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The aim of the present work is to compare thermal efficiency of three flat-plate collectors, which are different in the type of coatings used in the absorber plate. The thermal efficiency of the collector was investigated using three types of absorber plate: the black painted, the black chrome coating, and the carbon coating. The thermal performance of the collectors was considered based on American Society of Heating, Refrigerating and Air-Conditioning Engineers Standard 93 (2010). The volume flow rate varied from 0.5 to 1.5 L min−1. The field emission scanning electron microscope images demonstrated that the carbon coating had high absorption due to trapping the light and avoiding the reflection of the light. The collector with the carbon-coated absorber plate at the flow rate of 1.5 L min−1 has the maximum thermal efficiency of approximately 69.4%. Furthermore, the thermal efficiency of the carbon-coated absorber plate and black chrome-coated absorber plate is averagely 13% and 11.3% higher than the black-painted absorber plate, respectively. Additionally, the removed energy parameter ( FRUL ) at the flow rate of 1.5 L min−1 decreases approximately 35.4% for the collector with the carbon-coated absorber plate and 28.4% for the collector with the black chrome-coated absorber plate compared to the black-painted absorber plate.

Many buildings comply with thermal comfort standards yet still feel uncomfortable to occupants, leading to complaints and suboptimal indoor environmental control. This gap arises because common comfort representations do not capture how humidity, air movement, local nonuniformities, and short‐term dynamics jointly influence perceived comfort under realistic indoor conditions. This study proposes a thermal comfort index (TCI), an interpretable, physics‐informed mixture‐of‐experts model for evaluating indoor thermal environments. The model integrates whole‐body drivers (operative temperature, humidity, air speed, clothing, and metabolism), local effects (radiant asymmetry, vertical temperature gradient, and floor temperature), and transient exposure into a bounded comfort score with three actionable classes. Training and calibration are based on standard‐relevant synthetic scenes anchored to ASHRAE and ISO criteria. Scenario‐based validation, sensitivity analysis, counterfactual audits, and psychrometric comparisons show that TCI captures regime‐dependent air‐movement effects and cumulative discomfort mechanisms that are often overlooked by conventional approaches. A lightweight MATLAB application supports practical assessment and decision‐making. The framework provides a transparent tool for managing thermal conditions as part of a healthy indoor environment.

Indoor Air Quality (IAQ) is the air quality within and around buildings and structures, particularly regarding building occupants’ health and comfort. IAQ assessments were performed using an objective measurement of molecular gaseous pollutants to determine the IAQ profile in the hospital’s critical areas. It also analyses the effects on patients in different environments and the sources that result in deviations from approved criteria. This comparative study is aimed to investigate the concentration of different compounds in different critical departments in the hospital and propose solutions to the related problem as an improvement in indoor air quality. The data was compared with the standards and regulations. It was found that the TVOC level in the CCU department, specifically in the fluoroscopy room, has exceeded the allowable limit. A few suggestions have been raised to lower the exceeded value. The risks and symptoms held by the occupants in the hospital buildings if they face poor indoor air quality were discussed. Further study can be conducted to relate the short and long-term health issues among medical staff to poor indoor air quality.

Recent evidence available in the literature has highlighted that the high-energy consumption rate associated with air conditioning leads to the undesired “overcooling” condition in arid-climate regions. To this end, this study quantified the effects of increasing the cooling setpoint temperature on reducing energy consumption and CO2 emissions to mitigate overcooling. DesignBuilder software was used to simulate the performance of a generic building operating under the currently adopted ASHRAE HVAC criteria. It was found that increasing the cooling setpoint temperature by 1 °C will increase the operative temperature by approximately 0.25 °C and reduce the annual cooling electricity consumption required for each 1 m2 of an occupied area by approximately 8 kWh/year. This accounts for a reduction of 8% in cooling energy consumption compared to the ASHRAE cooling setpoint (i.e., t_s = 26 °C) and a reduction in the annual CO2 emission rate to roughly 4.8 kg/m2 °C. The largest reduction in cooling energy consumption and CO2 emissions was found to occur in October, with reduced rates of approximately–1.3 kWh/m2 °C and −0.8 kg/m2 °C, respectively.

Reliable prediction of thermal sensation plays a critical role in enabling energy‐efficient and occupant‐oriented building operation. However, existing approaches remain limited under real‐world conditions. This study establishes a comprehensive and methodologically consistent framework to evaluate the predictive performance of thermal sensation vote (TSV) models by integrating a broad set of classical machine learning algorithms with a hybrid quantum–classical approach. The analysis is based on a dataset of 19,291 observations from naturally ventilated environments, including both environmental and personal variables. A total of 14 regression models were benchmarked using a unified fivefold cross‐validation protocol, followed by hyperparameter optimization of the best‐performing model and a structured feature ablation analysis. In parallel, a hybrid quantum–classical model was developed using parameterized quantum circuits and evaluated under the same experimental conditions to ensure direct comparability. The results show that ensemble‐based classical models outperform all other approaches, with tuned XGBoost achieving the highest predictive performance (R2: 0.386 and RMSE: 0.983). However, improvements from hyperparameter optimization remain marginal, indicating a practical predictive ceiling. The feature ablation analysis reveals that personal variables provide the most significant contribution to prediction accuracy, while derived thermal indicators such as operative temperature offer no additional benefit when their constituent variables are included. The hybrid quantum model demonstrates stable training behavior but significantly lower predictive performance (R2: 0.246 and RMSE: 1.085) and early convergence, suggesting limited representational capacity for tabular and noisy thermal comfort data. The findings indicate that TSV prediction is fundamentally constrained by data characteristics and human variability rather than model complexity. The study highlights the critical importance of occupant‐related variables, identifies diminishing returns from conventional feature engineering, and demonstrates that current quantum machine learning approaches are not yet competitive for this problem domain. These results provide a robust benchmark and emphasize the need for data‐centric and human‐centric modeling strategies to advance thermal comfort prediction.

Thermal bridging through cold-formed steel (CFS) studs significantly reduces the thermal performance of light gauge steel frame (LSF) wall systems, particularly in climates demanding higher thermal resistance (R-value). While thermal breaks are commonly used, they increase material costs and construction complexity. According to NCC 2022, the minimum total R-value requirement for external walls ranges between 2.8 and 3.8 m2·K/W depending on the climate zone and building class. This study therefore evaluated web-perforated steel studs as a passive strategy to enhance thermal resistance of LSF walls, analysing 120 configurations with validated 3D finite element models in Abaqus and benchmarking in THERM. The results showed that web perforations consistently improved R-values by 14 to 20%, as isotherm contours and heat flux vectors demonstrated disruption of direct heat flow through the stud, thereby mitigating thermal bridging. Although the axial compression capacity of web-perforated CFS studs decreased by 29.5%, the use of 4 mm hole-edge stiffeners restored 96.8% of the original capacity. The modified NZS 4214:2006 and ASHRAE Modified Zone methods, incorporating steel area reduction and heat flux redistribution, closely matched Abaqus predictions, with coefficients of variation (COV) below 0.009, corresponding to less than 1% relative deviation between analytical and numerical R-values. Furthermore, application of web-perforated CFS studs in five external wall systems demonstrated improved thermal resistance, ensuring compliance with NCC 2022 R-value requirements across all Australian climate zones. Overall, the findings establish web-perforated studs as an effective solution for improving the energy performance of LSF building envelopes.

HVAC (Heating, Ventilation and Air Conditioning) system in buildings is a major component of energy consumption, and realizing high-precision energy consumption prediction is of great significance for intelligent building management. Aiming at the problems of insufficient modeling ability of nonlinear features and insufficient portrayal of long time-series dependencies in prediction methods, this paper proposes an HVAC energy consumption prediction model that combines time-sequence convolutional network (TCN), bi-directional gated recurrent unit (BiGRU), and Attention mechanism. The model takes advantage of TCN’s parallel computing and multi-scale feature extraction, BiGRU’s bidirectional temporal dependency modeling, and Attention’s weight assignment of key features to effectively improve the prediction accuracy. In this work, the HVAC load is represented by the building-level electricity meter readings of office buildings equipped with centralized, electrically driven heating, ventilation, and air-conditioning systems. Therefore, the proposed method is mainly applicable to building-level HVAC energy consumption prediction scenarios where aggregated hourly electricity or cooling energy measurements are available, rather than to the control of individual terminal units. The experimental results show that the model in this paper achieves better performance compared to the method on ASHRAE dataset, the proposed model outperforms the baseline by 2.3%, 22.2%, and 34.7% in terms of MAE, RMSE, and MAPE, respectively, on the one-year time-by-time data of the office building, and meanwhile it is significant 54.1% on the MSE metrics.

Traditional interactions between the built environment and the electric utility industry are changing rapidly. Rather than a one-way flow of electricity where utilities produce and transmit the electricity that buildings then consume, buildings are becoming efficient energy consumers that can dynamically adjust loads to ensure grid-provided energy is used effectively. Buildings are transitioning to now be at the intersection of forecasting loads and demand response, forecasting and control of output from distributed generation and renewable energy systems, and the optimal dispatch of energy storage. Because residential and commercial buildings consume 74.7% of the electricity generated in the United States, grid-integrated buildings represent an important solution to addressing challenges facing the power sector. In addition to the electric power grid, buildings will be increasingly interactive with water, transportation, and telecommunications networks, and will also more strongly affect surrounding buildings and other civic infrastructure.

Heat loss quantification (HLQ) is an essential step in improving a building’s thermal performance and optimizing its energy usage. While this problem is well-studied in the literature, most of the existing studies are either qualitative or minimally driven quantitative studies that rely on localized building envelope points and are, thus, not suitable for automated solutions in energy audit applications. This research work is an attempt to fill this gap of knowledge by utilizing intensive thermal data (on the order of 100,000 plus images) and constitutes a relatively new area of analysis in energy audit applications. Specifically, we demonstrate a novel process using deep-learning methods to segment more than 100,000 thermal images collected from an unmanned aerial system (UAS). To quantify the heat loss for a building envelope, multiple stages of computations need to be performed: object detection (using Mask-RCNN/Faster R-CNN), estimating the surface temperature (using two clustering methods), and finally calculating the overall heat transfer coefficient (e.g., the U-value). The proposed model was applied to eleven academic campuses across the state of North Dakota. The preliminary findings indicate that Mask R-CNN outperformed other instance segmentation models with an mIOU of 73% for facades, 55% for windows, 67% for roofs, 24% for doors, and 11% for HVACs. Two clustering methods, namely K-means and threshold-based clustering (TBC), were deployed to estimate surface temperatures with TBC providing consistent estimates across all times of the day over K-means. Our analysis demonstrated that thermal efficiency not only depended on the accurate acquisition of thermal images but also relied on other factors, such as the building geometry and seasonal weather parameters, such as the outside/inside building temperatures, wind, time of day, and indoor heating/cooling conditions. Finally, the resultant U-values of various building envelopes were compared with recommendations from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE) building standards.

Globally, a variety of factors, ranging from ethnicity and occupants’ lifestyles to the local climate characteristics of any studied location, as well as people’s age, can affect thermal comfort assessments. This review paper investigates the energy effectiveness of state-of-the-art passive systems in providing neutral adaptive thermal comfort for elderly people by exploring passive design strategies in four distinct climates, namely Canada, India, Abu Dhabi and the Eastern Mediterranean basin. The aim of the study is to analyse the available data provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers’ (ASHRAE) Global Thermal Comfort Database II, version 2.1. The main objective of the study is to develop an effective methodological framework for the on-going development of adaptive thermal comfort theory. To this extent, this study presents a comprehensive review of the assessment of energy effectiveness of passive design systems. To accomplish this, the impact of climate change factors in passive design systems was investigated. A meta-analysis method was adopted to determine the input variables for the statistical analysis. Cramer’s V and Fisher’s Exact tests were used to assess occupants’ thermal sensation votes (TSVs). The findings revealed that there are discrepancies detected between the in situ field experiments and the data recorded in the ASHRAE Global Thermal Comfort Database II. The study findings contribute to the development of adaptive thermal comfort theory by reviewing the existing methodologies globally. Furthermore, a critical review of the significance of occupants’ age differences should be conducted in the identification of neutral adaptive thermal comfort.