Search Results (224)

Reflective materials, characterized by high albedo and thermal emissivity, offer effective passive cooling strategies for reducing building energy demand. While prior studies have developed thermal transfer models validated under laboratory conditions or conducted short-term monitoring in non-air-conditioned spaces, their effectiveness in operational buildings remains underexplored. This research evaluates the change in cooling energy demand and indoor thermal comfort in a retrofitted office building with reflective materials in China’s Hot Summer and Cold Winter (HSCW) zone. The calibrated WUFI^®Plus simulations show that the application of reflective roof and window materials can result in an 11.3% reduction in cooling energy demand. Moreover, occupant surveys indicate improved thermal perception, with the mean Thermal Comfort Vote (TCV) rising from −0.75 to −0.30, thermal acceptability increasing from 0.10 to 0.35, and 80% of occupants reporting cooler conditions. These subjective results align with simulated Predicted Mean Vote (PMV) reductions (0.82 → 0.74), confirming the retrofit’s effectiveness. While the energy savings are more modest than those reported in Mediterranean climates, they are generally consistent with the energy saving ratios of buildings in the HSCW region as evaluated by previous studies. This study provides a framework for assessing retrofits in occupied buildings with reflective materials and indicates the practicality of such retrofits as an economic, low-disruption strategy for upgrading aging office building stocks in the HSCW zone. Full article

Against the global backdrop of energy transition, the precise assessment of urban rooftop photovoltaic (PV) system capacity is recognized as crucial for optimizing the energy structure and enhancing the sustainable utilization efficiency of spatial resources. Publicly available aerial imagery is characterized by non-orthorectified issues; direct utilization is known to lead to geometric distortions in rooftop PV and errors in capacity prediction. To address this, a dual-optimization framework is proposed in this study, integrating monocular vision-based 3D reconstruction with a lightweight linear model. Leveraging the orthogonal characteristics of building structures, camera self-calibration and 3D reconstruction are achieved through geometric constraints imposed by vanishing points. Scale distortion is suppressed via the incorporation of a multi-dimensional geometric constraint error control strategy. Concurrently, a linear capacity-area model is constructed, thereby simplifying the complexity inherent in traditional multi-parameter fitting. Utilizing drone oblique photography and Google Earth public imagery, 3D reconstruction was performed for 20 PV-equipped buildings in Wuhan City. Two buildings possessing high-precision field survey data were selected as typical experimental subjects for validation. The results demonstrate that the 3D reconstruction method reduced the mean absolute percentage error (MAPE)—used here as an estimator of measurement uncertainty—of PV area identification from 10.58% (achieved by the 2D method) to 3.47%, while the coefficient of determination (R²) for the capacity model reached 0.9548. These results suggest that this methodology can provide effective technical support for low-cost, high-precision urban rooftop PV resource surveys. It has the potential to significantly enhance the reliability of energy planning data, thereby contributing to the efficient development of urban spatial resources and the achievement of sustainable energy transition goals. Full article

Green roofs are effective passive strategies for enhancing building energy efficiency and indoor thermal comfort, particularly in response to climate change. This study presents an experimental and numerical assessment of an ultra-lightweight, soil-free green roof system for Mediterranean climates. In situ thermal monitoring was carried out on two identical test rooms in Rome (Italy), comparing the green roof to a traditional tiled roof under winter conditions. Results revealed a 45% reduction in thermal transmittance. These data were used to calibrate a dynamic TRNSYS 18 model and then applied to annual simulations of energy demand and indoor comfort across different roof configurations, including expanded polystyrene-insulated reference roofs. The model was calibrated in accordance with ASHRAE Guideline 14, achieving an MBE within ±10% and a CV(RMSE) within ±30% for hourly data, ensuring the simulation’s reliability. The green roof reduced cooling energy demand by up to 58.5% and heating demand by 11.6% relative to the uninsulated reference case. Compared to insulated roofs, it maintained similar winter performance while achieving summer operative temperature reductions up to 0.99 °C and PPD decreases up to 2.94%. By combining field measurements with calibrated simulations, this work provides evidence of the green roof’s effectiveness as a passive retrofit solution for Mediterranean buildings. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

Achieving the construction sector’s dual carbon objectives necessitates scaling green energy adoption in new residential buildings. The current literature critically overlooks four unresolved problems: oversimplified penalty mechanisms, ignoring escalating regulatory costs; static subsidies misaligned with market maturity evolution; systematic exclusion of innovation feedback from energy suppliers; and underexplored behavioral evolution of building owners. This study establishes a government–suppliers–owners evolutionary game framework with dynamically calibrated policies, simulated using MATLAB multi-scenario analysis. Novel findings demonstrate: (1) A dual-threshold penalty effect where excessive fines diminish policy returns due to regulatory costs, requiring dynamic calibration distinct from fixed-penalty approaches; (2) Market-maturity-phased subsidies increasing owner adoption probability by 30% through staged progression; (3) Energy suppliers’ cost-reducing innovations as pivotal feedback drivers resolving coordination failures, overlooked in prior tripartite models; (4) Owners’ adoption motivation shifts from short-term economic incentives to environmentally driven decisions under policy guidance. The framework resolves these gaps through integrated dynamic mechanisms, providing policymakers with evidence-based regulatory thresholds, energy suppliers with cost-reduction targets, and academia with replicable modeling tools. Full article

Traditional Chinese Kang heating systems have been used for over two millennia in northern China, yet their thermal efficiency and optimal design parameters lack scientific validation. This study aims to establish evidence-based guidelines for Kang-to-room area ratios to enhance thermal comfort and energy efficiency in rural architecture. We conducted direct measurements in a controlled experimental house (24 m²) in Huludao City, collecting temperature and humidity data from Kang surfaces and interior spaces over five-day periods. A benchmark curve for heat flux density was developed based on specific fuelwood consumption rates (1 kg/m²). TRNSYS simulations were employed to validate experimental data and analyze thermal performance in the historical Qingning Palace (352 m²) at Shenyang Imperial Palace. The benchmark curve demonstrated high accuracy with a Mean Absolute Error of 0.46 °C and Root Mean Square Error of 0.53 °C when compared to measured temperatures over the 48 h validation period; these values are well within acceptable ranges for calibrated thermal models. Simulations revealed optimal thermal comfort conditions when heat dissipation parameters were scaled appropriately for building size. The optimal Kang-to-room area ratio ranges from 0.28 to 0.69, with the existing Qingning Palace ratio (0.34) falling within this range, validating traditional design wisdom. This research provides a scientific foundation for sustainable architectural practices, bridging traditional knowledge with contemporary thermal engineering principles for both heritage preservation and modern rural construction applications. Full article

This study provides a detailed dataset from two modern homes constructed inside an environmentally controlled chamber. These data are used to carefully calibrate a dynamic thermal simulation model of these homes. The calibrated models show good agreement with measurements taken under controlled conditions. The two case study homes, “The Future Home” and “eHome2”, were constructed within the University of Salford’s Energy House 2.0, and high-quality data were collected over eight days. The calibration process involved updating U-values, air permeability rates, and modelling refinements, such as roof ventilation, ground temperatures, and sub-floor void exchange rates, set as boundary conditions. Results demonstrated a high level of accuracy, with performance gaps in whole-house heat transfer coefficient reduced to 0.5% for “The Future Home” and 0.6% for “eHome2”, falling within aggregate heat loss test uncertainty ranges by a significant amount. The study highlights the improved accuracy of calibrated dynamic thermal simulation models, compared to results from the steady-state Standard Assessment Procedure model. By providing openly accessible calibrated models and a clearly defined methodology, this research presents valuable resources for future building performance modelling studies. The findings support the UK’s transition to dynamic modelling approaches proposed in the recently introduced Home Energy Model approach, contributing to improved prediction of energy efficiency and aligning with goals for zero carbon ready and sustainable housing development. Full article

The city of Al Ain is a fast-developing area. With building typology varying from low-rise to mid-rise, sustainable design in buildings is needed. As the majority of the city’s population is Emirati Citizens, the percentage of expats is increasing. The expats tend to live in mid-rise buildings. One of the central midrise areas is AL Ain Square. This study aims to investigate how an optimized mashrabiya pattern can impact the energy and the Predicted Mean Vote (PMV) in a 3-bedroom apartment, fully oriented to the south, of an expat family. The methodology is as follows: case study selection, Weather analysis, Modeling/Validation of the base case scenario, Optimization of the mashrabiya pattern, Simulation of various scenarios, and Results. Analyzing the selected case study is the initial step of the methodology. This analysis begins with the district, building typology, and the chosen apartment. The weather analysis is relevant for using the mashrabiya (screen device) and the need to improve energy consumption and thermal comfort. The modeling of the base case shall be performed in Rhino Grasshopper. The validation is based on a one-year electricity bill provided by the owner. The optimization of mashrabiya patterns is an innovative process, where various designs are compared and then optimized to select the most efficient pattern. The solutions to the selected scenarios will then yield the results of the optimal scenario. This study is relevant to industry, academia, and local authorities as an innovative approach to retrofitting buildings. Additionally, the research presents a creative vision that suggests optimized mashrabiya patterns can significantly enhance energy savings, with the hexagonal grid configuration demonstrating the highest efficiency. This finding highlights the potential for geometry-driven shading optimization tailored to specific climatic and building conditions. Contrasting earlier mashrabiya studies that assess one static pattern, we couple a geometry-agnostic evolutionary solver with a utility-calibrated EnergyPlus model to test thousands of square, hexagonal, and triangular permutations. This workflow uncovers a previously undocumented non-linear depth perforation interaction. It validates a hexagonal screen that reduces annual cooling energy by 12.3%, establishing a replicable, grid-specific retrofit method for hot-arid apartments. Full article

Coarse-grained beaches consisting of gravel, pebbles, and cobbles play a crucial role in coastal protection. On the Croatian Adriatic coast, there are artificial gravel pocket beaches created for recreational and protective purposes. However, these beaches are subject to constant morphological changes due to natural forces and human intervention. This study investigates the morphodynamics of artificial gravel pocket beaches, focusing on berm formation and crest build-up processes characteristic for low to moderate wave conditions. Despite mimicking natural formations, artificial beaches require regular maintenance due to sediment shifts dominantly caused by wave action and storm surges. Structure-from-Motion (SfM) photogrammetry and UAV-based surveys were used to monitor morphological changes on the artificial gravel pocket beach Ploče (City of Rijeka). The XBeach-Gravel model, originally adapted to simulate the effects of high-energy waves, was calibrated and validated to analyze low to moderate wave dynamics on gravel pocket beaches. The calibration includes adjustments to the inertia coefficient (ci), which influences sediment transport by shear stress at the bottom; the angle of repose (ϕ), which controls avalanching and influences sediment transport on sloping beds; and the bedload transport calibration coefficient (γ), which scales the transport rates linearly. By calibrating XBeach-G for low to moderate wave conditions, this research improves the accuracy of the model for the cases of morphological responses “berm formation” and “crest build-up”. Full article

Expanded polystyrene (EPS) foam is widely used in energy-absorbing structures for packaging applications; however, its mechanical behavior under dynamic loading conditions remains insufficiently characterized. To address this, the dynamic responses of EPS foam used in television packaging were first examined experimentally through drop tests. Building on these findings, a rate-sensitive constitutive model was developed to incorporate tensile damage mechanisms and tension–compression asymmetry, enabling unified modeling of both tensile and compressive deformation in complex structural applications. The proposed model was calibrated using standardized tension, compression, and shear tests, and subsequently employed to simulate three-point bending and dynamic compression scenarios involving EPS foam components. The simulation results demonstrated favorable agreement with experimental observations, confirming the accuracy and robustness of the proposed constitutive model in predicting the dynamic mechanical behavior of EPS foam. Full article

This study develops a calibrated multiscale simulation of three lightweight industrial warehouses located in Tecámac, Mexico, to evaluate the dual role of rooftop photovoltaic (PV) arrays as renewable energy generators and passive thermal modifiers. Dynamic energy models were developed using EnergyPlus via Ladybug Tools v. 1.8.0 and calibrated against 2021 real-world electricity billing data, following ASHRAE Guideline 14. Statistical analyses conducted in RStudio v2024.12.1 Build 563 confirmed significant passive cooling effects induced by PV integration, achieving up to 15.3 °C reductions in peak indoor operative temperatures and improving thermal comfort rates by approximately 10 percentage points. While operational energy savings were evident, the primary focus of this research was on the multiscale modeling of thermal performance enhancement in composite metallic-PV roofing systems under semi-arid climatic conditions. These results provide new insights into computational approaches for optimizing passive thermal performance in lightweight industrial envelopes. Full article

Amid global climate change and energy constraints, green building represents a critical pathway for the construction industry’s decarbonization, yet its market development mechanisms remain underexplored. This study constructs a tripartite evolutionary game model analyzing dynamic interactions among consumers, construction enterprises, and the government, proposing a “Technology–Reputation–Policy” synergistic framework. The results reveal that the green building market equilibrium depends on government subsidy probabilities, subsidy amounts, stakeholder benefits, and cost reduction. While incentives significantly impact consumer behavior, their influence on enterprises is limited due to rapid strategic evolution. Government subsidy decisions balance reputational gains against expenditures, with market stability maintainable during subsidy reduction when technology-driven cost decreases reach threshold levels. Empirical calibration using Shenzhen data suggests a phased strategy: initial consumer subsidy prioritization, followed by technology cost-reduction alliances with gradual enterprise subsidy phase-outs, culminating in consumer subsidy reduction to ensure market self-sustainability. This study aims to explore “why” subsidy mechanisms effectively drive sustainable construction practices and the interaction mechanism among consumers, enterprises, and the government. These findings provide theoretical foundations and actionable policies for advancing green building markets under China’s dual carbon goals. Full article

The heat transfer coefficient, or the HTC, is an industry-standard indicator of building energy performance. It is predicated on an assumption that it is of a constant value, and several different methods have been developed to measure and calculate the HTC as a constant. Whilst there are limited variations in the results obtained from these different methods, none of these methods consider a possibility that the HTC could be dynamically variable. Our experimental work shows that the HTC is not a constant. The experimental evidence based on our environmental chambers, which contain detached houses and in which the ambient air temperature can be controlled between −24 °C and +51 °C, with additional relative humidity control and with weather rigs that can introduce solar radiation, rain, and snow, shows that the HTC is dynamically variable. The analysis of data from the fully instrumented and monitored houses in combination with calibrated simulation models and data processing scripts based on genetic algorithm optimization provide experimental evidence of the dynamic variability of the HTC. This research increases the understanding of buildings physics properties and has the potential to change the way the heat transfer coefficient is used in building performance analysis. Full article

Nowadays, reducing energy consumption and obtaining thermal comfort are significant for making educational buildings more climate resilient, more sustainable, and more comfortable. To achieve these goals, a sustainable passive method is that of applying green walls and roofs that provide extra thermal insulation, evaporative cooling, a shadowing effect, and the blockage of wind on buildings. Therefore, the objective of this study is to evaluate the impact of green wall and roof applications on energy consumption and thermal comfort in an educational building. For this purpose, a university building in the Csb climate zone is selected and monitored during one year, as a case study. Then, the case building is modelled in a well-calibrated dynamic building energy simulation tool and twenty-one different plant species, which are mostly used for green walls and roofs, are applied to the envelope of the building in order to determine a reduction in energy consumption and an increase in thermal comfort. The Hedera canariensis gomera (an ivy species) plant is used for green walls due to its aesthetic appeal, versatility, and functional benefits while twenty-one different plants including Ophiopogon japonicus (Mando-Grass), Phyllanthus bourgeoisii (Waterfall Plant), and Phoenix roebelenii (Phoenix Palm) are simulated for the green roof applications. The results show that deploying Hedera canariensis gomera to the walls and Phyllanthus bourgeoisii to the roof could simultaneously reduce the energy consumption by 9.31% and increase thermal comfort by 23.55% in the case building. The authors acknowledge that this study is solely based on simulations due to the high cost of all scenarios, and there are inherent differences between simulated and real-world conditions. Therefore, the future work will be analysing scenarios in real life. Considering the limited studies on the effect of different plant species on energy performance and comfort, this study also contributes to sustainable building design strategies. Full article