Building energy performance multi-target optimization by simulation-based model predictive control strategy

  • Introduction:
    1. Energy conservation as a key method to tackle the climate change
    2. Buildings consume about 40% of the primary energy
    3. Smart control systems of buildings have shown potential in energy savings
    4. Good indoor climate increases productivity and well-being
  • The aim of my research:
    1. To investigate the potential of energy savings of the model predictive control procedure
    2. The goal is to optimize a building hourly set point temperatures, based on
      • weather forecasts
      • solar loads
      • energy costs
      • occpancy profile
      • themral comfort criteria
    3. Objectives of the optimization:
      • operating costs
      • occupant thermal comfort
  • Major phases in my research:
    1. Basic control theory. Construct LEGO robots and implement the PID control algorithms.
    2. Simple model predictive control. Learn Simulink + Matlab MPC function. Construct LEGO robots and implement the control algorithms.
    3. Simulation based model and multi-objective optimization. Matlab + Simulink (MPC Block) + TRNSYS/IDA ICE.
      Simulation steps:
      1. Pre-processing phase
      2. Optimization phase
      3. Post processing phase

Simulation based model:


Model predictive control description: