Journal Article10.1109/TII.2021.3107406
Intelligent Data-Driven Decision-making Method for Dynamic Multi-Sequence: An E-Seq2Seq Based SCUC Expert System
Nan Yang,Cong Yang,Lei Wu,Xun Shen,Junjie Jia,Zhengmao Li,Daojun Chen,Binxin Zhu,Songkai Liu +8 more
112
TL;DR: An expanded sequence-to-sequence (E-Seq2Seq)-based data-driven SCUC expert system for dynamic multiple-sequence mapping samples and results indicate that the proposed approach could possess strong generality, high solution accuracy, and efficiency over traditional methods.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Comprehensive Review of Security-constrained Unit Commitment
TL;DR: In this article , the basic mathematical model of the standard SCUC is summarized, and the characteristics and application scopes of common solution algorithms are presented, and customized models focusing on diverse mathematical properties are then categorized and the corresponding solving methodologies are discussed.
Underfrequency Load Shedding Scheme for Islanded Microgrids Considering Objective and Subjective Weight of Loads
01 Mar 2023
TL;DR: In this paper , an underfrequency load shedding (UFLS) scheme based on the comprehensive weight of loads is proposed, which evaluates the multiple attributes of loads and the user's preference for load attributes based on entropy method with high adaptability (EMHA) and the analytical hierarchy process (AHP) to obtain the objective and subjective weights of loads.
35
References
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
- 01 Jan 2014
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.
LSTM: A Search Space Odyssey
TL;DR: This paper presents the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling, and observes that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment.
6.8K
Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
TL;DR: In this article, the root mean square error (RMSE) and the mean absolute error (MAE) are used to evaluate model performance and it is shown that the RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian.
A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem
Miguel Carrión,Jose M. Arroyo +1 more
TL;DR: In this paper, a new mixed-integer linear formulation for the unit commitment problem of thermal units is presented, which requires fewer binary variables and constraints than previously reported models, yielding a significant computational saving.
1.8K
Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem
TL;DR: In this paper, a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty is proposed, which only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data.