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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.

Recently, the attention towards Urban Building Energy Modelling has been growing due to the large contribution of cities on the worldwide energy consumption rate. In fact, many models have been developed to simulate buildings and urban energy systems. This article presents a new open-source tool for city-scale simulations. The platform implements the electrical analogy to model buildings thermal behaviour through resistance-capacitance networks, thus predicting the energy demand of urban areas with a bottom-up approach and low computational resources. The model has been applied to two urban areas: a small and well-known neighbourhood and a broader urban district. Results show the accordance of simplified models with respect to single building detailed simulations in the evaluation of the seasonal energy demand. When comparing results at hourly resolution, the accuracy is increased by considering a thermal network that splits buildings' surfaces into two capacitances. A model based on a single capacitance leads to a higher deviation, especially on the daily peak power. Regarding the district simulation, these simplified networks show a good prediction of the buildings energy demand according to relevant European references, thus demonstrating the reliability of the proposed approach despite their low computational effort. • EUReCA, an UBEM platform based on lumped-capacitance building models is presented. • EUReCA handles both 2D shapefiles and 3D CityJSON georeferenced data. • A restricted well-known neighbourhood and a wider city district are analyzed. • A two-capacitances building model enhances power peak and load profile prediction. • Aggregated results at district level demonstrate EUReCA's reliability. [ABSTRACT FROM AUTHOR]

In recent decades, the cooling energy demand in urban areas is increasing ever faster due to the global warming and the growth of developing economies. In this perspective, the urban building energy modelling community is focusing its research activities on innovative tools and policy actions to improve cities' sustainability. This work aims to present a novel module of the EUReCA (Energy Urban Resistance Capacitance Approach) platform for evaluating the effects of the interaction between district's buildings in the cooling season. EUReCA predicts the urban energy demand using a bottom-up approach and low computational resources. The new module allows us to evaluate the mutual shading between buildings and the urban heat island effects, and it is well integrated with the calculation of the energy demand of buildings. The analysis was carried out considering a real case study in Padua (Italy). Results show that the urban heat island causes an average increase of 2.2 °C in the external air temperature mainly caused by the waste heat rejected from cooling systems. This involves an increase in urban cooling energy and electricity demand, which can be affected between 6 and 8%. The latter is the most affected by the urban heat island (UHI), due to the degradation it causes on the HVAC systems' efficiency. [ABSTRACT FROM AUTHOR]

Ground coupled heat pumps are a notoriously efficient system for heating and cooling buildings. Sometimes the characteristics of the building and the user's needs are such that the amount of heat extracted from the ground during the winter season can be considerably different from the amount injected in summer. This situation can cause a progressive cooling or heating of the ground with a negative effect on the energy efficiency and correct operation of the system. In these cases, an accurate sizing has to be done. In systems already built, it could be necessary to intervene a posteriori to remedy an excessive ground thermal drift due to the energy unbalance. In this work, such a situation relating to a real office building in Italy is investigated and several solutions are examined, one of which has been then implemented. In particular, a hybrid heat pump using as heat sink both the ground and external air is compared with common solutions through computer simulations using a dedicated numerical model, which has also been compared with monitoring data. As a result, the hybrid heat pump shows better performance and limits the thermal drift of the ground temperature. [ABSTRACT FROM AUTHOR]