Search Results (591)

Hakka traditional vernacular dwellings embody regionally specific climatic adaptation strategies. This study takes the Meizhou Guangludi enclosed house as a case study to evaluate its climate adaptability with longevity and passive survivability factors of the Hakka three-hall enclosed house under subtropical climatic conditions. A mixed research method is employed, integrating visualized parametric modeling analysis and on-site measurement comparisons to quantify wind, temperature, solar radiation/illuminance, and humidity, along with human comfort zone limits and building environment. The results reveal that nature erosion in the Guangludi enclosed house is the most pronounced during winter and spring, particularly on exterior walls below 2.8 m. Key issues include bulging, spalling, molding, and fractured purlins caused by wind-driven rain, exacerbated by low wind speeds and limited solar exposure, especially at test spots like the E8–E10 and N1–N16 southeast and southern walls below 1.5 m. Fungal growth and plant intrusion are severe where surrounding trees and fengshui forests restrict wind flow and lighting. In terms of passive survivability, the Guangludi enclosed house has strong thermal insulation and buffering, aided by the Huatai mound; however, humidity and day illuminance deficiencies persist in the interstitial spaces between lateral rooms and the central hall. To address these issues, this study proposes strategies such as adding ventilation shafts and flexible partitions, optimizing patio dimensions and window-to-wall ratios, retaining the spatial layout and Fengshui pond to enhance wind airflow, and reinforcing the identified easily eroded spots with waterproofing, antimicrobial coatings, and extended eaves. Through parametric simulation and empirical validation, this study presents a climate-responsive retrofit framework that supports the sustainability and conservation of the subtropical Hakka enclosed house. Full article

Biomimetic structures inspired by evolutionary optimized biological systems offer promising solutions to overcome current limitations in passive daytime radiative cooling (PDRC) technology, which efficiently scatters solar radiation through atmospheric windows and radiates surface heat into space without additional energy consumption. While structural biomimicry provides excellent optical performance and feasibility, its complex manufacturing and high costs limit scalability due to micro–nano fabrication constraints. Material-based biomimicry, utilizing environmentally friendly and abundant raw materials, offers greater scalability but requires improvements in mechanical durability. Adaptive biomimicry enables intelligent regulation with high responsiveness but faces challenges in system complexity, stability, and large-scale integration. These biologically derived strategies provide valuable insights for advancing radiative cooling devices. This review systematically summarizes recent progress, elucidates mechanisms of key biological structures for photothermal regulation, and explores their application potential across various fields. It also discusses current challenges and future research directions, aiming to promote deeper investigation and breakthroughs in biomimetic radiative cooling technologies. Full article

School buildings in hot-humid climates encounter considerable difficulties in balancing energy use and thermal comfort due to this environment, necessitating optimized design strategies to reduce energy consumption while enhancing occupant comfort. This study presents sustainable design strategies for educational structures in hot-humid regions, aiming to optimize energy efficiency and thermal comfort for environmental preservation and occupant welfare. The present work introduces a multi-objective optimization framework for window design in school buildings situated in hot-humid climates, targeting a balance between Energy Use Intensity (EUI) and Thermal Comfort Time Ratio (TCTR). Exploring multi-objective optimization through NSGA-II genetic algorithms, the study conducts Sobol sensitivity analysis for parameter assessment and applies Gaussian Process Regression (GPR) for effective model validation, identifying optimal window configurations that reduce energy consumption while enhancing thermal comfort. It finds that the Window-to-Wall Ratio (WWR) and Solar Heat Gain Coefficient (SHGC) are the most significant factors, with WWR and SHGC accounting for 28.1% and 23.7% of the variance in EUI and TCTR, respectively. The results reveal a non-linear trade-off between the objectives, with the Balanced Solution offering a practical compromise: a 6.7% decrease in energy use and a 14.3% enhancement in thermal comfort. The study examined various ranges of window parameters, including WWR (0.1–0.50), SC (0.20–0.80), K (1.0–2.5 W·m⁻²·K⁻¹), SHGC (0.1–0.4), Shading width (0.3–2.0 m), and Shading angle (0°–90°). The recommended compromise, known as the Balanced Solution, suggests optimal values as follows: WWR = 0.40, SC = 0.30, SHGC = 0.40, K = 1.2 W·m⁻²·K⁻¹, Shading width = 1.22 m, and Shading angle = 28°. The GPR model exhibited high predictive precision, with R² values of 0.91 for EUI and 0.95 for TCTR, underscoring the framework’s effectiveness. This research offers actionable insights for designing energy-efficient and comfortable school buildings in hot-humid climates, enriching sustainable architectural design knowledge. Full article

Accurate rice yield prediction remains a major challenge due to the complex and nonlinear interactions among cultivar, environment, and phenology. Existing approaches often focus on analyzing individual components while ignoring their interdependencies, which results in limited predictive accuracy and generalizability. To overcome these problems, this study proposes a novel interpretable random forest model that integrates cultivar, environmental, and phenological dimensions. Different from conventional approaches, the proposed method incorporates a factor-combination optimization strategy to identify the most effective information for yield estimation. For analysis, 24 key determinants were screened, including the geographical location, meteorological conditions, phenological events, and cultivar traits. The RF models were also evaluated when built with seven factor combinations. The results reveal the following: (1) Meteorological conditions play a dominant role during the vegetative growth period, including net solar radiation (r = 0.42), daylength (r = 0.38), and thermal summation (r = 0.29). On the other hand, thermal summation (r = 0.28), mean minimum temperature (r = −0.23), and mean temperature (r = −0.20) are most relevant during the reproductive growth period. (2) The full-factor model achieves optimal performance (RMSE = 601.45 kg/ha and MAE = 454.98 kg/ha, R² = 0.77). (3) Importance analysis reveals that meteorological factors provide the greatest contribution (53.59%), followed by phenological factors (20.39%), geographical factors (17.20%), and cultivar (8.82%), respectively. The results also reveal that threshold effects of key determinants on yield, and identify mid-April to early May as the optimal sowing window. These findings demonstrate that integrating cultivar, environment, and phenology factors creates a powerful predictive model for rice yields. Full article

YVO₄ based phosphors have aroused extensive interest in the field of optoelectronics due to their good chemical stability and unique luminescence properties. However, commercialization of YVO₄ phosphors requires high luminescence intensity, enhanced conversion efficiency, and a wide excitation spectrum. In this work, Eu³⁺, Ba²⁺, Bi³⁺ co-doped YVO₄ was prepared by the sol–gel method. The XRD of YVO₄: 5%Eu³⁺, 5%Ba²⁺, 0.5%Bi³⁺ phosphor analysis confirms the pure tetragonal phase, with a fairly large size of approximately 100 nm for the optimal composition. And the SEM and TEM revealed well-dispersed spherical nanoparticles with sizes of 100–120 nm. The introduction of Ba²⁺ ions enhanced the luminescence intensity, while the incorporation of Bi³⁺ ions improved the excitation width of the phosphor. The resulting YVO₄: 5%Eu³⁺, 5%Ba²⁺, 0.5%Bi³⁺ phosphor exhibited a 1.39-times broader excitation bandwidth and a 2.72-times greater luminescence intensity at 618 nm compared to the benchmark YVO₄: 5% Eu³⁺ sample. Additionally, the transmittance of the films in the 350 nm to 800 nm region exceeded 85%. The YVO₄: 5%Eu³⁺, 5%Ba²⁺, 0.5%Bi³⁺ film effectively absorbed ultraviolet light and converted it to red emission, enabling potential applications in solar cell window layers, dye-sensitized cell luminescence layers, and solar cell packaging glass. Full article

Thermal analyses of space station windows in Low Earth Orbit (LEO) are usually focused on a specific orbiting scenario, namely the one with the longest eclipse duration and the greatest temporal fluctuation in solar radiation, that is typically considered the most critical for satellites. However, for windows made of materials such as acrylic glass, whose mechanical properties are sensitive to temperature, alternative orbital configurations can lead to significantly higher heating than previously estimated. In particular, this study identifies a critical condition, occurring when the orbit plane is highly inclined with respect to the Sun rays, so that one surface is exposed to prolonged and intense radiation. Here, it is demonstrated that, under this scenario, the Sun-facing surface may reach temperatures above the glass transition point, risking material degradation and structural failure, while the opposite surface experiences low temperatures, potentially leading to embrittlement. These findings emphasize the need to evaluate transient thermal behavior under diverse orbital geometries when designing large windows for future space stations. The results highlight key trade-offs between material properties, glazing dimensions, and orbital parameters to ensure safety and performance. Full article

Urbanization in China and the proliferation of high-rise office buildings have led to increased demand for daylighting and thermal comfort. These requirements often result in reliance on active systems, including heating, cooling, and artificial lighting, which increase energy consumption. Existing studies have often focused on individual cases or room-scale models, which makes it difficult to generalize findings to the design of various high-rise office building types. Therefore, in this study, parametric prototype building models for high-rise office buildings were developed based on surveys of completed and under-construction projects. These surveys reflected actual design practices and were used to support systematic performance evaluation and typology-level optimization. Building performance was simulated using Grasshopper and Honeybee to generate large-scale datasets, and stacking ensemble learning models were used as surrogate predictors for energy use, daylighting, and thermal comfort. Multi-objective optimization was conducted using the non-dominated sorting genetic algorithm III (NSGA-III), followed by strategy formulation. The results revealed the following: (1) the proposed prototype model establishes clear parameter ranges for geometry, envelope design, and thermal performance, offering reusable models and data; (2) the stacking ensemble model outperforms individual models, improving the coefficient of determination (R²) by 0.5–16.1%, with mean squared error (MSE) reductions of 4.4–70.6%, and mean absolute error (MAE) reductions of 2.8–45.8%; (3) space length, aspect ratio, usable area ratio, window U-value, and solar heat gain coefficient (SHGC) were identified as primary performance drivers; and (4) optimized solutions reduced energy use by 3.79–11.81% and enhanced daylighting comfort by 40.16–50.32% while maintaining thermal comfort. The proposed framework provides localized, data-driven guidance for early-stage performance optimization in high-rise office building design. Full article

In Pakistan and most other developing nations, the residential building sector is one of the highest energy-consuming domains. The residential sector has the highest share of 50% of final electricity use of the country. Though Energy Conservation Building Codes (ECBC-2023) provide structured energy guidelines, no work has been performed to quantify the actual energy-saving potential of code-compliant retrofits in residential buildings. This study investigates the performance of ECBC-compliant retrofitting strategies for residential buildings under Pakistan’s diverse climatic conditions. The Passive House Planning Package (PHPP), a validated simulation tool, was used to assess energy performance improvements through building envelope interventions such as thermal insulation, solar shading, window glazing, and optimal orientation. Field data were collected from three representative cities, Multan (hot desert), Taxila (humid subtropical), and Quetta (cold semi-arid), to simulate both conventional and energy-efficient building scenarios. The results showed substantial seasonal energy savings in all three climates. During the heating period, energy savings were 48%, 50%, and 60% for Taxila, Multan, and Quetta, respectively. Similarly, energy savings during the cooling season were 44%, 33%, and 16%. Life cycle economic analysis revealed that these retrofits yielded Net Present Values (NPVs) of USD 752 (Taxila), USD 1226 (Multan), and USD 1670 (Quetta) over a 30-year period, with discounted payback periods ranging from 6 to 10 years. Furthermore, a life cycle assessment demonstrated that retrofitted buildings yielded up to 26% reduction in overall carbon emissions, combining both embodied and operational sources. The findings highlight that ECBC-2023 is not only a technically viable solution for energy savings but also financially attractive in residential retrofitting. By incorporating localized climate responsiveness into ECBC-compliant building design, the study provides a practical roadmap for achieving Pakistan’s energy efficiency goals. Additionally, the outcomes serve as a basis for informing policy initiatives, supporting building code adaptation, and raising public awareness of sustainable housing practices. Full article

This study investigates the influence of rear setback geometry and façade design parameters on microclimatic conditions, indoor thermal comfort, and energy performance in multi-story residential buildings in hot arid climates, addressing the growing need for climate-responsive design in regions with extreme temperatures and high solar radiation. Despite increasing interest in sustainable strategies, the combined effects of urban geometry and building envelope design remain underexplored in these environments. A coupled simulation framework was developed, integrating ENVI-met for outdoor microclimate modeling with Design Builder and EnergyPlus for dynamic building performance analysis. A total of 270 simulation scenarios were examined, combining three rear setback aspect ratios (1.5, 1.87, and 2.25), three window-to-wall ratios (10%, 20%, and 30%), three glazing types (single-, double-, and triple-pane), and two wall insulation states, using customized weather files derived from microclimate simulations. Global sensitivity analysis using rank regression and multivariate adaptive regression splines identified the glazing type as the most influential parameter (sensitivity index ≈ 0.99), especially for upper floors. At the same time, higher aspect ratios reduced peak Physiological Equivalent Temperature (PET) by up to 5 °C and decreased upper-floor cooling loads by 37%, albeit with a 9.3% increase in ground-floor cooling demand. Larger window-to-wall ratios lowered lighting energy consumption by up to 35% but had minimal impact on cooling loads, whereas wall insulation reduced annual cooling demand by up to 29,441 kWh. The results emphasize that integrating urban morphology with optimized façade components, particularly high-performance glazing and suitable aspect ratios, can significantly improve thermal comfort and reduce cooling energy consumption in hot arid residential contexts. Full article

This paper is a result of the search for design assumptions for a sensor to detect oil dispersed in the sea waters (oil-in-water emulsions). Our approach is based on analyzing changes in the underwater solar radiance (L) field caused by the presence of oil droplets in the water column. This method would enable the sensor to respond to the presence of oil contaminants dispersed in the surrounding environment, even if they are not located directly at the measurement point. This study draws on both literature sources and the results of current numerical modeling of the spread of solar light in the water column to account for both downward and upward irradiance (Es). The core principle of the analysis involves simulating the paths of a large number of virtual solar photons in a seawater model defined by spatially distributed Inherent Optical Properties (IOPs). The IOPs data were taken from the literature and pertain to the waters of the southern Baltic Sea. The optical properties of the oil used in the model correspond to crude oil extracted from the Baltic shelf. The obtained results were compared with previously published spectral analyses of an analogous polluted sea model, considering vertical downward radiance, vertical upward radiance, and downward and upward irradiance. It was found that the optimal wavelength ratio of 555/412, identified for these quantities, is also applicable to scalar irradiance. The findings indicate that the most effective way to determine this index is by measuring it using a sensor with its window oriented in the direction of upward-traveling light. Full article

This article evaluates the effectiveness of various energy retrofit solutions—both passive and active—for reducing energy demand and improving indoor thermal conditions in apartments of typical multifamily buildings in Central Europe, considering current and future climate conditions. This study combines computer-based co-simulations (EnergyPlus and CONTAM) with in situ thermal measurements to identify challenges in maintaining indoor thermal conditions and to support model validation. Key indicators include the number of thermal discomfort hours and heating and cooling demand. The evaluated strategies include passive measures (wall insulation, green or reflective roofs, roller blinds, solar protective glazing) and active solutions such as mechanical cooling. The comfort operative temperature range of the adaptive model is adopted as a measure of thermal comfort and the energy demand in individual apartments as a measure of energy efficiency. The simulation results showed that solar protective glazing combined with a reflective roof reduced thermal discomfort hours by up to 95%, while modern windows alone decreased them by 90% and lowered heating demand by 18%. In contrast, typical passive solutions such as internal blinds or balconies were significantly less effective, reducing discomfort hours by only 11–42%. These findings highlight that, while no single retrofit measure is universally optimal, well-selected passive or hybrid strategies can substantially improve summer comfort, maintain winter efficiency, and reduce long-term reliance on energy-intensive cooling systems in aging multifamily housing. Full article

Space layout design has been recognized as a key technical challenge in achieving low-energy and low-carbon rural houses. Adjustment of room location can influence building energy performance and is subject to both meteorological and design parameters. To elucidate the impact of these parameters on the energy saving potential of room relocation (ESR), this study investigated rural houses in Northwest China using dynamic simulations to compare the relative energy saving rates (RES) associated with three types of single heated room location changes: from the west side to the middle (WM), from the east side to the middle (EM), and from the west side to the east side (WE). Simulations were conducted across different climate regions (Lhasa, Xi’an, Tuotuohe, and Altay) and design parameters, including exterior wall U-value, building orientation (BO), building height (BH), and window-to-wall ratio (WWR). Additionally, the maximum differences in energy consumption (MD) among six layouts with multiple heated rooms were assessed. The results demonstrated that ESR varied significantly with room relocation. The ranges of RES_WM, RES_EM, and RES_WE were −7.89% to 13.20%, −7.82% to 10.25%, and −2.29% to 3.36%, respectively. The MD values ranged from 2.42% to 15.01%. For single heated rooms, including direct normal irradiance (I_dn), the difference between east and west solar-air temperature (△T_sa), outdoor dry bulb temperature (T_e), exterior wall heat transfer coefficient (U), and WWR significantly influenced RES_WM and RES_EM. The ranking of the factor contributions was U > △T_sa > I_dn > T_e > WWR for RES_WM and U > I_dn > △T_sa > T_e > WWR for RES_EM. In the case of RES_WE, I_dn, △T_sa, T_e, exterior wall U value, and BO had significant effects, ranking I_dn > △T_sa > T_e > BO > U. For MD, the key influencing factors were I_dn, △T_sa, T_e, exterior wall U value, and WWR, which were ranked as I_dn > △T_sa > U > T_e > WWR. The effects of design parameters on ESR varied under different climatic conditions. In high-temperature regions, the exterior wall U-value had a stronger influence on the ESR of WE. In regions with larger |△T_sa|, BO exerted a more pronounced effect on the ESR of WE. In regions characterized by high temperatures and radiation, WWR and BH significantly influenced the ESR of WM and EM. Similarly, in these regions, WWR and BH exhibited a greater impact on MD. Finally, among the meteorological parameters, I_dn and △T_sa were significantly correlated with ESR (p < 0.01). These findings provide a valuable reference for the energy-efficient layout design of rural houses in Northwest China and cold regions and support the future development of intelligent and automated rural residential spatial layout design. Full article

The neural architecture search technique is used to automate the engineering of neural network models. Several studies have applied this approach, mainly in the fields of image processing and natural language processing. Its application generally requires very long computing times before converging on the optimal architecture. This study proposes a hybrid approach that combines transfer learning and dynamic search space adaptation (TL-DSS) to reduce the architecture search time. To validate this approach, Long Short-Term Memory (LSTM) models were designed using different evolutionary algorithms, including artificial bee colony (ABC), genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO), which were developed to predict trends in global horizontal irradiation data. The performance measures of this approach include the performance of the proposed models, as evaluated via RMSE over a 24-h prediction window of the solar irradiance data trend on one hand, and CPU search time on the other. The results show that, in addition to reducing the search time by up to 89.09% depending on the search algorithm, the proposed approach enables the creation of models that are up to 99% more accurate than the non-enhanced approach. This study demonstrates that it is possible to reduce the search time of a neural architecture while ensuring that models achieve good performance. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber resilience. Real-time electricity prices are treated as dynamic decision variables influenced by charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management in renewable-rich grid environments. Full article

Accurately predicting the ionosphere’s Total Electron Content (TEC) is significant for ensuring the regular operation of satellite navigation and communication systems and space weather prediction. To further improve the accuracy of TEC prediction, this paper proposes a TEC prediction model based on the grid-optimized Gate Recurrent Unit (GRU). This model has the following main features: (1) it uses statistical learning methods to interpolate the missing data of TEC observations; (2) it constructs a sliding time window by using the multi-dimensional time series features of two types of solar activity indices to support modeling; (3) It adopts grid search combined with optimization of network depth, time step length, and other hyperparameters to significantly enhance the model’s ability to extract the characteristics of the ionospheric 11-year cycle and seasonal variations. Taking the EGLIN station as an example, the proposed model is verified. The experimental results show that the root mean square error of the GRU model during the period from 2019 to 2020 was 0.78 TECU, which was significantly lower than those of the CCIR, URSI, and statistical machine learning models. Compared with the other three models, the RMSE error of the GRU model was reduced by 72.73%, 72.64%, and 57.38%, respectively. The above research verifies the advantages of the proposed model in predicting TEC and provides a new idea for ionospheric modeling. Full article