Data-Based Prediction for Energy Scheduling of Steel Industry

  • Jun Zhao
  • Wei Wang
  • Chunyang Sheng
Part of the Information Fusion and Data Science book series (IFDS)


Based on the results of a number of different forecasting modes introduced in the previous chapters, this chapter provides a practical case study related to the optimal scheduling for energy system in steel industry based on the prediction outcomes. As for the by-product gas scheduling problem, a two-stage scheduling method is introduced here. On the prediction stage, the states of the optimized objectives, the consumption of the outsourcing natural gas and oil, the power generation, and the gas holder levels are forecasted by using the previous data-driven learning methods. On the optimal scheduling stage, a rolling optimization procedure is performed by employing the predicted results. More typically, with respect to the scheduling task for the oxygen/nitrogen system in steel industry, a similar two-stage method is also developed, in which a granular-computing (GrC)-based prediction model is firstly established on the stage of a long-term prediction, and the scheduling solution is also optimized later. Furthermore, the results of the scheduling system applications also indicate the effectiveness of the real-time prediction and scheduling optimization.


Steel industry Multi-energy system Energy scheduling Prediction By-product gas Oxygen/nitrogen Intelligent optimization Mixed Gaussian kernel PIs construction Long-term prediction Mathematical programming FCM Fuzzy rule Decision-making 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jun Zhao
    • 1
  • Wei Wang
    • 1
  • Chunyang Sheng
    • 2
  1. 1.Dalian University of TechnologyDalianChina
  2. 2.Shandong University of Science and TechnologyQingdaoChina

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