Search Results (415)

The coastal sandy land in northeast Hainan Province is typical for this land type, also exhibiting strong sand activity. This study is based on wind speed, wind direction, and sediment transport data obtained at a field meteorological station using an omnidirectional sand accumulation instrument from 2020 to 2024, studying the coastal aeolian environment and sediment transport distribution characteristics in the region. Its findings provide a theoretical basis for comprehensively analyzing the evolution of coastal aeolian landforms and the evaluation and control of coastal aeolian hazards. The research results show the following: (1) The annual average threshold wind velocity for sand movement in the study area is 6.84 m/s, and the wind speed frequency (frequency of occurrence) is 51.54%, dominated by easterly (NE, ENE) and southerly (S, SSE) winds. (2) The drift potential (DP) refers to the potential amount of sediment transported within a certain time and spatial range, and the annual drift potential (DP) and resultant drift potential (RDP) of Mulan Bay from 2020 to 2024 were 550.82 VU and 326.88 VU, respectively, indicating a high-energy wind environment. The yearly directional wind variability index (RDP/DP) was 0.59, classified as a medium ratio and indicating blunt bimodal wind conditions. The yearly resultant drift direction (RDD) was 249.45°, corresponding to a WSW direction, indicating that the sand in Mulan Bay is generally transported in the southwest direction. (3) When the measured data extracted from the sand accumulation instrument in the study area from 2020 to 2024 were used for statistical analysis, the results showed that the total sediment transport rate (the annual sediment transport of the observation section) in the study area was 110.87 kg/m·a, with the maximum sediment transport rate in the NE direction being 29.26 kg/m·a. These results suggest that when sand fixation systems are constructed for relevant infrastructure in the region, the construction direction of protective forests and other engineering measures should be perpendicular to the net direction of sand transport. Full article

Photovoltaic–thermal (PVT) technology has attracted considerable attention for its ability to significantly improve solar energy conversion efficiency by simultaneously providing electricity and heat during the day. PVT technology serves a purpose in condensers and subcoolers for passive cooling in refrigeration systems at night. In our previous work, we proposed a switchable film-insulated photovoltaic–thermal–passive cooling (PVT-PC) module to address the structural incompatibility between diurnal and nocturnal modes. However, the performance of the proposed module strongly depends on two key design parameters: the structural height and the vacuum level of the air cushion. In this study, a numerical model of the proposed module is developed to examine the impact of design and meteorological parameters on its all-day performance. The results show that diurnal performance remains stable across different structural heights, while nocturnal passive cooling power shows strong dependence on vacuum level and structural height, achieving up to 103.73 W/m² at 10 mm height and 1500 Pa vacuum, which is comparable to unglazed PVT modules. Convective heat transfer enhancement, induced by changes in air cushion shape, is identified as the primary contributor to improved nocturnal cooling performance. Wind speed has minimal impact on electrical output but significantly enhances thermal efficiency and nocturnal convective cooling power, with a passive cooling power increase of up to 31.61%. In contrast, higher sky temperatures degrade nocturnal cooling performance due to diminished radiative exchange, despite improving diurnal thermal efficiency. These findings provide fundamental insights for optimizing the structural design and operational strategies of PVT-PC systems under varying environmental conditions. Full article

The Complementary Relationship (CR) principle of evapotranspiration provides an efficient approach for estimating actual evapotranspiration (ET_a), owing to its simplified computation and effectiveness in utilizing meteorological factors. Accurate estimation of actual evapotranspiration (ET_a) is crucial for understanding surface energy and water cycles, especially in permafrost regions. This study aims to evaluate the applicability of two Complementary Relationship (CR)-based methods—Bouchet’s in 1963 and Brutsaert’s in 2015—for estimating ETa on the Qinghai–Tibetan Plateau (QTP), using observations from Eddy Covariance (EC) systems. The potential evapotranspiration (ET_p) was calculated using the Penman equation with two wind functions: the Rome wind function and the Monin–Obukhov Similarity Theory (MOST). The comparison revealed that Bouchet’s method underestimated ET_a during frozen soil periods and overestimated it during thawed periods. In contrast, Brutsaert’s method combined with the MOST yielded the lowest RMSE values (0.67–0.70 mm/day) and the highest correlation coefficients (r > 0.85), indicating superior performance. Sensitivity analysis showed that net radiation (Rn) had the strongest influence on ET_a, with a daily sensitivity coefficient of up to 1.35. This study highlights the improved accuracy and reliability of Brutsaert’s CR method in cold alpine environments, underscoring the importance of considering freeze–thaw dynamics in ET modeling. Future research should incorporate seasonal calibration of key parameters (e.g., ε) to further reduce uncertainty. Full article

Under the dual challenges of global energy crisis and climate change, the building sector, as a major carbon emitter consuming 33% of global primary energy, has seen its energy efficiency optimization become a critical pathway towards achieving carbon neutrality goals. The Window-to-Wall Ratio (WWR), serving as a core parameter in building envelope design, directly influences building energy consumption, with its optimized design playing a decisive role in balancing natural daylighting, ventilation efficiency, and thermal comfort. This study focuses on the traditional One-Seal dwellings (Yikeyin) in Kunming, China, establishing a dynamic wind field-thermal environment coupled analysis framework to investigate the impact mechanism of window dimensions (WWR and aspect ratio) on indoor thermal comfort under natural wind conditions in transitional climate zones. Utilizing the Grasshopper platform integrated with Ladybug, Honeybee, and Butterfly plugins, we developed parametric models incorporating Kunming’s Energy Plus Weather meteorological data. EnergyPlus and OpenFOAM were employed, respectively, for building heat-moisture balance calculations and Computational Fluid Dynamic (CFD) simulations, with particular emphasis on analyzing the effects of varying WWR (0.05–0.20) on temperature-humidity, air velocity, and ventilation efficiency during typical winter and summer weeks. Key findings include, (1) in summer, the baseline scenario with WWR = 0.1 achieves a dynamic thermal-humidity balance (20.89–24.27 °C, 65.35–74.22%) through a “air-permeable but non-ventilative” strategy, though wing rooms show humidity-heat accumulation risks; increasing WWR to 0.15–0.2 enhances ventilation efficiency (2–3 times higher air changes) but causes a 4.5% humidity surge; (2) winter conditions with WWR ≥ 0.15 reduce wing room temperatures to 17.32 °C, approaching cold thresholds, while WWR = 0.05 mitigates heat loss but exacerbates humidity accumulation; (3) a symmetrical layout structurally constrains central ventilation, maintaining main halls air changes below one Air Change per Hour (ACH). The study proposes an optimized WWR range of 0.1–0.15 combined with asymmetric window opening strategies, providing quantitative guidance for validating the scientific value of vernacular architectural wisdom in low-energy design. Full article

Forecasting the energy consumption of different consumers became an important procedure with the creation of the European Electricity Market. This study presents a methodology for 24-hour ahead prediction of the energy consumption, which is suitable for application in animal husbandry facilities, such as pig farms. To achieve this, 24 individual models are trained using artificial neural networks that forecast the energy production 1 to 24 h ahead. The selected features include power consumption over the last 72 h, time-based data, average, minimum, and maximum daily temperatures, relative humidities, and wind speeds. The models’ Normalized mean absolute error (NMAE), Normalized root mean square error (NRMSE), and Mean absolute percentage error (MAPE) vary between 16.59% and 19.00%, 22.19% and 24.73%, and 9.49% and 11.49%, respectively. Furthermore, the case studies showed that in most situations, the forecasting error does not exceed 10% with several cases up to 25%. The proposed methodology can be useful for energy managers of animal farm facilities, and help them provide a better prognosis of their energy consumption for the Energy Market. The proposed methodology could be improved by selecting additional features, such as the variation of the controlled meteorological parameters over the last couple of days and the schedule of technological processes. Full article

Accurate short-term weather forecasting plays a vital role in disaster response, agriculture, and energy management, where timely and reliable predictions are essential for decision-making. Graph neural networks (GNNs), known for their ability to model complex spatial structures and relational data, have achieved remarkable success in meteorological forecasting by effectively capturing spatial dependencies among distributed weather stations. However, most existing GNN-based approaches rely on pairwise station connections, limiting their capacity to represent higher-order spatial interactions. Moreover, their dependence on supervised learning makes them vulnerable to spatial heterogeneity and temporal non-stationarity. This paper introduces a novel spatial–temporal pretraining framework, Hypergraph-enhanced Meteorological Pretraining (HyMePre), which combines hypergraph neural networks with self-supervised learning to model high-order spatial dependencies and improve generalization across diverse climate regimes. HyMePre employs a two-stage masking strategy, applying spatial and temporal masking separately, to learn disentangled representations from unlabeled meteorological time series. During forecasting, dynamic hypergraphs group stations based on meteorological similarity, explicitly capturing high-order dependencies. Extensive experiments on large-scale reanalysis datasets show that HyMePre outperforms conventional GNN models in predicting temperature, humidity, and wind speed. The integration of pretraining and hypergraph modeling enhances robustness to noisy data and improves generalization to unseen climate patterns, offering a scalable and effective solution for operational weather forecasting. Full article

Hybridization of solar and wind energy sources is a promising solution to enhance the dispatch capability of renewables. The complementarity of wind and solar radiation, as well as the sharing of transmission lines and other infrastructures, can notably benefit the deployment of renewable power. Mapping of hybrid solar–wind potential can help identify new emplacements or existing power facilities where an extension with a hybrid system might work. This paper presents an analysis of a hybrid solar–wind potential by considering a reference power plant of 40 MW in the Iberian Peninsula and comparing the hybrid and non-hybrid energy generated. The generation of energy is estimated using SAM for a typical meteorological year, using PVGIS and ERA5 meteorological information as input. Modeling the hybrid plant in relation to individual PV and wind power plants minimizes the dependence on technical and economic input data, allowing for the expression of potential hybridization analysis in relative numbers through maps. Correlation coefficient and capacity factor maps are presented here at different time scales, showing the complementarity in most of the spatial domain. In addition, economic analysis in comparison with non-hybrid power plants shows a reduction of around 25–30% in the LCOE in many areas of interest. Finally, a sizing sensitivity analysis is also performed to select the most beneficial sharing between PV and wind. Full article

With the growing global demand for clean energy, the accuracy of wind-power forecasting plays a vital role in ensuring the stable operation of power systems. However, wind-power generation is significantly influenced by meteorological conditions and is characterized by high uncertainty and multiscale fluctuations. Traditional recurrent neural network (RNN) and long short-term memory (LSTM) models, although capable of handling sequential data, struggle with modeling long-term temporal dependencies due to the vanishing gradient problem; thus, they are now rarely used. Recently, Transformer models have made notable progress in sequence modeling compared to RNNs and LSTM models. Nevertheless, when dealing with long wind-power sequences, their quadratic computational complexity (O(L²)) leads to low efficiency, and their global attention mechanism often fails to capture local periodic features accurately, tending to overemphasize redundant information while overlooking key temporal patterns. To address these challenges, this paper proposes a wind-power forecasting method based on dimension-transformed collaborative multidimensional multiscale attention (DTCMMA). This method first employs fast Fourier transform (FFT) to automatically identify the main periodic components in wind-power data, reconstructing the one-dimensional time series as a two-dimensional spatiotemporal representation, thereby explicitly encoding periodic features. Based on this, a collaborative multidimensional multiscale attention (CMMA) mechanism is designed, which hierarchically integrates channel, spatial, and pixel attention to adaptively capture complex spatiotemporal dependencies. Considering the geometric characteristics of the reconstructed data, asymmetric convolution kernels are adopted to enhance feature extraction efficiency. Experiments on multiple wind-farm datasets and energy-related datasets demonstrate that DTCMMA outperforms mainstream methods such as Transformer, iTransformer, and TimeMixer in long-sequence forecasting tasks, achieving improvements in MSE performance by 34.22%, 2.57%, and 0.51%, respectively. The model’s training speed also surpasses that of the fastest baseline by 300%, significantly improving both prediction accuracy and computational efficiency. This provides an efficient and accurate solution for wind-power forecasting and contributes to the further development and application of wind energy in the global energy mix. Full article

Accurate extraction of representative operating conditions is crucial for optimizing systems in renewable energy applications. This study proposes a novel framework that combines the Parzen window estimation method, ideal for nonparametric modeling of wind, solar, and load datasets, with a game theory-based time scale selection mechanism. The novelty of this work lies in integrating probabilistic density modeling with multi-indicator evaluation to derive realistic operational profiles. We first validate the superiority of the Parzen window approach over traditional Weibull and Beta distributions in estimating wind and solar probability density functions. In addition, we analyze the influence of key meteorological parameters such as wind direction, temperature, and solar irradiance on energy production. Using three evaluation metrics, the main result shows that a 3-day representative time scale offers optimal accuracy when determined through game theory methods. Validation with real-world data from Inner Mongolia confirms the robustness of the proposed method, yielding low errors in wind, solar, and load profiles. This study contributes a novel 3-day typical profile extraction method validated on real meteorological data, providing a data-driven foundation for optimizing energy storage systems under renewable uncertainty. This framework supports energy sustainability by ensuring realistic modeling under renewable intermittency. Full article

The worldwide air pollution situation reveals significant environmental challenges. In addition to being a major contributor to the deterioration of air quality, particulate matter (PM) is also an important factor affecting the performance of solar energy systems given its ability to decrease light transmission to solar panels. As part of our research, the present investigation involves monitoring concentrations of PM using a high-performance optical instrument, the in situ calibration protocol of which is described in detail. For the city of Rabat, observations revealed significant variations in concentrations between day and night, with peaks observed around 8 p.m. correlating with high relative humidity and low wind speeds, and the highest levels recorded in February with a monthly average value reaching 75 µm/m³. In addition, an experimental protocol was set up for an analysis of the elemental composition of particles in the same city using SEM/EDS, providing a better understanding of their morphology. To assess the impact of meteorological variables on PM concentrations in two distinct climatic environments, a database from the city of Marrakech for the year 2024 was utilized. Overall, the distribution of PM values during this period did not fluctuate significantly, with a monthly average value not exceeding 45 µm/m³. The random forest method identified the most influential variables on these concentrations, highlighting the strong influence of the type of environment. The findings provide crucial information for the modeling of solar installations’ soiling and for improving understanding of local air quality. Full article

Boao Jade Beach, on the east coast of Hainan Island, is a typical sandy beach and is one of the areas where typhoons frequently land in Hainan. This study examined wind speed, wind direction, and sediment transport data obtained from field meteorological stations and omnidirectional sand accumulation instruments from 2020 to 2024 to study the coastal aeolian environment and sediment transport distribution characteristics in the region. The findings provide a theoretical basis for comprehensive analyses of the evolution of coastal aeolian landforms and the evaluation and control of coastal aeolian hazards. The research results showed the following: (1) The annual average threshold wind velocity for sand movement in the study area was 6.13 m/s, and the wind speed frequency was 20.97%, mainly dominated by easterly winds (NNE, NE) and southerly winds (S). (2) The annual drift potential (DP) and resultant drift potential (RDP) of Boao Jade Belt Beach from 2020 to 2024 were 125.99 VU and 29.59 VU, respectively, indicating a low-energy wind environment. The yearly index of directional wind variability (RDP/DP) was 0.23, which is classified as a small ratio and indicates blunt bimodal wind conditions. The yearly resultant drift direction (RDD) was 329.41°, corresponding to the NNW direction, indicating that the sand on Boao Jade Belt Beach is generally transported in the southwest direction. (3) When the measured data from the sand accumulation instrument in the study area from 2020 to 2024 were used for a statistical analysis, the results showed that the total sediment transport rate in the study area was 39.97 kg/m·a, with the maximum sediment transport rate in the S direction being 17.74 kg/m·a. These results suggest that, when sand fixation systems are constructed for relevant infrastructure in the region, the direction of protective forests and other engineering measures should be perpendicular to the net direction of sand transport. Full article

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field. Full article

The aerial measurement of wind speed and direction is important for the development of the low-altitude economy, meteorology, climate research, and renewable energy systems. Existing UAV-based wind measurements, whether instrument-based or flight-dynamic-based, consistently exhibit bias and significant errors, limiting their reliability for precise wind estimation. This study introduces a machine learning (ML) approach to improve the accuracy of the wind speed and direction estimation using UAVs. The proposed method leverages data from sensors onboard UAV platforms, combined with advanced ML algorithms trained on ground-truth measurements obtained through high-resolution LiDAR systems. The experiments reveal that incorporating a 10 s smoothing window yields a root mean square error (RMSE) value of 0.39 m/s for the wind speed (horizontal) and an even lower bias (≤0.069 m/s) when using a 60 s smoothing window, representing a marked improvement over traditional techniques. These results are particularly promising at longer smoothing windows (>50 s), where the ML-based approach achieves superior accuracy compared to LiDAR measurements. The findings underscore the potential of integrating machine learning with UAV-based wind measurement systems to achieve higher precision and reliability in wind characterization. Full article

Wind power generation provides clean and renewable electricity for microgrids, but its intermittency and uncertainty pose challenges to the operation and power quality of microgrids. Accurate forecasting is conducive to maintaining the stability of microgrids and improving the efficiency of energy management. Therefore, this study proposes a dual-branch frequency transformer (DBFformer), which leverages multi-scale spectral transformation and the multi-head attention mechanism to improve the prediction accuracy of microgrid wind turbines. In the encoder, two parallel branches are designed to extract the global features and local dynamic features of meteorological data based on Fourier transform and wavelet transform, respectively. In the decoder, an exponential smoothing attention (ESA) mechanism and a frequency attention (FA) mechanism are introduced to extract multi-scale temporal features. ESA enhances the model’s ability to capture long-term growth trends, whereas FA focuses on periodic pattern recognition. Additionally, to further optimize the model’s performance, a periodic weight coefficient (PWC) mechanism is employed to dynamically adjust the fusion coefficients to further improve the fusion performance and prediction accuracy. The factors influencing wind turbine power are analyzed; then, the most relevant factors are selected for the experiment. According to the experimental results, the proposed DBFformer accurately predicts the output power of wind turbines and exhibits superior performance. It achieves lower mean squared error (MSE) and mean absolute error (MAE) values than other state-of-the-art models. Specifically, its MSE values are 0.195, 0.216, 0.457, and 0.583, and the corresponding MAE values are 0.318, 0.335, 0.474, and 0.503 for different rated wind turbines. Furthermore, comprehensive ablation experiments validate that the dual-branch structure, frequency transformations, dual-attention mechanisms, and PWC module have a positive impact on the proposed model. Therefore, this research offers a novel and effective approach for wind power forecasting and supports the broader goal of integrating clean energy into microgrids. Full article

The southern Baltic Sea is characterized by highly variable weather conditions, particularly in autumn and winter, when storms, strong westerly winds, and temporary sea ice formation disrupt maritime operations. This study presents a climatographic overview and evaluates key hydrometeorological factors that influence the safe and efficient use of liquefied natural gas (LNG) as bunkering fuel in the region. The analysis draws on long-term meteorological and hydrological datasets (1971–2020), including satellite observations and in situ measurements. It identifies operational constraints, such as wind speed, wave height, visibility, and ice cover, and assesses their impact on LNG logistics and terminal functionality. Thresholds for safe operations are evaluated in accordance with IMO and ISO safety standards. An ice severity forecast for 2011–2030 was developed using the ECHAM5 global climate model under the A1B emission scenario, indicating potential seasonal risks to LNG operations. While baseline safety criteria are generally met, environmental variability in the region may still cause temporary disruptions. Findings underscore the need for resilient port infrastructure, including anti-icing systems, heated transfer equipment, and real-time environmental monitoring, to ensure operational continuity. Integrating weather forecasting into LNG logistics supports uninterrupted deliveries and contributes to EU goals for energy diversification and emissions reduction. The study concludes that strategic investments in LNG infrastructure—tailored to regional climatic conditions—can enhance energy security in the southern Baltic, provided environmental risks are systematically accounted for in operational planning. Full article