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Big data driven predictive production planning for energy-intensive manufacturing industries

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  • Ma, Shuaiyin
  • Zhang, Yingfeng
  • Lv, Jingxiang
  • Ge, Yuntian
  • Yang, Haidong
  • Li, Lin

Abstract

Improving energy and resource efficiency in manufacturing is an important goal for enterprises to sustain their competitive advantages. Predictive production planning is a new solution to achieve such goal, following the direct improvement of energy efficiency and indirect energy savings through better scheduling. With the emergence of new information and communication technologies under the background of Industry 4.0, the amount of various energy and resource data obtained through Internet of Things is reaching the magnitude of big data. It poses a challenge to traditional data processing and mining methods for predictive production. To solve this challenge, in this paper, the big data driven predictive production planning is proposed to improve energy and resource efficiency for energy-intensive manufacturing industries. Additionally, the cube-based energy consumption models and long short-term memory based energy prediction model are established for data preprocessing and mining correspondingly. An industrial case study is presented to process energy big data and predict energy consumption parameters and production status. The performance evaluation results indicate that the proposed long short-term memory models outperform back propagation neural network, autoregressive moving average and support vector regression. Based on data preprocessing and forecasting results, the energy and resource efficiency could be improved during the whole manufacturing process for energy-intensive manufacturing industries.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:211:y:2020:i:c:s0360544220314274
    DOI: 10.1016/j.energy.2020.118320
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    8. Mengmeng Xu & Ruipeng Tan, 2024. "Digital economy as a catalyst for low-carbon transformation in China: new analytical insights," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
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