文章基本信息

标题：Evolutionary Machine Learning-Based Energy Consumption Prediction for the industry
本地全文：下载
作者：Mouad Bahij ; Moussa Labbadi ; Chakib Chatri 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2022
卷号：351
页码：1-5
DOI：10.1051/e3sconf/202235101091
语种：English
出版社：EDP Sciences
摘要：In the digitalization of industry and the industry 4.0 environment, it is important to master the accurate forecasting of energy demand in order to guarantee the continuity of production service as well as to improve the reliability of the electrical system while promoting energy efficiency strategies in the industrial sector. This paper proposes machine learning models to predict the energy consumption demand in an industrial plant, which takes into account the at-tributes that directly the consumption. The proposed models in this work include Multiple Linear Regression (MLR), Decision Tree (DT), Recurrent Neural Networks (RNN) and Gated Recurrent United (GRU), which are compared according to their performances criteria which help to find the best forecasting models. Basing on simulation results, it is proven that the MLR approach is the best forecasting method.