The integration of Foamed Concrete (FC) into 3D Concrete Printing (3DCP) processes facilitates the design of energy-efficient building envelopes. However, strategies for optimizing material porosity and printing topology to balance winter and summer performance remain underexplored. This study presents a 2D numerical thermal analysis of an innovative 3D-printed building envelope block characterized by sinusoidal internal partitions. Through a parametric variation in porosity (ranging from 10% to 50%) and internal geometry (amplitude and period of the partitions), 45 distinct configurations were simulated. Performance was evaluated by calculating the steady-state thermal transmittance (U) and the periodic thermal transmittance (Yie) under dynamic climatic conditions. The results demonstrate that porosity is the governing parameter; increasing porosity from 10% to 50% reduces U by 31% and, contrary to traditional assumptions for massive structures, also improves Yie by 12.3%. These outcomes are physically driven by the drastic reduction in thermal conductivity, which overcompensates for the loss of thermal mass, leading to a net reduction in overall thermal diffusivity. While internal topology plays a secondary role, its optimization allows for fine-tuning dynamic damping without compromising insulation. The study confirms that 3D printing with foamed concrete enables the overcoming of the traditional trade-off between insulation and thermal inertia. High-porosity configurations (50%) with optimized internal topology emerge as the most effective solution, simultaneously guaranteeing beneficial steady-state and dynamic thermal performance for sustainable buildings.

The rising power density of modern electronics requires effective thermal management to maintain performance and reliability. Phase change materials (PCMs) provide passive cooling via latent heat but are limited by low thermal conductivity, which hinders their efficiency. This study investigates the application of topology optimization for designing thermally efficient fin structures in a PCM-filled domain. The PCM numerical model is validated against experimental data, ensuring code fidelity. The optimization process is conducted within a 2D finite element framework, where fins are designed on a fixed-temperature wall while maintaining the same material volume as a baseline configuration with three rectangular fins. Three objective functions - convective heat flux, diffusive heat flux, and thermal compliance - are considered using a steady-state approach. Their influence on the melting process is systematically evaluated. Then, the best performing one is used in a multi-step approach (MSTE) that resembles the unsteady PCM melting but allows to obtain performing design through an iterative process. Results demonstrate that the choice of a convective heat flux as objective accelerates the melting process by 32.1% compared to the baseline. Then, the MSTE shows that with two iterations it is possible to generate a design that fasten the melting process by 48.9% compared to the baseline. Further analysis includes comparisons of melt fraction, Nusselt number, energy storage and mean thermal power across all cases to provide valuable insights into the advantage of the MSTE approach for optimizing thermal management systems.

The widespread adoption of ground-mounted photovoltaic systems is facing increasing opposition due to conflicts over land use with agriculture. Agrivoltaics (AV) offers a synergistic solution; however, current design methodologies often treat agricultural constraints and panel layouts as static boundaries, resulting in suboptimal trade-offs between PV-energy conversion and the irradiance available to crops. This study addresses this issue by proposing AGRO: a comprehensive optimization framework that combines the Non-dominated Sorting Genetic Algorithm (NSGA-II), the EnergyPlus dynamic simulation engine, and Python algorithms, treating both agricultural and PV layouts as active decision variables rather than fixed boundary conditions. Applied to a representative Mediterranean case study constrained by Italy’s regulatory framework, the co-optimized layouts improve the trade-off between PV energy generation density and cumulative solar irradiance on cultivated soil, with the system adapting its morphological configuration to protect crop irradiance even during critical seasonal periods for PV-energy conversion. The variable-layout strategy proves effective not only in raising ground-level irradiance but, more distinctly, in making it more uniform across the cultivated cells. This work shows that dynamic spatial allocation enables PV systems to fully comply with Italian legislative constraints while enhancing the solar irradiance available to crops.

Forecasting indoor temperature and thermal loads plays a crucial role in improving energy efficiency, enabling advanced control strategies, and supporting design optimization. However, many data-driven approaches struggle to ensure reliability when applied across different climates and operational scenarios, which limits their robustness and transferability. To overcome this limitation, this study proposes a novel training procedure for nonlinear autoregressive with exogenous input (NARX) neural networks that accounts for multiple heating and cooling setpoint scenarios to maximize prediction reliability. The approach is tested on a representative office building prototype developed by ENEA (Italian national agency for new technologies, energy and sustainable development), typical of central Italy constructions from 1946 to 1970. EnergyPlus is used to perform dynamic simulations across 52 locations covering several Italian climatic zones. The resulting datasets are employed for multi-phase training of NARX networks: first under fixed setpoints, then validated and tested under different conditions. Results show strong predictive performance, with mean square errors between 0.038 and 0.14 degrees C2 for indoor temperature, and between 10-5 and 0.33 kW2 for thermal loads, and coefficients of determination consistently close to 1. By integrating climate variability and operational scenarios into the training process, the proposed method provides a versatile and accurate forecasting tool, adaptable to other buildings and contexts.

Buildings significantly contribute to global energy consumption and CO2 emissions, and their performance is increasingly challenged by rising cooling demands under climate change. While many studies assess envelope retrofit measures, most focus on a single building use. This study addresses this gap through a comparative analysis across building typologies, uses, and climatic conditions. The effects of climate change and envelope retrofit strategies on thermal energy demand (TED) and CO2 emissions are evaluated for four representative Italian building typologies-apartment block, multifamily building, terraced house, and single-family house-considering residential and office uses, standard and high internal loads, and current and 2050 climate conditions. Dynamic simulations are performed using EnergyPlus for four Italian climatic zones. Several retrofit options are analyzed, including insulation measures, window replacement, cool roofs, and a global retrofit. The global retrofit provides the largest benefits, reducing TED and CO2 emissions by up to 65% and 60% under current conditions, with a slight decrease in effectiveness (2-4%) toward 2050. Office buildings with high internal loads show smaller improvements than residential ones. Compact buildings exhibit greater climate resilience, whereas less compact typologies experience larger performance declines. Cool roofs are effective in mitigating cooling-related emissions under warmer climates, though their impact is limited in high-load office scenarios. Results highlight the need for adaptive and climate-resilient retrofit strategies in Mediterranean contexts.

Energy renovation in historic buildings requires balancing architectural, historical, and esthetic preservation with eco-friendly and sustainable refurbishment. This study develops a novel bio-based polyurethane (bio-PUR) foam and evaluates its thermal and mechanical properties through laboratory tests, real-scale applications, and numerical simulations. Laboratory tests show that bio-PUR has higher mechanical strength than conventional PUR and comparable thermal performance, with a thermal conductivity of 0.036 W/mK, confirmed using nitrogen thermal control. A real-scale test, conducted in a laboratory at the University of Sannio in wintertime, validated the material's performance, showing only a 4% deviation from theoretical thermal transmittance values and consistent heat flux data. Numerical simulations applied bio-PUR foam in two historical buildings in Milan and Naples, comparing it to traditional insulation materials. Primary energy demand for heating is reduced in both climates, with a slight higher efficacy for traditional PUR, because of its lower thermal conductivity. Similar trends were observed in summer season. Indoor air temperature analysis revealed improved thermal stability with bio-PUR in winter, while potential overheating in summer can occur under free-running conditions. Overall, bio-based PUR foam provides competitive thermal properties and environmental benefits, resulting in a promising solution for the green, resilient, and sustainable renovation of heritage buildings.

Optimizing photovoltaic (PV) installations requires precise understanding of the annual energy yield, which depends heavily on geographical location, panel technology, tilt, and azimuth. This study establishes the framework for a “European Photovoltaic Atlas”. In this pilot phase, the dynamic tool is applied to representative European climatic zones to compare diverse latitudes and technologies. Consequently, we aim to create a robust database and interactive visualization tool that allows users to analyze technology-specific yields based on variable orientation parameters. The study employs a large-scale simulation campaign using EnergyPlus coupled with a PVWatts model. Two photovoltaic technologies (PERC and TOPCon monocrystalline) have been simulated in seven European reference cities: Naples, Madrid, Berlin, Paris, London, Stockholm, and Warsaw. For each city and technology, simulations have been performed for a complete grid of orientations. The tilt was varied from 0° to 90° in 5° increments, and the azimuth was varied from 0° to 360° in 5° increments. All panels have been simulated at a height of 15 m to represent typical rooftop installations. The main result is a comprehensive database that links location, technology, tilt, azimuth, and normalized annual energy yield. This database feeds an interactive application developed in Python. This tool generates 2D heatmaps showing the surface orientation factor of any selected city–technology pair, 3D surface plots comparing performance across multiple technologies or locations simultaneously, and 2D charts estimating hourly annual productivity by varying technology efficiency values. The “Photovoltaic Atlas” serves as a practical decision support tool for architects and engineers by enabling the rapid optimization of photovoltaic systems and clearly illustrating performance in the European context.

Accurate thermal load forecasting is crucial for optimizing the operation of heating systems, particularly under predictive control strategies. The choice of the forecasting method is strongly influenced by data availability and compatibility with control logic. However, there is still a lack in scientific literature on the detailed evaluation of forecast methods for the operation of buildings in weather-based control mode and in forecast control mode of building heating system. This study presents a structured multi-method approach to model space heating demand of a residential building in Poland, which has been monitored since 2018 and is being operated in forecasting mode since 2021 through the forHEAT system developed by the Lublin University of Technology. The analysis is articulated in three methods. Method 1 uses a calibrated EnergyPlus model to simulate building behavior under both weather-based (2018-2021) and forecasting-based control (2021-2024), offering insights into seasonal load patterns and about the limitations of static modeling under dynamic control. Method 2 develops 26 nonlinear autoregressive neural networks with exogenous inputs (NARX), exploring combinations of seasonal subsets, training durations, and control paradigms. Results show that recent, seasonally aligned training data enable the best performance (R 2 = 0.937, cvRMSE = 9.96%), while shorter or misaligned datasets lead to higher error and reduced generalization. Method 3 introduces a simplified empirical model based on equivalent outdoor temperature (T eq ) for real-time implementation. While T eq maintains low bias (NMBE within +/- 0.6%), it suffers from higher variability (cvRMSE up to 27%) and limited adaptability. Overall, NARX networks consistently outperform both EnergyPlus and T eq across metrics, particularly in scenarios with transitional or mismatched regimes, making them the most robust tool for accurate heat load forecasting. Future developments can expand the model to other building typologies and climatic locations.

This study extends numerical analyses of 3D-printed cementitious walls incorporating microencapsulated phase change materials (MPCMs). Two wall configurations are examined to assess the combined effects of conduction, natural convection, and surface radiation, with particular attention to emissivity-driven radiative heat transfer. A finite-element CFD model is developed, using thermophysical properties derived from experimental data and homogenised via a three-phase Maxwell-Eucken scheme. Natural-convection predictions are validated against benchmark measurements. A parametric analysis, varying wall geometry and MPCM content, evaluates their influence on thermal performance under Mediterranean summer conditions. Key performance indicators, including periodic transmittance, temperature damping, time lag, and latent-energy storage, are computed. Results show that MPCM content is the dominant factor governing thermal evolution in time, whereas divider geometry has only a minor impact because cavity convection remains laminar and radiative exchange controls heat transfer near peak temperatures. Increasing MPCM content yields large reductions in periodic transmittance and average heat flux (up to 50% and 40%, respectively), followed by pronounced attenuation of indoor temperature oscillations and thermal-wave delays exceeding 9 h. A maximum reduction of 2 °C in peak indoor wall surface temperature is observed under severe conditions. Melt-fraction fields reveal that phase change occurs primarily in the external wall region, while interior layers contribute minimally to latent-energy cycling. This indicates that uniform MPCM distribution is suboptimal and that concentrating MPCM in the outer part of the wall, or adopting asymmetric material grading, would further enhance thermal buffering. Results demonstrate the strong potential of MPCM-enhanced 3D-printed walls to improve dynamic thermal inertia.

The energy retrofit of existing residential buildings is crucial to achieve the decarbonization targets, however, identifying optimal envelope insulation solutions is a complex challenge due to the interplay between environmental impacts, economic performance, and the different building typology and climatic conditions.This study proposes an integrated parametric framework combining dynamic energy simulation, life cycle assessment, and economic evaluation to support multi-objective decision-making in insulation retrofit design. A fully automated workflow is developed by coupling MATLAB with EnergyPlus to conduct large-scale parametric simulations. Three representative residential building archetypes, i.e., apartment block, multi-family house, and single-family house are analyzed across Italian climatic zones B to E. The framework, aiming to create a comprehensive mapping of retrofit configurations, explores combinations of several design variables: vertical and roof insulation thicknesses for five insulation materials (expanded polystyrene, rock wool, glass wool, hemp, and cork). For each scenario, operational energy consumption, life-cycle global warming potential, and net present value over a 50-year service life are assessed, incorporating three possible policy incentive schemes (0%, 50%, and 70% investment subsidies).The results highlight the strong influence of building compactness and climatic conditions on both cost- and carbon-optimal insulation. Buildings with lower surface-to-volume exhibit reduced sensitivity to insulation thickness, while detached buildings show significantly higher benefits from enhanced envelope performance. Although bio-based insulation materials present lower embodied emissions, synthetic materials, e.g., expanded polystyrene often emerge as optimal solutions when considering embodied and operational impacts. Policy incentives significantly shift the Pareto-optimal solutions, improving the economic feasibility of deeper retrofit strategies.

Smart control of energy supply for cooling in buildings can significantly improve energy efficiency. However, existing modelling methods are often complex and rely mainly on artificial neural networks (ANN) and other machine learning techniques, posing various difficulties for integrating them in forecast control of cooling. Moreover, accurate cooling energy models are generally more demanding to develop than heating models. To address this research gap, this study proposes a novel, simple and physically based method for creating building cooling energy models that also take into account the characteristics of the existing cooling system. The approach uses measured cooling energy consumption and meteorological data-outdoor air temperature, wind speed and solar irradiance - to derive an equivalent outdoor temperature that represents the real thermal behaviour of the building during cooling operation. Proper selection of operating and weather data is essential to minimise the influence of unrelated factors. The method is demonstrated on an office building in Poland and a university building in Cyprus. For both case studies, the developed cooling energy models were validated, achieving for outdoor temperatures above 26 degrees C mean absolute percentage error (MAPE) values of 13.15% for the office building and 17.87% for the university building. To further assess robustness, Multilayer Perceptron (MLP) ANN models were trained using the same hourly inputs-outdoor temperature, wind speed and solar radiation. The ANN models did not significantly improve prediction accuracy, yielding higher MAPE values of 19.6-24.7% for the office building and 29.4-34.9% for the university building. The results highlight that buildings must be considered individually and show that the proposed method can provide a practical, transparent and accurate tool for estimating cooling energy performance. Future work will address occupant influence and integration into predictive control of air-conditioning systems.

Climate change is accelerating global warming, leading in turn to increased thermal stress and indoor overheating, particularly in buildings with high occupancy in southern Europe. This study examines the thermal performance of secondary school buildings in southern Spain, focusing on the influence of ventilation on thermal comfort. Given the reliance on natural ventilation of a significant portion of the Mediterranean school building stock, this research aims to characterize thermal comfort conditions using validated parametric simulation models on a regional scale. The study analyses current and future comfort conditions across different climatic zones, incorporating climate change projections, and assessing overheating and undercooling risks. Results show how ventilation without thermal treatment plays a crucial role in both overheating and undercooling. Higher ventilation rates generally lead to discomfort during winter but improve comfort in summer. Building orientation and solar exposure further influence comfort, with south-facing buildings benefiting from solar gains. Projections for 2050 suggest an increase in overheating risks, particularly in cities with higher cooling degree days (CDD). Despite the benefits of higher ventilation rates, these may not fully mitigate the anticipated increase in overheating, which showcases the need for additional strategies, such as active ventilation systems, to address these challenges. The findings highlight the need for improved adaptation strategies to mitigate the effects of climate change.

This work aims to explore the potential of topology optimization in the design of forced air-cooled heat sinks for inverters equipping hybrid-electric aeronautical propulsion systems. Compared to the automotive applications that are driving the electrification of the transportation sector, the design of heat sinks in aircraft requires minimizing not only the thermal resistance and pumping power of the fans, but also the weight and volume. The challenge is further made more difficult by ambient air temperature and density that vary with the aircraft's altitude. Considering the inverter of a real aircraft equipped with a hybrid electric propulsion system, the authors first designed conventional heat sinks with finned configurations by applying semi-empirical formulations and then a free-form heat sink by exploiting topology optimization. Conventional heat sinks serve as a reference for carrying out the evaluation with heat sinks based on topology optimization. The latter prove to be characterized by superior performance as the thermal resistance is up to 7 % lower, the pumping power of the fan is reduced by 34 % at the same inlet velocity and the weight saving is around 45 %. Finally, the heat sinks were verified at the system level by having their models integrated into a completed aircraft model.

Topology optimization (TO) is a design algorithm providing the optimal material layout within a design domain to minimize/maximize an objective function. In thermal science, for instance, it can be used to optimize the design of heat sinks to minimize thermal compliance, entropy generation, average temperature, etc. Recently, classical TO frameworks have been enhanced in multi-material TO in order to include more materials, thereby enhancing the degrees of freedom of the system, and thus ensuring better thermal performance. This work implements both TO and multi-material TO (MMTO) to address a benchmark heat conduction problem, i.e., the cooling of a circular heat generating volume through heat conduction paths. The heat generation is uniform in the disc, the rim is adiabatic, while the centre is set at a fixed temperature - Dirichlet boundary condition - and serves as heat sink. In TO the choice is between void and high- conductivity material, while in MMTO variable-porosity metal foams are integrated. The interpolation of the materials' thermal conductivity is conducted via an ordered solid isotropic material penalization (SIMP) algorithm. The distinction between materials is attained by setting different thresholds in the interpolation and projection functions. The dimensionless global thermal resistance and domain average temperature are alternately addressed as objective functions to be minimized at equal weight of the system. The findings unveil that MMTO outperforms TO, which outperforms constructal tree networks, considering in the latter case different configurations of different complexity.

In the European Union, the building sector accounts for almost 40% of CO 2 -eq emissions, with 75% of existing buildings being inefficient. Thus, the European Directive 2024/1275 underscores the urgent need for energy efficiency measures and integration on renewables to achieve a deep renovation of the building stock and newly built zero emission buildings (ZEBs). In this frame, balancing operational and embodied energy is crucial to achieve real ZEBs. Accordingly, this study investigates the net energy performance (operational + embodied) of two key components used for both building energy retrofit and new ZEB design: thermal insulation in expanded polystyrene (EPS) and monocrystalline silicon photovoltaic (PV) panels. EPS was selected due to its wide use in Italy’s retrofit practices and well-documented environmental data, and two variants with different densities were compared to assess the impact of material choice within the same insulation family. The analysis focuses on a representative sample of theoretical buildings belonging to 1961-‘75 Italian residential stock. The total non-renewable primary energy (PENRT) related to EPS installation is assessed using EnergyPlus simulations to evaluate operational energy, and environmental product declarations (EPD) for embodied energy. The results unveil significant potential energy savings but with not-negligible energy payback times, up to six years, highlighting the importance of balancing embodied and operational energy while optimizing insulation thickness. Moreover, the study investigates PV panels, determining their energy payback time to be between 3 and 4 years in relation to their lifetime energy production. The findings highlight the necessity of a comprehensive life cycle perspective in sustainable construction, from material extraction to building’s disposal. Addressing inefficiencies within the construction supply chain emerges as a critical factor in reducing environmental impact. By integrating embodied and operational energy considerations, this research gives insights to actually achieve zero emission buildings.

This study presents a comparative Life Cycle Assessment (LCA) and economic analysis of three insulation materials — expanded polystyrene (EPS), rockwool, and hemp — applied to residential buildings in four Italian climatic zones. Environmental impact is evaluated through Global Warming Potential (GWP), while economic performance is assessed via Net Present Value (NPV) over the life cycle. An automated simulation workflow using EnergyPlus enables the assessment of multiple configurations by varying material properties, thickness, and climatic conditions. Results highlight trade-offs between environmental and economic performance. Compliance with regulatory thresholds can be achieved with varying insulation thickness depending on climate and material. Bio-based solutions like hemp may offer greater environmental benefits, although in most climates they require incentives to be cost-effective. This integrated approach provides useful insights for designers, policymakers, and stakeholders aiming to balance cost-effectiveness with sustainability in building retrofit and construction.

Buildings account for about around 1/3 of energy consumption and related carbon emissions at national (Italy), EU (European Union) and World levels. Nonetheless, their energy consumption continues to rise due to urbanization, economic growth, and growing demand for space cooling driven by higher comfort needs and climate change. Space cooling is expected to become the most energy-intensive function in buildings, especially in high-load environments like offices. This investigates the impact of common retrofit actions and climate change on space cooling demand. The case study is a is a two-storey building proposed by ENEA (Italian national agency for new technologies, energy and sustainable development), representative of Italian offices built in 1946-1970. Energy simulations are performed by coupling EnergyPlus and MATLAB®, considering different weather locations in Italian climatic zones B (the warmest), C, D, E (the coldest). The investigation is performed for current climatic conditions (EnergyPlus weather data files) and for a pessimist 2050 climate change scenario, i.e., SRES (special report on emission scenarios) A2. Common energy retrofit actions for the building envelope are simulated, i.e., walls and roof insulation, windows replacement, cool roofs, and their combinations. Results show that thermal insulation can increase electricity demand for cooling due to summer overheating from internal and solar gains. In contrast, cool roofs are the most effective measure, reducing cooling demand by over 10%. Climate change can cause a demand increase between around 30% (zone B) and 50% (zone E). Retrofit actions can slightly mitigate this increase. The findings highlight the need to consider space cooling demands and climate change in building envelope retrofits, especially for buildings with high internal loads. In such cases, adding insulation may be counterproductive, showing the importance of a holistic approach to energy design.