Search Results (7,598)

To enhance the seismic performance of existing reinforced concrete (RC) buildings during retrofitting, the study introduces a new type of joint connected by a T-shaped steel plate. Compared with previous similar strengthening methods, this novel structure incorporating a post-installed beam not only effectively improves the mechanical properties of RC columns, but the connectors also further enhance the integrity of the post-installed beam. Low-cycle reversed loading tests on one cast-in-place specimen (RC) and three T-shaped steel plate connection specimens (TRC1–TRC3) were conducted to evaluate failure modes, hysteresis and skeleton curves, and energy dissipation. Results show that the novel joint failure concentrates at beam-end–column steel jacket weld seams and column-side steel plate cracking, while the core-zone concrete remains intact. Compared with RC, the novel joints TRC1–TRC3 exhibit bearing capacity variations of −1.03%~+15.80% and significantly enhanced energy dissipation. The thickness of the beam’s wrapped steel improves the carrying capacity and energy dissipation, whereas the T-shaped connector thickness has limited influence on bearing capacity. ABAQUS parametric analysis indicates that bolt quantity, concrete strength, and connector thickness have limited influence and serve as secondary design factors. These findings provide a theoretical basis for retrofitting existing buildings. Full article

The Greek power system includes 42 non-interconnected islands grouped into 28 autonomous electrical systems operated by the Hellenic Electricity Distribution Network Operator. Although these systems possess substantial wind and solar potential, the technical constraints of isolated microgrids lead to systematic renewable energy curtailment. Building on our previous methodology for estimating curtailed wind energy and hydrogen production, this study develops and evaluates a dispatch-oriented power-to-power pathway in which curtailed wind electricity is converted into hydrogen and subsequently reconverted into electricity. The study integrates hydrogen-to-power technology selection, annual energy recovery, dispatch strategy, and operational environmental and economic benefits for a representative non-interconnected island. A comparative assessment of commercially relevant hydrogen-to-power technologies identified proton exchange membrane fuel cells as the most suitable option because of their absence of direct CO₂ and NO_x emissions, rapid start-up, load-following performance, modularity, and compatibility with remote island operation. Applying the previously developed curtailment methodology to 2024 data yielded 9334.5 MWh of exploitable curtailed wind energy. This energy could produce 155.6–233.4 tonnes of hydrogen and recover 2437.1–4277.9 MWh of electricity annually. Two dispatch strategies were evaluated: continuous integration of hydrogen-derived electricity into the island’s generation mix, and strategic hydrogen storage with priority dispatch during periods of emergency diesel generator operation. Under the reference case, both strategies recovered approximately 2935.1 MWh annually, avoided 1868.9 tonnes of CO₂ emissions, and reduced fuel expenditure by €359,000. Full article

Rural houses in cold regions of China usually have high energy demands, particularly for space heating and domestic hot water. Passive design can reduce building energy demand, but additional renewable energy systems are still needed to improve on-site energy supply. This study evaluates the performance and limitations of photovoltaic (PV) and solar water heating (SWH) systems in a passive-designed rural house in Xi’an, China. Hourly simulations were conducted for PV-only and PV–battery configurations with different south-facing roof coverage ratios and battery capacities, together with an evacuated-tube SWH system. The results show that PV electricity supply was limited by the mismatch between household electricity demand and PV generation. Household demand mainly occurred in the morning and evening, whereas PV generation was concentrated around noon. The 13 m² PV case achieved approximately 11% electricity supply capacity with a utilization ratio of 62%, while increasing the PV area to 50 m² raised the supply capacity to only 15% and reduced the utilization ratio to 23%. With battery storage, the largest PV–battery configuration supplied 48% of annual household electricity demand, while the overall electricity utilization ratio was 73%, indicating a trade-off between household electricity self-supply and system utilization. The SWH system showed better applicability for domestic hot water supply, with an annual average hot water supply capacity of 60.2% and an average device efficiency of 43.5%, but its winter performance remained weak. These results indicate that PV and SWH are useful but insufficient solar energy strategies for passive-designed rural houses. PV is mainly constrained by daily time mismatch, while SWH is mainly constrained by seasonal climate variation. Full article

Although deep learning has transformed protein structure prediction, the controlled generation of functional and experimentally tractable protein structures remains a major challenge in structural bioinformatics. Diffusion models offer a versatile approach to generating protein backbones, motif-conditioned scaffolds, all-atom structures and biomolecular interaction geometries, while accommodating explicit structural and functional constraints. This review focuses on coordinate- and residue-frame-based diffusion approaches for generating protein structures, paying particular attention to geometric equivariance, conditioning strategies, all-atom modelling and interaction-aware design. We compare representative methods derived from RoseTTAFold, frame-diffusion architectures, and oriented-residue-cloud representations according to their molecular representation, generative objective, and validation strategy. We examine the criteria used to evaluate generated proteins, such as stereochemical quality, structural consistency, designability, novelty, diversity, computational efficiency, and experimental performance. Particular attention is given to the distinction between learned structural distributions and condition-dependent thermodynamic ensembles. Future progress will depend on the integration of generative models with molecular mechanics, conformational sampling, uncertainty estimation, free-energy methods, and experimental design–build–test–learn cycles. Within this framework, diffusion models offer candidate generation and constraint satisfaction capabilities within broader protein engineering workflows. Full article

As urban areas grow and face climate-related challenges, rooftop greenhouses (RTGs) offer a promising approach for Controlled Environment Agriculture (CEA) to enhance urban food production while providing potential energy-saving benefits. However, the energy performance of RTGs integrated with mid-rise residential buildings, which represent a substantial proportion of the existing urban housing stock in cold climates, remains poorly understood. This study investigated the influence of RTG integration on the energy demand and indoor thermal conditions of a four-story residential building across cold-climate regions. Dynamic simulations using IDA-ICE were performed to analyze the influence of different glazing and shading configurations, comparing rooftop and ground-based greenhouse scenarios and host building performance. RTG integration reduced the host building’s annual heating demand by 6.2–7.6%, with thermal buffering primarily benefiting the top floor, where heating demand decreased by up to 24.2%, while lower floors remained largely unaffected. No summertime overheating risks were observed. Compared to a ground-based greenhouse, the RTG had a lower heating energy use by 2.8–14.6% depending on glazing and shading configuration, by using free heat from the host building. This study highlights the potential of RTG integration as a passive energy-saving measure, while supporting sustainable urban food production, contributing to more sustainable and climate-resilient cities. Full article

This paper examines the results of in-situ U-value measurements across 36 traditional uninsulated solid walls and early mass concrete construction in Ireland. As the retrofit of poorly performing buildings becomes increasingly important, establishing an accurate thermal performance baseline for common solid wall types is essential. Existing assumptions in Ireland are based on default U-values used for Energy Performance Certificates or on calculated values derived using BR443 or ISO 6946 with reference thermal conductivities, while no large-scale dataset of in-situ U-values for Irish solid wall construction currently exists. This paper introduces a new dataset for such walls and compares it to the associated default and calculated U-values. Brick walls < 325 mm performed on average 36.8% better than default assumptions, while brick walls ≥ 325 mm aligned on average reasonably well with the defaults. However, the results ranged from −18.7 to +33.1% of the defaults. Stone walls performed on average 7.2% better than the defaults but with substantial variability at −54.8 to +40.8%. In contrast, mass concrete walls performed significantly worse than assumed (+37.2%), while an earthen wall exceeded expectations by 21.5%. The findings demonstrate that in-situ U-value measurements provide a valuable, non-invasive means of improving the accuracy of thermal performance assessments for traditional solid walls. Full article

Occupancy detection is fundamental to the operational intelligence of smart buildings, driving critical functions in energy management, HVAC automation, and physical security. While modern Deep Learning (DL) models have achieved high accuracy in parsing complex environmental sensor data, they remain highly vulnerable to adversarial examples, imperceptibly perturbed inputs designed to deceive neural networks. These vulnerabilities pose severe real-world risks, ranging from energy sabotage, in which systems heat empty rooms, to critical security breaches in which intruders go undetected. To address this security gap, we propose ADS-Guard, a novel Adversarial Detection and Sanitization (ADS) framework rooted in sequence-to-sequence autoencoder purification. Unlike standard denoising techniques, ADS-Guard incorporates a latent consistency regularization mechanism that encourages alignment between clean and adversarial representations in the latent feature space. We evaluated ADS-Guard using a comprehensive experimental pipeline comprising five distinct DL architectures (LSTM, GRU, 1D-CNN, MLP, and Transformer) across three diverse datasets: (1) The UCI Occupancy dataset (20,699 samples) for standard binary detection; (2) Building59 dataset (7200 samples) for three-class occupancy-level classification (Low, Medium, High); and (3) Room Occupancy dataset (10,129 samples), representing a highly imbalanced binary occupancy-detection task. We evaluate ADS-Guard against both Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks across diverse occupancy datasets and model architectures. We further assess the framework under adaptive white-box attacks and compare its performance with FGSM-based and PGD-based adversarial training baselines. Our results demonstrate that adversarial attacks can substantially degrade occupancy-detection performance across datasets and model architectures. ADS-Guard consistently improves robustness relative to undefended models against both FGSM and PGD attacks, recovering a substantial portion of the lost performance in binary occupancy tasks and providing meaningful gains in the more challenging multi-class setting. Furthermore, ADS-Guard remains effective under stronger adaptive threat models while providing a practical retraining-free defense that can be integrated with existing occupancy-detection systems without modifying downstream classifiers. Full article

The valorization of waste cooking oil (WCO) offers a sustainable pathway for improving building’s energy efficiency while supporting circular economy principles. This study developed a novel shape-stabilized bio-based phase change material (Bb-PCM) derived from WCO fatty acids for passive thermal regulation of building envelopes. Purified fatty acids were obtained through filtration, saponification, acidification, and solvent purification. The resulting Bb-PCM was then incorporated into a natural clay–cellulose supporting matrix containing four activated-carbon loading levels using a direct impregnation method. The composites were characterized using spectroscopic, thermal, and microstructural techniques. Differential scanning calorimetry under nitrogen at 2 °C min⁻¹ showed melting temperatures of 34–35 °C and melting latent heats of 35.2–45.9 J g⁻¹. Thermogravimetric analysis confirmed thermal stability below 100 °C, while microstructural characterization demonstrated differences in matrix densification and structural ordering among the composite formulations investigated. The composite containing 25 wt.% activated carbon exhibited the highest melting latent heat (45.9 J g⁻¹) and favorable thermal conductivity (0.29 W m⁻¹ K⁻¹), representing the optimum composite formulation that balances thermal storage capacity, heat transfer, and structural stability. Outdoor evaluation demonstrated stable thermal performance, reducing indoor temperatures by approximately 2 °C, indicating strong potential for sustainable passive cooling applications in buildings. Full article

This study assessed the perceived effectiveness of sustainable building technologies and design strategies in improving building energy efficiency within the South African construction industry. Adopting a quantitative research approach, data were collected from 118 construction professionals in the Free State Province, South Africa, using a structured survey questionnaire administered through purposive sampling. Data analysis was conducted using SPSS, employing descriptive statistics, reliability analysis, correlation analysis, and Principal Component Analysis (PCA). The findings indicate that all investigated technologies and design strategies positively contribute to building energy efficiency and are significantly interrelated. The strong correlations among these technologies, supported by PCA results explaining 76.85% of the total variance through two principal components, suggest that sustainable building technologies are most effective when implemented as an integrated system rather than as isolated interventions. This implies that integrating energy-efficient technologies such as passive design strategies, renewable energy systems, green infrastructure, and intelligent building management systems can yield greater energy performance benefits than deploying individual technologies independently. These findings offer practical guidance for construction professionals and policymakers aiming to promote energy-efficient buildings. While limited to the Free State Province, the study provides valuable empirical evidence on sustainable building technologies within the South African context. Full article

The twentieth (20th) century represents a radical transformation in architectural history. Unlike previous centuries, characterized by gradual stylistic continuity, it witnessed a succession of theoretical ruptures, technological innovations, material discoveries, and experimental design paradigms. Each movement emerged by questioning the assumptions of its predecessors, generating an experimentation laboratory that transformed the relationship between buildings, resources, climate, and society. Despite this exceptional intellectual and technological legacy, 20th-century architecture is still predominantly interpreted through its technological shortcomings, while the historical evolution of its contributions to environmental sustainability and energy efficiency is still lacking. This study reconstructs the historical genealogy of environmental sustainability and energy efficiency across the principal architectural movements of the 20th century. Through a comparative analysis of design theories and architectural principles, it identifies environmental and energy paradigms, drawbacks, and design lessons. Environmental sustainability evolved around material durability, adaptability, resource optimization, contextual integration, and life cycle thinking, whereas energy efficiency developed through passive climatic strategies, building morphology, envelope performance, daylight, natural ventilation, and environmental control. The study demonstrates that many of the principles underpinning contemporary sustainable and energy-efficient architecture originated during the 20th century. Approaches such as Parametricism, Computational Design, Biomimetic Architecture, Climate-Responsive Design, Circular Architecture, and Regenerative Design extend these concepts through new scientific understanding, digital technologies, and environmental performance objectives. Full article

Recently, the Internet of Things (IoT) has evolved into the Artificial Intelligence of Things (AIoT) through its combination with artificial intelligence (AI) technology and has become capable of providing intelligent services in all industrial sectors. Globally, energy consumption within buildings is continuously increasing alongside the advancement of IT and AI technologies. Since this increase is attributed to various causes—ranging from large-scale climate change to small-scale indoor environmental factors (air quality) and health factors—research aimed at reducing indoor energy consumption is actively underway. In particular, in the home environment where people spend a significant portion of their day, maintaining indoor air quality (IAQ) is critical for health, and energy conservation in heating, ventilation, and air conditioning (HVAC) systems is essential. In Korea, the number of single-person households is increasing and was expected to reach 36.1% of all households by 2024, leading people to live in increasingly smaller homes. This study aimed to verify residents using contactless facial recognition to prevent pandemics such as COVID-19 and to provide comfortable indoor air quality. Resident facial recognition was performed by identifying residents’ faces in images captured by the Pi camera using OpenCV’s Haar feature-based cascade classifier. Indoor air quality measurements were conducted in four indoor locations, measuring various environmental factors (PM_2.5, CO₂, etc.) based on environmental sensors and the IoT. Furthermore, to manage indoor air quality, AI was utilized based on the measurement data to classify the four spaces, with a success rate of 96%. Additionally, considering the indoor area of the experimental environment (97 m²), it was confirmed that operating a 70 W air purifier only when the resident is indoors can reduce power consumption by approximately 33–75% compared to running it 24 h a day. Full article

University dormitories in cold climate regions face the dual challenges of high heating energy consumption and poor outdoor pedestrian comfort during winter. Existing studies on university dormitories have primarily focused on individual building performance optimization, while insufficient attention has been paid to the optimization of dormitory cluster layouts and their multi-objective performance. To address this gap, this study establishes a parametric multi-objective optimization framework to simultaneously minimize building energy use intensity, minimize wind speed at pedestrian height, and maximize outdoor thermal comfort. Based on three floor area ratio scenarios, 24 dormitory prototypes are extracted from three building typologies: row-type buildings, detached buildings, and enclosed buildings. The optimization process was implemented on the Grasshopper platform using the NSGA-II algorithm. Cluster analysis is conducted on the Pareto front, and Pearson correlation analysis is applied to investigate the relationships between six urban morphological parameters and the three optimization objectives. The results indicate that: (1) enclosed buildings (E-1 type) and detached buildings (D-1 type) dominate the Pareto-optimal solution set; (2) high-FAR buildings are predominantly distributed in the northeastern part of the site, while public spaces are concentrated in the central-southern area; (3) correlation analysis indicates that shape coefficient (SC) exhibits the strongest correlations with the three objectives; and (4) compared with dominated solutions, Pareto-optimal solutions reduce WS by 10.46% and EUI by 6.57%, while improving UTCI by 0.05 °C. This study provides quantitative decision-making support for efficient planning and low-carbon design of university dormitory clusters in cold climate regions. Full article

Green roofs have emerged as a critical nature-based solution for urban climate mitigation, offering potential for carbon sequestration alongside thermal regulation and stormwater management. This literature review synthesizes recent research (2020–2025) on green roof carbon dynamics, with emphasis on temperate climate zones and methodological approaches relevant to environmental impact assessment. We systematically analyzed 47 peer-reviewed studies published between 2020 and 2025 through comprehensive database searches, focusing on substrate composition effects, vegetation type performance, seasonal variation patterns, and Life Cycle Assessment methodologies. Key findings from extensive systems in maritime and temperate–arid climates reveal that substrate organic carbon typically dominates total carbon storage, significantly exceeding plant biomass contributions. Extensive green roofs demonstrate a wide range of annual carbon fluxes—from initial net emissions of +20.2 g C m⁻² yr⁻¹ during establishment phases to substantial net sequestration rates reaching up to −1762 g CO₂ m⁻² yr⁻¹ in mature systems—depending strongly on substrate age, vegetation type, and local climate conditions. Native grass and forb mixtures consistently outperform Sedum monocultures in long-term carbon storage through enhanced root biomass and substrate organic matter accumulation. Substrate depth, composition, and moisture retention capacity emerge as primary controls on carbon balance, with recycled waste materials showing promise for enhanced storage. Life cycle assessment studies indicate that indirect carbon savings from reduced building energy consumption frequently exceed direct biological sequestration by one to two orders of magnitude. However, significant methodological heterogeneity, limited long-term monitoring datasets, and geographic gaps—particularly for Central and Eastern European temperate zones—constrain robust comparative analysis and transferability of findings. This review identifies critical research priorities, including standardized carbon accounting frameworks, dynamic life cycle assessments incorporating temporal sequestration trajectories, multi-decadal monitoring programs, and region-specific validation studies for temperate continental climates similar to Romania’s Cfb/Dfb zones. Full article

Foundation models for time series forecasting have recently been applied to energy prediction tasks, where they can produce accurate forecasts without task-specific training. This study investigates the impact of two types of covariates, meteorological variables and calendar-based day type indicators, on the forecasting performance of Chronos-2, a state-of-the-art foundation model, in building energy consumption prediction. Using real-world monitoring data from two non-residential buildings at the ENEA Research Centre in Portici, Italy, we systematically evaluate seven configurations combining past and future covariates across multiple observation window lengths (7–28 days). Future meteorological covariates are derived from historical weather forecasts rather than observed weather data, ensuring that the evaluation reflects realistic operational forecasting conditions. The results show that incorporating day type indicators as both past and future covariates consistently delivers the highest forecasting accuracy, reducing CV-RMSE from 14.58% for the covariate-free baseline to 10.41% with a 28-day observation window. A day-stratified analysis further reveals that these improvements are concentrated on regime transition days, for which recent load history alone provides limited information about the operating conditions of the day being forecast. By contrast, meteorological variables, whether obtained from weather forecasts or historical observations, yield only marginal performance gains, suggesting that calendar-driven operational schedules are the primary determinants of energy demand in the buildings considered. These findings provide practical guidance for deploying foundation models in real-world energy building management systems and show that covariate selection is a key determinant of forecasting performance. Full article

With urbanisation resulting in increased demand for indoor comfort, HVAC (heating, ventilation, and air-conditioning) systems, particularly air handling units (AHUs), are essential for indoor climate control. The advent of big data and artificial intelligence (AI) has opened new avenues for enhanced safety and reliability in HVAC operations. Hence, this study focused on the predictive performance evaluation of AHUs, which is receiving less attention compared to its fault detection and optimal control issues. Utilising real-time operational data from the Oak National Laboratory, the proposed model employs multi-task learning (MTL) to refine prediction accuracy for AHU return air properties, including temperature, moisture content, and power consumption. This is achieved without allowing any single task to dominate others during the training phase. Moreover, the model introduces an ensemble approach that synergises the capabilities of the different MTL algorithms using a boosting technique via a gradient boosting regression tree (GBRT). The study evaluated four MTL weighting strategies: average loss (MTL), geometric loss (MTL-GL), dynamic weighting (MTL-DW), and uncertainty weighting (MTL-UW). The baseline MTL achieved the highest accuracy for environmental variables, with an MSE of 0.0022, MAE of 0.0391, and an R² score of 0.98 for return air temperature. In contrast, MTL-UW performed best for RTU power prediction, attaining an MSE of 6702, MAE of 45.6620, and an R² of 0.88. In this light, we developed an ensemble multi-task learning (E-MTL) framework. The novel strategy significantly surpassed conventional data-driven baselines, as the E-MTL models consistently outperformed individual MTL variants, achieving the lowest RMSE and MAE across all target variables. This study bridges advanced artificial intelligence and environmental stewardship by mapping precise multi-task predictions directly onto building sustainability metrics. The paper culminates by showcasing the significant role of the proposed model as a metric for AHU performance evaluation and its contribution to smart decision-making in a real-world context. By enabling actionable operational foresight, the proposed model serves as an important framework for lowering HVAC operational carbon footprints, minimising net energy demands, and advancing sustainability goals. Essentially, the developed model is poised to facilitate optimal decision-making regarding HVAC components and foster proactive strategies to ensure consistent operation and extend the lifespan of HVAC systems. Full article