Modeling of a Building System and its Parameter Identification

Abstract

-This study proposes a low order dynamic model of a building system in order to predict thermal behavior within a building and its energy consumption. The building system includes a thermally well-insulated room and an electric heater. It is modeled by a second order lumped RC thermal network based on the thermal-electrical analogy. In order to identify unknown parameters of the model, an experimental procedure is firstly detailed. Then, the different linear parametric models (ARMA, ARX, ARMAX, BJ, and OE models) are recalled. The parameters of the parametric models are obtained by the least square approach. The obtained parameters are interpreted to the parameters of the physically based model in accordance with their relationship. Afterwards, the obtained models are implemented in Matlab/Simulink® and are evaluated by the mean of the sum of absolute error (MAE) and the mean of the sum of square error (MSE) with the variable of indoor temperature of the room. Quantities of electrical energy and converted thermal energy are also compared. This study will permit a further study on Model Predictive Control adapting to the proposed model in order to reduce energy consumption of the building.

On the Evolution and Application of the Thermal Network Method for Energy Assessments in Buildings

“…The first studies of appliance effects in contact with thermal networks started by adding a new branch to a building model. Park [78,79] introduced a 1R1C branch to consider appliances in a building, as shown in Figure 3a. She determined the thermal network parameters as a function of identified parameters in different parametric models (ARX (Autoregressive exogenous), ARMAX, BJ (Box-Jenkins), and OE (Output error)).…”

Knowledge engineering and management in thermal multi-zone building studies: a systematic review

“…The purpose of this step was to figure out and select the keywords that would be used to search into the databases. The keywords used in this research process were obtained from seven random papers (RAMIREZ-LABOREO; SAGUES; LLORENTE, 2014) (SWIFT; MOLINSKI; LEHN, 2001) (CHEN; FU; XU, 2015) (FLOREZ; MANTELLI; NUERNBERG, 2013) (LEEUWEN et al, 2015) (PARNIS, 2012) (PARK et al, 2013) which were aligned with the subject to be searched.…”

Identification of criteria for the selection of buildings with elevated energy saving potentials from hydraulic balancing-methodology and case study

“…The first two options can be implemented in different ways and can significantly reduce heat loss. Nowadays the degree of thermal retrofitting varies significantly, ranging from single measures delivering small overall improvements to deep comprehensive retrofits that may enhance the buildings' energy efficiency performance by 50% or more (Olson, 2011;Park et al, 2013). However, the latter implies relatively high investment costs and is characterized by long payback periods (Lefter & Popescu, 2015;Streicher et al, 2020), e.g.…”

“…The process to balance the hydronic system, the so-called hydraulic balancing includes re-adjustment of hydraulic components, such as pipes, balancing valves, pumps and radiators that influence flow rates and heat transfer rates, followed by more precise temperature control. Given the complexity of operation of a thermal system, computer simulation models have been developed and applied to identify the optimal set-point value for temperature and flow rates (Park et al, 2013;Rulff, 2011). As recommended by EN 12975, quasi steady-state methods are frequently used (Afonso et al, 2015).…”