Search Results (4,304)

Existing office buildings in Australia contribute to 24% of the nation’s electricity consumption and 10% of greenhouse gas emissions, with energy use projected to rise by 84%. Meeting the 2050 sustainability target and United Nations (UN) 17 Sustainable Development Goals (SDGs) requires improving sustainability within existing office buildings. This systematic review examines net zero energy and climate resilience strategies in these buildings by analysing 74 studies from scholarly literature, government reports, and industry publications. The literature search was conducted across Scopus, Google Scholar, and Web of Science databases, with the final search in early 2025. Studies were selected based on keywords and research parameters. A narrative synthesis identified key technologies, evaluating the integration of net zero principles with climate resilience to enhance energy efficiency through HVAC modifications. Technologies like heat pumps, energy recovery ventilators, thermal energy storage, and phase change materials (PCMs) have been identified as crucial in reducing HVAC energy usage intensity (EUI). Lighting control and plug load management advancements are examined for reducing electricity demand. This review highlights the gap between academic research and practical applications, emphasising the need for comprehensive field studies to provide long-term performance data. Current regulatory frameworks influencing the net zero transition are discussed, with recommendations for policy actions and future research. This study links net zero performance with climate adaptation objectives for existing office buildings and provides recommendations for future research, retrofit planning, and policy development. Full article

In regions with hot summers and cold winters, elderly care buildings face the dual challenges of high energy consumption and stringent thermal comfort requirements. Using Nanchang as a case study, this research presents an optimization approach that integrates phase change material (PCM) walls with the window-to-wall ratio (WWR). PCM wall performance was tested experimentally, and EnergyPlus simulations were conducted to assess building energy use for WWR values ranging from 0.25 to 0.50, with and without PCM. The phase change material (PCM) used in this study is paraffin (an organic phase change material), which has a melting point of 26 °C and can store and release heat during temperature fluctuations. The experimental results show that PCM walls effectively reduce heat transfer, lowering the surface temperatures of external, central, and internal walls by 3.9 °C, 3.8 °C, and 3.7 °C, respectively, compared to walls without PCM. The simulation results predict that the PCM wall can reduce air conditioning energy consumption by 8.2% in summer and total annual energy consumption by 14.2%. The impact of WWR is orientation-dependent: east and west façades experience significant cooling penalties as WWR increases and should be maintained at or below 0.30; the south façade achieves optimal performance at a WWR of 0.40, with the lowest total energy load (111.2 kW·h·m-2); and the north façade performs best at the lower bound (WWR = 0.25). Under the combined strategy (south wall with PCM and WWR = 0.40), annual total energy consumption is reduced by 9.8% compared to the baseline (no PCM), with indoor temperatures maintained between 18 and 26 °C. This range is selected based on international thermal comfort standards (e.g., ASHRAE) and comfort research specifically targeting the elderly population, ensuring comfort for elderly occupants. These findings offer valuable guidance for energy-efficient design in similar climates and demonstrate that the synergy between PCM and WWR can reduce energy consumption while maintaining thermal comfort. Full article

Dual Carbon Goals are driving transformation in China’s power system, where increased renewable energy penetration is accompanied by heightened fluctuations on the generation and load sides. Energy storage and microgrid coordination have emerged as key solutions. However, existing research faces the challenge of balancing microgrid operations, energy storage services, and the alignment of user demand with stakeholder interests. This paper establishes a tripartite collaborative optimization framework to balance multi-stakeholder interests and enhance system efficiency, assuming fixed energy storage capacity. Centering on a principal-agent game between microgrid operators and consumer aggregators, energy storage service providers are integrated into this dynamic. Microgrid operators set 24-h electricity and heat pricing while adhering to tariff constraints, prompting consumer aggregators to adjust energy consumption and storage strategies accordingly. The KKT conditional method is employed to solve the model, deriving optimal user energy consumption strategies at the lower level while solving marginal pricing equilibrium relationships at the upper level, balancing accuracy with information privacy. The creative contribution of this article lies in the first construction of a tripartite collaborative optimization architecture in which energy storage service providers are embedded in a game of ownership and subordination. It proposes a dynamic coupling mechanism between pricing power, energy consumption decision-making, and energy storage configuration under fixed energy storage capacity constraints, achieving a balance of interests among multiple parties. By building a case study using MATLAB (R2022b), we compare operation costs, benefits, and absorption rates across different scenarios to validate the framework’s effectiveness and provide a reference for engineering applications. Full article

The relevance of this study is driven by the increasing requirements for the energy efficiency and indoor comfort of residential and public buildings, particularly in regions with extreme climatic conditions characterized by substantial daily and seasonal temperature fluctuations. Effective management of heat transfer through building envelopes has become a key factor in reducing energy consumption and improving indoor comfort. This paper presents the results of an experimental–numerical investigation of the thermal behavior of an adaptive exterior wall system with a controllable air cavity. Steady-state and transient simulations were performed for three envelope configurations: a baseline design, a design with vertical air channels, and an adaptive configuration equipped with adjustable openings. Quantitative analysis showed that during the winter period, the adaptive configuration increases the interior surface temperature by 1.5–2.3 °C compared to the baseline design, resulting in a 12–18% reduction in the specific heat flux through the wall. In the summer period, the temperature of the exterior cladding decreases by 3–5 °C relative to the baseline, which reduces heat gains by 8–14% and lowers the cooling load. Additional analysis of temperature fields demonstrated that the presence of vertical air channels has a limited effect during winter: temperature differences at the surfaces do not exceed 1 °C. A similar pattern is observed in warm periods; however, due to controlled air circulation, the adaptive configuration provides an improved thermal regime. The results confirm the effectiveness of the adaptive wall system under the climatic conditions of southern Kazakhstan, characterized by high solar radiation and large diurnal temperature variations. The practical significance of the study lies in the potential application of adaptive façades to enhance the energy efficiency of buildings during both winter and summer seasons. Full article

This work introduces ultrafast high-temperature graphitization (UHG) as an effective method to synthesize graphite with significantly reduced processing times of about 100 s and reduced consumed energy, as opposed to conventional methods that require several days at 2800 K. This novel process achieves graphitization of up to 90% within a few minutes due to the accelerated kinetics occurring at temperatures as high as 3400 K. Samples processed using UHG attained stable cyclic capacities of 350 mAh/g, which is fully comparable to commercially available graphite. Finite Element Simulations were also used to calculate the energy consumption for a scaled-up configuration, and it was found that the UHG approach reaches ultra-low energy consumption, requiring only 2.4 MJ/kg for the direct conversion of coke into graphite. By minimizing the duration of high-temperature processing and employing localized heating, UHG is envisioned to mitigate some of the challenges associated with traditional Acheson furnaces that have been in use for more than a century. Full article

Carbon capture is pivotal for achieving carbon neutrality; however, its high energy consumption severely limits the operational flexibility of power plants and remains a key challenge. This study, targeting a full flue gas carbon capture scenario for a 1000 MW coal-fired power plant, identified the dual-element (“steam” and “power generation”) coupling convergence mechanism. Based on this mechanism, a comprehensive set of mathematical model equations for the “carbon–electricity–heat” coupling process is established. This model quantifies the dynamic relationship between key operational parameters (such as unit load, capture rate, and thermal consumption level) and system performance metrics (such as power output and specific power penalty). To address the challenge of flexible operation, this paper further proposes two innovative coupled modes: steam thermal storage and chemical solvent storage. Model-based quantitative analysis indicated the following: (1) The power generation impact rate under full THA conditions (25.7%) is lower than that under 30% THA conditions (27.7%), with the specific power penalty for carbon capture decreasing from 420.7 kW·h/tCO₂ to 366.7 kW·h/tCO₂. (2) Thermal consumption levels of the capture system are a critical influencing factor; each 0.1 GJ/tCO₂ increase in thermal consumption leads to an approximate 2.83% rise in unit electricity consumption. (3) Steam thermal storage mode effectively reduces peak-period capture energy consumption, while the chemical solvent storage mode almost fully eliminates the impact on peak power generation and provides optimal deep peak-shaving capability and operational safety. Furthermore, these modeling results provide a basis for decision-making in plant operations. Full article

This study explored the effects of Bifidobacterium animalis subspecies lactis CECT 8145 (B. animalis CECT 8145)—in both live probiotic and heat-treated postbiotic form—on metabolic health and digestion in male and female Labrador Retrievers during weight gain and loss. The study consisted of two, seven-week phases: weight gain (200% maintenance energy intake; Phase (1) and weight loss (100% maintenance energy requirement for ideal weight; Phase (2), separated by a 2-week washout period. In each phase, forty-five adult Labrador Retrievers (1.6–12.5 years) were randomly assigned to daily supplementation with live B. animalis CECT 8145 probiotic (PRO, n = 15), heat-treated B. animalis CECT 8145 postbiotic (POST, n = 15), or placebo control (CON, n = 15). Body weight, body condition score, fecal quality and food consumption were monitored throughout the study, and body composition, fecal, and blood samples were analyzed at the beginning and end of each phase. Digestibility was evaluated at the end of each phase. Post-prandial glucose responses were affected by intervention during weight loss, with a 6% reduction in the area under the curve (AUC) in POST compared to CON dogs (p = 0.035). Glucagon was decreased in females supplemented with POST (p = 0.0014), while POST males showed increased glucagon-like peptide-1 (GLP-1) compared to CON (p = 0.016) during weight gain. Serum GGT levels decreased, within the normal reference range, in POST compared to CON dogs during weight gain (post hoc p = 0.041). Fecal isovalerate was also reduced and fat digestibility increased (p = 0.026) in POST compared to CON (p = 0.018) during weight gain. There was a significant association between the group and gastric inhibitory polypeptide (GIP), with a decrease in GIP in POST over time (p = 0.030), and glucagon tended to be decreased in POST compared to CON (p = 0.073). Overall, these findings suggest supplementation with postbiotic B. animalis CECT 8145 may improve certain markers of Labrador retrievers’ metabolic health. Full article

Heating, ventilation, and air conditioning (HVAC) systems account for a significant portion of building energy consumption and play a crucial role in maintaining indoor comfort. However, hidden faults in air-handling units (AHUs) often lead to energy waste and degraded performance, highlighting the importance of reliable fault detection and diagnosis (FDD). This study proposes a simulation-driven FDD framework that integrates a standardized prototype dataset and an independent evaluation dataset generated from a calibrated EnergyPlus model representing a target facility, enabling controlled experimentation and transfer evaluation within simulation environments. Training data were generated from the DOE EnergyPlus Medium Office prototype model, while evaluation data were obtained from a calibrated building-specific EnergyPlus model of a research facility operated by Company H in Korea. Three representative fault scenarios—outdoor air damper stuck closed, cooling coil fouling (65% capacity), and air filter fouling (30% pressure drop)—were systematically implemented. A Deep Belief Network (DBN) classifier was developed and optimized through a two-stage hyperparameter tuning strategy, resulting in a three-layer architecture (256–128–64 nodes) with dropout and regularization for robustness. The optimized DBN achieved diagnostic accuracies of 92.4% for the damper fault, 98.7% for coil fouling, and 95.9% for filter fouling. These results confirm the effectiveness of combining simulation-based dataset generation with advanced deep learning methods for HVAC fault diagnosis. The results indicate that a DBN trained on a standardized EnergyPlus prototype can transfer to a second, independently calibrated EnergyPlus building model when AHU topology, control logic, and monitored variables are aligned. This study should be interpreted as a simulation-based proof-of-concept, motivating future validation with field BMS data and more diverse fault scenarios. Full article

Thermal comfort and energy efficiency are two main goals of heating, ventilation, and air conditioning (HVAC) systems, which use about 40% of the total energy in buildings. This paper aims to predict optimal room temperature, enhance comfort, and reduce energy consumption while avoiding extra energy use from overheating or overcooling. Six Machine Learning (ML) models were tested to predict the optimal temperature in the classroom based on the occupancy characteristic detected by a Deep Learning (DL) model, You Only Look Once (YOLO). The decision tree achieved the highest accuracy at 97.36%, demonstrating its effectiveness in predicting the preferred temperature. To measure energy savings, the study used RETScreen software version 9.4 to compare intelligent temperature control with traditional operation of HVAC. Genetic algorithm (GA) was further employed to optimize HVAC energy consumption while keeping the thermal comfort level by adjusting set-points based on real-time occupancy. The GA showed how to balance comfort and efficiency, leading to better system performance. The results show that adjusting from default HVAC settings to preferred thermal comfort levels as well controlling the HVAC to work only if the room is occupied can reduce energy consumption and costs by approximately 76%, highlighting the substantial impact of even simple operational adjustments. Further improvements achieved through GA-optimized temperature settings provide additional savings of around 7% relative to preferred comfort levels, demonstrating the value of computational optimization techniques in fine-tuning building performance. These results show that intelligent, data-driven HVAC control can improve comfort, save energy, lower costs, and support sustainability in buildings. Full article

Regenerative hardfacing of steel substrates is an important technology for restoring the surface layer of components operating under wear conditions, supporting the goals of the circular economy (CE) by extending the service life of components, reducing material and energy consumption throughout their life cycle, and shortening downtime during machine repairs. The article provides a synthetic analysis of the literature on the production of functional layers exclusively on steels and systematizes process → structure → properties (PSP) relationships in the context of technological quality and the prediction of the functional properties of welds. The review covers methods used and developed in steel hardfacing (including arc processes and variants with increased energy concentration), analyzed on the basis of measurable process indicators: energy parameters (arc energy/heat input/volume energy), dilution, bead geometry, heat-affected zone characteristics, and the risk of welding defects. It has been shown that these factors determine the structural effects in the weld and the area at the fusion boundary (including phase composition and morphology, hardness gradient, and susceptibility to cracking), which translates into functional properties (hardness, wear resistance, adhesion, and fatigue life) and durability after regeneration. The main result of the work is the development of a PSP table dedicated to hardfacing on steel substrates, mapping the key “levers” of the process to structural consequences and trends in functional properties. This facilitates the identification of optimization directions (minimization of energy input and dilution while ensuring fusion continuity), which translates into longer durability after regeneration and a lower risk of defects—key, measurable effects of CE. Research gaps have also been identified regarding the comparability of results (standardization of energy metrics) and the need to determine and verify “technology windows” within the WPS/WPQR (welding procedure specification/welding procedure qualification record) for layers deposited on steels. Full article

The conventional reactive cooling strategy, which relies on static thresholds, has become inadequate for managing dynamically changing heat loads, often resulting in energy inefficiency and increased risk of local hot spots. In this study, we develop a data center cooling optimization system that integrates distributed sensor arrays for predictive analysis. By deploying high-density temperature and humidity sensors both inside and outside server racks, a real-time, high-fidelity three-dimensional digital twin of the data center’s thermal environment is constructed. Time-series analysis combined with Long Short-Term Memory algorithms is employed to forecast temperature and humidity based on the extensive environmental data collected, achieving high predictive accuracy with a root mean square error of 0.25 and an R² value of 0.985. Building on these predictions, a proactive cooling control strategy is formulated to dynamically adjust fan speeds and the opening degree of chilled-water valves in computer room air conditioning units, changing the cooling approach from passive to preemptive prevention of overheating. Compared with conventional proportional–integral–differential control, the developed system significantly reduces overall energy consumption and maintains all equipment within safe operating temperatures. Specifically, the framework has reduced the energy consumption of the cooling system by 37.5%, lowered the overall power usage effectiveness of the data center by 12% (1.48 to 1.30), and suppressed the cumulative hotspot duration (temperature 27 °C) by nearly 96% (from 48 to 2 h). Full article

This study investigates the feasibility of using waste heat from generator sets as the low-temperature heat source for heat pumps in off-grid energy systems, addressing the need for more efficient and self-sufficient heating solutions. A conceptual model was developed in which a generator and an air-to-water heat pump operate within an insulated thermal chamber, enabling the recovery of waste heat to maintain a stable 15 °C inlet temperature for the heat pump. Theoretical analysis was supplemented with preliminary experimental tests performed on a small generator placed in a thermally insulated enclosure. Measurements of temperature rise and heat output allowed for verification of the real heat-recovery efficiency, which reached approximately 28%. Based on real household heating demand, this study evaluated annual heat demand, heat pump electricity consumption, and fuel requirements for several recovery scenarios (28%, 45%, and 60%). The results show that maintaining a constant 15 °C source temperature significantly improves heat-pump efficiency, reducing annual electricity demand. Increasing heat-recovery efficiency from 28% to 60% reduces fuel consumption by more than half and lowers the annual operating costs. The findings confirm the potential of generator-supported heat-pump systems to enhance energy efficiency in off-grid applications and provide a sound basis for further optimization and real-scale validation. Full article

Based on the pressing need to develop efficient desulfurization technologies for fuel oils, this study presents a novel photoresponsive adsorbent for the removal of refractory thiophenic sulfides. Conventional hydrodesulfurization exhibits limited efficiency for such compounds, while adsorption–desorption processes often suffer from high energy consumption during regeneration. Inspired by natural stimuli-responsive systems, we designed a photothermal adsorbent by incorporating silver nanoparticles (Ag NPs) into a porous aromatic framework (PAF) via a green photoreduction method. The resulting materials, denoted as Ag(0)PBPAF-n (n = 1, 2, 3), were thoroughly characterized to confirm successful synthesis and structural integrity. The introduced Ag NPs serve as adsorption sites, enhancing uptake capacity through weak interactions with sulfur atoms in thiophenic molecules. More significantly, under light irradiation, the localized surface plasmon resonance (LSPR) of Ag NPs enables efficient photothermal conversion, triggering rapid desorption without conventional heating. Adsorption–desorption tests demonstrated that up to 48% of adsorbed thiophenic sulfur could be released upon illumination. Fixed-bed experiments further verified that light can effectively stimulate regeneration and improve energy efficiency. This work offers a promising strategy for designing recyclable adsorbents with low-energy regeneration driven by clean solar energy. Full article

The thermal influence of Urban Heat Islands (UHIs) is not limited to periods of high temperature but persists throughout the year. The present study utilizes hourly data collected over a period of one year from a network of hygrothermal monitoring stations with a high density, which were deployed across the city of Cáceres (Spain). The network was designed in accordance with the World Meteorological Organization’s guidelines for urban measurements (employing radiation footprints and surface roughness) and ensures representation of each Local Climate Zone (LCZ), characterized by those factors (such as building typology and density, urban fabric, vegetation, and anthropogenic activity, among others) that influence potential solar radiation absorption. The magnitude of the heat island effect in this city has been determined to be approximately 7 °C in summer and winter at the first hours of the morning. In order to assess the energy impact of UHIs, Cooling and Heating Degree Days (CDD and HDD) were calculated for both summer and winter periods across the different LCZs. Following the implementation of rigorous quality control procedures and the utilization of gap-filling techniques, the analysis yielded discrepancies in energy demand of up to 10% between LCZs within the city. The significance of incorporating UHIs into the design of building envelopes and climate control systems is underscored by these findings, with the potential to enhance both energy efficiency and occupant thermal comfort. This methodology is particularly relevant for extrapolation to larger and denser urban environments, where the intensification of UHI effects exerts a direct impact on energy consumption and costs. The following essay will provide a comprehensive overview of the relevant literature on the subject. Full article

Maintaining optimal humidity in livestock buildings during winter is a major challenge in cold climate regions due to the conflict between moisture-removing ventilation and the need for heat preservation. To address this issue, a novel condensation dehumidification system is proposed that utilizes the natural low temperature of cold winters. An integrated energy consumption model, coupling moisture and thermal balances, was developed to evaluate room temperature drop, dehumidification rate (DR), and the internal circulation coefficient of performance (IC-COP). The model was calibrated and validated with experimental data comprising over 150 operational cycles under varied operation conditions, including initial temperature differences (ranging from −20 to −5 °C), air flow rates (0.6–1.5 m/s), refrigerant flow rates (3–7 L/min), and high-humidity conditions (>90% RH). Correlation analysis showed that higher indoor humidity improved both DR and IC-COP. Four machine learning models—Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and Multilayer Perceptron (MLP)—were developed and compared with a stacking ensemble learning model. Results demonstrated that the stacking model achieved superior prediction accuracy, with the best R² reaching 0.908, significantly outperforming individual models. This work provides an energy-saving dehumidification solution for enclosed livestock housing and a case study on the application of machine learning for energy performance prediction and optimization in agricultural environmental control. Full article