Data-Based RC Dynamic Modelling Incorporating Physical Criteria to Obtain the HLC of In-Use Buildings: Application to a Case Study

Abstract

This paper reports the application of RC dynamic models (network of resistances and capacitances analogous to electrical networks) to obtain the heat loss coefficient (HLC) from a dynamic test campaign carried out in an in-use building. It is a well-insulated building located in Gainsborough, U.K. This case study and data were made available to participants in the IEA–EBC Annex 71 project Building Energy Performance Assessment Based on In-Situ Measurements. The analysis reported in this paper is mainly focused on the identification of the main heat transfer contributions and also on the translation of these phenomena to the RC models used to obtain the required HLC. First pre-processing and qualitative analysis were carried out. Afterwards several candidate models were constructed according to different plausible assumptions and approximations. The validity of the results obtained using these models has been evaluated taking into account the agreement among different data series and also the levels of the residuals obtained using the different models. The work concludes obtaining accurate estimates of the HLC from the energy balance including the following relevant contributions: space heating, solar gains, internal gains due to appliances, and internal gains due to metabolic activity. These terms were modelled using the following driving variables: consumption of gas and water, electricity production by the photovoltaic (PV) panels and electricity consumption (modelling internal gains due to appliances and occupancy patterns).

Time-dependent solar aperture estimation of a building: Comparing grey-box and white-box approaches

“…For example, Eq. ( 5) embedded grey-box models are used to determine building energy performance [35][36][37], serve as control model in model predictive control (MPC) [9,38,39] for optimizing building energy performance, and etc. However, a constant value gA-value is not in line with the dynamic nature of solar aperture (gA), determined by both the angle dependent solar transmittance (Fig.…”

Estimating dynamic solar gains from on-site measured data: An ARX modelling approach

“…For example, Eq. ( 1) embedded in linear regression methods [21][22][23], ARX models (Auto-regressive with eXogenous input) [21][22][23][24], and grey-box models are used for determining building energy performance [22,25,26]. Meanwhile, Eq.…”

“…However, the above-mentioned studies [6][7][8]19,[21][22][23][24][26][27][28][29][30][31][32] all rely on a constant solar aperture (gA) integrated in Eq. (1), which is not in line with its dynamic nature, which is highly influenced by the sun's movement.…”

Data-Based RC Dynamic Modelling to Assessing the In-Situ Thermal Performance of Buildings. Analysis of Several Key Aspects in a Simplified Reference Case toward the Application at On-Board Monitoring Level

“…Wide activity has been developed in this area [3,27], with a broad development on techniques for the identification of the parameters characterizing the thermal performance of building components, from dynamic tests in outdoor test cells, usually obtained from linear and time-invariant models [28]. The extension and generalization of the current scope of application of these techniques is not simple, but some researches are undertaking this challenge [29].…”

“…LORD software has been used as tool to identify the required parameters from the measured data series [30]. The potential to apply this approach to in-use buildings has been demonstrated by a previously published work [29]. This paper repots a further systematic analysis focusing on the following aspects: the effect of the solar radiation on the heat flux through the opaque walls versus the performance of the models including this effect.…”