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 This study experimentally evaluates the thermal and mechanical performance of cement mortar enhanced with a bio-based shape-stabilized composite phase change material (SSCPCM) for passive cooling in building envelopes. A eutectic mixture of lauric acid and beeswax (LA–BW) was developed, exhibiting a melting temperature of 38.7 °C and latent heat of 242.3 J/g. The eutectic PCM was impregnated into zeolite, and the optimal composite, containing 35 wt.% PCM loading was selected based on leakage resistance at 70–75 °C. This SSCPCM exhibited a melting temperature of 36.3 °C, a latent heat of 84.6 J/g, and maintained stability after 750 thermal cycles. The SSCPCM was incorporated into cement mortar at varying replacement levels, and mortars containing moderate SSCPCM content retained a 28-day compressive strength of 79–95 kN, which is sufficient for interior plaster applications. To assess real-world performance, the optimized SSCPCM-mortar was applied as an 8-mm-thick interior plaster layer in test chambers exposed to natural climatic conditions in Varanasi, India. The PCM-integrated mortar achieved a peak indoor temperature reduction of 6.3°C and a peak temperature delay of 78 min compared to the reference mortar, demonstrating significant attenuation of heat gain under hot-humid conditions.

ABSTRACT To achieve energy-efficient buildings, many studies optimize retrofit or design in a single climate, although relevant parameters vary by climate. Additionally, surrogate models used in optimization are often developed without proper tuning, and the effect of dataset size on accuracy remains understudied. This study optimizes energy retrofit of a residential building across various Iranian climates using an efficient surrogate model. Two surrogate models are developed, one predicts heating load, cooling load, and renewable energy, and the other forecasts thermal discomfort hours (TDH). Four machine learning algorithms including Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB) are evaluated using datasets of 250, 500, 1000, 2000, and 4000 samples, with hyperparameters tuned using a genetic algorithm. Retrofit measures are then optimized using NSGA-III considering net energy consumption (NEC), TDH, and life cycle cost (LCC) under limited and unlimited budget scenarios. Results show MLP as the best-performing model, with the 1000-sample dataset achieving R2 above 0.99 for all targets. Climate type significantly influences optimal measure selection. Applying optimal measures reduces NEC by 53–99% and TDH by 45–97% compared to the base case. The proposed framework offers a powerful tool for promoting energy-efficient buildings.

ABSTRACT This study investigates the thermal insulation performance of multilayer wall systems incorporating indigenous materials: palm fibers, Drinn, and sheep wool, in a desert climate typical of Ouargla, Algeria. Traditional Algerian construction often employs an air gap between two brick walls for insulation. In this work, an experimental prototype housed within a wooden cell was designed, where the air gap was replaced in separate tests with a 5 cm layer of each material, inserted without compression at its natural bulk density to ensure reproducibility. Laboratory projectors simulated solar radiation uniformly across the external wall, with an average measured intensity of 501.55 W/m². Temperature data were collected using thermocouples placed on both wall surfaces and within the cavity. Results showed that the conventional air gap offered the poorest insulation. Palm fibers reduced total heat flow by approximately 26%, while both Drinn and sheep wool achieved reductions of about 55%. Notably, Drinn, a perennial plant abundant in Saharan camel pastures, has not previously been reported as an insulation material, despite its low cost and wide availability. These findings highlight the potential of valorizing local, underutilized resources to improve energy efficiency and promote sustainable construction practices in hot, arid regions.

ABSTRACT Growing concerns over energy usage are revitalizing interest in applying thermal remote sensing technologies to obtain information that can support decision-makers in developing strategies that reduce energy consumption and CO2 emissions within residential environments. The purpose of this study was to evaluate the feasibility of aerial thermography as a tool for in-situ determination of the thermal insulation quality of residential dwellings in South Africa. Utilizing high-resolution orthomosaic airborne thermal infrared imaging, the study surveyed selected residential dwellings across the KwaZamokuhle settlement in Mpumalanga Province. The study findings were that the use of aerial thermal infrared imaging can differentiate residential dwelling quality, thus identifying poorly insulated homes, which are often linked to higher energy consumption and increased carbon emissions. The results suggest that high-resolution aerial thermal infrared imagery has considerable potential as a tool to evaluate the thermal insulation status of residential dwellings, especially in large settlements where rapid assessments might be needed. The approach holds a lot of potential in supporting energy efficiency interventions and informing policy development towards improving residential dwellings’ energy efficiency in South Africa and other low-income countries that face housing quality challenges.

ABSTRACT This study utilizes advanced machine learning (ML) algorithms to assess the influence of external weather-related factors on the energy efficiency (EE) of residential buildings. Two deep neural network (DNN) models were developed: a standard DNN and an enhanced one incorporating residual layers and attention mechanisms. The models were trained using a dataset comprising historical meteorological data and energy performance data from residential homes in York, UK. The investigation indicated that energy consumption is substantially influenced by meteorological factors including temperature (denoted as heating degree days (HDD) and cooling degree days (CDD)), humidity, wind velocity, and sun radiation. The random forest model surpassed previous models, attaining root mean square error (RMSE) of 0.0819 and a R 2 score of 1.0000, underscoring its exceptional capacity to represent the intricate interactions between meteorological factors and energy usage.The advanced deep neural network demonstrated favourable outcomes with an RMSE of 4.12 and a R 2 of 0.878, underscoring the significance of employing complicated architectures to represent intricate relationships. The results underscore the need of integrating ML for EE forecasting and identify critical domains for enhancing household energy consumption. The study compared multiple ML models, highlighting their benefits and weaknesses for prediction accuracy and resilience. This is followed by the application of SHAP (SHapley Additive exPlanations) to interpret model outputs and obtain an understanding of the significance of these weather features.

ABSTRACT Through the establishment of optimal thermal conditions, educational institutions can develop an environment favourable to learning, concentration, and academic achievements. Additionally, comfortable temperatures can contribute to the overall well-being and satisfaction of both students and teachers, fostering a positive educational experience. In this study, we aimed to determine the impacts of environmental factors on students’ thermal comfort in three state colleges of Madhya Pradesh. A total of 784 students participated in a questionnaire study conducted during May (Summer season), August (Monsoon season), October (Autumn season), and December (Winter season) of 2023 to assess occupants’ degree of clothing, temperature preference, and metabolic rates. Indoor and outdoor temperatures, relative humidity, and air velocity were measured using an anemometer. Throughout the study period, the thermal comfort range was determined to be between 21.5°C and 28.5°C, with 25°C as the neutral temperature. It has been noted that the clothing pattern varies during the year (0.47-0.85 Clo). Additionally, implementation of the adaptation strategies, such as switching on and off fans or opening windows, helped lessen discomfort in the classroom. The findings indicate that, in contrast to the other seasons, the summers brought exceptionally high levels of heat discomfort to the occupants.

ABSTRACT Urban densification, a response to population growth, poses adverse impacts on built environments. Limited access to sunlight is one of these consequences. Solar radiation and daylight affect urban canyons in different ways, including thermal comfort and illuminance levels. A balance between summer and solar heat gain can alleviate solar thermal considerations. This becomes even more important in an urban context like Western Sydney with an air temperature of around 6 to 10 degrees hotter than Inner Sydney (during extreme heat events). Additionally, Western Sydney is predicted to absorb two-thirds of the population growth of the Sydney region. Nevertheless, the urban development trend in this region doesn’t seem to meet the requirements of an environmentally sustainable scheme covering solar geometry-based design principles. Therefore, this study aims to develop a general urban model based on the spatial analysis of the western Sydney suburbs and propose a novel integrated methodology to provide the optimal solar-based urban design solution. This process uses an evolutionary algorithm as a computational tool to optimize solar radiation and daylight levels in extreme weather conditions. Results offer the optimum value for a wide range of design parameters regarding solar geometry.

ABSTRACT Rapid population growth has increased housing demand. This demand was mainly addressed using conventional and unsustainable construction materials, which raised concerns about intensifying global warming. Economically challenged communities lacking access to cooling systems are more vulnerable to climate change effects. This research proposes strategies to address future climate change challenges, assessing the energy efficiency of a lab-developed eco-friendly construction material. A TRNSYS-based model was used in this study to evaluate four materials: rammed earth, hemp-lime bricks, DOUM fibre-stabilized material with quicklime, and cinder blocks in a real-scale building. Prior to the energy assessment, GenOpt software was used to ensure efficient building design. Optimizing the building's envelope can reduce annual thermal loads by up to 52%. The DOUM-clay brick house, with high thermal mass, exhibits significant potential. In comparison, the cooling loads of the cinder block, the rammed earth, and the hemp clay houses are 24.8%, 11.9%, and 9.2% lower, respectively. Additionally, the study considers the impact of climate change on buildings in a hot, semi-arid climate. The findings reveal a 20.3% lower thermal load for the lab-developed brick house compared to the cinder block house. The study also anticipates a reduction in natural ventilation’s potential in the future.

ABSTRACT As climate change is globally widespread and intensifying, there is an urgent call for buildings to undergo energy reduction and decarbonization in order to reach carbon-neutrality by 2050. This article comes in response to this need, by comparing the thermal energy savings and discomfort mitigation from implementing climate-responsive design strategies with respect to the hot semi-arid climate of Morocco. To do so, we’ve used EnergyPlus to evaluate the thermal energy performance and indoor thermal comfort conditions of two passive unoccupied homes: Home_1 and home_2; and the accuracy of the building energy models has been approved via forty days of indoor temperature measurements in two different thermal zones for each home. Furthermore, the comparison of the annual thermal needs of both passive homes has demonstrated that passive design strategies influence heavily the thermal performance of the buildings and help mitigate indoor comfort conditions, especially in the cooling period. A combination of semi-arid climate-responsive passive design strategies for home_2 has allowed to reach a thermal energy intensity of 23.31 kWh/m2/year, which is less by 73.3% in comparison with the thermal energy needs of home_1 and also Discomfort Hours (DH) in home_2 is 2069 h, against 5744 h in home_1.