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

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

Energy, 2022, vol. 241, issue C

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.

Keywords: Raw meal grinding; Electricity consumption prediction; Long short-term memory networks; Spatial attention; Advance prediction (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:241:y:2022:i:c:s0360544221030176

DOI: 10.1016/j.energy.2021.122768

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