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.
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© 2013 Springer-Verlag London
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Hu, R., Zhang, Q. (2013). Prediction of Energy Consumption in Steel Enterprises based on BP Adaboost Algorithm. In: Xu, J., Yasinzai, M., Lev, B. (eds) Proceedings of the Sixth International Conference on Management Science and Engineering Management. Lecture Notes in Electrical Engineering, vol 185. Springer, London. https://doi.org/10.1007/978-1-4471-4600-1_35
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DOI: https://doi.org/10.1007/978-1-4471-4600-1_35
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