%0 Journal Article
%T Improved data-driven subspace algorithm for energy prediction in iron and steel industry<br>改进的数据驱动子空间算法求解钢铁企业能源预测问题
%A ZHANG Yan-yan
%A TANG Li-xin
%A <br>张颜颜
%A 唐立新
%J 控制理论与应用
%D 2012
%I 
%X Using the production and energy system in Iron and Steel industry as the research background, we propose a data-driven subspace (DDS) method for predicting the energy consumption of production operations. The characteristics of energy consumption and regeneration are fully investigated to find the crucial factors for building the model. The features of practical conditions and data are analyzed in designing the efficient solving method. The subspace method is improved by introducing the feedback factor and the forgetting factor, values of which are optimized by particle swarm optimization (PSO) algorithm in order to improve the prediction accuracy. The performance of the improved method is demonstrated by experimental tests using the practical data, which provide beneficial results in energy prediction and management.
%K data-driven subspace
%K particle swarm optimization
%K energy prediction<br>数据驱动子空间
%K 粒子群优化
%K 能源预测
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=E4EB2AADE1AB1F60E4D048EBFC01C4EC&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=59906B3B2830C2C5&sid=9C467F963DDC525B&eid=498BA6789EF40614&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=0