Search Results (1,080)

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

This paper presents a multimodal transformer-based framework for the joint prediction of indoor thermal comfort and energy efficiency using real-world building management system (BMS) datasets. Unlike traditional comfort models that rely on fixed physical assumptions and subjective surveys, the proposed approach adopts physics-guided, data-driven learning to capture nonlinear and time-dependent interactions among environmental conditions, HVAC operation, and occupancy-related variables. Thermal comfort labels are computed using the PMV–PPD formulation defined by ASHRAE Standard 55, assuming standard metabolic rate and clothing insulation due to the lack of direct measurements in routine BMS data. A temperature-driven baseline HVAC energy proxy is derived using change-point regression. The proposed transformer architecture fuses multivariate temporal sequences to jointly predict both comfort and energy baseline targets through a dual-head regression formulation. The model is validated on two complementary datasets representing steady-state and dynamically perturbed thermal conditions. The proposed approach consistently outperforms linear regression, random forest, and LSTM baselines, achieving mean absolute errors below 0.03 and $R^2$ values exceeding 0.98 with corresponding RMSE values below 0.035 for both targets. Residual and calibration analyses confirm stable, unbiased prediction behavior across wide temperature ranges. The results highlight the strong potential of attention-based multimodal learning for future comfort-aware building energy optimization and digital twin integration. Full article

The HVAC/R sector in Europe is undergoing significant transformation driven by climate policy, technological innovation, and increasing digitalization of industrial services. This study examines the sustainability and digital transformation of the Slovak business-to-business (B2B) HVAC/R market in the context of EU F-gas regulation and emerging workforce constraints. The research applies a qualitative–interpretive design supported by structured secondary-data analysis, a review of European and Slovak regulatory frameworks, comparative benchmarking against selected European markets, and exploratory semi-structured interviews with industry professionals. The analysis indicates that regulatory pressure associated with the phase-down of fluorinated greenhouse gases, rising demand for energy-efficient systems, and the growing role of digital communication channels are reshaping procurement behaviour and market competition. At the same time, the sector faces structural barriers, particularly the limited availability of certified technicians and uneven digital adoption among small and medium-sized enterprises. The findings suggest that firms integrating transparent sustainability communication, environmental performance indicators, and digital engagement strategies can strengthen their competitive positioning within the evolving European HVAC/R ecosystem. Full article

Non-Intrusive Load Monitoring (NILM) represents a powerful approach for energy disaggregation, which enables detailed insights into energy consumption patterns without requiring extensive sensor deployment. While significant advances have been achieved in residential NILM applications, commercial and industrial buildings remain largely underexplored despite their substantial contribution to global energy consumption. This study addresses this gap by developing and evaluating multiple artificial intelligence approaches for energy disaggregation across residential, commercial, and industrial buildings under a unified experimental protocol. We implement and compare several AI-based models, including Vision Transformer (ViT), Variational Autoencoder (VAE), Random Forest (RF), and custom architectures inspired by TimeGPT and Prophet, alongside traditional baseline methods. The proposed framework is validated using three benchmark datasets representing residential (AMPds), commercial (COmBED), and industrial (IMDELD) environments. Experimental results demonstrate that architecture–load interactions, rather than model complexity alone, are the primary determinants of disaggregation accuracy: the ViT-small configuration achieves superior performance for complex industrial loads with R² values exceeding 0.94, Random Forest proves most effective for finite-state commercial HVAC systems with R² up to 0.97, and the Prophet-inspired model excels in capturing seasonal patterns in residential appliances. These findings provide evidence-based guidelines for selecting appropriate AI models based on load characteristics, signal-to-noise ratio, and building type, contributing to the practical deployment of NILM in heterogeneous building environments. Full article

The high energy intensity of public buildings, especially those with HVAC systems, calls for advanced control strategies such as Model Predictive Control (MPC) to balance energy efficiency and thermal comfort. However, the performance of MPC relies critically on the accuracy and robustness of building cooling and heating load calculations, which remain challenging, particularly for buildings with complex dynamic characteristics. This study proposes a simplified modeling-based MPC approach and investigates the influence of three different load calculation methods on controller performance: a physics-driven white-box model, a data-driven black-box model, and a novel Closed-Loop Load Grey Model (CLLGM). Under identical outdoor conditions during summer cooling operation, the three controllers exhibit distinct performance disparities: although the proposed CLLGM-based controller only reduces the load prediction MAPE by 0.63% compared with the black-box model, it improves the temperature control stability index (TDI) by 80.43% and increases the comprehensive score from the MPC multi-objective optimization function by 16.55%. Its key advantage is that it can use on-site temperature measurements as feedback to correct the cooling load, making it better suited for simulation and computation in MPC. 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

Accurate performance prediction of variable-speed centrifugal chillers is important for building energy optimization and the development of digital twins in HVAC systems. In practice, obtaining extensive operational data is costly, creating a prevalent “small-sample” dilemma under which conventional data-driven models are prone to overfitting with poor extrapolation capability. While recent Physics-Informed Neural Networks (PINNs) incorporate system-level thermodynamic constraints (e.g., COP definitions), they typically treat the centrifugal compressor as a thermodynamic black box, neglecting its inherent fluid dynamic characteristics; consequently, extrapolated predictions may be physically inconsistent or fall into unsafe operating regions such as compressor surge. To address this gap, this paper proposes an Aero-thermodynamic Physics-Informed Neural Network (Aero-PINN) that introduces three mechanisms into the PINN loss function: (1) dimensionless aerodynamic similarity mapping governed by affinity laws, (2) a surge boundary constraint that prevents non-physical extrapolations, and (3) an aerodynamic–electrical energy coupling validation. Experimental validation on 420 real-world variable-speed test records shows that the Aero-PINN achieves a COP RMSE of 0.04 and a COP MAPE of 0.3%, outperforming standard MLP and polynomial baselines. Moreover, 100% of the extrapolated operating points satisfy all fluid dynamic safety and energy efficiency constraints. This framework provides a reliable, physics-constrained small-sample learning approach, facilitating factory calibration and reduced-test digital modeling for chiller plants. Full article

Indoor air quality (IAQ) in educational buildings plays a critical role in the health, cognitive performance, and well-being of occupants. Aging university facilities often rely on outdated ventilation systems that are not designed to meet current demands or respond to dynamic occupancy levels. This study investigates the performance and feasibility of various advanced ventilation strategies in comparison to an existing balanced mechanical ventilation (BMV) system in a university classroom accommodating 100 students. Using a Dynamic Building Energy Simulation Program, simulations were conducted to evaluate IAQ (using CO₂ levels), energy consumption, and thermal comfort under three retrofitting scenarios: BMV, demand-controlled ventilation (DCV), and hybrid ventilation combining natural and mechanical airflow. The simulations indicate that DCV cuts annual HVAC energy use by 33% relative to the baseline, while the hybrid strategy achieves the greatest reduction of 42% and maintains CO₂ levels and thermal comfort within recommended limits. Although hybrid systems provide seasonal advantages, their complexity may limit applicability. In addition to technical analysis, this study also explores the financial and tax-related challenges associated with retrofitting ventilation systems in university buildings. Investment payback periods, operational costs, and potential tax incentives are discussed to evaluate economic viability. Overall, the endorse hybrid ventilation as the most cost-effective strategy where mixed-mode control is feasible, and DCV as a practical alternative for buildings unable to employ natural ventilation. Full article

This paper presents a design research study that develops a comprehensive conceptual framework for an integrated digital twin system for architectural heritage. The framework aims to explore mechanisms for real-time monitoring and the coupled regulation of structural health, microclimatic conditions, and energy performance. In the context of the ongoing global warming emergency, this framework supports climate adaptation strategies for heritage sites. It enables a fully coordinated operational process encompassing real-time sensing, predictive analysis, coupled control, and decision support. In the structural dimension, the framework is designed to utilise sensors to monitor and warn against cracks, settlement, and deformation, whilst integrating models to analyse stress conditions. In the microclimate dimension, the study envisages predicting and adjusting HVAC and lighting systems based on environmental parameters and footfall monitoring data via algorithms, with the aim of balancing occupant comfort with humidity control and mould prevention. Regarding energy, the framework optimises equipment operation through smart metering and algorithms and we propose a modelling tool for the quantitative assessment of energy-saving retrofit effects. Furthermore, the framework incorporates the establishment of an open-access dataset covering structural, microclimate, and energy use data, providing data standards and a foundation for subsequent empirical research. Full article

The built environment in the EU accounts for 40% of the total energy consumption and 36% of the total greenhouse gas emissions. To address the inefficiency of existing buildings, renovation could reduce their total energy consumption by 5–6% and lower carbon dioxide emissions by approximately 5%. A retrofit solution for existing buildings involves the use of lightweight prefabricated systems, some of which include integrated HVAC components that are able to enhance their functionality. Indeed, such prefabricated facade elements with integrated HVAC systems can represent a minimally invasive method for reducing the energy consumption of an existing building. To assess the potential of this approach, a full-scale mock-up of a prefabricated timber facade with integrated HVAC system was tested at the Facade System Interactions Lab (FSIL) of Eurac Research, Bolzano. The experimental data were used to develop a calibrated and validated 3D finite element model in COMSOL Multiphysics. The validated model was used to evaluate the facade’s thermal performance under standard heating conditions through a proposed equivalent thermal transmittance indicator (U_eq). Results show that the active facade achieves 0.07 W m⁻² K⁻¹, compared to 0.21 W m⁻² K⁻¹ for the passive facade with identical materials but without active components. Full article

The decarbonization of the building stock by 2050, as set by the European Green Deal, calls for an unprecedented wave of energy renovations. Yet, reliable evidence on the real costs and performance of retrofit interventions remains scarce. This paper presents the results of a large-scale technical and economic analysis conducted on 34 residential buildings, all renovated under a national Italian programme supporting energy efficiency improvements. For each building, pre- and post-renovation energy performances were assessed using standardised procedures, while detailed investment cost data were collected for all implemented measures, including envelope insulation, HVAC system upgrades, and renewable integrations. By combining these datasets, the study evaluates the actual cost-effectiveness of different retrofit strategies, revealing the true financial effort required to achieve substantial energy improvements. The results highlight both the opportunities and limitations of current approaches, showing a significant gap between theoretical models and real outcomes. The findings contribute to the European debate on the economic sustainability of deep renovation policies. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems used in modern buildings are among the largest contributors to energy consumption. Therefore, it is necessary to carefully balance between thermal comfort and energy efficiency when operating these systems. This study proposes a Symbolic Discrete Controller Synthesis (SDCS) approach for HVAC management that simultaneously enforces comfort-band constraints at the supervisory level and optimizes energy efficiency. Unlike traditional continuous controllers tuned per zone, the proposed method coordinates zone-level actuation through discrete power levels and node-level constraints (including an aggregate peak cap), exploiting thermal inertia to redistribute service over time without increasing comfort-band violations. Experimental evaluations on a multi-zone building model demonstrate that the SDCS approach provides comparable small comfort violations and provides superior energy savings when benchmarked against Model Predictive Control (MPC) and traditional Proportional-Integral-Derivative (PID) controllers. These results highlight the potential of SDCS as a robust and scalable solution for sustainable building management and energy-aware HVAC coordination in multi-zone buildings. Full article

This review paper discusses the technological advancements and innovative strategies of heating, ventilation, and air conditioning (HVAC) systems for buildings. Buildings are a major contributor to energy consumption and greenhouse gas (GHG) emissions, representing about 35% of global final energy use and 26% of energy-related GHG emissions. In Canada, the building sector accounts for roughly 31% of energy demand and 18% of total GHG emissions, with HVAC systems responsible for 40–50% of this energy use. The current challenges, emerging trends, and future prospects for HVAC and related technologies are systematically reviewed to promote sustainability, affordability, and resilience in buildings. The literature scanning begins with an overview of the prevailing energy scenario in buildings, HVAC technologies, and other regulatory and policies. The paper thoroughly examines the critical role of HVAC systems in reducing energy consumption, minimizing environmental impact, improving building affordability and enhancing occupant health and productivity. It discusses emergent technological opportunities, energy efficiency measures, sensors, smart controllers, Internet of Things (IoT) and AI-based technologies. The paper highlights the barriers to adopting new technologies and strategies. It provides an evolving topography of HVAC technologies, their current state and emerging directions to tackle environmental challenges, including net zero energy and zero carbon building goals. The review suggests that while there are promising advancements in HVAC technology, further research and practical demonstrations of innovative solutions are necessary to maintain the momentum in building modernization efforts. Full article

Hydrogen fuel cell buses (HFCBs) offer a promising zero-emission solution for sustainable public transportation. However, the high energy consumption of auxiliary systems, particularly heating, ventilation and air conditioning (HVAC), significantly impacts overall vehicle efficiency by increasing hydrogen consumption. This study investigates the influence of the HVAC load on the energy efficiency of hydrogen fuel cell buses under different driving conditions and seasonal ambient temperatures. Using a MATAB/Simulink-based simulation framework, the interaction between the fuel cell system, battery dynamics, and HVAC operation is modeled to quantify energy consumption under urban, highway and mixed driving conditions. Simulation was conducted at 7 °C and 35 °C with varying HVAC load levels of 50% and 100% to represent harsh winter and summer conditions. Results demonstrated that HVAC operation can account for a substantial portion of total energy consumption, reducing the vehicle range and fuel cell efficiency. Full article

Phase change materials (PCMs) have emerged as a key enabler of high-performance, low-carbon buildings through latent heat-based thermal energy storage. This paper presents a systematic and critical synthesis of advances in PCM technologies for building applications published between 2022 and 2025, analyzing over 300 peer-reviewed studies to evaluate thermal performance, economic viability, environmental impact, and climate adaptability across three integration approaches: passive, active, and hybrid systems. The studies analyzed show that passive envelope integration employing macroencapsulated or form-stable PCMs in walls, roofs, and glazing is reported to deliver 15–45% energy savings with payback periods of 8–15 years, primarily through enhanced thermal inertia and indoor temperature stabilization. Active systems, which couple PCMs with HVAC, heat pumps, or air handling units, are found to achieve 20–40% energy reductions and shorter payback periods (3–8 years) by enabling load shifting, peak shaving, and improved coefficient of performance (COP). Hybrid configurations integrating passive and active strategies with AI-driven control demonstrate, in the literature, the highest potential, with reported energy savings of up to 50%, though they entail greater complexity and capital cost. The review further highlights material-level innovations, including ternary composite PCMs, bio-based alternatives, and nano-enhanced formulations that address intrinsic limitations such as low thermal conductivity (0.1–0.3 W/m·K for organics) and cycling instability. Despite significant progress, critical gaps persist in standardized testing protocols, long-term field validation, comprehensive lifecycle assessments, and real-world scalability, particularly in tropical and cold climates. By bridging material science, building physics, and energy system engineering, this work provides a forward-looking roadmap to accelerate the deployment of PCM-based solutions in the global decarbonization of the built environment. Full article