A Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics

Tianyu Wang; Fan Zhou; Yangjie Wu; Jun Zhao; Wei Wang

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > In Press, Accepted Manuscript

T. Wang, F. Zhou, Y. Wu, J. Zhao, and W. Wang, “A multi-condition sequential network ensemble for industrial energy storage prediction considering the condition switching characteristics,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 2, pp. 1–12, Feb. 2025.

Citation:

T. Wang, F. Zhou, Y. Wu, J. Zhao, and W. Wang, “A multi-condition sequential network ensemble for industrial energy storage prediction considering the condition switching characteristics,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 2, pp. 1–12, Feb. 2025.

Citation:

T. Wang, F. Zhou, Y. Wu, J. Zhao, and W. Wang, “A multi-condition sequential network ensemble for industrial energy storage prediction considering the condition switching characteristics,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 2, pp. 1–12, Feb. 2025.

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A Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics

Tianyu Wang^{,
,},
Fan Zhou^,,
Yangjie Wu^,,
Jun Zhao^{,
,},
Wei Wang^,

Funds: This work was supported by the National Natural Sciences Foundation of China (62125302, 62203087), Liaoning Revitalization Talents Program (XLYC2002087), Sci-Tech Talent Innovation Support Program of Dalian (2022RG03), and Young Elite Scientist Sponsorship Program by China Association for Science and Technology (YESS20220018)

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Author Bio:
Tianyu Wang (Member, IEEE) received the B.E. and Ph.D. degrees in automation from the Dalian University of Technology in 2013 and 2020, respectively. He was a Visiting Student with the University of Calgary, Canada, from 2018 to 2019. He is currently an Associate Professor with the School of Control Science and Engineering, Dalian University of Technology. His current research interests include industrial system modeling and optimization, production scheduling, and artificial intelligence

Fan Zhou received the B.S. degree in automation from Qufu Normal University in 2011, and the M.S. and Ph.D. degrees in control engineering from the Dalian University of Technology, in 2014 and 2023, respectively. He is currently a Lecturer with the School of Information Science and Engineering, Dalian Polytechnic University. His current research interests include industrial system modeling and optimization, production scheduling, and intelligent control

Yangjie Wu received the B.E. and M.S. degrees in automation from the Wuhan Institute of Technology, in 2018 and 2021, respectively. He is currently a Ph.D. candidate in control science and control engineering at the School of Control Science and Engineering, Dalian University of Technology. His current research interests include industrial system modeling and optimization, production scheduling

Jun Zhao (Senior Member, IEEE) received the B.S. degree in control theory from Dalian Jiaotong University in 2003, and the Ph.D. degree in engineering from the Dalian University of Technology in 2008. He is currently a Professor with the School of Control Science and Engineering, Dalian University of Technology. His current research interests include industrial production scheduling, computer integrated manufacturing, intelligent optimization, and machine learning

Wei Wang (Senior Member, IEEE) received the B.S., M.S. and Ph.D. degrees from Northeastern University, in 1982, 1986, and 1988, respectively, all in industrial automation. He was a Post-Doctoral Fellow with the Division of Engineering Cybernetics, Norwegian Science and Technology University, Norway, from 1990 to 1992, a Professor and Vice Director of the National Engineering Research Center of Metallurgical Automation of China, from 1995 to 1999, and a Research Fellow with the Department of Engineering Science, University of Oxford, UK, from 1998 to 1999. He is currently a Professor with the School of Control Sciences and Engineering, Dalian University of Technology. His current research interests include adaptive controls, computer integrated manufacturing, and computer controls of industrial processes
Corresponding author: Tianyu Wang, e-mail: wangty@dlut.edu.cn; Jun Zhao, e-mail: zhaoj@dlut.edu.cn
Received Date: 2024-04-12
Revised Date: 2024-08-14
Accepted Date: 2024-10-06

Available Online: 2024-11-15

Abstract

Abstract

As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status (mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-condition time sequential network ensembled method is proposed. In order to especially consider the time dependence of different conditions, a central-wise condition sequential network is developed, where the network branches are specially designed based on the condition switching sequences. A branch combination transfer learning strategy is developed to tackle the sample imbalance problem of different condition data. Since the condition or status data are real-time information that cannot be recognized during the prediction process, a pre-trained and ensemble learning approach is further proposed to fuse the outputs of the multi-condition networks and realize a transient-state involved prediction. The performance of the proposed method is validated on practical energy data coming from a domestic steel plant, comparing with the state-of-the-art algorithms. The results show that the proposed method can maintain a high prediction accuracy under different condition switching cases, which would provide effective guidance for the optimal scheduling of the industrial energy systems.
- Ensemble learning,
- industrial energy prediction,
- multi-working condition,
- structural deep network,
- transfer learning

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References

[1]	V. B. de Oliveira Junior, J. G. C. Pena, and J. L. F. Salles, “An improved plant-wide multiperiod optimization model of a byproduct gas supply system in the iron and steel-making process,” Appl. Energy, vol. 164, pp. 462–474, Feb. 2016. doi: 10.1016/j.apenergy.2015.11.043
[2]	Z. Han, J. Zhao, Q. Liu, and W. Wang, “Granular-computing based hybrid collaborative fuzzy clustering for long-term prediction of multiple gas holders levels,” Inf. Sci., vol. 330, pp. 175–185, Feb. 2016. doi: 10.1016/j.ins.2015.10.020
[3]	F. Jin, J. Zhao, Y. Liu, W. Wang, and Q. Xu, “A scheduling approach with uncertainties in generation and consumption for converter gas system in steel industry,” Inf. Sci., vol. 546, pp. 312–328, Feb. 2021. doi: 10.1016/j.ins.2020.06.063
[4]	I. Matino, S. Dettori, V. Colla, V. Weber, and S. Salame, “Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management,” Appl. Energy, vol. 253, p. 113578, Nov. 2019. doi: 10.1016/j.apenergy.2019.113578
[5]	S.-L. Jiang, G. Peng, I. D. L. Bogle, and Z. Zheng, “Two-stage robust optimization approach for flexible oxygen distribution under uncertainty in integrated iron and steel plants,” Appl. Energy, vol. 306, p. 118022, Jan. 2022. doi: 10.1016/j.apenergy.2021.118022
[6]	Z. Han, W. Pedrycz, J. Zhao, and W. Wang, “Hierarchical granular computing-based model and its reinforcement structural learning for construction of long-term prediction intervals,” IEEE Trans. Cybern., vol. 52, no. 1, pp. 666–676, Jan. 2022. doi: 10.1109/TCYB.2020.2964011
[7]	J. Zhao, T. Wang, W. Pedrycz, and W. Wang, “Granular prediction and dynamic scheduling based on adaptive dynamic programming for the blast furnace gas system,” IEEE Trans. Cybern., vol. 51, no. 4, pp. 2201–2214, Apr. 2021. doi: 10.1109/TCYB.2019.2901268
[8]	L. Su, J. Zhao, and W. Wang, “Distributed adaptive fuzzy modeling for industrial gas-electricity networks with multioperation modes,” IEEE Trans. Ind. Inf., vol. 19, no. 5, pp. 6647–6658, May 2023.
[9]	H. Zhou, S. Gao, Y. Xie, C. Zhang, and J. Liu, “Multi-condition wear prediction and assessment of milling cutters based on linear discriminant analysis and ensemble methods,” Measurement, vol. 216, p. 112900, Jul. 2023. doi: 10.1016/j.measurement.2023.112900
[10]	T. Wang, J. Zhao, H. Leung, and W. Wang, “A condition knowledge representation and feedback learning framework for dynamic optimization of integrated energy systems,” IEEE Trans. Cybern., vol. 54, no. 5, pp. 2880–2890, May 2024. doi: 10.1109/TCYB.2023.3234077
[11]	K. Zhang, Z. Zheng, M. Zhu, H. Chen, T. Guo, K. Zhang, S. Ma, and L. Sun, “Hybrid prediction method of blast furnace gas generation considering multi-operation modes in steel plants near the city,” Sustain. Energy Technol. Assess., vol. 56, p. 103031, Mar. 2023.
[12]	W. Li, L. Chen, Z. Han, J. Zhao, and W. Wang, “Multi-operation conditions prediction based on least square support vector machine for blast furnace gas system in steel industry,” in Proc. 7th Int. Conf. Information, Cybern., and Computational Social Systems, Guangzhou, China, 2020, pp. 19–24.
[13]	K. Bandara, C. Bergmeir, and H. Hewamalage, “LSTM-MSNet: Leveraging forecasts on sets of related time series with multiple seasonal patterns,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 4, pp. 1586–1599, Apr. 2021. doi: 10.1109/TNNLS.2020.2985720
[14]	A. Essien and C. Giannetti, “A deep learning model for smart manufacturing using convolutional LSTM neural network autoencoders,” IEEE Trans. Ind. Inf., vol. 16, no. 9, pp. 6069–6078, Sep. 2020. doi: 10.1109/TII.2020.2967556
[15]	S. Liu and W. Sun, “Attention mechanism-aided data- and knowledge-driven soft sensors for predicting blast furnace gas generation,” Energy, vol. 262, p. 125498, Jan. 2023. doi: 10.1016/j.energy.2022.125498
[16]	T. Wang, H. Leung, J. Zhao, and W. Wang, “Multiseries featural LSTM for partial periodic time-series prediction: A case study for steel industry,” IEEE Trans. Instrum. Meas., vol. 69, no. 9, pp. 5994–6003, Sept. 2020. doi: 10.1109/TIM.2020.2967247
[17]	J. Liu, A. Shahroudy, D. Xu, A. C. Kot, and G. Wang, “Skeleton-based action recognition using spatio-temporal LSTM network with trust gates,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 12, pp. 3007–3021, Dec. 2018. doi: 10.1109/TPAMI.2017.2771306
[18]	T. Wang, J. Zhao, Q. Liu, and W. Wang, “Granular-based multilayer spatiotemporal network with control gates for energy prediction of steel industry,” IEEE Trans. Instrum. Meas., vol. 70, p. 2516512, Oct. 2021.
[19]	C. Zhang, P. Lim, A.-K. Qin, and K. C. Tan, “Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics,” IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 10, pp. 2306–2318, Oct. 2017. doi: 10.1109/TNNLS.2016.2582798
[20]	Z. Li, D. Wu, C. Hu, and J. Terpenny, “An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction,” Reliab. Eng. Syst. Saf., vol. 184, pp. 110–122, Apr. 2019. doi: 10.1016/j.ress.2017.12.016
[21]	Y. Chen, H. Chen, P. Ye, Y. Lv, and F.-Y. Wang, “Acting as a decision maker: Traffic-condition-aware ensemble learning for traffic flow prediction,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 4, pp. 3190–3200, Apr. 2020.
[22]	S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997. doi: 10.1162/neco.1997.9.8.1735
[23]	F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He, “A comprehensive survey on transfer learning,” Proc. IEEE, vol. 109, no. 1, pp. 43–76, Jan. 2021. doi: 10.1109/JPROC.2020.3004555
[24]	Y. Guo, H. Shi, A. Kumar, K. Grauman, T. Rosing, and R. Feris, “Spottune: Transfer learning through adaptive fine-tuning,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, USA, 2019, pp. 4800–4809.
[25]	S. Gong, C. Shao, and L. Zhu, “Energy efficiency evaluation in ethylene production process with respect to operation classification,” Energy, vol. 118, pp. 1370–1379, Jan. 2017. doi: 10.1016/j.energy.2016.11.012
[26]	M. H. D. M. Ribeiro and L. dos Santos Coelho, “Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series,” Appl. Soft Comput., vol. 86, p. 105837, Jan. 2020. doi: 10.1016/j.asoc.2019.105837
[27]	Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. doi: 10.1038/nature14539
[28]	L. Jiang, L. Wu, Y Tian, and Y. Li, “An ensemble fault diagnosis method for rotating machinery based on wavelet packet transform and convolutional neural networks,” Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., vol. 236, no. 24, pp. 11600–11612, Dec. 2022. doi: 10.1177/09544062221102721
[29]	S. Davoodi, H. V. Thanh, D. A. Wood, M. Mehrad, V. S. Rukavishnikov, and Z. Dai, “Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites,” Exp. Syst. Appl., vol. 222, p. 119796, Jul. 2023. doi: 10.1016/j.eswa.2023.119796
[30]	S.-Y. Shih, F.-K. Sun, and H.-Y. Lee, “Temporal pattern attention for multivariate time series forecasting,” Mach. Learn., vol. 108, pp. 1421–1441, 2019.

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A Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics

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