Detailed Thermodynamic Modeling of Multi-Zone Buildings with Resistive-Capacitive Method

Abstract

Increased use of energy in buildings and HVAC systems requires advanced control schemes like model-based control to improve energy efficiency, which in turn requires accurate thermodynamic models of buildings. The Resistive-Capacitive (RC) method is a popular and versatile approach for thermal modeling of buildings. Despite this, it is not easy to find practical solutions of implementation of the RC method. It is the goal of this paper to clarify the RC method and demonstrate simple implementation of this method, especially for multi-zone buildings, which have more potential for energy savings from use of model-based control. This paper provides two contributions. First is a detailed explanation of the RC method, focusing on its use for developing a structure of a model and first-principles approach for estimation of parameters of a model. Second is a demonstration of an algorithm that enables automatic development of the structure of a model from basic information about a building (layout, construction elements) and its combination with data-based parameter estimation. Use of the algorithm is presented with a case-study on industrial multi-zone building, for which such a grey-box model is developed and analyzed. The resulting model is rapidly developed and used in a simulation with the measured data. The outputs of the model are compared with the measured temperatures and they show good fit.

Simulation of a Building with Hourly and Daily Varying Ventilation Flow: An Application of the Simulink S-Function

“…This circuit can be easily solved by the node potential method and represented in the state-space notation, which is commonly used when describing thermal models of buildings [15,20,52]. The details of this derivation can be found in [46,67].…”

“…Recently, thermal networks have been more and more frequently used for load estimation of buildings [12] and dynamic modelling of buildings [13,14]. The growing importance of the energy assessment of buildings has made thermal network models more frequently used in the prediction of indoor conditions [15] or thermal loads [16]. These two fields of application resulted in the development of many schemes of various complexities [17][18][19][20][21].…”

Implementation of Building a Thermal Model to Improve Energy Efficiency of the Central Heating System—A Case Study

“…Despite the common opinion among researchers and experts about the great complexity of building the issue of energy modeling, it was assumed that the model prepared based on many solutions described in the scientific and industry literature [31][32][33][34] would be as functional as possible. That is, it will consider (qualitatively and, if possible, quantitatively) the physical phenomena occurring in the building in a representative way and will be utilitarian, i.e., operational enough to be used in the process of analysis and creation without the use of advanced IT tools (e.g., Matlab package) and algorithms that control the topology of a heat substation, which are used in construction.…”

“…Based on a simple hourly method an equivalent resistances and one capacitance (5R1C) network model of a building (zone) [29,30] where the thermal-electrical analogy is based on the similarity between electric current and heat flux, an original thermodynamic model was built with a resolution to the type of partition (on the passive side) and the many possible sources of thermal energy supply to the system and dissipation fluxes (Figure 5). Despite the common opinion among researchers and experts about the great complexity of building the issue of energy modeling, it was assumed that the model prepared based on many solutions described in the scientific and industry literature [31][32][33][34] would be as functional as possible. That is, it will consider (qualitatively and, if possible, quantitatively) the physical phenomena occurring in the building in a The model uses the concentrated heat capacity C of the partition to write a differential equation modeling changes in its temperature as a building structure.…”

Machine learning driven parameter identification for grey-box thermal modelling for buildings

“…The inverse approach consists of selecting a generic structure then performing parameter identification to find the set of RC parameters that reproduce best the thermal dynamics observed in the data. The structure can be either single zone or multiple zoned, the latter consists of multiple single zone models connected together (Belić, Slišković, and Hocenski 2021). The complexity of zone model can range from 1R1C (1 resistance 1 capacitance) up to 6R4C (Li et al 2021).…”