Prediction of Energy Consumption in Steel Enterprises based on BP Adaboost Algorithm

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 185)

Abstract

Low-carbon production is the aim of every heavy industry enterprises. Iron and steel companies are no exceptions. This thesis complished the prediction goal by applying the framework of BP neural network and Adaboost algorithm based on Matlab platform. Data used as training set were the energy consumption from 2005–2009 and the data of 2010–2015 were set as target. The result indicated that the main trend of the energy consumption in this industry was declining and the gaps between companies were decreasing eventually.

Keywords

BP neural network Energy consumption Prediction Iron and steel enterprise 

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.School of Dongling Economics and ManagementUniversity of Science and TechnologyBeijingPeople Republic of China

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