The thermal resistance of retrofitted building components based on in-situ measurements

Buildings are responsible for a large share of the worldwide energy use. For new buildings very strict objectives for the energy performance of buildings are set. The main energy use however occurs in existing buildings emphasizing the need for renovation of the older building stock. In order to detect deviations from the theoretical performance, in-situ measurements of the envelope performance after renovation can give insight in the workmanship and issues with different renovation techniques. Therefore, the focus of this paper is to determine the thermal resistance of building components from in-situ measurements, before and after renovation. Different methods were applied to examine the thermal resistance of the building components: the average method, linear regression, Anderlinds’ method, ARX modelling (Auto-Regressive models with eXogenous inputs) and Grey Box Modelling. All of these methods seem to lead to similar results with only a small variation in confidence intervals, except for linear regression, which couldn’t capture dynamic heat flows due to solar radiation. For the assessed exterior walls, different phenomena influencing the thermal resistance were noted. The measured thermal resistance answers the estimated theoretical value of the building components quite well before renovation, but after renovation the difference is variating due to cavity air flows

The co-heating test as a means to evaluate the efficiency of thermal retrofit measures applied on residential buildings

In order to reduce the energy use of residential buildings, regional governments in Belgium established, amongst others, mandatory criteria for the energy performance to be achieved after retrofitting. However, due to construction deficiencies, deviating boundary conditions, and nonmodeled physical phenomena and interactions, the actual energy performance may differ significantly from theoretical design value. Several studies indicate this as the performance gap. This paper focuses on analyzing the actual impact of the refurbishment measures applied to a single-family home in Belgium. Hereto, in-situ measurements assessing the building envelope’s thermal performance, described by the overall heat loss coefficient HLC [W/K], are performed both before and after the retrofit. To analyze this HLC, a quasi-steady state test, the so-called co-heating test, has been performed before and after renovation of a single-family home in Belgium, renovated to the nearly Zero Energy Building (nZEB) level. As a result, the HLC determined with linear regression and an Auto-Regressive model with eXogenous inputs (ARX) show similar estimates, except for a smaller confidence interval for the ARX. Furthermore, it is shown that data set lengths shorter than 10 days are quite sensitive to sample times. For our case study, the gap between the theoretical and measured HLC enlarges after retrofit. Finally, the influence of a unheated neighboring zone on the HLC is assessed

Modelling of a naturally ventilated BIPV system for building energy simulations

Two major causes of energy yield reduction in PV systems are partial shading and high operating temperatures. Both issues are particularly critical for BIPV systems. The correct assessment of the BIPV contribution to the built environment depends, therefore, on the accurate prediction of PV temperature and on the possibility of simulating shading effects. This paper describes the development of a multi-physics model for a naturally ventilated façade BIPV system within the openIDEAS environment for building and district energy simulations. The PV electrical model used here follows a physics-based approach that takes into account solar intensity and temperature spatial variations within the PV module, enabling the simulation of shading effects both at cell and module level. A detailed thermal model has been developed and coupled to the electrical model to estimate the PV temperature. Four case studies illustrate the importance of temperature and shading effects on the PV power output. The model has been validated using data from an experimental BIPV setup deployed in Belgium. The results indicate that the model is able to predict both the PV surface temperature and the power production, given the correct boundary conditions are applied

Mapping the pitfalls in the characterisation of the heat loss coefficient from on-board monitoring data using ARX models

Several studies have demonstrated the capability of data-driven modelling based on on-site measurements to characterise the thermal performance of building envelopes. Currently, such methods include steady-state and dynamic heating experiments and have mainly been applied to scale models and unoccupied test buildings. Nonetheless, it is proposed to upscale these concepts to characterise the thermal performance of in-use buildings based on on-board monitoring (OBM) devices which gather long-term operational data (e.g., room temperatures, gas and electricity consumption...). It remains, however, to be proven whether in-use data could be a cost-effective, practical and reliable alternative for the dedicated tests whose more intrusive measurements require on-site inspections. Furthermore, it is presently unclear what the optimal experimental design of the OBM would be and which data analysis methods would be adequate. This paper presents a first step in bridging this knowledge gap, by using on-board monitoring data to characterise the overall heat loss coefficient (HLC) [W/K] of an occupied, well-insulated single-family house in the UK. With the aid of a detailed building physical framework and specifically selected data subsets a sensitivity analysis is carried out to analyse the impact of the measurement set-up, the duration of the measurement campaign and the applied data analysis method. Although the exact HLC of the building is unknown and no absolute errors could hence be calculated, this paper provides a new understanding of the decisions that have to be made during the process from design of experiment to data analysis. It is demonstrated that such judgements can lead to differences in the mean HLC estimate of up to 89.5%

Creating common exercises for modelling building and district energy systems: lessons learnt from the IBPSA Project 1 - DESTEST

The District Energy Simulation Test (DESTEST) is a series of common exercises about modelling building stocks and district heating networks aiming at testing, benchmarking and verifying different urban-scale energy system simulation tools. For each common exercise, participants are modelling a case with well-defined characteristics, grid topology and boundary conditions. The DESTEST allows participants to discuss common mistakes and pitfalls and define guidelines from the experience and feedback. These common exercises can also be used for training purposes. This article discusses the development process of these common modelling exercises and presents the main lessons learnt during the creation of the DESTEST