| 引用本文: | 张颜颜,唐立新.改进的数据驱动子空间算法求解钢铁企业能源预测问题[J].控制理论与应用,2012,29(12):1616~1622.[点击复制] |
| ZHANG Yan-yan,TANG Li-xin.Improved data-driven subspace algorithm for energy prediction in iron and steel industry[J].Control Theory and Technology,2012,29(12):1616~1622.[点击复制] |
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| 改进的数据驱动子空间算法求解钢铁企业能源预测问题 |
| Improved data-driven subspace algorithm for energy prediction in iron and steel industry |
| 摘要点击 1734 全文点击 1734 投稿时间：2012-02-29 修订日期：2012-06-14 |
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| DOI编号 10.7641/j.issn.1000-8152.2012.12.CACP120183 |
| 2012,29(12):1616-1622 |
| 中文关键词 数据驱动子空间 粒子群优化 能源预测 |
| 英文关键词 data-driven subspace particle swarm optimization energy prediction |
| 基金项目 国家自然科学基金重点资助项目(71032004); 教育部基本科研业务费资助项目(N100304012, N090104002, N100704002); 国家“111”资助项目(B08015). |
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| 中文摘要 |
| 本文以钢铁企业生产与能源系统作为研究背景, 提出一种数据驱动的子空间方法(data-driven subspace, DDS)预测各生产工序的能源消耗. 为了准确表达该问题, 本文全面分析了能源消耗和回收的特点, 以提取建模因素; 为了设计有效的求解方法, 对实际生产和数据的特征进行了分析. 为了提高预测准确率, 文中引入了反馈因子和遗忘因子来改进子空间方法, 因子的取值采用粒子群算法(particle swarm optimization, PSO)来优化. 对实际生产数据的测试验证了本文所提出的方法的有效性, 该结果能够为钢铁企业的能源预测和管理提供有效的决策支持. |
| 英文摘要 |
| 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. |
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