Search Results (469)

The thermal performance of multilayer building wall systems under variable outdoor temperature conditions is an important factor in evaluating building energy efficiency and indoor thermal comfort. This study presents a simplified analytical formulation based on the classical transient heat conduction theory together with a numerical model based on the finite difference method (FDM) implemented in the MATLAB R2026a (Update 5) environment. The analysis includes five types of multilayer wall systems with different structural compositions and thermal masses, combined with three thermal insulation materials (expanded polystyrene (EPS), mineral wool, and aerogel) and various insulation thicknesses, resulting in a total of 55 wall assembly configurations. The investigated wall systems are evaluated using the thermal transmittance coefficient (U-value), decrement factor, time lag, maximum heat flux, and the temporal variation in the interior wall surface temperature. The results demonstrated that the dynamic thermal behavior of multilayer wall systems depends on the combined effects of the thermal mass of the load-bearing layer, the type and thickness of the thermal insulation, and the thermophysical properties of the constituent materials. The comparison between the analytical formulation and the MATLAB simulations demonstrates consistent trends in the predicted thermal behavior of the investigated wall systems, supporting the applicability of the proposed analytical–numerical approach for the preliminary assessment of the thermal performance of multilayer building wall systems. Full article

Large mineral flotation workshops in severe cold regions represent special industrial indoor environments characterized by the coexistence of limited ventilation and intense heat release. Such conditions generate pronounced spatial thermal stratification and localized heat accumulation within the workshop, leading to uneven worker thermal exposure and increased thermal discomfort and heat stress risk. However, conventional thermal comfort models were primarily developed for ordinary buildings with relatively stable thermal environments. Their applicability to large industrial workshops remains insufficiently validated. Nine representative monitoring points were arranged in the summer operating areas of the workshop, and thermal comfort surveys were conducted among 35 workers who had adapted to the local climate and working environment. The predicted mean vote (PMV) model was used as the baseline assessment framework, while an adaptive predicted mean vote (aPMV) model was further calibrated using field-based thermal sensation information. Monte Carlo simulation was employed to evaluate uncertainty propagation under field-data constraints, and Sobol sensitivity analysis was conducted to identify the dominant factors affecting thermal comfort predictions. The results demonstrated that the conventional PMV model exhibited a clear warm prediction bias under the investigated industrial conditions. After adaptive correction, the deviation from the field-based TSV was reduced by 82.93%, indicating improved agreement with workers’ actual thermal perception. Sensitivity analysis identified metabolic rate as the dominant contributor to aPMV output variance, with first-order and total-effect Sobol indices of 0.530 and 0.535. The proposed framework provides a scenario-specific approach for thermal comfort assessment in the investigated flotation workshop and offers preliminary methodological references for similar large-scale flotation workshops. Full article

Electrochromic devices (ECDs) in smart buildings have been advanced as a potential solution for improving energy savings and visual and thermal comfort. The current paper is a review of advanced control strategies for ECDs with respect to predictive, environmental, and adaptive strategies for improving building performance. One of the most frequently employed methods is rule-based control (RBC). RBC is being complemented by more sophisticated model predictive control (MPC) and machine learning (ML) procedures. By adjusting ECD behaviour in dynamic response to changing external circumstances, like daylight, glare, temperature, and solar radiation, these improved techniques ensure a major improvement in real-time adaptivity, energy savings, and occupant comfort. The paper systematically examines ECD control techniques available in the literature, detailing performance indicators, energy conservation, and comfort enhancement for various climatic conditions. It also examines hybrid techniques based on MPC and ML models that tackle the obstacles faced by conventional control systems. Furthermore, their compatibility with renewable energy sources such as PV and thermochromic systems is outlined in relation to net-zero energy buildings. The paper ends with a review of future directions that could lead towards standardization in the form of models, sensor networks and AI-based adaptive frameworks to increase the scale as well as the real-world relevance of ECDs in varying building contexts. Full article

The extensive use of high-resolution digital elevation data, along with continuous improvements in computing power and geographic information system (GIS) tools. Has driven the development of spatial data processing, management, and spatial interpolation methods. This study aims to construct a high-quality spatial distribution map of thermal comfort in densely populated areas of northwestern Jordan using climate data collected from six meteorological stations between 1991 and 2024, based on the indoor temperature index (IAT). To analyze the spatial variability of climate elements, a digital elevation model (DEM) with a spatial resolution of 30 m was used and resampled to a 0.5-km grid. Spatial interpolation employed inverse distance weighting (IDW), with each grid cell using data from the three nearest meteorological stations. The results showed that areas with higher temperatures inside the villas were clearly concentrated in the summer, especially in the lowlands near the Jordan Valley. Indicating that these areas are more susceptible to thermal stress. The model results also show that it performs well in predicting thermal comfort, with a coefficient of determination (R²) between 0.95 and 0.98 and mean squared error (MSE) between 0.35 and 0.50. which reflects the ability of these models to represent the relationship between climate variables and predict thermal comfort levels with a high degree of accuracy. The results indicate significant spatiotemporal differences in thermal comfort within the study area, with longer durations of heat stress in summer. This highlights the importance of combining geospatial methods with numerical simulations in studying the impacts of climate change and supporting urban planning and climate adaptation strategies. Full article

Indoor humidity regulation is important for building comfort, occupant health, and energy saving. Passive moisture-regulating concrete provides a low-energy solution; however, the moisture-regulating performance of thermally modified zeolite concrete and related prediction methods remain insufficiently studied. This study thermally modified natural clinoptilolite zeolite powder and used it as an equal-mass cement replacement. X-ray diffraction, moisture adsorption–desorption tests, and orthogonal design were employed to evaluate the modification effect and mixture parameters. A multilayer perceptron (MLP) enhanced using an adaptation of the Synthetic Minority Over-sampling Technique (SMOTE) and optimized by a mapping-improved Mountain Gazelle Optimizer (MMGO) was developed to predict unit-area moisture absorption. The novelty lies in integrating thermally modified zeolite concrete with an intelligent prediction framework. The results demonstrate improved moisture regulation of concrete and the potential of the proposed model for performance prediction. Further validation with larger independent datasets is required. Full article

Tuned viscous mass dampers (TVMDs) are effective devices for wind-induced vibration control in supertall buildings, but their performance depends on a precise resonance condition that can be disturbed by manufacturing tolerances. This study identifies an insufficiently examined asymmetric sensitivity mechanism, termed the “dangerous diagonal effect”, in which opposite-sign errors in TVMD inertance and stiffness amplify tuning-frequency drift and create a worst-case sensitivity space that conventional symmetric uncertainty models may underestimate. To tackle this challenge without requiring prior statistical distributions unavailable at the design stage, an Info-Gap Decision Theory (IGDT) robust optimization framework tailored to TVMDs under stochastic wind excitation is developed. A Kriging-metamodel-assisted Efficient Global Optimization bi-level strategy reduces the computational burden of the nested worst-case search. Applied to a 76-story, 306 m benchmark building under a dual-criterion constraint combining the ISO 10137 comfort limit and a 30% relative degradation bound, the framework certifies comfort compliance for manufacturing errors up to 23.44% along the dangerous-diagonal direction. Under the most severe coupled degradation scenario, which integrates opposite-sign manufacturing detuning, 50-year power-law aging, and Arrhenius thermal drift, the nominal H₂-optimal design collapses to 36.7% vibration reduction efficiency while the IGDT robust design sustains 51.7%, reducing the Monte Carlo failure probability from 3.8% to 1.2% across 500 random realizations. An aeroelastic wind tunnel campaign spanning 620 detuning configurations on a 1:350 scaled model provides physical validation of IGDT design reliability for a TVMD system. The experiments corroborate the dangerous-diagonal sensitivity asymmetry, support the predicted robustness plateau under severe parameter detuning, and show that the IGDT framework maintains comfort compliance where the H₂-optimal design fails. Full article

The real-time optimization of indoor thermal comfort and ventilation efficiency in offices is limited by the high computational cost of traditional Computational Fluid Dynamics (CFD) simulations (single simulation taking hours to days). Furthermore, the strong nonlinearity and coupling characteristics of temperature fields further increase the prediction difficulty of surrogate models. This study proposes a two-stage CFD surrogate model that maps five-dimensional operating condition parameters (supply temperature, supply velocity and position (x, y, z)) to POD modal coefficients through a deep neural network, followed by linear reconstruction of the flow field based on POD theory. The main contributions are: (1) a multi-branch temperature network (weighted fusion architecture of main branch + auxiliary branch + residual connection); (2) a Temperature-Aware Attention Mechanism (generating adaptive attention weights in the 2D temperature modal coefficient space); (3) a combination of hierarchical regularization with an intelligent data augmentation strategy. Experiments based on 510 office CFD scenarios demonstrate that the model achieves a Mean Absolute Error (MAE) of 0.210 K for temperature field prediction (34.4% improvement compared to the baseline with MAE of 0.320 K) and 0.0075 m/s for velocity field prediction (24.2% improvement compared to the baseline with MAE of 0.0099 m/s); the coefficient of determination (R²) reaches 0.98 (temperature) and 0.92 (velocity), respectively. The single prediction time is approximately 0.0008 s, 3–4 orders of magnitude faster than traditional CFD. This model provides an effective approach for temperature field prediction in office ventilation scenarios and provides a practical framework for real-time control, optimization, and digital twin applications. Full article

Improving HVAC energy efficiency while maintaining collective thermal comfort remains challenging in multi-occupant shared indoor environments, where occupants differ in thermal sensation, activity level, clothing condition, and spatial distribution. This study develops and field-validates an integrated edge-cloud IoT framework that connects non-invasive occupant-state sensing, INT8 edge thermal-sensation inference, and group-comfort-oriented HVAC setpoint optimization for classroom-based shared spaces. The proposed system integrates localized temperature–humidity sensing, vision-derived occupancy, posture, and clothing estimation, cloud-based thermal sensation model training, and edge-deployed real-time control on a HUB 8735 ULTRA device. A 4-day model-training data collection campaign with structured questionnaires was first conducted to obtain occupants’ Thermal Sensation Votes (TSVs) as ground-truth labels. The trained model was compressed from Float32 to INT8 through post-training quantization and deployed on the edge device for real-time inference. Predicted individual TSV values were then transformed into a PPD-inspired TSV-derived dissatisfaction index and used to determine the HVAC setpoint through rolling-horizon group comfort optimization. A separate eight-school-day single-blind daily-block A/B field validation was conducted, with four validation days assigned to the proposed smart control strategy and four days assigned to a fixed 25 °C baseline. The validation dataset included 2194 valid TSV questionnaire responses, which were aggregated into 116 valid 30 min classroom sessions for statistical comparison. The proposed control achieved a session-level mean TSV of −0.13, compared with −0.66 under the baseline, with Welch’s t(100) = 11.2, p < 0.001 and Cohen’s d = 2.11. Daily HVAC energy use decreased from 2.61 to 2.32 kWh/day, corresponding to a cumulative reduction of 1.16 kWh, or 11.1%, over the validation period. These results support the short-term feasibility of the proposed classroom-level human-centric HVAC control framework. However, because the validation was limited to a short-term classroom setting without full weather/load normalization, longer multi-season and multi-room studies are required to further evaluate generalizability and long-term energy performance. Full article

Buildings account for a significant share of final energy consumption, with space heating representing one of the major energy uses in residential buildings. Therefore, improving the thermal performance of building envelopes is an important strategy for reducing energy demand. Green roofs can contribute to this objective by modifying roof thermal properties and reducing heat losses through the building envelope. This study investigates the use of machine learning to predict annual heating demand and potential heating energy savings associated with replacing conventional roof configurations with a selected green roof assembly in a representative stock of Macedonian buildings. A representative dataset comprising 2934 building cases based on post-2013 buildings designed in accordance with the national energy-performance regulations was assembled. The dataset covers a wide range of building typologies, envelope thermal properties, climatic conditions and heating schedules. Three supervised learning models, Random Forest, Artificial Neural Network and Extreme Gradient Boosting (XGBoost), were developed and compared. The results show that XGBoost achieved the highest predictive accuracy and the best computational efficiency, with test coefficients of determination of 0.9901 for the heating demand of conventional roof buildings and 0.9956 for green-roof-related heating energy savings. Most simulated buildings showed heating energy savings of up to 10% following green roof implementation, while only a limited number of cases exhibited increases in heating demand of up to 3%. The feature importance analysis identified heated floor area, heating duration and wall area as the major drivers of heating demand in conventional roof buildings, whereas roof thermal transmittance was the most influential factor governing green-roof-related heating energy savings. The findings demonstrate that machine learning can reliably reproduce the results of the established energy performance assessment methodology and provide rapid estimates of the potential heating energy savings associated with replacing conventional roofs with a selected green roof system across a representative building stock. The proposed approach can support engineers, urban planners and architects in the early-stage assessment of green roofs as an energy-efficient measure. Full article

Model predictive control enhances building energy performance; however, its reliability is highly dependent on the robustness of internal prediction models under severe operating conditions. To address this, a risk-aware model-then-control (RAMC) training framework is proposed in this study. This approach augments conventional prediction loss with a conditional value-at-risk (CVaR) penalty on operational costs under perturbed inputs, embedding tail-risk awareness directly into the prediction model. The framework is trained via standard backpropagation, avoiding the computational burden of differentiating through the controller. The proposed methodology is evaluated on a simulated commercial building equipped with a hydronic heating system under three weather scenarios. Compared to a standard fidelity-trained baseline, the strongest risk-aware configuration reduced occupied cold degree-hours by 22–26% and peak cold violations by 14–27%, demonstrating the greatest benefit under forecast bias. These comfort improvements were achieved alongside a 17–31% increase in weekly heating energy consumption. The results indicate that embedding tail-risk awareness into model training improves closed-loop comfort robustness relative to standard accuracy-based training. An ablation study attributes this improvement directly to the CVaR tail term, while the risk weight formalizes a tunable energy–comfort trade-off dictated by operational priorities. In this case study, a fixed setpoint-margin baseline reached comparable cold protection at lower energy; the distinct contribution of RAMC is that it relocates a tunable tail-risk preference into the prediction model itself, leaving the downstream controller unchanged. Full article

Driven by the demand for high-quality housing, fourth-generation residential buildings—known internationally as “Vertical Forest” and in China as “Urban Forest Garden”—have developed rapidly. Initially built in mild southern regions, they have recently expanded to colder northern areas, with over 50 projects underway in provinces such as Shanxi, Hebei, Shaanxi, and Gansu. Several cities have introduced design standards and incentives, and the China Association for Standardization of Engineering Construction has issued the “Design Standards for Urban Forest Garden Housing.” However, in cold regions, where winters are long and cold and summers are short and hot, there is a lack of systematic quantitative research on how balcony design affects building carbon reduction, energy efficiency, and indoor thermal comfort. To address this research gap, this paper poses the following research questions: (1) In fourth-generation residential buildings in cold regions, how do different combinations of balcony orientations affect annual energy consumption and indoor thermal comfort? (2) Which balcony configurations offer the best balance between carbon reduction, energy efficiency, and thermal comfort? Based on statistical analysis of terrace configurations from more than 40 projects, 12 typical configuration models were identified. Using Ladybug and Honeybee tools on the Grasshopper platform, building energy consumption and indoor thermal comfort were simulated. Multi-objective trade-off analysis was performed using the Pareto front method. In this study, indoor thermal comfort was evaluated using the PMV (Predicted Mean Vote) index. PMV is an index proposed by Professor Fanger that comprehensively reflects human thermal sensation, taking into account air temperature, humidity, wind speed, mean radiant temperature, human metabolic rate, and clothing thermal resistance. Its typical range is −3 (cold) to +3 (hot); in this study, the comfort zone was defined as −1 ≤ PMV ≤ 1. Key findings: (1) The southwest + south terrace configuration shows the highest annual energy consumption, exceeding the lowest (northwest + west) by 2.7%, indicating that south-facing terraces are less favorable for carbon reduction. (2) The best thermal comfort is achieved with east, west, and south orientations. Compared to the least comfortable combination (southwest + northwest), the difference in PMV comfort percentage reaches 2.4%. (3) The Pareto front reveals that beyond a certain comfort level, energy consumption increases sharply. The west + south and east + south combinations yield the highest thermal comfort (49.4%) while maintaining relatively low energy consumption (17.98 kWh/m²). Therefore, in cold regions, fourth-generation residential designs should prioritize terrace combinations integrating south-facing and side-facing orientations and avoid pure corner configurations to balance winter solar gain and summer shading. Full article

Energy efficiency is a critical challenge in heating, ventilation, and air conditioning (HVAC) systems in battery electric vehicles (BEVs), as they are among the main auxiliary systems directly affecting driving range. This review examines control-oriented strategies for EV cabin thermal management, focusing on how advanced control can improve energy utilization while maintaining thermal comfort. Specifically, the review examines predictive control methods, optimization-based strategies, and data-driven learning approaches applied to HVAC systems, with particular emphasis on model predictive control, dynamic programming, and reinforcement learning frameworks. The literature shows that advanced controllers can reduce HVAC energy consumption while maintaining thermal comfort; however, most existing studies still focus on whole-cabin air regulation. In contrast, localized actuators, including seat heaters, radiant panels, infrared heaters, and targeted airflow systems, are rarely optimized or incorporated as explicit manipulated variables in control frameworks. This review identifies the lack of coordinated local–global actuator optimization and control as a major research gap. Future EV cabin thermal management should therefore prioritize human-centric, prediction-aware, and safety-constrained control frameworks that jointly optimize global HVAC operation and localized comfort actuation. Full article

Thermal comfort in mixed-mode buildings is challenging due to individual differences in perception, particularly in humid subtropical climates. In Florianópolis, Brazil, dynamic indoor conditions influence occupants’ thermal perception and adaptation. This study investigates how thermal disposition shapes comfort perception. A total of 1032 responses from heat-sensitive users and 733 from cold-sensitive users were collected through electronic questionnaires. The data were analysed using Predicted Mean Vote (PMV), Actual Mean Vote (AMV), and a linear mixed-effects model. Although both groups exhibited average PMV values within the ASHRAE 55 comfort range, their subjective evaluations differed significantly: heat-sensitive users reported warmer sensations, whereas cold-sensitive users reported cooler sensations under similar conditions. Among heat-sensitive users, the PMV–AMV correlation was moderate and strongest under air-conditioning, whereas it was weak and non-significant for cold-sensitive users. Dissatisfaction levels frequently exceeded 20% among heat-sensitive users. Adaptive comfort analysis indicated that most observations fell within acceptability limits for mixed-mode buildings. The mixed-effects model confirmed that thermal disposition significantly moderates the relationship between operative temperature and thermal sensation. These findings highlight the importance of incorporating individual thermal sensitivity into occupant-centred comfort assessments. Full article

Building energy consumption accounts for 30% of global energy use, making building management pivotal to achieving global sustainability. Occupants have profound impacts on the building environment. Incorporating occupant-related factors into the environmental control process is essential for optimizing the efficiency of building management systems (BMSs), which thus gives rise to the concept of occupant-centric control (OCC). Conventional methods rely on simplified models and fixed schedules that fail to satisfy environmental control and occupant requirements, while constructing credible models places strict requirements on the dataset. In this paper, we propose a Model-Aware Predictive Control (MAPC) framework that can construct credible models with limited data and provide room-level control strategies to optimize the trade-off between occupant comfort and energy consumption. The technological innovations of this research are twofold. On the one hand, we design a model construction and fine-tuning method that combines data-driven subspace projection approach with physical priors that can construct credible thermal dynamic models with limited data. On the other hand, to balance the potential conflicts between enhancing occupant comfort and saving energy, we present a hierarchical decision-making mechanism that enables adaptive multi-objective room-level control considering dynamic occupant comfort requirements and energy usage. The experimental results obtained on an EnergyPlus-based simulation dataset and a publicly available dataset demonstrate that MAPC can provide room-level control strategies based on dynamic occupant requirements and user preferences and achieve superior trade-offs between occupant comfort and energy consumption. The ablation experiments also demonstrated the superiority of MAPC in constructing reliable models on limited datasets. MAPC provides pivotal support for the advancement of the intelligent buildings and sustainable indoor environment. Full article

This paper investigates the potential of localized air-curtain microclimate control to reduce HVAC energy consumption in electric vehicles while maintaining occupant thermal comfort. The study compares conventional full-cabin cooling with driver-focused and passenger-focused air-curtain configurations under controlled ambient conditions of 32 °C. The experimental framework combines analytical airflow and heat-transfer modeling with comparative HVAC performance evaluation using power consumption, time to reach thermal comfort, and Predicted Mean Vote (PMV) analysis. The results show that the air-curtain configurations reduce HVAC power consumption from 3.2 kW for conventional cooling to 2.3 kW and 2.5 kW for the driver- and passenger-focused configurations, corresponding to energy savings of approximately 22–28%. In addition, localized airflow significantly accelerates thermal comfort attainment, reducing stabilization time from 8 min to 4–5 min while maintaining PMV values within acceptable comfort limits. The findings demonstrate that occupant-centered air-curtain microclimate strategies can improve HVAC energy efficiency, reduce auxiliary energy demand, and support more sustainable and range-efficient operation of next-generation electric vehicles. Full article