ALBERT

Description: Since households are one of the most energy-intensive sectors in Europe, retrofit of dwellings is promoted to increase energy efficiency. Recent research, however, shows that the energy performance after retrofit does not always meet the target values, which can be caused by amongst other things, a deviating building envelope performance. This paper compares the theoretical and measured building envelope performance for a real-life case study in post-retrofit state, in order to illustrate the limitations of calculation methods and characterization models. First, the performance is evaluated on building scale by verifying the correspondence between the default theoretical heat loss coefficient (HLC) and the measured HLC, which was determined by following the guidelines formulated within IEA EBC Annex 58 and Annex 71. In order to illustrate the limitations of the standard calculation method in real-life conditions, the theoretical variability of the HLC is evaluated, generated by variating infiltration heat losses and heat exchange with neighboring dwellings. Second, the performance is investigated on a component scale by assessing the theoretical and measured thermal resistances, identified from heat flux tests. Additionally, nonhomogeneous assembled components and air leaks are simulated to verify probable causes for the locally varying measured values and to illustrate the limitations of calculations and characterization methods. The results illustrate the limitations of the calculation methods by the assessment of the strong variability of the theoretical HLC, depending on assumptions regarding infiltration and heat exchange with neighboring dwellings. In addition, component simulations indicated that deficiencies on a component scale could be caused by a nonhomogeneous assembly and air cavity flows of the component. Moreover, a detailed assessment of an unreliable thermal resistance illustrates the limitations of the used characterization method. Finally, a contrast was found between the quite good performance on building scale (15% deviation between the theoretical and measured HLC) and poor performance on a component scale (only one out of nine monitored components met their theoretical target values), which illustrates the complexity of the building envelope performance.

Description: Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

Description: Network flexibility is the use of the thermal capacity of water that is contained in the district heating network pipes to store energy and shift the heat load in time. Through optimal control, this network flexibility can aid in applications such as peak shaving and operational heat pump optimisation. Yet, optimal control requires perfect predictions and complete knowledge of the system characteristics. In reality, this is not the case and uncertainties exist. To obtain insight into the importance of these uncertainties, this paper studies the influence of imperfect knowledge of building parameters on the optimal network flexibility activation and its performance. It is found that for the optimisation of heat pump operation, building parameter uncertainties do not present large risks. For peak shaving, a more robust result can be achieved by activating more network flexibility than may be required.