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In recent years, building stock models have been developed by researchers to examine the aggregate performance of stacks of buildings within large areas, thereby giving rise to the concept of urban building energy models (UBEMs). The input data for such models consists of geometric and non-geometric attributes of the buildings, in addition to meteorological information. In this perspective, the acquisition of precise and comprehensive data poses a challenge, as the existing datasets often lack certain parameters or are not in a standardized format. This study aims to address the challenges by proposing a workflow for generating an input data frame tailored for incorporation into UBEMs. The data frame should include all the essential parameters of the buildings, and its constitution should be reflective of the real-world data. Moreover, the proposed workflow should remain consistent with databases available at national or regional levels. Acknowledging this non-uniformity in databases across regions, the methodology proposed in this study strategically considers various alternatives. For this reason, the proposed automatized workflow ensures flexibility and adaptability to changes in data availability. The workflow proposed in this study is a QGIS based geographical calculation method. The method can combine data from various sources into one shapefile that can be used to simulate the energy performance of buildings in urban areas.

Data-driven models are gaining traction in Building Energy Simulation, driven by the increasing role of smart metering and control in buildings. This paper aims to enhance the knowledge in this sector by introducing a practical method to analyse heating consumption. The methodology involves the analysis of hourly total heating demand and outdoor temperature measurements to create and calibrate Energy Signature Curves. Importantly, the building Energy Signature Curve is calibrated independently for each daily hour, resulting in a subset of 24 data-driven models. After calibration, a disaggregation algorithm is proposed to distinguish space heating from domestic hot water usage. The method also evaluates the building’s thermal inertia, examining the correlation between the hourly global energy consumption and the outdoor air temperature moving average. It also presents a methodology for improving the DHW heat consumption model. The methodology is applied to a case study of 51 buildings in Tartu, Estonia, with complete yearly demand measurements from the district heating operator. Thanks to the hourly calibration approach, R2 is 0.05 higher on average than the yearly Energy Signature Curve approach. The difference between estimated and measured annual energy consumption is 8% on average, demonstrating the practicality and effectiveness of the proposed method.

In this work, the climatic chamber CORE-CARE is presented. The laboratory was built at the Industrial Engineering Department of the University of Padova, in order to further investigate issues related to indoor environmental quality, the comfort perceived by people in confined spaces and thermal performance of radiant systems. CORE-CARE is equipped with radiant panels in each surface, which can be controlled separately. The room presents two real windows on the East side, equipped with shading systems. Furthermore, the mechanic controlled ventilation system installed in the room can act as integration of sensible or latent heat both in heating and in cooling conditions. The climatic chamber is provided with a data acquisition system, consisting of 38 sensors, that allow the evaluation of the surface temperatures in different positions, the air temperature at different heights, the mean radiant temperature, relative humidity and CO2 concentration. The CORE-CARE chamber is thermally characterized using a detailed model developed at the Industrial Engineering Department of the University of Padova and the simulations results are compared with test measurements. Moreover, the impact of radiant asymmetry is investigated.