Search Results (387)

Natural cooling and ventilation have been fundamental principles in vernacular architecture for millennia, shaping sustainable building practices across diverse climatic regions. This paper examines the historical evolution, technological advancements, environmental benefits, and prospects of passive cooling strategies, with a particular focus on wind catchers. Originating in Mesopotamian, Egyptian, Caucasia, and Iranian architectural traditions, these structures have adapted over centuries to maximize air circulation, thermal regulation, and humidity control, ensuring comfortable indoor environments without reliance on mechanical ventilation. This study analyzes traditional wind catcher designs, highlighting their geometric configurations, airflow optimization, and integration with architectural elements such as courtyards and solar chimneys. Through a comparative assessment, this paper contrasts passive cooling systems with modern HVAC technologies, emphasizing their energy neutrality, low-carbon footprint, and long-term sustainability benefits. A SWOT analysis evaluates their strengths, limitations, opportunities for technological integration, and challenges posed by urbanization and regulatory constraints. This study adopts a comparative analytical method, integrating a literature-based approach with qualitative assessments and a SWOT analysis framework to evaluate passive cooling strategies against modern HVAC systems. Methodologically, the research combines historical review, typological classification, and sustainability-driven performance comparisons to derive actionable insights for climate-responsive design. The research is grounded in a comparative assessment of traditional and modern cooling strategies, supported by typological analysis and evaluative frameworks. Looking toward the future, the research explores hybrid adaptations incorporating solar energy, AI-driven airflow control, and retrofitting strategies for smart cities, reinforcing the enduring relevance of vernacular cooling techniques in contemporary architecture. By bridging historical knowledge with innovative solutions, this paper contributes to ongoing discussions on climate-responsive urban planning and sustainable architectural development. Full article

China has committed to carbon neutrality by 2060 by necessitating a comprehensive transformation of its building sector, particularly in rapidly urbanizing areas such as the Greater Bay Area (GBA), where subtropical climates, urban heat island effects, and extreme weather events present distinct challenges for achieving carbon reduction objectives through green building practices. The goal of this study is to establish an analysis method for green building success in the GBA’s subtropical environment, paying attention to the challenging goals of reducing carbon and making buildings more climate-resilient. Research techniques involved performing building energy simulations with EnergyPlus and DesignBuilder, applying LightGBM models for machine learning, using case studies from 32 buildings in Shenzhen, Hong Kong and Guangzhou and carrying out an evaluation of the policy using a PEI. Energy usage in green buildings was 45.3% less than in conventional structures, with Energy Use Intensity ranging from 65.1 to 72.4 kWh/m²/year, while traditional buildings used between 118.5 and 124.2 kWh/m²/year. Also, the carbon footprint during the life cycle of buildings was decreased by 38.4% and they became more resilient to typhoons, giving residents 72.4 h of power during storms, while conventional buildings gave only 8.3 h. HVAC system efficiency was the leading factor, accounting for 24.3% of the difference in energy performance. A detailed approach is developed for optimizing subtropical green buildings, based on unique design features and helpful policy ideas to promote carbon neutrality in swiftly growing metropolitan areas around the world. Full article

Water distribution and pumping systems consume a large share of energy in metro HVAC operations and remain a major challenge to energy-efficient performance. This study, grounded in a practical metro project, investigates four control strategies for chilled water systems, focusing on chiller sequencing, pump frequency modulation, and variable flow regulation. A dynamic system model was developed using Dymola to simulate and evaluate the performance of each strategy. The results indicate that Strategy 2, which integrates real-time outdoor weather parameters into the frequency control logic, enhances operational stability and maintainability while achieving a 4.42% reduction in total energy consumption compared to the baseline. Strategy 4 employs a genetic algorithm to optimize chiller load distribution, resulting in improved system efficiency and energy savings of up to 8.62%. Further analysis reveals that chillers account for approximately 80% of the system’s total energy consumption, underscoring their central importance in system-wide energy optimization. Additionally, cooling towers show significant energy-saving potential under low wet-bulb temperatures. A 1 °C decrease in wet-bulb temperature results in an estimated 7% reduction in energy use. These findings offer quantitative insights and practical guidance for the low-carbon optimization of metro chilled water systems. Full article

This study introduces a comprehensive framework combining building information modeling (BIM), project management body of knowledge (PMBOK), and machine learning (ML) to optimize energy efficiency and reduce environmental impacts in Riyadh’s construction sector. The suggested methodology utilizes BIM for dynamic energy simulations and design visualization, PMBOK for integrating sustainability into project-management processes, and ML for predictive modeling and real-time energy optimization. Implementing an integrated model that incorporates building-management strategies and machine learning for both commercial and residential structures can offer stakeholders a thorough solution for forecasting energy performance and environmental impact. This is particularly essential in arid climates owing to specific conditions and environmental limitations. Using a simulation-based methodology, the framework was evaluated based on two representative case studies: (i) a commercial complex and (ii) a residential building. The neural network (NN), reinforcement learning (RL), and decision tree (DT) were implemented to assess performance in energy prediction and optimization. Results demonstrated notable seasonal energy savings, particularly in spring (15% reduction for commercial buildings) and fall (13% reduction for residential buildings), driven by optimized heating, ventilation, and air conditioning (HVAC) systems, insulation strategies, and window configurations. ML models successfully predicted energy consumption and greenhouse gas (GHG) emissions, enabling targeted mitigation strategies. GHG emissions were reduced by up to 25% in commercial and 20% in residential settings. Among the models, NN achieved the highest predictive accuracy (R² = 0.95), while RL proved effective in adaptive operational control. This study highlights the synergistic potential of BIM, PMBOK, and ML in advancing green project management and sustainable construction. Full article

Integrating renewable energy, particularly wind power, into modern power systems introduces challenges concerning stability and reliability. These issues require enhanced regulation to balance power supply with load demand. Flexible loads and energy storage provide viable solutions to stabilize the grid without relying on new resources. This paper proposes building thermostatically controlled loads (BTLs), such as heating, ventilation, and air conditioning (HVAC) systems, as flexible demand-side management tools to address the challenges of intermittent energy sources. A new concept is introduced, portraying BTLs as a stochastic battery with losses, offering a compact representation of their dynamics. BTLs’ thermal characteristics, user-defined set points, and ambient temperature changes determine the power limits and energy capacity of this stochastic battery. The model is simulated using DIgSILENT Power Factory, which includes thermal power plants, gas turbines, wind power plants, and BTLs. A dynamic dispatch strategy optimizes power generation while utilizing BTLs to balance grid fluctuations caused by variable wind energy. Performance analysis shows that integrating BTLs with conventional thermal plants can reduce variability and improve grid stability. The study highlights the dual role of simulating overall flexibility and applying dynamic dispatch strategies to enhance power systems with high renewable energy integration. Full article

We propose a hybrid CNN-LSTM architecture for energy-efficient spatiotemporal modeling in sports venue analytics, addressing the dual challenges of computational efficiency and prediction accuracy in dynamic environments. The proposed method integrates layered mixed-precision training with gradient accumulation, dynamically allocating bitwidths across the spatial (CNN) and temporal (LSTM) layers while maintaining robustness through a computational memory unit. The CNN feature extractor employs higher precision for early layers to preserve spatial details, whereas the LSTM reduces the precision for temporal sequences, optimizing energy consumption under a hardware-aware constraint. Furthermore, the gradient accumulation over micro-batches simulates large-batch training without memory overhead, and the computational memory unit mitigates precision loss by storing the intermediate gradients in high-precision buffers before quantization. The system is realized as a ResNet-18 variant with mixed-precision convolutions and a two-layer bidirectional LSTM, deployed on edge devices for real-time processing with sub 5 ms latency. Our theoretical analysis predicts a 35–45% energy reduction versus fixed-precision models while maintaining <2% accuracy degradation, crucial for large-scale deployment. The experimental results demonstrate a 40% reduction in energy consumption compared to fixed-precision models while achieving over 95% prediction accuracy in tasks such as occupancy forecasting and HVAC control. This work bridges the gap between energy efficiency and model performance, offering a scalable solution for large-scale venue analytics. Full article

Heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems account for a significant share of global energy demand, prompting intensive research into advanced thermal enhancement techniques. Among these, nanofluids—colloidal suspensions of nanoparticles in base fluids—have shown promise in boosting heat transfer performance. This review provides a structured and critical evaluation of nanofluid applications in HVAC&R systems, synthesizing research published from 2015 to 2025. A total of 200 peer-reviewed articles were selected from an initial pool of over 900 through a systematic filtering process. The selected literature was thematically categorized into experimental, numerical, hybrid, and AI/ML-based studies, with further classification by fluid type, performance metrics, and system-level relevance. Unlike prior reviews focused narrowly on thermophysical properties or individual components, this work integrates recent advances in artificial intelligence and hybrid modeling to assess both localized and systemic enhancements. Notably, nanofluids have demonstrated up to a 45% improvement in heat transfer coefficients and up to a 51% increase in the coefficient of performance (COP). However, the review reveals persistent gaps, including limited full-system validation, underexplored real-world integration, and minimal use of AI for holistic optimization. By identifying these knowledge gaps and research imbalances, this review proposes a forward-looking, data-driven roadmap to guide future research and facilitate the scalable adoption of nanofluid-enhanced HVAC&R technologies. Full article

Maintaining air quality in operating rooms is critical for infection control and patient safety. Particulate matter, originating from surgical instruments, personnel, and external sources, is influenced by airflow patterns and ventilation efficiency. This study employs Computational Fluid Dynamics (CFD) simulations using Simcenter STAR-CCM+ 2410 to analyze airflow and particulate behavior in a surgical-grade operating room. A steady-state solver with the k–ε turbulence model was used to replicate airflow, while the Lagrangian multiphase method simulated particle trajectories (0.5 µm, 1 µm, and 5 µm). The simulation results demonstrated close agreement with the experimental data, with average errors of 17.3%, 17.7%, and 39.7% for 0.5 µm, 1 µm, and 5 µm particles, respectively. These error margins are considered acceptable given the device’s 10% measurement sensitivity and the observed experimental asymmetry—attributable to equipment placement—which resulted in variations of 17.2%, 18.0%, and 26.5% at corresponding symmetric points. Collectively, these findings support the validity of the simulation model in accurately predicting particulate transport and deposition within the operating room environment. Findings confirm that optimizing airflow can achieve ISO Class 7 cleanroom standards and highlight the potential for future studies incorporating dynamic elements, such as personnel movement and equipment placement, to further improve contamination control in critical environments. Full article

The goals of the European Union towards the energy transition imply profound changes in the energy field, so as to promote sustainable energy development while fostering economic growth. To achieve these changes, the incorporation of sustainable technologies supporting decentralisation, energy efficiency, renewable energy production, and demand flexibility is of vital importance. Blockchain has the potential to change energy services towards this direction. To optimally exploit blockchain, innovative business models need to be designed, identifying the opportunities emerging from unmet needs, while also considering potential risks so as to take action to overcome them. In this context, the scope of this paper is to examine the opportunities and the risks that emerge from the adoption of blockchain in four innovative business models, while also identifying mitigation strategies to support and accelerate the energy transition, thus proposing optimal approaches of exploitation of blockchain in energy services. The business models concern Energy Performance Contracting with P4P guarantees, improved self-consumption in energy cooperatives, energy efficiency and flexibility services for natural gas boilers, and smart energy management for EV chargers and HVAC appliances. Firstly, the value proposition of the business models is analysed and results in a comprehensive SWOT analysis. Based on the findings of the analysis and consultations with relevant market actors, in combination with the examination of the relevant literature, risks are identified and evaluated through a qualitative assessment approach. Subsequently, specific mitigation strategies are proposed to address the detected risks. This research demonstrates that blockchain integration into these business models can significantly improve energy efficiency, reduce operational costs, enhance security, and support a more decentralised energy system, providing actionable insights for stakeholders to implement blockchain solutions effectively. Furthermore, according to the results, technological and legal risks are the most significant, followed by political, economic, and social risks, while environmental risks of blockchain integration are not as important. Strategies to address risks relevant to blockchain exploitation include ensuring policy alignment, emphasising economic feasibility, facilitating social inclusion, prioritising security and interoperability, consulting with legal experts, and using consensus algorithms with low energy consumption. The findings offer clear guidance for energy service providers, policymakers, and technology developers, assisting in the design, deployment, and risk mitigation of blockchain-enabled business models to accelerate sustainable energy development. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems represent a significant portion of global energy use, yet they are often operated without optimized control strategies. This study explores the application of deep learning to accurately model heating system behavior as a foundation for predictive control and energy-efficient HVAC operation. Experimental data were collected under controlled laboratory conditions, and 288 unique hyperparameter configurations were developed. Each configuration was tested three times, resulting in a total of 864 artificial neural network models. Five key hyperparameters were varied systematically: number of epochs, network size, network shape, learning rate, and optimizer. The best-performing model achieved a mean squared error of 0.469 and featured 17 hidden layers, a left-triangle architecture trained for 500 epochs with a learning rate of 5 × 10⁻⁵, and Adam as the optimizer. The results highlighted the importance of hyperparameter tuning in improving model accuracy. Future research should extend the analysis to incorporate cooling operation and real-world building operation data for broader applicability. Full article

Effective building operation requires a careful balance between energy conservation and indoor environmental comfort. Although numerous methods have been developed to reduce energy consumption during the operational phase, their objectives and applications vary widely. However, the complexity of building energy management makes it challenging to identify the most suitable methods that simultaneously achieve both comfort and efficiency goals. Existing studies often lack a systematic framework that supports integrated decision-making under comfort constraints. This research aims to address this gap by proposing a decision-making tree for selecting energy conservation methods during building operation with an explicit consideration of indoor environmental comfort. A comprehensive literature review is conducted to identify four main energy-consuming components during building operation: the building envelope, HVAC systems, lighting systems, and plug loads and appliances. Three key comfort indicators—thermal comfort, lighting comfort, and air quality comfort—are defined, and energy conservation methods are categorized into three strategic groups: passive strategies, control optimization strategies, and behavioural intervention strategies. Each method is assessed using a defined set of evaluation criteria. Subsequently, a questionnaire survey is administered for the calibration of the decision tree, incorporating stakeholder preferences and expert judgement. The findings contribute to the advancement of understanding regarding the co-optimization of energy conservation and occupant comfort in building operations. Full article

Electric buses (EBs) are increasingly adopted for their environmental and operational benefits, yet their real-world efficiency is influenced by climate, route characteristics, and auxiliary energy demands. While most existing research identifies winter as the most energy-intensive season due to cabin heating and reduced battery performance, this study presents a contrasting perspective based on a three-year longitudinal analysis of the LYMMO fleet in Orlando, Florida—a subtropical U.S. region. The findings reveal that summer is the most energy-intensive season, primarily due to sustained HVAC usage driven by high ambient temperatures—a seasonal pattern rarely reported in the current literature and a key regional contribution. Additionally, idling time exceeds driving time across all seasons, with HVAC usage during idling emerging as the dominant contributor to total energy consumption. To mitigate these inefficiencies, a proxy-based HVAC energy estimation method and an optimization model were developed, incorporating ambient temperature and peak passenger load. This approach achieved up to 24% energy savings without compromising thermal comfort. Results validated through non-parametric statistical testing support operational strategies such as idling reduction, HVAC control, and seasonally adaptive scheduling, offering practical pathways to improve EB efficiency in warm-weather transit systems. Full article

This article presents a new method of parametric shaping of buildings with buffer spaces characterized by complex forms and effective thermal operation in the moderate climate of the Central Europe Plane. The parameterization of an elaborated thermal qualitative model of buildings with buffer spaces and its configuration based on computer simulations of thermal operation of many discrete models are the specific features of the method. The model uses various original building shapes and a new parametric artificial neural network (a) to automate the calculations and recording of results and (b) to predict a number of new buildings with buffer spaces characterized by effective thermal operation. The configuration of the parametric quantitative model was carried out based on the simulation results of 343 discrete models defined by means of ten independent variables grouping the properties of the building and buffer space related to their forms, materials and air circulation. The analysis performed for the adopted parameter variability ranges indicates a varied impact of these independent variables on the thermal operation of buildings located in a moderate climate. The infiltration and ventilation and physical properties of the windows and walls are the independent variables that most influence the energy savings utilized by the examined buildings with buffer spaces. The optimal values of these variables allow up to 50–60% of the energy supplied by the HVAC system to be saved. The accuracy and universality of the method will continuously be increased in future research by increasing the types and ranges of independent variables. Full article

Maintaining the required relative humidity values in the vehicle cabin is an important HVAC task, along with considerations related to the temperature, velocity, air pressure and noise. Deviation from the optimal values worsens the psycho-physiological state of the driver and affects the energy efficiency of the train. In this study, a model of liquid film formation on and removal from various cabin surfaces was constructed using the fundamental Navier–Stokes hydrodynamic equations. A special transport model based on the liquid vapor diffusion equation was used to simulate the air environment inside the cabin. The evaporation and condensation of surface films were simulated using the Euler film model, which directly considers liquid–gas and gas–liquid transitions. Numerical results were obtained using the RANS equations and a turbulence model by means of the finite volume method in Ansys CFD. Conjugate fields of temperature, velocity and moisture concentration were constructed for various time intervals, and the dependence values for the film thicknesses on various surfaces relative to time were determined. The verification was conducted in comparison with the experimental data, based on the protocol for measuring the microclimate indicators in workplaces, as applied to the train cabin: the average ranges encompassed temperature changes from 11% to 18%, and relative humidity ranges from 16% to 26%. Comparison with the results of other studies, without considering the phase transition and condensation, shows that, for the warm mode, the average air temperature in the cabin with condensation is 12.5% lower than without condensation, which is related to the process of liquid evaporation from the heated walls. The difference in temperature values for the model with and without condensation ranged from −12.5% to +4.9%. We demonstrate that, with an effective mode of removing condensate film from the window surface, including recirculation modes, the energy consumption of the climate control system improves significantly, but this requires a more accurate consideration of thermodynamic parameters and relative humidity. Thus, considering the moisture condensation model reveals that this variable can significantly affect other parameters of the microclimate in cabins: in particular, the temperature. This means that it should be considered in the numerical modeling, along with the basic heat transfer equations. Full article

Heating, ventilation, and air-conditioning (HVAC) systems account for the largest share of energy consumption in European Union (EU) buildings, representing approximately 40% of the final energy use and contributing significantly to carbon emissions. Latent thermal energy storage (LTES) using phase change materials (PCMs) has emerged as a promising strategy to enhance HVAC efficiency. This review systematically examines the role of latent thermal energy storage using phase change materials (PCMs) in optimizing HVAC performance to align with EU climate targets, including the Energy Performance of Buildings Directive (EPBD) and the Energy Efficiency Directive (EED). By analyzing advancements in PCM-enhanced HVAC systems across residential and commercial sectors, this study identifies critical pathways for reducing energy demand, enhancing grid flexibility, and accelerating the transition to nearly zero-energy buildings (NZEBs). The review categorizes PCM technologies into organic, inorganic, and eutectic systems, evaluating their integration into thermal storage tanks, airside free cooling units, heat pumps, and building envelopes. Empirical data from case studies demonstrate consistent energy savings of 10–30% and peak load reductions of 20–50%, with Mediterranean climates achieving superior cooling load management through paraffin-based PCMs (melting range: 18–28 °C) compared to continental regions. Policy-driven initiatives, such as Germany’s renewable integration mandates for public buildings, are shown to amplify PCM adoption rates by 40% compared to regions lacking regulatory incentives. Despite these benefits, barriers persist, including fragmented EU standards, life cycle cost uncertainties, and insufficient training. This work bridges critical gaps between PCM research and EU policy implementation, offering a roadmap for scalable deployment. By contextualizing technical improvement within regulatory and economic landscapes, the review provides strategic recommendations to achieve the EU’s 2030 emissions reduction targets and 2050 climate neutrality goals. Full article