Chen, WanghuZhai, ChenhanWang, XinLi, JingLv, PengboLiu, Chen2022-08-122022-08-122022-07-25W. Chen, C. Zhai, X. Wang, J. Li, P. Lv and C. Liu, "GCN and GRU Based Intelligent Model for Temperature Prediction of Local Heating Surfaces," in IEEE Transactions on Industrial Informatics, 2022, doi: 10.1109/TII.2022.3193414.https://doi.org/10.1109/TII.2022.3193414http://hdl.handle.net/11603/25382A boiler heating surface is composed of hundreds of tubes, whose temperatures may be different because of their positions, the influences of attempering water and flue gas. Using a criteria based on DBI, we propose to partition a heating surface into local ones, whose interactions in temperature are represented as a weighted Heating Surface Graph (HSG) at each point of time, and their current features are embedded in the HSG's nodes. Then, a local heating surface temperature prediction model WGCN-GRU is proposed. Graph Convolutional Network (GCNs) receive a series of HSGs, and extract the features of local heating surfaces and their spatial dependences in a time window. Features output by GCNs are finally directed to Gated Recurrent Units (GRUs) for temperature predictions. Experiments show that WGCN-GRU can averagely maintain the prediction error below 0.5°C. Compared with other models, it can reduce the errors by a rate from 5.6% to 46.8%, and shows advantages in RMSE and R2. It also shows that the node-to-node weights for GCN can reduce the prediction error by 11.4%.12 pagesen-US© 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Text