基于改进差分进化算法的步进式加热炉能耗预测方法  

Energy Consumption Prediction of Walking Beam Furnace Based on Improved Differential Evolution Algorithm

作　　者：仝翠芝 张惠 刘洪斌 TONG Cuizhi;ZHANG Hui;LIU Hongbin(State Grid Jibei Electric Power Company Limited Smart Distribution Network Center,Qinhuangdao 066100,China)

机构地区：[1]国网冀北电力有限公司智能配电网中心,河北秦皇岛066100

出　　处：《工业加热》2024年第1期47-51,共5页Industrial Heating

基　　金：国家电网公司科技项目(52010118001P);国家重点研发计划资助项目(2018YFE0122200);国家自然科学基金资助项目(51777066)。

摘　　要：为了快速寻找到全局最优解,提高加热炉能耗的预测精度,提出基于改进差分进化算法的步进式加热炉能耗预测方法。首先,采用小波包原理对步进式加热炉的历史运行能耗数据展开去噪处理,提高能耗数据精度;其次,采用时间卷积网络建立步进式加热炉能耗预测模型;最后,采用差分进化算法对预测模型进行求解,采用禁忌搜索算法对适应度值排名前10%的优良个体进行进一步的搜索,使差分进化算法的全局最优解寻求能力得到改进提升。实验结果表明,所提出预测方法去噪效果较高,预测结果与实际能耗结果基本一致,并且最长预测耗时不超过2 min。In order to quickly find the global optimal solution and improve the prediction accuracy of the energy consumption of the reheating furnace,a method for predicting the energy consumption of the walking beam reheating furnace based on the improved differential evolution algorthm is proposed.Firstly,the wavelet packet principle is used to de-noise the historical operation energy consumption data of the walking beam heating furnace to improve the accuracy of energy consumption data;Secondly,the time convolution network is used to establish the energy consumption prediction model of the walking beam furnace;Finally,the differential evolution algorithm is used to solve the prediction model,and the tabu search algorithm is used to further search the top 10%of the excellent individuals with fitness values,so that the global optimization search ability of the differential evolution algorithm is improved.The experimental results show that the proposed prediction method has high denoising effect,the prediction results are basically consistent with the actual energy consumption results,and the longest prediction time is less than 2 minutes.

关 键 词：步进式加热炉 改进差分进化算法 能耗预测 小波包 禁忌搜索算法 

分 类 号：TF062[冶金工程—冶金物理化学] TG155.12[金属学及工艺—热处理]