1. Intelligent Data-Driven Decision-Making Method for Dynamic Multisequence: An E-Seq2Seq-Based SCUC Expert System
- Author
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Zhengmao Li, Daojun Chen, Xun Shen, Nan Yang, Junjie Jia, Lei Wu, Songkai Liu, Cong Yang, and Binxin Zhu
- Subjects
Sequence ,Basis (linear algebra) ,SIMPLE (military communications protocol) ,business.industry ,Computer science ,Deep learning ,Construct (python library) ,computer.software_genre ,Expert system ,Computer Science Applications ,Data-driven ,Power system simulation ,Control and Systems Engineering ,Artificial intelligence ,Data mining ,Electrical and Electronic Engineering ,business ,computer ,Information Systems - Abstract
An expanded sequence-to-sequence (E- Seq2Seq) based data-driven security-constrained unit commitment (SCUC) expert system for dynamic multiple-sequence mapping samples is proposed in this paper. First,the 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 the deep learning model,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 from Hunan province in China. The results indicate that the proposed approach possesses strong generality,high solution accuracy,and efficiency over traditional methods.
- Published
- 2022