Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-BiLSTM)-Attention-Based Prediction of the Amount of Silica Powder Moving in and out of a Warehouse

Dudu Guo, Pengbin Duan (), Zhen Yang, Xiaojiang Zhang and Yinuo Su
Additional contact information
Dudu Guo: School of Transportation Engineering, Xinjiang University, Urumqi 830017, China
Pengbin Duan: School of Business, Xinjiang University, Urumqi 830017, China
Zhen Yang: Xinjiang Hualing Logistics & Distribution Co., Urumgi 830017, China
Xiaojiang Zhang: Xinjiang Xinte Energy Logistics Co., Urumqi 830017, China
Yinuo Su: School of Business, Xinjiang University, Urumqi 830017, China

Energies, 2024, vol. 17, issue 15, 1-22

Abstract: Raw material inventory control is indispensable for ensuring the cost reduction and efficiency of enterprises. Silica powder is an essential raw material for new energy enterprises. The inventory control of silicon powder is of great concern to enterprises, but due to the complexity of the market environment and the inadequacy of information technology, inventory control of silica powder has been ineffective. One of the most significant reasons for this is that existing methods encounter difficulty in effectively extracting the local and long-term characteristics of the data, which leads to significant errors in forecasting and poor accuracy. This study focuses on improving the accuracy of corporate inventory forecasting. We propose an improved CNN-BiLSTM-attention prediction model that uses convolutional neural networks (CNNs) to extract the local features from a dataset. The attention mechanism (attention) uses the point multiplication method to weigh the acquired features and the bidirectional long short-term memory (BiLSTM) network to acquire the long-term features of the dataset. The final output of the model is the predicted value of silica powder and the evaluation metrics. The proposed model is compared with five other models: CNN, LSTM, CNN-LSTM, CNN-BiLSTM, and CNN-LSTM-attention. The experiments show that the improved CNN-BiLSTM-attention prediction model can predict inbound and outbound silica powder very well. The accuracy of the prediction of the inbound test set is higher than that of the other five models by 7.429%, 11.813%, 15.365%, 10.331%, and 5.821%, respectively. The accuracy of the outbound storage prediction is higher than that of the other five models by 14.535%, 15.135%, 1.603%, 7.584%, and 18.784%, respectively.

Keywords: improved CNN-BiLSTM-attention; inbound and outbound forecasts; deep learning; silica powder; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/15/3757/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/15/3757/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:15:p:3757-:d:1446003

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().