(DBN)、卷积神经网络(CNN)等不同深度学习方法预测模型.对比于传统的负荷预测方法,深 度学习方法具有更高的预测精度,对于各种外部影响因素具有更好的鲁棒性.
Abstract:
In the rapid development of integrated energy systems and energy network, power load forecasting played an important role in the economic and safe operation of energy and power systems. The traditional load forecasting modelling methods have been widely used in power systems. However, the simple computational model structure limited by traditional methods could not guarantee the dynamic load prediction accuracy under high randomness and big data background. In recent years, in the context of the continuous upgrading of computing tools and the increasing large-scale of training data volume, the application of deep learning methods in the field of power system load forecasting atrracted extensive attentions. This paper analyzed the applications of various deep learning methods in the field of load forecasting, and revieed the Recurrent Neural Network (RNN) , Long- and Short-Term Memory Network ( LSTM) , Deep Belief Network ( DBN) , and Convolutional Neural Network ( CNN). Compared with the traditional load forecasting method, the deep learning method showed higher prediction accuracy and better robustness to various external influences.

相似文献/References:

[1]朱晓东,王颖,杨之乐,等.启发式多目标优化算法在能源和电力系统中的典型应用综述[J].郑州大学学报(工学版),2019,40(05):1.
[2]马民,秦佳,杨东升,等.人工智能在电力系统中的应用综述[J].郑州大学学报(工学版),2019,40(05):22.
[3]黄文锋,徐珊珊,孙燚,等.基于多分辨率卷积神经网络的火焰检测[J].郑州大学学报(工学版),2019,40(05):79.

更新日期/Last Update: 2019-10-26