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SA-LSTMs: A new advance prediction method of energy consumption in cement raw materials grinding system

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  • Liu, Gang
  • Wang, Kun
  • Hao, Xiaochen
  • Zhang, Zhipeng
  • Zhao, Yantao
  • Xu, Qingquan

Abstract

Electricity consumption is a major energy efficiency indicator in cement raw materials grinding system. Advance prediction of electricity consumption provides the basis for cement production scheduling and achieves the energy saving. However, due to the influence of strong coupling, delay, intrinsic non-linearity and uncertainty, it is difficult to model the raw materials grinding system accurately. To address this problem, a new model of long short-term memory networks based on spatial attention is proposed in this paper. The proposed method uses LSTM neural networks appropriately to capture the long-term temporal dependencies of the system variables appropriately to solve the problem of time delay. Spatial attention is designed to improve the spatial perception of the model and increase the sensitivity to important information. Thus, the proposed method can efficiently capture the temporal and spatial characteristics of the cement raw materials grinding system. In addition, to verify the superiority of the proposed method, we introduce LSTM, Seq2Seq, ARIMA, SVM and XGBoost for comparison. The experimental result confirms that the proposed model achieves the highest performance, which provides an encouraging perspective for real-time energy consumption prediction in raw materials grinding.

Suggested Citation

  • Liu, Gang & Wang, Kun & Hao, Xiaochen & Zhang, Zhipeng & Zhao, Yantao & Xu, Qingquan, 2022. "SA-LSTMs: A new advance prediction method of energy consumption in cement raw materials grinding system," Energy, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:energy:v:241:y:2022:i:c:s0360544221030176
    DOI: 10.1016/j.energy.2021.122768
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    References listed on IDEAS

    as
    1. Kazemzadeh, Mohammad-Rasool & Amjadian, Ali & Amraee, Turaj, 2020. "A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting," Energy, Elsevier, vol. 204(C).
    2. Chang, Zihan & Zhang, Yang & Chen, Wenbo, 2019. "Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform," Energy, Elsevier, vol. 187(C).
    3. Kwak, Sanghyeok & Choi, Jaehong & Lee, Min Chul & Yoon, Youngbin, 2021. "Predicting instability frequency and amplitude using artificial neural network in a partially premixed combustor," Energy, Elsevier, vol. 230(C).
    4. Liu, Xianjie & Feng, Qian & Peng, Zhigang & Zheng, Yong & Liu, Huan, 2020. "Preparation and evaluation of micro-encapsulated thermal control materials for oil well cement slurry," Energy, Elsevier, vol. 208(C).
    5. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    6. Yi, Yong & Wang, Liming & Chen, Zhengying, 2021. "Adaptive global kernel interval SVR-based machine learning for accelerated dielectric constant prediction of polymer-based dielectric energy storage," Renewable Energy, Elsevier, vol. 176(C), pages 81-88.
    7. Li, Tenghui & Liu, Xiaolei & Lin, Zi & Morrison, Rory, 2022. "Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm," Energy, Elsevier, vol. 239(PD).
    8. Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
    9. Ma, Shuaiyin & Zhang, Yingfeng & Lv, Jingxiang & Ge, Yuntian & Yang, Haidong & Li, Lin, 2020. "Big data driven predictive production planning for energy-intensive manufacturing industries," Energy, Elsevier, vol. 211(C).
    10. Wang, Jian Qi & Du, Yu & Wang, Jing, 2020. "LSTM based long-term energy consumption prediction with periodicity," Energy, Elsevier, vol. 197(C).
    11. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    12. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    13. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    14. Hossain, Syed Raihan & Ahmed, Istiak & Azad, Ferdous S. & Monjurul Hasan, A S M, 2020. "Empirical investigation of energy management practices in cement industries of Bangladesh," Energy, Elsevier, vol. 212(C).
    15. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    16. Shen, Weiguo & Liu, Yi & Yan, Bilan & Wang, Jing & He, Pengtao & Zhou, Congcong & Huo, Xujia & Zhang, Wuzong & Xu, Gelong & Ding, Qingjun, 2017. "Cement industry of China: Driving force, environment impact and sustainable development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 618-628.
    17. Liu, Jiangyan & Zhang, Qing & Dong, Zhenxiang & Li, Xin & Li, Guannan & Xie, Yi & Li, Kuining, 2021. "Quantitative evaluation of the building energy performance based on short-term energy predictions," Energy, Elsevier, vol. 223(C).
    18. Schinke-Nendza, A. & von Loeper, F. & Osinski, P. & Schaumann, P. & Schmidt, V. & Weber, C., 2021. "Probabilistic forecasting of photovoltaic power supply — A hybrid approach using D-vine copulas to model spatial dependencies," Applied Energy, Elsevier, vol. 304(C).
    19. Dittmer, Celina & Krümpel, Johannes & Lemmer, Andreas, 2021. "Power demand forecasting for demand-driven energy production with biogas plants," Renewable Energy, Elsevier, vol. 163(C), pages 1871-1877.
    20. Atmaca, Adem & Kanoglu, Mehmet, 2012. "Reducing energy consumption of a raw mill in cement industry," Energy, Elsevier, vol. 42(1), pages 261-269.
    21. Hao, Xiaochen & Guo, Tongtong & Huang, Gaolu & Shi, Xin & Zhao, Yantao & Yang, Yue, 2020. "Energy consumption prediction in cement calcination process: A method of deep belief network with sliding window," Energy, Elsevier, vol. 207(C).
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