ABSTRACT Buildings account for 37% of energy demand globally, and cooling consumes more energy and contributes more emissions in hot climates. This study evaluates the thermal performance of a non-air-conditioned hostel room at Shiv Nadar Institution of Eminence, Delhi NCR, India, through an integrated approach combining 3D energy modelling (Google SketchUp), transient thermal simulations (TRNSYS 18), and simulating the dynamic movement of air, in and out of a building using TRNFLOW (Transient Network Flow). A calibrated model was developed using real-time sensor data and validated against two weather datasets to assess comfort hours (indoor temperatures: 18°C–27°C). Bioclimatic potential analysis guided passive cooling strategies, including optimized natural ventilation, enhanced thermal mass, and improved glazing. Heat flux analysis revealed that outward-facing insulation reduced peak heat gain by 27%. At the same time, double-glazed units minimized convective losses (decrement factor: 0.4). TRNFLOW simulations further refined airflow dynamics by incorporating wind pressure and temperature gradients, extending summer comfort hours by 18–28% though winter ventilation trade-offs were noted. The findings demonstrate that sequential heat flux-airflow analysis is critical. The study advances sustainable building practices by quantifying the efficacy of passive interventions and proposing a simulation-integrated design process for high-occupancy buildings.

ABSTRACT The study examines the influence of indoor relative humidity on thermal and humidity perception, working memory performance, and physiological responses, with an emphasis on gender-based differences. A data-driven model was used to predict working memory based on humidity-related preferences, identifying key physiological and perceptual factors that contributed to the prediction of cognitive performance. Sensations and preferences related to humidity and temperature, as well as heart rate and electrodermal activity (EDA), were recorded under different humidity conditions, while the Operation Span Task was employed to assess working memory. Results revealed that humidity sensation responded sensitively to a 20% change in humidity, whereas temperature sensation required a 30% change. In contrast, temperature preference shifted significantly in response to smaller humidity changes than humidity preference. Higher humidity levels increased heart rate and EDA and reduced working memory, with these effects more pronounced in males. Moreover, males’ perceptions of temperature and humidity showed significant correlations with both physiological responses and working memory, while females displayed limited correlations. Notably, the model demonstrated that working memory can be predicted from subjective humidity-related preferences rather than the actual humidity level as an input variable, underscoring the value of human perception in forecasting cognitive outcomes.

ABSTRACT Residential buildings are a major contributor to urban energy consumption, and their spatial morphology significantly affects energy performance. Although many Urban Building Energy Modelling (UBEM) studies include microclimate effects, the quantitative influence of spatial parameter on high-rise residential clusters remains underexplored. This study establishes a coupled ‘spatial morphology–microclimate–energy consumption’ framework by integrating Rhino & Grasshopper for geometric modelling, UWG (Urban Weather Generator) for microclimate simulation, and EnergyPlus for energy analysis. The framework systematically quantifies how spatial parameters affect building energy use and proposes corresponding optimization strategies. Results show that neglecting microclimate effects leads to 3.51% and 2.57% deviations in cooling and heating loads, respectively. Among six equally dense layouts, the staggered layout performs best, reducing cooling energy by 5.85% and total energy by 3.6% compared with the courtyard layout. Optimal performance occurs when the building spacing is 1.6–1.8 times the minimum required distance and the orientation is 5° west of south. This work deepens understanding of the morphology–energy relationship and supports energy-efficient planning of high-rise residential clusters in hot summer and cold winter (HSCW) regions of China.

ABSTRACT Accurately measuring air leakage is essential for predicting energy use in skin-load-dominated buildings, especially in cooling-intensive regions. This study evaluates the impact of air infiltration on the energy performance of a single-family house, using methods applicable to various climates. Two modelling approaches were tested: a constant air change rate and results from a blower door test (BDT) at 50 Pa pressure. The constant air change rate overestimated energy use by 21.63%, while the BDT method underestimated it by 59.14% due to unaccounted HVAC-induced pressurization effects, common in hot climates. By calibrating air leakage through HVAC-induced ventilation, the energy deviation was reduced to 2.1%, offering a more accurate model. This study demonstrates that relying solely on BDT results can lead to significant underestimation of energy use, whereas calibrating the leakage rate through HVAC systems significantly improves accuracy. The proposed method provides a replicable framework for pressurization-dominated buildings in hot climates and advances the state-of-the-art in energy modelling. Key contributions include a comparative analysis of modelling approaches, quantification of deviation magnitudes, and the introduction of a calibration technique that enhances energy prediction accuracy in cooling-intensive climates worldwide.

ABSTRACT Despite significant advances in energy-efficient building (EEB) technologies, energy demand in the global building sector continues to rise. Addressing this gap, this study investigates the key dynamics constraining energy demand reduction, focusing on the multidimensional barriers hindering the adoption and diffusion of EEBs. In contrast to the predominantly single-issue approaches in the existing literature, this study adopts a holistic and multi-scalar perspective. It simultaneously examines macro-level demand trends alongside policy and governance structures, construction-sector, and user-related behavioural factors. By explicitly analyzing cause–effect relationships across these interacting dimensions, the study provides an integrated understanding of how seemingly independent barriers collectively undermine the effectiveness of energy efficiency strategies. Methodologically, the study employs a PRISMA-based systematic literature review, qualitatively analyzing 331 peer-reviewed studies published between 2005 and 2025 using thematic analysis. The findings show that energy-efficient building adoption is constrained by weak governance, construction sector–related barriers, and persistent behavioural mismatches that undermine realized energy savings. By synthesizing 54 challenges into interdisciplinary problem–cause–solution roadmaps, this study offers a comprehensive, evidence-based framework to support energy demand reduction and accelerate EEB adoption. The results highlight the necessity of addressing institutional, sectoral, and behavioural barriers, providing actionable insights for policymakers, practitioners, and researchers.

ABSTRACT The integration of phase change materials (PCMs) into porous bricks offers a promising avenue for enhancing building energy efficiency. However, prevailing design strategies frequently prioritize PCM dosage over spatial configuration, resulting in suboptimal performance. This study challenges that convention, demonstrating that spatial layout-rather than merely the amount of PCM-is the pivotal factor governing thermal regulation. Through systematic numerical simulations of ten distinct configurations, we reveal that strategically positioning a single PCM layer towards the interior reduces the inner surface temperature amplitude by 25.7% (from 12.69°C to 9.43°C). This is achieved by optimizing the temporal synergy between the PCM's phase change process and external thermal fluctuations. Furthermore, we quantitatively establish the law of diminishing marginal returns as PCM dosage increases. The optimal single inner-layer layout achieves a high latent heat utilization rate of 0.97, outperforming double- and triple-layer configurations (0.82 and 0.83, respectively) that suffer from ‘thermal shielding’ and incomplete phase activation. Crucially, a newly proposed Cost-Benefit Index (CBI) analysis reveals that this configuration maximizes economic efficiency, scoring 1.76 times higher than fully-filled models. These findings advocate a paradigm shift in energy-storage envelope design toward strategic spatiotemporal control.

ABSTRACT In developed countries, people spend most of their time indoors; therefore, indoor environmental quality has a direct impact on human health. Assessing indoor conditions requires the continuous monitoring of several parameters. In this context, the present study aims to analyze the influence of sensor placement on the monitoring of CO2 concentration, temperature and relative humidity in a primary school classroom. To this end, several sensors were installed in different locations within the classroom, and real-time data were collected for CO2 concentration, temperature and relative humidity. The results showed that CO2 measurements exhibited deviations from the mean ranging from 1% to 11% depending on sensor position, with the highest concentrations recorded near the students’ working area. In contrast, deviations in temperature and relative humidity were minimal, ranging between 1–3% and 0–5%, respectively. It is concluded that the most appropriate location for sensor placement in this type of study is on the teacher’s desk, as it allows for minimal interference with teaching activities, facilitates teacher supervision of the device, is positioned near the main emission source, ensures proper sensor operation, and yields representative data of the classroom environment in terms of CO2, temperature and relative humidity.

ABSTRACT The accurate simulation of courtyard microclimates remains a critical challenge in evaluating passive cooling strategies in dense urban environments, particularly under variable window operation. This study investigates the combined effect of courtyard geometry and natural ventilation control on indoor thermal comfort in a Mediterranean urban block, using a hybrid experimental – numerical approach. A 1:10 scaled physical model of a courtyard building was constructed and monitored under summer conditions in northern Morocco, capturing indoor and courtyard air temperatures, surface temperatures, and meteorological data. A detailed multizone thermal and airflow model was developed using TRNSYS 18 coupled with CONTAM, incorporating bidirectional airflow modeling at window openings and radiative interactions based on Gebhart factors. Model validation against experimental measurements yielded high accuracy, with mean absolute errors below 1.5 °C and coefficients of determination (R²) exceeding 0.96 for both air and surface temperatures.Parametric simulations explored three courtyard configurations (W/L = 1.0, 0.67, 0.5), seven orientations (0°-90°), and four window control strategies (closed, open, night ventilation, conditional). Results indicate that night ventilation reduced cumulative indoor discomfort by up to 56% compared to sealed conditions, while compact courtyards improved ventilation and decreased UTCI by up to 3 °C during peak hours. Orientations aligned with prevailing winds (15-30°) significantly enhanced airflow rates. This study demonstrates the applicability of validated TRNSYS-CONTAM coupling for modeling courtyard-scale microclimates. The proposed methodology provides a replicable framework for assessing passive cooling efficiency through geometry - ventilation interactions in warm climate contexts.

ABSTRACT Phase change materials (PCMs) have shown strong potential for enhancing thermal comfort and reducing cooling loads in buildings. The experimental investigation was conducted on a test building situated in Chennai, India, a region characterized by hot and humid climatic conditions. Experimental results indicate that PCM-integrated roofs lower the inner surface temperatures by an average of 1.8 °C compared to conventional roofs. A validated numerical model, with a maximum relative error of 10.83% and an average error of 6.32%, was developed to assess performance. Two PCMs, HS29 and OM29, paired with steel and aluminium panels, were evaluated for energy savings, carbon emission reductions, and payback periods. Results show that HS29 and OM29 can reduce annual energy costs by 1294.38 and 2872.89 INR, and carbon emissions by 204.23 and 453.28 kg/kWh, respectively. Payback periods range from 8.3 to 29.8 years, with OM29 showing longer periods due to higher costs. PCM technology supports green building initiatives by lowering environmental impact and promoting energy efficiency, contributing to sustainability in the construction sector.

ABSTRACT The optimization of surrogate modelling used in building thermal load management of District Heating Network (DHN) layouts for heating load prediction is crucial to reducing the contribution of building energy consumption globally. However, state-of-the-art surrogate models often struggle to capture the dynamic thermal mechanics of DHNs that occur in hourly intervals without sacrificing their low computational cost. Consequently, the model modification should be expanded beyond the present literature scope of conventional archetypes or multi-surrogate structures only. To this aim, this work proposes a novel surrogate model construction methodology, which emphasizes bolstering surrogate model performance through the combination of a model archetype, a multi-surrogate structure using rule-based data splitting and a criterion-based restriction of training data. Applying the modelling methodology on a real substation shows that the predictive performance of surrogate models depends on dataset restriction on single-surrogate cases, across all archetypes. Contrarily, in multi-surrogate cases, it depends on the model archetype used, as well as insufficient data quality and thermal mechanics manifesting in certain periods of the operation of the DHN substation. In general, this novel methodology can assist in future heat load prediction endeavours, by detecting surrogate modelling limitations posed in each DHN case and encouraging further improvements.