Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks

By Qiong Li, Q. Meng, Jiejin Cai, H. Yoshino, A. Mochida. 2009

355 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Building and Construction, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/71d180fc1bf3d83c5bdd52d944c85a80fb6dc181

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Musa Buyukada
2. Jian Sun
3. Wuming Xie
4. Jiahong Kuo
5. Xiaochun Zhang
6. Chao Liu
7. Jingyong Liu
8. Candie Xie

Citations in the corpus

• Co-combustion thermal conversion characteristics of textile dyeing sludge and pomelo peel using TGA and artificial neural networks (2018, background): “Machine learning techniques such as artificial neural networks (ANN) have been widely accepted to tackle such complex issues owing to their strong ability of mapping non-linear behaviors [23].”