Search Results (623)

The reduction in driving range during winter remains a major barrier to the widespread adoption of battery electric buses (BEBs), as battery performance degradation and increased Heating, Ventilation and Air Conditioning (HVAC) energy demand significantly raise total energy consumption. This study investigates the use of proton exchange membrane fuel cells (PEMFCs) as auxiliary power units for range-extended electric buses (FC-REEBs) under low-temperature conditions to address this challenge. A comprehensive dynamic model was developed in MATLAB/Simulink 2025a version, integrating a fuel cell system, lithium-ion battery, power conversion unit, vehicle dynamics, and cabin thermal model. The model was evaluated under the World Harmonized Vehicle Cycle (WHVC) to compare three fuel cell operation strategies defined by fuel cell capacity and operating modes for cabin heating and battery charging. Performance was compared in terms of SOC variation, fuel cell loading patterns, hydrogen consumption, and equivalent fuel economy. Results indicate that the high-capacity strategy improves SOC stability but increases hydrogen consumption and reduces overall efficiency. In contrast, the strategy prioritizing cabin heating with minimal battery charging effectively utilizes waste heat and achieves the highest equivalent fuel economy. These findings highlight key trade-offs among different operating strategies and demonstrate that fuel cells can significantly enhance BEB efficiency and driving performance in cold environments while reducing battery load. Full article

Buildings account for a substantial proportion of global energy consumption and greenhouse-gas emissions, largely due to the widespread use of conventional heating, ventilation, and air-conditioning (HVAC) systems. Hybrid systems that integrate passive and active technologies have emerged as a promising strategy for reducing energy demand while maintaining adequate indoor environmental conditions. This study evaluates the thermal and airflow performance of a hybrid air-conditioning system (HACS) that combines transparent thermal insulation (TTI) filled with R-410A refrigerant and a pig-fat-based organic phase-change material (PCM). Experimental measurements of heat flux, temperature, airflow velocity, and CO₂ concentration were conducted in a controlled prototype system. In parallel, computational simulations were performed using computational fluid dynamics (CFD) and multizone airflow modeling. The hybrid system incorporates a TTI container acting as a solar absorber and a galvanized-steel PCM container filled with 10 kg of pig fat used as latent heat storage. Heat-flux measurements were obtained using an HFS-5 sensor connected to a data acquisition system, while airflow velocity and temperature were monitored with analog data loggers. Indoor CO₂ concentrations were recorded using a dedicated CO₂ meter and simulated using CONTAMW software version 3.4.0.8. The experimental results show that the TTI and PCM containers reached average heat-flux values of 77.04 W/m² and 55.31 W/m², respectively. Airflow within the system is induced by buoyancy forces arising from temperature gradients generated by heat transfer processes at the surfaces of the TTI and PCM, resulting in a mixed air stream with an average temperature of 37.54 °C during winter operation. Recorded CO₂ concentrations remained between 290 and 413 ppm, indicating high indoor air quality levels. The overall experimental campaign spanned 6 years and 3 months. CFD simulations confirmed the airflow patterns and heat-transfer behavior observed experimentally. The findings demonstrate that hybrid air-conditioning systems combining refrigerant-filled transparent insulation with bio-based phase-change materials can effectively enhance passive thermal performance while maintaining acceptable indoor air quality. The integration of photovoltaic-powered ventilation systems could further the operational autonomy and overall energy efficiency of such hybrid systems. Full article

Home energy management systems (HEMS) aim to provide intelligent control of the thermal comfort inside smart buildings with the minimum energy cost, while satisfying the energy consumption requests and increasing the use of energy from renewable sources. The capabilities of these intelligent HEMS agents are restricted due to the personalized observability of the environment, resulting in limited knowledge gathering and potentially sub-optimal decisions. Furthermore, several buildings have recently been organized into small energy communities, with the ultimate goal of sharing intelligence between agents in federated learning schemes.In this context, we propose a personalized federated deep reinforcement learning method using Moreau envelopes (pFedMe) for joint energy cost and household comfort optimization in energy communities that consist of multiple smart homes. Specifically, a Twin-Delayed Deep Deterministic Policy Gradient (TD3) actor–critic model is introduced, dynamically observing the state of the smart home environment and suggesting control actions on the operation of the Energy Storage System and on the regulation of the indoor temperature. The TD3 actor–critic model leads to improved policy performance in the continuous control of these systems, mitigating the overestimation bias and improving the training stability of the intelligent agents. The efficiency of the proposed method is verified via simulations based on real data, achieving a beneficial trade-off between the energy cost and the thermal comfort compared to FedAvg and Fedprox baselines. The results show that the proposed pFedMe framework consistently outperforms FedAvg and FedProx in both convergence speed and overall reward, achieving an energy cost reduction of approximately 10% compared to the other schemes, while exhibiting marginal thermal comfort behavior. Full article

Road noise has become a dominant interior noise source in electrified vehicles, especially at low and medium speeds. In practical active road noise control (ARNC) systems, the error microphones capture not only the road noise component correlated with the reference sensors but also non-coherent disturbances such as wind noise, engine harmonics, and heating, ventilation and air conditioning (HVAC) noise. These disturbances degrade the convergence stability and steady-state attenuation of the conventional filtered-x least mean square (FxLMS) algorithm. This study analyzes FxLMS under non-coherent interference and develops two robustness optimization methods. Under the small-step-size assumption, a statistical convergence model is derived for stationary random inputs, together with the corresponding convergence region and steady-state error expressions. Based on this analysis, a multichannel cascaded controller (MCC) and a bounded variable-step-size (VSS) FxLMS algorithm are proposed. Offline simulations and dSPACE-based experiments are conducted on a single-channel HVAC duct ANC test platform and a vehicle test bench. The vehicle-bench tests use controlled tonal excitations and should be interpreted as an intermediate validation step before real-driving broadband tests. Average noise reduction (ANR) and the norm of the adaptive-filter coefficients are used to evaluate robustness. Both MCC and VSS improve attenuation and reduce coefficient fluctuations under non-coherent interference. Relative to fixed-step FxLMS, the maximum ANR improvement reaches 15.8 dB in simulation and 14.2 dB in the real-time duct experiment. Within the controlled tonal and tonal-plus-white-noise conditions tested here, VSS achieves robustness improvements close to those of MCC with much lower computational cost; therefore, it is a practical candidate for further onboard ARNC evaluation rather than a completed validation under real-driving broadband road noise. Full article

Pipe networks are essential components of modern building infrastructure, including heating, ventilation, and air conditioning (HVAC) water systems, water distribution networks (WDNs), and district heating and cooling (DHC) systems. Leakage in these systems can lead to increased energy consumption, loss of thermal efficiency, and unstable system operation, thereby affecting indoor environmental quality and overall building performance. Despite differences in scale and application, similar leakage mechanisms are also observed in other pipe network systems, such as oil and gas pipelines and liquid cooling networks. These shared characteristics motivate a unified analytical perspective across different applications. This review provides a systematic analysis of leakage diagnosis methods, with a focus on machine learning (ML) approaches. The results indicate that ML methods have become a dominant research direction due to their ability to capture nonlinear relationships and process high-dimensional data. However, their effectiveness is often constrained by the limited availability of labeled leakage data, sensitivity to dynamic operating conditions, and insufficient physical interpretability. This review provides a structured framework for understanding ML-based leakage diagnosis and offers insights into the integration of data-driven and physics-based approaches for pipe network systems. In addition, the potential role of reinforcement learning (RL) is briefly discussed as an emerging direction for handling dynamic and adaptive scenarios. Compared with ML-based methods, RL has not yet been systematically explored in leakage diagnosis and remains at an early stage of development. This review synthesizes current methodologies, identifies key challenges, and outlines future research directions. Full article

This study develops an advanced optimization framework for heating, ventilation, and air conditioning (HVAC) systems in multi-zone buildings with highly dynamic and uncertain internal heat loads. Unlike conventional models that assume static occupancy, the proposed approach captures time-varying, spatially heterogeneous thermal disturbances driven by occupant activity. The building is modeled as a coupled cyber-physical system integrating multi-zone thermal dynamics, nonlinear HVAC energy consumption, and behavior-driven heat generation. To address uncertainty, a distributionally robust optimization framework based on Wasserstein ambiguity sets is employed, enabling reliable performance without requiring precise probability distributions. In addition, a physics-informed reinforcement learning mechanism is incorporated to derive adaptive control policies while ensuring thermodynamic feasibility. A multi-zone coordination strategy is further introduced to manage spatial thermal interactions and maintain stable comfort across different areas. Case study results demonstrate that the proposed method reduces peak energy consumption by 28–32%, decreases comfort violation rates by 65–75%, and improves thermal stability, with temperature variance reduced by over 60% compared to baseline strategies. Full article

Heating, ventilation, and air conditioning (HVAC) systems struggle to maintain optimal thermal comfort because perception is subjective and varies significantly across individuals. Traditional uniform cooling strategies often overlook demographic diversity, leading to inequitable comfort outcomes and inefficient building operations. To address this limitation, this study analyzed a web-based survey of 366 university occupants using a partial proportional odds model with multiple imputation and inverse-frequency weighting. Interaction terms, specifically Age–Activity, Gender–Clothing, and Age–Clothing, were included to assess combined effects that reflect demographic disparities in adaptive capacity. The results show that clothing insulation, activity, age, gender, race/ethnicity, and space type significantly influence thermal responses. Notably, male occupants were more than three times as likely to report feeling too warm (odds ratio [OR] = 3.24), whereas older adults exhibited significantly lower odds of reporting feeling too warm (OR = 0.42). Substantial variation was observed across racial and ethnic groups (ORs ranging from 2.4 to 6.5). These findings highlight the limitations of traditional population-average comfort approaches and provide valuable scientific insights for demand-response-ready HVAC strategies that adjust temperature setpoints dynamically without sacrificing comfort. By offering accurate, real-time estimates across diverse thermal ranges, these occupant-centric models reduce HVAC energy use and associated emissions at the building scale while supporting ancillary services for flexible load shifting and smarter coordination within low-carbon electric grids. Ultimately, incorporating demographic and contextual diversity into building controls reduces unnecessary cooling waste while promoting thermal equity, establishing a human-centric foundation for sustainable built environments. Full article

This systematic review builds upon 51 published empirical studies out of 354 studies that were published between 2020 and 2025 to assess the effectiveness of building-scale digital twins (DTs) in providing thermal comfort and energy efficiency, and improving the indoor environment and system reliability. The results show that there is a rapidly developing field focused on five thematic clusters: system architecture, artificial intelligence and machine learning (AI/ML)-driven control, human-centric engagement, predictive maintenance, and blockchain-enabled cybersecurity. Existing DT frameworks not only achieve real-time building information modeling (BIM)–Internet of Things (IoT) integration with prediction errors under 10%, but reinforcement learning controllers are also able to achieve 25–40% heating, ventilation, and air conditioning (HVAC) energy savings, and human-centric interfaces increase thermal satisfaction from 0.64 up to 1.2 Likert points. Predictive maintenance models have diagnostic accuracies of 91–97%, and new blockchain applications enhance data integrity, but largely at the prototype level. The cross-cluster convergence signifies the transition towards adaptive, socio-technical systems with an equilibrium of efficiency, comfort, reliability, and trust. The major weaknesses identified in this paper were a lack of longitudinal validation, climatic bias and ethical governance. A framework of a modular six-layer architecture is proposed after the review of 51 studies, which facilitates scalable, interoperable, and ethically robust DT deployments. Full article

Background/Objectives: Healthcare facilities are among the most energy-intensive public buildings, yet hospital decision-support models rarely integrate energy-related performance indicators alongside operational metrics. This study aims to address this gap by developing a discrete-event simulation framework capable of jointly evaluating clinical efficiency and energy consumption in elective orthopedic surgical pathways. Methods: A comprehensive discrete-event simulation model was developed to represent the diagnostic imaging and orthopedic surgical process. The model was parameterized using a hybrid data-collection approach that combined clinical activity data, scientific literature, and expert judgment. Energy consumption was modeled by differentiating fixed loads, such as heating, ventilation, and air-conditioning systems and lighting, from activity-dependent loads associated with diagnostic and surgical equipment. Baseline performance was assessed and compared with alternative scenarios for organizational and technological improvements. Results: The analysis showed that fixed infrastructural loads, particularly HVAC systems, were the main drivers of per-patient energy consumption, with inefficient space utilization and prolonged idle times. Scenario analysis demonstrated that organizational interventions, such as increasing operating room throughput and optimizing MRI scheduling, can substantially reduce energy intensity by diluting fixed loads and decreasing idle consumption. Technological interventions, such as replacing conventional surgical lamps with LED systems, produced smaller but still beneficial reductions. The combined implementation of organizational and technological strategies yielded the greatest overall improvement. Conclusions: Integrating energy metrics into discrete-event simulation provides effective support for hospital decision-making by revealing the interaction between workflow design, resource utilization, and environmental performance. The findings indicate that organizational redesign, particularly when combined with technological upgrades, can significantly improve both operational efficiency and sustainability in hospital settings. This study highlights discrete-event simulation as a promising tool for energy-aware healthcare planning. Full article

This paper proposes a deep-learning-based scheduling approach for community microgrids that explicitly accounts for building thermal dynamics and customer comfort preferences. Traditional heating, ventilation, and air-conditioning (HVAC) scheduling models are NP-hard and scale poorly, especially for large systems with many buildings. To address this challenge, we develop a dual-encoder deep learning model that predicts building-level HVAC ON/OFF schedules using temporal load and temperature profiles, along with static building thermal parameters. The proposed model is trained in a supervised manner using solutions generated by an optimization-based HVAC scheduling framework, thereby serving as a computationally efficient surrogate for predicting HVAC schedules within a microgrid. The model is trained on samples generated by the optimization-based HVAC scheduling framework and evaluated using precision, recall, and F1-score. The results indicate strong predictive performance. Full article

Heating, ventilation, and air conditioning (HVAC) systems are essential for indoor air quality and thermal comfort but can simultaneously act as vectors for microbial contamination, particularly bacteria and fungi. While the COVID-19 pandemic intensified focus on airborne viral transmission, bacterial and fungal contamination in indoor environments remains a persistent and significant health risk. This study presents a detailed case study of a restaurant HVAC system, analysing the impact of different ventilation strategies on bacterial contamination, infection transmission risk, energy consumption, and thermal comfort. By focusing on a real-world application, the research evaluates practical challenges and trade-offs associated with HVAC operation modifications aimed at mitigating microbial risks while maintaining acceptable energy and comfort levels. The research compares three operational scenarios: normal operation with air recirculation, 24 h operation with 100% outdoor air, and extended operation periods. Results demonstrate that while strategies emphasizing outdoor air intake and extended operation reduce infection probability by up to 60–65%, they simultaneously increase energy consumption by over 1700% and compromise thermal comfort parameters. In the h24 case, the pre-heat coil rises from 2421.7 to 43,923.7 kWh and the post-heat coil from 24,812.8 to 152,970.4 kWh, while the Plus 2 h strategy reduces the energy penalty by roughly 42–51% with respect to the h24 case. The findings are contextualized within current research on bacterial and fungal risks in HVAC systems, highlighting the critical need for balanced ventilation strategies that integrate health protection, energy efficiency, and comfort considerations. Full article

Conventional Heating, Ventilation, and Air Conditioning (HVAC) control systems primarily rely on environmental and physiological parameters, largely ignoring the critical influence of psychological states on thermal comfort. Overlooking this factor often leads to suboptimal occupant satisfaction, energy inefficiency and thus carbon dioxide (CO₂) emissions. To this aim, this study introduces a novel mood-adaptive HVAC control system integrating psychological feedback to decrease CO₂ emissions in office buildings by reducing energy consumption and optimizing comfort. A total of 7000 thermal facial measurement records and high-resolution camera images were collected across seven mood state conditions using video stimuli and the Profile of Mood States (POMS) questionnaire to evaluate mood variations. A dual artificial intelligence system was developed: a Convolutional Neural Network (CNN) for analyzing facial expressions and an Artificial Neural Network (ANN) for processing facial temperatures via thermal imaging. These models collectively predict occupant mood in real-time, and a custom-designed wearable necklace interface transmits this data to dynamically adjust HVAC setpoints. To evaluate system performance, energy consumption was directly measured in real-life operations using an energy analyzer, without relying on simulations. Results indicate that this prototype personalized mood-driven system has the potential to enhance perceived thermal comfort while achieving up to a 20% reduction in carbon emissions compared to conventional systems. This human-centered approach significantly advances intelligent building management and climate change mitigation. Full article

Hospital buildings account for 60–80% of healthcare sector electricity consumption, yet robust causal evidence on the relationship between building energy efficiency and emissions per unit of clinical activity remains scarce. This study applies within–group fixed effects estimation to an unbalanced panel of 12 intermediate–complexity hospitals in Madrid, Spain (2016–2024; N = 107 hospital–year observations), controlling for activity volume and exogenous shocks. Cluster–robust standard errors and Wild Cluster Bootstrap inference address the limited number of cross–sectional units (N = 12). We propose a methodological correction for the artificial 74.6% discontinuity in Spain’s electricity emission factor (2020–2021) caused by regulatory change. The within–hospital building energy use intensity (EUIe) coefficient is β = 0.646 (p < 0.001), remarkably stable across six robustness specifications (range: 0.599–0.648; 8.2% variation). Wild Cluster Bootstrap confirms statistical significance despite 75% larger standard errors. A 20 kWh/m²·year EUIe reduction achievable through Heating, Ventilation and Air Conditioning (HVAC) retrofit in 1980s–era buildings translates into 13 kWh/stay savings, equivalent to 216 tCO₂/year for median–sized facilities (8.2% reduction). Within R² exceeds 0.97 across all specifications. Building envelope and HVAC retrofit constitute the dominant structural intervention for hospital Scope 2 emissions reduction. Facilities with EUIe > 150 kWh/m²·year should be prioritized for energy efficiency interventions using NextGenerationEU funds. Full article

Climate change poses a growing threat to global health, yet healthcare systems contribute substantially to environmental harm through energy use, waste, and greenhouse gas (GHG) emissions. Among hospital departments, Intensive Care Units (ICUs) are among the most resource- and energy-intensive, generating disproportionately high greenhouse gas (GHG) emissions. The aim of this systematic review is to synthesize the literature on the environmental footprint of ICUs and to develop evidence-based strategies for creating sustainable ‘Green ICUs’ in accordance with the PRISMA 2020 guidelines. Peer-reviewed studies published between 2012 and October 2025 were identified through searches of major biomedical databases. Eligible studies examined the impacts of climate change on human health and infectious diseases, the ecological footprint of medical imaging and personal protective equipment, and sustainability interventions relevant to adult intensive care units. The environmental footprint of ICUs ranges from 88 to 178 kg CO₂-equivalents per patient per day. High electricity consumption, especially from heating, ventilation, and air-conditioning (HVAC) systems, along with single-use medical supplies and diagnostic imaging, drives this impact. Life-cycle assessments consistently demonstrate that reusable textiles, optimized energy systems, and rationalized diagnostic practices significantly reduce emissions and waste. Educational and behavioral interventions were effective in reducing unnecessary consumable use while maintaining patient safety. A “Green ICU” model integrating energy efficiency, sustainable procurement, waste reduction, and staff education can substantially reduce environmental harm without compromising quality of care. Full article

Short auditory cues in enclosed built environments (such as elevator calls, access control, navigation, and heating, ventilation, and air conditioning (HVAC) notifications) influence not only usability but also stress and perceptions of well-being in daily indoor life. However, acoustic research remains largely focused on physical properties, and the psychophysiological impact of such short auditory cues remains under-quantified. To address this gap, a neuroscience-based evaluation approach, the Acoustic User Experience and Emotion (AUEX) model, is proposed. This model integrates functional near-infrared spectroscopy (fNIRS), electrodermal activity (EDA), and the User Experience Questionnaire (UEQ). With 33 in-cabin prompt sounds as a controlled typology of short auditory cues in an enclosed setting, we set up a simulated interaction experiment with 20 participants in a driving simulator vehicle cabin to investigate the relationship between acoustic properties and cognitive load, arousal, and user experience. The results show that timbre is the key factor, which was correlated positively with overall UX (r = 0.414) and negatively with prefrontal ΔHbO (CH3: r = −0.368; l-DLPFC: r = −0.449), indicating a decrease in cognitive load and a relaxed affective state. Conversely, high-frequency signals improved pragmatic quality but increased physiological arousal, which negatively affected hedonic assessment. To facilitate the translation of evaluation results into practice, we also completed a design phase that converted the AUEX results into scenario-based parameter targets and prototype designs for functional, warning, and brand/affective cues, illustrating how evidence-based relationships can be translated into design-ready outputs for enclosed built environments. These results confirm the AUEX approach as a transferable method for designing short auditory cues for well-being and provide parameter-level implications for therapeutic and human-centered sound design in smart buildings, intelligent vehicles, and other enclosed built environments. Overall, the AUEX approach provides a transferable evaluation-to-design workflow for short auditory cues in enclosed interactive contexts; however, direct generalization from a single controlled vehicle cabin setting to real-world building environments should be validated through future field studies. Accordingly, the present findings are positioned as evidence from a controlled enclosed case rather than universal conclusions for all enclosed spaces. Full article