Smart heating

The goal of this project is to develop a thermal model of the building and fit the parameters of this model based on the temperature measurements of the rooms in the building, the set temperatures, the outside temperature and heating powers. In order to finaly use model predictive control to minimize the temperature difference between the room temperature and the set temperature

The considered house

Steps

1. Literature review

   • Review two scientific publications on thermal models of buildings.
   • Summarize the approach and the model used to thermally characterise the building.
   • Propose a simple model related to our case.
   • Report

2. Parameter estimation

   • Develop a multi-zone model of the building.
   • Complete case where the parameters of your model must be estimated for each room.
   • The interactions between the rooms must therefore be considered and each room has a different temperature evolution as a function of the adjacent rooms, the outside temperature and its heating input power.
   • Report

3. Probabilistic modeling

   • Make your model probabilistic. Integrate uncertainty on
     • the model parameters
     • the transition between timesteps
     • the temperature measurements.
     • Report
   • Consolidation from previous milestone
     • Report

4. Predictive control

   • Formulate an optimization problem to control the boiler and radiator operations to minimize the difference between the room temperature and its setpoint temperature (model predictive control).
   • Implement your objective and constraints function using Pyomo.
   • Solve your optimization problem using an appropriate solver and analyze your results.
   • Report

Results

Results from the simulator

Authors

Julien Gustin, Joachim Houyon and Romain Charles.