Hybrid teaching-learning artificial neural network for city-level electrical load prediction

被引：2

作者：

Li, Kangji ^{[1
]}

Xie, Xianming ^{[1
]}

Xue, Wenping ^{[1
]}

Chen, Xu ^{[1
]}

机构：

[1] Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Jiangsu, Peoples R China

来源：

SCIENCE CHINA-INFORMATION SCIENCES | 2020年 / 63卷 / 05期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

D O I：

10.1007/s11432-018-9594-9

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

引用

页数：3

共 5 条

[1] Parameters identification of solar cell models using generalized oppositional teaching learning based optimization [J].

Chen, Xu ;

Yu, Kunjie ;

Du, Wenli ;

Zhao, Wenxiang ;

Liu, Guohai .

ENERGY, 2016, 99 :170-180

[2] Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis [J].

Li, Kangji ;

Hu, Chenglei ;

Liu, Guohai ;

Xue, Wenping .

ENERGY AND BUILDINGS, 2015, 108 :106-113

[3] A novel approach framework based on statistics for reconstruction and heartrate estimation from PPG with heavy motion artifacts [J].

Pang, Bo ;

Liu, Ming ;

Zhang, Xu ;

Li, Peng ;

Chen, Hongda .

SCIENCE CHINA-INFORMATION SCIENCES, 2018, 61 (02)

[4] Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems [J].

Rao, R. V. ;

Savsani, V. J. ;

Vakharia, D. P. .

COMPUTER-AIDED DESIGN, 2011, 43 (03) :303-315

[5] A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models [J].

Wang, Zeyu ;

Srinivasan, Ravi S. .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 75 :796-808

← 1 →