计算机科学
构造(python库)
适应性
序列(生物学)
人工神经网络
概括性
电力系统仿真
编码器
人工智能
深度学习
电力系统
数据挖掘
功率(物理)
实时计算
生物
操作系统
心理学
物理
程序设计语言
心理治疗师
量子力学
遗传学
生态学
作者
Nan Yang,Cong Yang,Lei Wu,Xun Shen,Junjie Jia,Zhengmao Li,Daojun Chen,Binxin Zhu,Songkai Liu
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2022-05-01
卷期号:18 (5): 3126-3137
被引量:69
标识
DOI:10.1109/tii.2021.3107406
摘要
Under the background of the rapid change of energy technology and the deep integration of artificial intelligence into the power system, it is of great significance to study the intelligent decision-making method of security-constrained unit commitment (SCUC) with high adaptability and high accuracy. Thus, in this article, an expanded sequence-to-sequence (E-Seq2Seq)-based data-driven SCUC expert system for dynamic multiple-sequence mapping samples is proposed. First, dynamic multiple-sequence mapping samples of SCUC are reconstructed by analyzing the input–output sequence characteristics. Then, an E-Seq2Seq approach with a multiple-encoder–decoder architecture and a fully connected extension layer is proposed. On this basis, the simple recurrent unit is introduced as a neuron of the E-Seq2Seq approach to construct deep learning models, and an intelligent data-driven expert system for SCUC is further developed. The proposed approach has been simulated on a typical IEEE 118-bus system and a practical system in Hunan province in China. The results indicate that the proposed approach could possess strong generality, high solution accuracy, and efficiency over traditional methods.
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