How to Derive and Implement a Minimalistic RC Model from Thermodynamics for the Control of Thermal Parameters for Assuring Thermal Comfort in Buildings

EasyChair Preprint 7119, version 1

Abstract

Heating, ventilation, and air conditioning (HVAC) systems of buildings account for a major part of global energy demand and HVAC optimization offers significant potential to improve energy efficiency. As a promising optimization technology, Model Predictive Control (MPC) can reduce the energy demand while maintaining thermal comfort in buildings, but it also requires a thermal building model. Most existing models are too complex for a reliable parameter identification from measurements or too simple to represent thermal comfort. In this paper, we derive and implement a minimalistic thermal building model that can be applied to (i) parameter identification from measurements (grey-box modeling), and (ii) control of thermal parameters for assuring thermal comfort. We derive our grey-box model from the laws of thermodynamics, heat transfer, and the electro-thermal RC analogy. As a novelty, we present not only the detailed theoretical derivation but also the open-source code for applying the identification to various buildings. The proposed minimalistic model ensures a reliable parameter estimation and requires only a few measurements of temperatures, heating, and global radiation. We identify and validate our model with measurements from a research building under real-world conditions.

Keyphrases: Building Control, Operative temperature, Wall temperature, building energy, grey box model, parameter identification, thermal building model, thermal comfort

@booklet{EasyChair:7119,
  author    = {Moritz Frahm and Felix Langner and Philipp Zwickel and Jörg Matthes and Ralf Mikut and Veit Hagenmeyer},
  title     = {How to Derive and Implement a Minimalistic RC Model from Thermodynamics for the Control of Thermal Parameters for Assuring Thermal Comfort in Buildings},
  howpublished = {EasyChair Preprint 7119},
  year      = {EasyChair, 2021}}