Neural network approach for modelling and solving the unit commitment problem of cascaded hydropower stations

2016 ◽  
Author(s):  
Gang Bao ◽  
Siyu Wen ◽  
Wenju Yu
Keyword(s):  
Neural Network ◽  
Unit Commitment ◽  
Network Approach ◽  
Unit Commitment Problem ◽  
Neural Network Approach ◽  
Commitment Problem ◽  
Hydropower Stations

Application of Hopfield neural network in unit commitment problem

2009 ◽  
Vol 29 (4) ◽  
pp. 1028-1031
Author(s):  
Wei-xin GAO ◽  
Xiang-yang MU ◽  
Nan TANG ◽  
Hong-liang YAN
Keyword(s):  
Neural Network ◽  
Unit Commitment ◽  
Hopfield Neural Network ◽  
Unit Commitment Problem ◽  
Commitment Problem

scholarly journals A Solution Method of Unit Commitment Problem by Means of Neural Network

1991 ◽  
Vol 111 (7) ◽  
pp. 729-734
Author(s):  
Hiroshi Sasaki ◽  
Yuhji Fujii ◽  
Masahiro Watanabe ◽  
Junji Kubokawa ◽  
Naoto Yorino
Keyword(s):  
Neural Network ◽  
Solution Method ◽  
Unit Commitment ◽  
Unit Commitment Problem ◽  
Commitment Problem

scholarly journals AUGMENTED LAGRANGE HOPFIELD NEURAL NETWORK BASED METHOD FOR UNIT COMMITMENT

2012 ◽  
Vol 15 (2) ◽  
pp. 39-49
Author(s):  
Khai Phuc Nguyen ◽  
Dieu Ngoc Vo ◽  
Tu Phan Vu
Keyword(s):  
Neural Network ◽  
Heuristic Search ◽  
Unit Commitment ◽  
Optimal Solution ◽  
Hopfield Neural Network ◽  
Computer Time ◽  
Test Results ◽  
Unit Commitment Problem ◽  
Priority List ◽  
Commitment Problem

This paper proposed an enhanced merit order (EMO) and augmented Lagrange Hopfield neural network (ALHN) for solving unit commitment problem. This problem is solved on 2 stages. At first, with the heuristic search EMO method we plan the unit scheduling. And then, we use ALHN, a continuous Hopfield neural network combines with augmented Lagrange relaxation, to solve the economic dispatch problem. The proposed method is tested on systems with 10 units, 17 units and up to 100 units. The obtained results is compared to conventional priority list (PL-ALHN) and other methods in literature. Test results show that the proposed method is totally more efficient than PLALHN and others for finding optimal solution of unit commitment problem. And the computer time of proposed method is vastly faster than other methods.

scholarly journals An Application of Gaussian Machine in Neural Network to the Unit Commitment Problem

1998 ◽  
Vol 118 (6) ◽  
pp. 721-727
Author(s):  
Yonghai Cui ◽  
Hiroyuki Kita ◽  
Ken-ichi Nishiya ◽  
Jun Hasegawa
Keyword(s):  
Neural Network ◽  
Unit Commitment ◽  
Unit Commitment Problem ◽  
Commitment Problem
Sign in / Sign up

Export Citation Format

Share Document