Energy consumption prediction in cement calcination process: A method of deep belief network with sliding window

Xiaochen Hao, Tongtong Guo, Gaolu Huang, Xin Shi, Yantao Zhao and Yue Yang

Energy, 2020, vol. 207, issue C

Abstract: Electricity consumption and coal consumption are two important indicators in the cement calcination process. Modeling predictions of cement energy consumption support efforts aimed at understanding energy use and energy conservation. However, due to the three characteristics of cement: time-varying delay, non-linearity and uncertainty, it is very difficult to establish accurate energy consumption prediction models. To solve the above problems, a multiple-index energy consumption prediction model based on sliding window deep belief network (SW-DBN) is proposed in this paper. Specifically, to avoid studying complex problem of time-varying delay, the sliding window method is introduced to deep belief network, which combines the previous and current variable data into time series data. As a result, all temporal information related to the energy consumption data is fed to the input layer of deep belief network. Then deep belief network is utilized to establish the multiple-index energy consumption prediction model on the temporal information, which is capable of predicting electricity consumption and coal consumption simultaneously. Experimental results show that the proposed model obtains improvement for multiple-index energy consumption prediction model in cement calcination process.

Keywords: Deep belief network; Sliding window; Energy consumption prediction; Multiple-index prediction (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:207:y:2020:i:c:s0360544220313633

DOI: 10.1016/j.energy.2020.118256

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