TY - JOUR
T1 - Intelligent Data-Driven Decision-Making Method for Dynamic Multisequence
T2 - An E-Seq2Seq-Based SCUC Expert System
AU - Yang, Nan
AU - Yang, Cong
AU - Wu, Lei
AU - Shen, Xun
AU - Jia, Junjie
AU - Li, Zhengmao
AU - Chen, Daojun
AU - Zhu, Binxin
AU - Liu, Songkai
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - 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.
AB - 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.
KW - Data-driven
KW - deep learning
KW - expanded sequence-to-sequence
KW - security-constrained unit commitment
KW - simple recurrent unit
UR - http://www.scopus.com/inward/record.url?scp=85113852631&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85113852631&partnerID=8YFLogxK
U2 - 10.1109/TII.2021.3107406
DO - 10.1109/TII.2021.3107406
M3 - Article
AN - SCOPUS:85113852631
SN - 1551-3203
VL - 18
SP - 3126
EP - 3137
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 5
ER -