Floor heating pre-on/off parameters based on Model Predictive Control feature extrapolation


  • Ettore Zanetti Politecnico di Milano
  • Rossella Alesci Politecnico di Milano
  • Rossano Scoccia Politecnico di Milano
  • Marcello Aprile Politecnico di Milano




Radiant floor, model predictive control, feature extrapolation, pre-heating, KPI


Floor heating systems are typically characterized by a relatively high thermal inertia, thus they react slowly to setpoint changes. When the system turns on, an under-heating period could occur for a relative long period, vice versa when the setpoint is decreased the floor thermal inertia could lead to overheating. In residential applications, the users try to avoid these discomfort problems by using a constant setpoint, higher than the setback. In this way the average energy consumption as well as the user’s bill increases. A smarter solution to mitigate this problem is to include a pre-on period parameter, so that the system will turn on a certain time before the increase in setpoint to avoid the under-heating period and a pre-off period so that it will switch off before overheating. Predictive controllers can be a solution to compensate the slow response of the radiant floor system. However, besides the need for more data, the computational power goes beyond what is available in heating systems micro controllers for residential cases. To avoid these issues, in this paper the optimal control trajectory obtained using a Model Predictive Control (MPC) approach is used to identify the pre-on and pre-off
parameters to be periodically updated in the micro controller (e.g. monthly). A simulation work was carried out to compare the performance between a baseline Rule Based Controller (RBC), an improved RBC and a MPC in terms of comfort and energy use. The result is a reduction from an average of 1.1°C to 0.2°C for the worst thermal zone meaning 80% reduction of the discomfort with respect to the baseline and a slight increase of the electrical consumption of the heat pump (less than 5%).




How to Cite

Zanetti, E., Alesci, R., Scoccia, R., & Aprile, M. (2022). Floor heating pre-on/off parameters based on Model Predictive Control feature extrapolation. CLIMA 2022 Conference. https://doi.org/10.34641/clima.2022.331