In recent years, public authorities around the world have used incentive strategies to encourage the renovation of the existing building stock to meet the set carbon neutrality targets. However, the design of the incentives typically does not consider that the subsidized energy efficiency measures should result in robust long-term improvements with respect to various objectives. Moreover, building energy retrofit analyses are commonly conducted at the individual building level rather than at urban scale, which could instead significantly accelerate the renovation rate. In this context, the current research aims to combine these different factors to support the design of building energy retrofit programs. We developed 21 subsidization strategies and their impact was evaluated on a parametric multi-objective optimization with respect to energy, economic, and environmental performance for a district located in Bolzano, Northern Italy. The optimization was performed considering a set of energy efficiency measures, pertaining to building envelope, climate change, economic scenarios, and two types of energy supplies. The results showed that (1) the impact of climate change is limited for the climate of Bolzano; (2) the type of energy supply strongly influences the economic feasibility of the retrofit investments; (3) when the investment is profitable, the optimal solutions include those measures with the largest impact on energy efficiency; and (4) subsidization strategies modify the number and composition of the Pareto solutions.

The design of renewable energy communities requires multi-disciplinary approaches to identify the most promising solutions from technical, economic, and environmental perspectives. Indeed, different simulations tools can be adopted, ranging from urban modelling to energy planning methods. In this framework, this research focused on the Santa Chiara district in Trento, Italy, to assess the performance of different decarbonization strategies, encompassing fossil and renewable energy systems. To achieve this goal, the district energy balance, CO2 emissions, total costs, and impact of incentives for energy communities were analyzed for each scenario. Furthermore, the effects of current and future hot climatic conditions were investigated, coupling urban building energy modelling (umi) and energy planning codes (EnergyPLAN).Results highlighted major modifications to the energy balance of the district due to climate change, with an important increase of space cooling needs. Heat pumps coupled with photovoltaic and solar thermal panels were identified as the most suitable solution, effectively contributing to the transformation of the considered district into an energy community. Finally, the adopted methodology pointed out the relevant role played by different calculation tools when used in an integrated workflow, allowing for a more comprehensive understanding of the available urban decarbonization strategies and more robust design choices.

Urban building energy modeling (UBEM) is crucial for assessing energy consumption patterns at the city-scale and for supporting data driven planning and decarbonization strategies. However, its practical deployment is often hindered by the need to balance detailed physics-based simulations with acceptable computation times when thousands of buildings are involved. This work presents a large-scale real world UBEM case study and proposes a workflow that combines EnergyPlus simulations, high-performance computing (HPC), and open urban datasets to model the energy consumption of the building stock in the Municipality of Bologna, Italy. Geometric data such as building footprints and heights were acquired from the Bologna Open Data portal and complemented by aerial light detection and ranging (LiDAR) measurements to refine elevations and roof geometries. Non-geometrical building characteristics, including wall materials, insulation levels, and window properties, were derived from local building regulations and the European TABULA project, enabling the assignment of archetypes in contexts where granular information about building materials is not available. The pipeline’s modular design allows us to analyze different combinations of retrofitting scenarios, making it possible to identify the groups of buildings that would benefit the most. A key feature of the workflow is the use of Leonardo, the supercomputer hosted and managed by Cineca, which made it possible to simulate the energy consumption of approximately 25,000 buildings in less than 30 min. In contrast to approaches that mainly reduce computation time by simplifying the physical model or aggregating representative buildings, the HPC-based workflow allows the entire building stock to be individually simulated (within the intrinsic simplifications of UBEM) without introducing further compromises in model detail. Overall, this case study demonstrates that the combination of open data and HPC-accelerated UBEM can deliver city-scale energy simulations that are both computationally tractable and sufficiently detailed to inform municipal decision-making and future digital twin applications.

Since Urban Building Energy Modeling and Multi-Objective Optimization require large computational resources, both could benefit of simplification techniques able also to make them more affordable to professionals. In this work, the capabilities of a simplification algorithm for urban scale application are assessed in the context of the optimization of energy efficiency measures for buildings. A group of buildings from different districts were selected along with a set of energy efficiency measures and four objectives, such as heating and cooling needs, thermal comfort, and costs. The performance of the simplification was assessed by using precision, recall and F1-score as metrics to check whether the simplified models could obtain the same optimal solutions as the detailed ones. Overall, the simplification produced adequate results with an F1-score greater than 0.8 for 85 % of the cases considered in all climates, allowing the simulation time to be reduced up to 15.7 times.