Public defence: Changhun Jeong

Changhun Jeong will defend his PhD degree in process, energy and automation engineering. The dissertation explores how model predictive control (MPC) can improve Norwegian hydropower systems.

12 Jun

Practical information

  • Date: 12 June 2024
  • Time: 10.00 - 16.00
  • Location: Porsgrunn, Room A-271 and Zoom
  • Download calendar file
  • Zoom link for digital participation


    10.00: Trial lecture: Title TBA.

    12.30: Public defence: Model predictive control for energy systems under uncertainty.

    Assessment committee

    • First opponent: Francisco Beltran Carbajal, professor, Metropolitan Autonomous University, Mexico City
    • Second opponent: Alex Alocer, professor, OsloMet
    • Administrator:  Ru Yan, associate professor Universitety of South-Eastern Norway


Any questions?

Changhun Jeong is defending his dissertation for the degree philosophiae doctor (PhD) at the University of South-Eastern Norway.Changhun Jeong portrait

The doctoral work has been carried out at the Faculty of Technology, Natural Sciences and Maritime Sciences in the program Process, Energy and Automation Engineering.

Welcome to follow the trial lecture and the public defence.


This research demonstrates that Model Predictive Control (MPC) significantly improves the efficiency and performance of energy systems, even in the presence of uncertainties.

Through simulations on the Dalsfoss and Hjartdøla hydropower systems in Norway, as well as a building temperature control system, we found that MPC frameworks can optimize resource utilization, reduce energy consumption, and ensure stable operations. These findings highlight the potential of MPC to address current energy challenges by making energy systems more resilient and efficient.

The application of MPC in hydropower plants maximizes electricity generation by optimally managing water resources, despite seasonal variations and complex regulatory requirements. For building temperature control, MPC maintains comfort while cutting down on energy usage, leading to substantial cost savings and reduced environmental impact. The improved robustness and computational efficiency of our proposed MPC methods make them practical for real-world implementation, paving the way for smarter, more sustainable energy management solutions.