Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Energy consumption modelling using deep learning embedded semi-supervised learning

Chen, Chong, Liu, Ying, Kumar, Maneesh, Qin, Jian and Ren, Yunxia 2019. Energy consumption modelling using deep learning embedded semi-supervised learning. Computers and Industrial Engineering 135 , pp. 757-765. 10.1016/j.cie.2019.06.052
Item availability restricted.

[img] PDF - Accepted Post-Print Version
Restricted to Repository staff only until 24 June 2020 due to copyright restrictions.

Download (860kB)

Abstract

Reduction of energy consumption in the steel industry is a global issue where government is actively taking measures to pursue. A steel plant can manage its energy better if the consumption can be modelled and predicted. The existing methods used for energy consumption modelling rely on the quantity of labelled data. However, if the labelled energy consumption data is deficient, its underlying process of modelling and prediction tends to be difficult. The purpose of this study is to establish an energy value prediction model through a big data-driven approach. Owing to the fact that labelled energy data is often limited and expensive to obtain, while unlabelled data is abundant in the real-world industry, a semi-supervised learning approach, i.e., deep learning embedded semi-supervised learning (DLeSSL), is proposed to tackle the issue. Based on DLeSSL, unlabelled data can be labelled and compensated using a semi-supervised learning approach that has a deep learning technique embedded so to expand the labelled data set. An experimental study using a large amount of furnace energy consumption data shows the merits of the proposed approach. Results derived using the proposed method reveal that deep learning (DLeSSL based) outperforms the deep learning (supervised) and deep learning (label propagation based) when the labelled data is limited. In addition, the effect on performance due to the size of labelled data and unlabelled data is also reported.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Engineering
Publisher: Elsevier
ISSN: 0360-8352
Date of First Compliant Deposit: 26 June 2019
Date of Acceptance: 23 June 2019
Last Modified: 18 Oct 2019 15:22
URI: http://orca.cf.ac.uk/id/eprint/123717

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics