عنوان انگلیسی مقاله:
Energy consumption modelling using deep learning embedded semi-supervised learning
ترجمه فارسی عنوان مقاله:
مدل سازی مصرف انرژی با استفاده از یادگیری عمیق یادگیری نیمه نظارت تعبیه شده
Sciencedirect - Elsevier - Computers & Industrial Engineering, 135 (2019) 757-765: doi:10:1016/j:cie:2019:06:052
Chong Chena, Ying Liua,⁎, Maneesh Kumarb, Jian Qina, Yunxia Renc
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.
Keywords: Energy modelling | Intelligent manufacturing | Deep learning | Semi-supervised learning | Data mining