Climate change is driven by global-scale warming, while cities additionally experience local amplification due to the urban heat island (UHI) effect (urban–rural temperature differences caused by urban form, materials, and reduced evapotranspiration). In this study, we address both dimensions by analyzing long-term near-surface climate variables and derived heat-exposure indicators for multiple South European cities and by translating climate signals into climate-suitability indicators for passive/evaporative cooling. In this study, heat-stress-relevant indicators and evaporative/adiabatic cooling opportunity across paired coastal and inland South European cities are quantified using long-term hourly reanalysis and scenario-based future projections. This paper compares coastal and inland city pairs from three regions: Nicosia and Limassol from Cyprus, Seville and Lisbon on the Iberian Peninsula, and Niš and Thessaloniki on the Balkans, to characterize recent heat stress and the prospective applications and limits of adiabatic cooling. ERA5/ERA5-Land variables from the Copernicus Climate Data Base, focusing on 2 m air temperature, 2 m dew point/relative humidity, and derived indicators: days above heat thresholds and “tropical nights”, were used to determine the differences between the local climate and compare severity of effects of global warming with respect to the specific climatic conditions of the chosen cities. Application of evaporative cooling was then tested with projections up to 2050 using Climate Consultant software, using regional temperature and humidity differences to explore comfort shifts and passive cooling applicability envelopes. Cross-city comparison of climate-suitability hours and cooling needs is included in the analysis. The novelty is a paired coastal–inland, multi-region South European design (Cyprus, Iberia, and Balkans) that combines long-term hourly reanalysis (1950–2025), scenario-based mid-century morphing, and a standardized psychrometric/adaptive-comfort framework to translate climate signals into comparable climate-suitability indicators for evaporative/adiabatic cooling across contrasting humidity regimes. The results provide planning direction by indicating that humid coastal cities should prioritize shading, reduced radiant load, ventilation/urban porosity and humidity-aware cooling, while hotter and drier inland cities retain a wider climatic window for evaporative cooling, subject to water-availability constraints.

If implemented properly in architectural design, passive measures can contribute to achieving the desired comfort in a building while reducing its energy consumption. Glazed additions in the form of sunspaces or greenhouses can influence the improvement of building energy efficiency and, at the same time, create appealing and pleasant building extensions. Through energy simulations performed using EnergyPlus software, this study aims to analyze the potential contribution of glazed additions to a detached house to reducing energy consumption and creating additional space for living. Research was performed as a case study at the following locations: Niš (Serbia), Berlin (Germany), and Tromsø (Norway). For the purposes of this study, five models (M0–M4) were developed and subjected to analysis across two different scenarios. The results of the conducted research showed that the integration of glazed elements can significantly contribute to energy savings: maximum total annual savings regarding heating and cooling go from 21% for Tromsø, up to 32% for Berlin and 40% for Niš, depending on whether the building to which the glazed element(s) is/are attached is insulated or not and the number and the position of glazed elements. Although glazed additions can create a pleasant microclimate around the house, the overheating observed in the study indicates that proper ventilation and shading are mandatory, especially in more southern locations.

The retrofit of existing buildings has a notable potential to save energy and reduce the environmental impact. This paper presents an approach to assess the primary energy saving potential related to the retrofit of a school building. The retrofit includes wall and roof insulation, fenestration replacement, installation of heat pumps with thermal storage, and usage of photovoltaic panels. The approach relies on building simulations and operation optimization evaluate a limited number of retrofit combinations. The results are used to formulate two kinds of surrogate models based on gradient boosting: classifier that finds feasible options and regressor that estimates the primary energy consumption. The results show high precision of the predictive models. The F1 score of the classifiers exceeds 0.99 even for very small training samples. The most important feature for estimating feasibility is the heat pump capacity. The coefficient of determination of the regressors is close to 1 and the root mean square error is lower than 1 kWh/m2, even for moderate sizes of the training set. The most important feature for predicting energy consumption is related to the area and orientation of photovoltaic panels.

Building energy consumption is related to a large share of the primary energy consumption on the global level. Energy retrofit of buildings, which might involve improving envelopes, as well as installing efficient energy supply systems based on the renewable energy sources, has a potential to considerably reduce the consumption of energy and the environmental impact. An accurate assessment of the energy efficiency measures is essential for careful planning and decision making related to their implementation. Detailed building energy simulations have became widely-used tools for the assessment of these measures. The optimization of energy system design and operation parameters is often a very useful addition. However, this approach is often time-consuming and resource-intensive, and, as such, is being replaced or supplemented with much faster, yet precise enough, so called surrogate models. These models are data-driven and based on machine learning techniques. This paper considers the enhancements of the previously-defined surrogate models for the assessment of the energy retrofit measures, including envelope improvements, and the installation of photovoltaic systems and heat-pump-based heating and cooling. It applies supervised machine learning regression models, which are able to learn from a very limited number of data points, obtained with detailed simulations, and predict the primary energy consumption related to particular measures. The models are based on decision trees and their ensembles: random forest and gradient boosting. They are further improved by implementing with linear regression, which uses modified input features, as well as with the optimization of hyperparameters. The presented approach is tested on a building of a primary school. It yielded the improvement in the prediction performance of the surrogate models of 64–75%, compared to the baseline models. The resulting models, although obtained with relatively small datasets, are accurate enough, so they can partially replace detailed building simulations and energy system optimization. The most precise regression model is based on the combination of linear regression and gradient boosting.

Buildings affect the wind flow pattern over an urban area. The wind has a significant effect on the comfort and safety of pedestrians, urban microclimate, and pollutant dispersion. It is important to include wind flow modeling in the early urban design to minimize wind safety issues and ensure pedestrian comfort. In this paper, numerical simulations based on Computational Fluid Dynamics (CFD) are performed to study the effect of four case study building layouts on the distribution of wind pressure, wind speed, and turbulence kinetic energy. The ANSYS software was used to simulate wind flow. Three-dimensional numerical simulation was employed, with the standard k-ε turbulence model. Simulation results show that urban space design with narrow passages between buildings leads to a significant increase in local wind speed and causes wind nuisance for pedestrians. When the wind is perpendicular to the buildings, the reverse flow zone behind buildings is much narrower compared to other cases. The corners of the buildings are the regions with high-speed flow due to the side vortices. Placing restaurant terraces, gardens, etc. close to these regions should be avoided due to safety issues and discomfort. Findings from this research can equip architects and urban planners with informed decision-making tools to create more comfortable urban environments.

In the modern world, where a uninterrupted energy supply is required for proper functioning, thermal energy systems are key components of the infrastructure. Considering the complexity of these systems, it is essential to develop procedures that enable specific determination of the reliability function of thermal energy systems. Analysis of the reliability of the thermal energy system is crucial for maintaining the stability and efficiency of the system. In this paper, by applying the mathematical theory of reliability to the exploitation research data and using simple and complex two-parameter Weibull distribution, the theoretical reliability functions of the thermal power plant Nikola Tesla, Block B2 were determined. A significant advantage of such a study is the possibility of an early and thorough understanding of the logic and mechanisms of risky behavior in the system, as well as a more precise assessment of its functioning throughout the future exploitation.