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This paper presents a low-order aquifer thermal energy storage (ATES) model for simulation of combine subsurface and above-surface energy systems. The model is included in the Modelica IBPSA Library, which is a free open-source library with basic models for building and district energy and control systems. The model uses a lumped- component method, in which the transient conductive-convective heat and mass transfer equation is radially discretized. To verify the accuracy of the model, we present an inter-model comparison from a simulation test suite. Results show that the Modelica ATES model is in good agreement, with a normalized mean bias error for yearly variation of aquifer temperatures of 1.6×10−2 and 9×10−5 at 1 m and 10 m distance from the well.

Social houses built after the Second World War to accommodate workers and low-income families represent one of the major energy consumers and greenhouse gas emitters in the residential sector. Plans for their renovation are underway in all European countries, and the process is more complicated for Italian cities due to the lack of space and the large number of historical buildings. This study addresses this challenge by proposing a methodology to renovate a low-income district in the city of Venice using CityBES to model and evaluate energy conservation measures. CityBES is a web-based tool that allows users to employ urban building energy modeling for large-scale energy and retrofit analyses of building stocks. In the case study conducted for Venice's Santa Marta district, due to the particular context, four common energy conservation measures covering both the building envelope and heat generation boilers have been applied. The evaluation of energy-saving performances at the district level showed that the four measures together achieved 67% energy savings, an abatement in energy cost equal to 67%, and annual carbon dioxide emissions reduction of 1.1 MtCO2. The case study demonstrates a method and workflow replicable for energy retrofit analysis of building stocks in other historical districts.

Urban building energy modeling (UBEM) seeks to evaluate strategies to optimize building energy use at urban scale to support a city's building energy goals. Prototype building models are usually developed to represent typical urban building characteristics of a specific use type, construction year, and climate zone, as detailed characteristics of individual buildings at urban scale are difficult to obtain. This study investigated the Italian building stock, developing 46 building prototypes, based on construction year, for residential and office buildings. The study included 16 single-family buildings, 16 multi-family buildings, and 14 office buildings. Building envelope properties and heating, ventilation, and air conditioning system characteristics were defined according to existing building energy codes and standards for climatic zone E, which covers about half the Italian municipalities. Novel contributions of this study include (1) detailed specifications of prototype building energy models for Italian residential and office buildings that can be adopted by UBEM tools, and (2) a dataset in GeoJSON format of Italian urban buildings compiled from diverse data sources and national standards. The developed prototype building specifications, the building dataset, and the workflow can be applied to create other building prototypes and to support Italian national building energy efficiency and environmental goals.

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.

One of the initiatives to reach the European decarbonization goal is the roll-out of smart heating meters in the building stock. However, these meters often record the total energy usage with only hourly resolution, without distinguishing between space heating (SH) and domestic hot water (DHW) production. To tackle this limitation, this paper presents the validation of a new methodology to estimate the SH and DHW from total measurements in different building types in three countries (Denmark, Switzerland, and Italy). The method employs a combined smoothing algorithm with a support vector regression (SVR) to estimate the different heating uses. The estimation results are compared with the different countries’ DHW compliance calculations. The comparison showed that the compliance calculations outperformed this method by considering the validation dataset characteristics.

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.

Nowadays, the search for new solutions to the pandemic situation caused by the spread of SARS-CoV-2 is one of the most important issues worldwide. The difficulties encountered in finding an effective cure for the infection and the ongoing vaccination campaign place a priority on minimising the transmission of this disease. This paper deals exclusively with the airborne route of transmission for COVID-19, proposing a model for the evaluation of infection risk. It starts from the well-known Wells-Riley model and its successive modifications and tries to couple it with a zonal model which analyses the air movement in an indoor environment. The objective of this model is evaluating whether the infection risk depends considerably on the position of the infected subject and the susceptible person in the room and how different ventilation concepts affect these aspects. The model was applied to an office room with a mixing ventilation system. The results obtained concern the sub-division of the considered volume in four cells, each one perfectly mixed. The evaluation of infection risk in different positions was carried out and an analysis on the parameters affecting this value was performed.