Data-driven learning-based Model Predictive Control for energy-intensive systems

Jiawei Chen, Gangyan Xu, Ziye Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


Efficient control of energy-intensive systems is essential for reducing energy consumption and realizing sustainable development. However, considering the complex inter-dependent energy-consumption devices, numerous control parameters, and dynamic environments, the energy-efficient control of energy-intensive system is always challenging. To address such problems, this paper proposes a data-driven learning-based Model Predictive Control (MPC) method for the integrated control of various devices in energy-intensive systems. Specifically, a hybrid prediction model based on two variants of RNN is integrated with the MPC scheme to learn and predict the system dynamics based on massive time-series sensing data. Then an efficient tree-based prioritized group control model for heterogeneous devices is developed with a rolling optimization and feedback correction mode. A real-life case study is provided to evaluate the performance of the proposed method, which demonstrates its superiority over existing methods on saving the energy consumption.

Original languageEnglish
Article number102208
JournalAdvanced Engineering Informatics
Publication statusPublished - Oct 2023


  • Data-driven system
  • Energy-efficient control
  • Energy-intensive system
  • Internet of Things (IoT)
  • Model Predictive Control (MPC)

ASJC Scopus subject areas

  • Information Systems
  • Artificial Intelligence


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