دانلود مقاله انگلیسی رایگان:مدل سازی مصرف انرژی با استفاده از یادگیری عمیق یادگیری نیمه نظارت تعبیه شده - 2019
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  • Energy consumption modelling using deep learning embedded semi-supervised learning Energy consumption modelling using deep learning embedded semi-supervised learning
    Energy consumption modelling using deep learning embedded semi-supervised learning

    سال انتشار:

    2019


    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


    منبع:

    Sciencedirect - Elsevier - Computers & Industrial Engineering, 135 (2019) 757-765: doi:10:1016/j:cie:2019:06:052


    نویسنده:

    Chong Chena, Ying Liua,⁎, Maneesh Kumarb, Jian Qina, Yunxia Renc


    چکیده انگلیسی:

    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


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 9
    حجم فایل: 1236 کیلوبایت

    قیمت: رایگان


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