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Renewable Energy Communities have been recently introduced in European legislation to promote distributed generation from renewable energy sources. In fact, they allow to produce and consume energy from shared local power plants. Low temperature district heating and cooling networks with distributed heat pumps have demonstrated their capability to exploit renewable and waste heat sources in the urban environment. Therefore, they are considered a promising infrastructure to help decarbonize the building sector. As their main operating cost is the electricity purchased by the utility for heat pumps and circulation pumps, this work investigates whether a Renewable Energy Community could help mitigate such cost by sharing electricity produced by local photovoltaic (PV) systems. The research relies on computer simulations performed with both physical and statistical models for the evaluation of electrical load profiles at the district level. Results show that due to the different seasonality between heating demand and PV production, the increase in self-consumption due to the distributed heat pumps is lower than 10%. The use of batteries does not seem convenient for the same reason. The environmental benefit of the proposed system is evident, with CO2 emissions reduced by 72–80% compared to the current situation depending on PV power installed. It also emerged that PV sharing significantly improves the self-consumption at the district level, in particular when the installed PV power is limited (+45%). In conclusion, results suggest that current incentives on PV-sharing make Renewable Energy Communities a viable option to improve the techno-economic performance of fifth-generation district heating and cooling networks.

Urban Building Energy Modeling (UBEM) plays a critical role in supporting data-driven strategies for the energy transition of cities. However, its application is often hindered by the lack of harmonized, high-quality input data representing the building stock. This paper presents the methodology and outputs of a national research project to construct an Italian library of reference buildings suitable for UBEM applications described with scorecards. The methodological workflow included six key phases: definition of a national data classification framework, acquisition and integration of heterogeneous data sources, data harmonization, statistical analysis and clustering, archetype formalization, and dissemination. The result is a library of 380 scorecards covering residential, educational, office, commercial, and catering buildings across multiple climate zones and construction periods. Each scorecard is based on empirical data from public databases, field surveys, or technical standards, and includes detailed descriptions of geometry, envelope characteristics, HVAC systems, internal gains, and ventilation. The scorecards are shared openly on the project’s website and were built to work with different UBEM platforms. Overall, both the method and the results help bring more consistency to UBEM practice and support better, data-driven urban energy planning.

The residential building stock represents one of the major players in energy use and greenhouse gas emissions; thus, it is fundamental to reduce the energy used. Simulation tools are becoming more and more accurate in compliance with the new requirements both at the single-building and at the district scale, although they are not affordable by non-specialist users such as policymakers. The research concerns the evaluation of the energy demand for space heating for a historical district that is representative of the Italian building stock. The work compares dynamic and specialist-oriented urban scale tools such as Energy Urban Resistance Capacitance Approach (EUReCA) and City Energy Analyst (CEA)) as well as a quasi-steady-state calculation method (Excel spreadsheet), which is more affordable for non-specialist users. The work was carried out to assess the possible deviation of the results between the dynamic and quasi-steady-state calculation methods, as well as to identify any limits and opportunities in the application of the latter procedure, which is currently the official national calculation tool for the implementation of Directive 2010/31/EU. The study shows how the quasi-steady-state method predicts a reliable building energy demand, in line with the results obtained by the two dynamic tools, when considering only geometry and infiltrations as input. However, the limits of the quasi-steady-state method emerge when introducing internal loads, significantly underestimating the energy demand compared to CEA and EUReCA simulations. The results underline the potential application of the quasi-steady-state method to predict energy demand, although dynamics tools are more reliable but far more complex. Major findings through two methods concern the impact of solar heat gains on the overall heating demand at both the single building and the district scale. The different results between the tools provided evidence of a gap in the use of the simplest tool and demonstrated the accuracy and reliability of the proposed approach with a lower computational effort.

In this study, the thermal behavior of the coaxial and double U borehole heat exchangers was investigated using numerical simulations in both the long- and short-term. As a reference for borehole heat exchanger specifications, the existing coaxial and double U probes of a geothermal heat pump installed within the Horizon 2020 research project named “Cheap GSHPs” were considered. Nine years of simulations revealed that when borehole heat exchangers are subjected to a balanced thermal load, and intermittent operating modes of the ground source heat pump system are set, the coaxial pipes’ configuration provides better thermal performance due to the higher thermal capacitance of the heat-carrier fluid and the lower borehole thermal resistance. The analysis was conducted considering two different types of ground with different thermal conductivity values. As result, the more conductive ground type highlights the higher yield of the coaxial probe.

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.

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.

The design phase of ground source heat pump systems is an extremely important one as many of the decisions made at that time can affect the system’s energy performance as well as installation and operating costs. The current study examined the interpretation of thermal response testing measurements used to evaluate the equivalent ground thermal conductivity and thus to design the system. All the measurements were taken at the same geological site located in Molinella, Bologna (Italy) where a variety of borehole heat exchangers (BHEs) had been installed and investigated within the project Cheap-GSHPs (Cheap and efficient application of reliable Ground Source Heat exchangers and Pumps) of the European Union’s Horizon 2020 research and innovation program. The measurements were initially analyzed in accordance with the common interpretation based on the first-order approximation of the solution for the infinite line source model and then by utilizing the complete solutions of both the infinite line and cylinder source models. An inverse numerical approach based on a detailed model that considers the current geometry of the BHE and the axial heat transfer as well as the effect of weather on the ground surface was also used. Study findings revealed that the best result was generally obtained using the inverse numerical interpretation.

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.

The design of ground source heat pumps is a fundamental step to ensure the high energy efficiency of heat pump systems throughout their operating years. To enhance the diffusion of ground source heat pump systems, two different tools are developed in the H2020 research project named, “Cheap GSHPs”: A design tool and a decision support system. In both cases, the energy demand of the buildings may not be calculated by the user. The main input data, to evaluate the size of the borehole heat exchangers, is the building energy demand. This paper presents a methodology to correlate energy demand, building typologies, and climatic conditions for different types of residential buildings. Rather than envelope properties, three insulation levels have been considered in different climatic conditions to set up a database of energy profiles. Analyzing European climatic test reference years, 23 locations have been considered. For each location, the overall energy and the mean hourly monthly energy profiles for heating and cooling have been calculated. Pre-calculated profiles are needed to size generation systems and, in particular, ground source heat pumps. For this reason, correlations based on the degree days for heating and cooling demand have been found in order to generalize the results for different buildings. These correlations depend on the Köppen–Geiger climate scale.

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.