RESEARCH ON INTELLIGENT SYSTEMS FOR ENERGY ENGINEERING

被引:0
作者
Rajora, M. [1 ]
Zou, P. [2 ]
Liang, S. Y. [1 ]
机构
[1] Georgia Inst Technol, George W Woodruff Sch Mech Engn, Atlanta, GA 30332 USA
[2] Donghua Univ, Mech Engn Coll, Shanghai 201620, Peoples R China
来源
ENERGY AND MECHANICAL ENGINEERING | 2016年
关键词
Artificial intelligence; Energy consumption; Energy conservation; Scheduling; SHOP SCHEDULING PROBLEMS; POWER-CONSUMPTION; NEURAL-NETWORKS; ELECTRICITY; PREDICTION; REDUCTION; ALGORITHM; HYBRID; MODEL;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
In the recent years, manufacturing industries have accounted for one-third of the world total energy consumption and CO2 production. These issues, paired with the growing concern over global warming and increasing energy cost, have led to growing efforts to minimize the energy consumption everywhere, especially in the manufacturing industries. The advancement in computation and information systems have enabled researchers to develop intelligent systems that can be used for power, energy efficient machinery, temperature control, and intelligent scheduling systems that consider both productivity and energy efficiency as their objectives. With the aim of minimizing the energy consumption, researchers have also focused on the production and distribution of electricity. The intelligent techniques have been applied to solve this problem and one of the successful applications is known as "smart grids". The application of these intelligent technologies is not only limited to manufacturing. It can also be applied to a variety of other fields in order to create a more energy efficient environment.
引用
收藏
页码:18 / 26
页数:9
相关论文
共 30 条
[1]   A review on applications of ANN and SVM for building electrical energy consumption forecasting [J].
Ahmad, A. S. ;
Hassan, M. Y. ;
Abdullah, M. P. ;
Rahman, H. A. ;
Hussin, F. ;
Abdullah, H. ;
Saidur, R. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2014, 33 :102-109
[2]  
[Anonymous], 1997, P 17 INT C PAR ARCH
[3]   An efficient model based on artificial bee colony optimization algorithm with Neural Networks for electric load forecasting [J].
Awan, Shahid M. ;
Aslam, Muhammad ;
Khan, Zubair A. ;
Saeed, Hassan .
NEURAL COMPUTING & APPLICATIONS, 2014, 25 (7-8) :1967-1978
[4]   New Continuous-Time Scheduling Formulation for Continuous Plants under Variable Electricity Cost [J].
Castro, Pedro M. ;
Harjunkoski, Iiro ;
Grossmann, Ignacio E. .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2009, 48 (14) :6701-6714
[5]   Modeling heating and cooling loads by artificial intelligence for energy-efficient building design [J].
Chou, Jui-Sheng ;
Bui, Dac-Khuong .
ENERGY AND BUILDINGS, 2014, 82 :437-446
[6]   Realizing energy reduction of machine tools through a control-integrated consumption graph-based optimization method [J].
Eberspaecher, Philipp ;
Verl, Alexander .
FORTY SIXTH CIRP CONFERENCE ON MANUFACTURING SYSTEMS 2013, 2013, 7 :640-645
[7]   Flow shop scheduling with peak power consumption constraints [J].
Fang, Kan ;
Uhan, Nelson A. ;
Zhao, Fu ;
Sutherland, John W. .
ANNALS OF OPERATIONS RESEARCH, 2013, 206 (01) :115-145
[8]   A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction [J].
Fang, Kan ;
Uhan, Nelson ;
Zhao, Fu ;
Sutherland, John W. .
JOURNAL OF MANUFACTURING SYSTEMS, 2011, 30 (04) :234-240
[9]   A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems [J].
Gao, Jie ;
Gen, Mitsuo ;
Sun, Linyan ;
Zhao, Xiaohui .
COMPUTERS & INDUSTRIAL ENGINEERING, 2007, 53 (01) :149-162
[10]   Optimal time-dependent operation of seawater reverse osmosis [J].
Ghobeity, Amin ;
Mitsos, Alexander .
DESALINATION, 2010, 263 (1-3) :76-88