Search Results (799)

Accurate global irradiance (GI) forecasting is essential for improving photovoltaic (PV) energy management, stabilizing renewable power systems, and enabling intelligent control in solar-powered applications, including electric vehicles and smart grids. The highly stochastic and non-stationary nature of solar radiation, influenced by rapid atmospheric fluctuations and seasonal variability, makes short-term GI prediction a challenging task. To overcome these limitations, this work introduces a new hybrid forecasting architecture referred to as WTX–CBO, which integrates a Wavelet Transform (WT)-based decomposition module, an encoder–decoder Transformer model, and an XGBoost regressor, optimized using the Chaotic Billiards Optimizer (CBO) combined with the Adam optimization algorithm. In the proposed architecture, WT decomposes solar irradiance data into multi-scale components, capturing both high-frequency transients and long-term seasonal patterns. The Transformer module effectively models complex temporal and spatio-temporal dependencies, while XGBoost enhances nonlinear learning capability and mitigates overfitting. The CBO ensures efficient hyperparameter tuning and accelerated convergence, outperforming traditional meta-heuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). Comprehensive experiments conducted on real-world GI datasets from diverse climatic conditions demonstrate the outperformance of the proposed model. The WTX–CBO ensemble consistently outperformed benchmark models, including LSTM, SVR, standalone Transformer, and XGBoost, achieving improved accuracy, stability, and generalization capability. The proposed WTX–CBO framework is designed as a high-accuracy decision-support forecasting tool that provides short-term global irradiance predictions to enable intelligent energy management, predictive charging, and adaptive control strategies in solar-powered applications, including solar electric vehicles (SEVs), rather than performing end-to-end vehicle or photovoltaic power simulations. Overall, the proposed hybrid framework provides a robust and scalable solution for short-term global irradiance forecasting, supporting reliable PV integration, smart charging control, and sustainable energy management in next-generation solar systems. Full article

Accurate forecasting of photovoltaic (PV) power generation is essential for mitigating weather-induced variability and maintaining power-system stability. This study aims to improve PV power forecasting accuracy by enhancing the quality of numerical weather prediction (NWP) inputs rather than modifying forecasting model structures. Specifically, systematic errors in temperature, wind speed, and solar radiation data produced by the Unified Model–Local Data Assimilation and Prediction System (UM-LDAPS) are corrected using a Model Output Statistics (MOS) approach. A case study was conducted for a 20 kW rooftop PV system in Buan, South Korea, comparing forecasting performance before and after MOS application using a random forest-based PV forecasting model. The results show that MOS significantly improves meteorological input accuracy, reducing the root mean square error (RMSE) of temperature, wind speed, and solar radiation by 38.1–62.3%. Consequently, PV power forecasting errors were reduced by 70.0–78.7% across lead times of 1–6 h, 7–12 h, and 19–24 h. After MOS correction, the normalized mean absolute percentage error (nMAPE) remained consistently low at approximately 7–8%, indicating improved forecasting robustness across the evaluated lead-time ranges. In addition, an economic evaluation based on the Korean renewable energy forecast-settlement mechanism estimated an annual benefit of approximately 854 USD for the analyzed 20 kW PV system. A complementary valuation using an NREL-based framework yielded an annual benefit of approximately 296 USD. These results demonstrate that improving meteorological data quality through MOS enhances PV forecasting performance and provide measurable economic value. Full article

The accurate short-term forecasting of global horizontal irradiance (GHI) is essential to optimizing the operation and integration of solar energy systems into the power grid. This study evaluates the performance of the Weather Research and Forecasting (WRF) model in predicting GHI over a 48 h forecast horizon at an Italian site: the ENEA Casaccia Research Center, near Rome (central Italy). The instantaneous GHI provided by WRF at model output frequency was post-processed to derive the mean GHI over the preceding hour, consistent with typical energy forecasting requirements. Furthermore, a decomposition model was applied to estimate direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) from the forecasted GHI. These derived components enable the estimation of solar energy yield for both concentrating solar power (CSP) and photovoltaic (PV) technologies (on tilted surfaces) by accounting for direct, diffuse, and reflected components of solar radiation. Model performance was evaluated against ground-based pyranometer and pyrheliometer measurements by using standard statistical indicators, including RMSE, MBE, and correlation coefficient (r). Results demonstrate that WRF-based forecasts, combined with suitable post-processing and decomposition techniques, can provide reliable 48 h predictions of GHI and DNI at the study site, highlighting the potential of the WRF framework for operational solar energy forecasting in the Mediterranean region. Full article

This article presents a database focused on measuring the experimental performance of a pilot parabolic trough collector (PTC) combined with the meteorological conditions corresponding to the installation site. Water was chosen as the fluid to recirculate through the PTC circuit. The data were recorded between August and September, assuming that global radiation was adequate for use in the concentration process. The database comprises seven experimental tests, which contain variables such as time, inlet temperature, outlet temperature, ambient temperature, global radiation, diffuse radiation, wind direction, wind speed, and volumetric flow rate. Based on the data obtained from this pilot PTC system, it is possible to provide relevant information for the installation and construction of large-scale solar collectors. Furthermore, the climatic conditions considered allow key factors in the design of multiple collectors to be determined, such as the type of arrangement (series or parallel) and manufacturing materials. In addition, the data collected in this study are key to validating future theoretical models of the PTC. Finally, considering the real operating conditions of a PTC in conjunction with meteorological variables could also be useful for predicting the system’s thermal performance using artificial intelligence-based models. Full article

The geographic distribution patterns of microorganisms and their underlying mechanisms are central topics in microbiology, crucial for understanding ecosystem functioning and predicting responses to global change. Cryoconite absorbs solar radiation to form cryoconite holes, and because it lies within these relatively deep holes, it faces limited interference from surrounding ecosystems, often being seen as a fairly enclosed environment. Moreover, it plays a dominant role in the biogeochemical cycling of key elements such as carbon and nitrogen, making it an ideal model for studying large-scale microbial biogeography. In this study, we analyzed bacterial communities in cryoconite across a transcontinental scale of glaciers to elucidate their biogeographical distribution and community assembly processes. The cryoconite bacterial communities were predominantly composed of Proteobacteria, Cyanobacteria, Bacteroidota, and Actinobacteriota, with significant differences in species composition across geographical locations. Bacterial diversity was jointly driven by geographical and anthropogenic factors: species richness exhibited a hump-shaped relationship with latitude and was significantly positively correlated with the Human Development Index (HDI). The significant positive correlation may stem from nutrient input and microbial dispersal driven by high-HDI regions’ industrial, agricultural, and human activities. Beta diversity demonstrated a distance-decay pattern along spatial gradients such as latitude and geographical distance. Analysis of community assembly mechanisms revealed that stochastic processes predominated across continents, with a notable scale dependence: as the spatial scale increased, the role of deterministic processes (heterogeneous selection) decreased, while stochastic processes (dispersal limitation) strengthened and became the dominant force. By integrating geographical, climatic, and anthropogenic factors into a unified framework, this study enhances the understanding of the spatial-scale-driven mechanisms shaping cryoconite bacterial biogeography and emphasizes the need to prioritize anthropogenic influences to predict the trajectory of cryosphere ecosystem evolution under global change. Full article

In photovoltaic (PV) power generation, the intermittency and uncertainty caused by meteorological factors pose challenges to grid operations. Accurate PV power prediction is crucial for optimizing power dispatching and balancing supply and demand. This paper proposes a PV power prediction model based on Density Peak Clustering Algorithm (DPCA)–Crested Porcupine Optimizer (CPO)–Random Forest (RF)–Gated Recurrent Unit (GRU)–Kolmogorov–Arnold Network (KAN). First, the DPCA is used to accurately classify weather conditions according to meteorological data such as solar radiation, temperature, and humidity. Then, the CPO algorithm is established to optimize the factor screening characteristic variables of the RF. Subsequently, a hybrid GRU model with a KAN layer is introduced for short-term PV power prediction. The Shapley Additive Explanation (SHAP) method values evaluating feature importance and the impact of causal features. Compared with other contrast models, the DPCA-CPO-RF-KAN-GRU model demonstrates better error reduction capabilities under three weather types, with an average fitting accuracy R² reaching 97%. SHAP analysis indicates that the combined average SHAP value of total solar radiation and direct solar radiation contributes more than 70%. Finally, the Kernel Density Estimation (KDE) is utilized to verify that the KAN-GRU model has high robustness in interval prediction, providing strong technical support for ensuring the stability of the power grid and precise decision-making in the electricity market. Full article

Pan evaporation (PE) is widely used as an indicator of atmospheric evaporative demand and is relevant to irrigation demand and climate-related hydrological changes. Using daily records from 759 meteorological stations across China during 2002–2018, this study investigated the temporal trends, spatial patterns, and climatic controls of PE across seven major climate zones. Multiple decomposition techniques revealed a dominant annual cycle and a pronounced peak in 2018, while a decreasing interannual trend was observed nationwide. Spatial analyses showed a clear north–south contrast, with the strongest declines occurring in northern China. A random forest (RF) model was employed to quantify the contributions of climatic variables, achieving high predictive performance. RF results indicated that the dominant drivers of PE varied substantially across climate zones: sunshine duration (as a proxy for solar radiation) and air temperature mainly controlled PE in humid regions, while wind speed and relative humidity (RH) exerted stronger influences in arid and semi-arid regions. The widespread decline in northern China is consistent with concurrent changes in wind speed and sunshine duration, together with humidity conditions, which modulate evaporative demand at monthly scales. These findings highlight substantial spatial heterogeneity in PE responses to climate forcing and provide insights for drought assessment and water resource management in a warming climate. Full article

Contemporary climate change, driven by anthropogenic greenhouse gas (GHG) emissions, has raised global temperatures by over 1 °C above pre-industrial levels, profoundly altering Earth’s energy balance. In marine turtles, which exhibit temperature-dependent sex determination (TSD), embryonic sex ratios are highly sensitive to nest temperature. Most studies predicting the effects of climate change on turtle sex ratios have used air temperature or sea surface temperature (SST) as proxies for nest temperature, despite limited empirical validation of this assumption. I question the validity of this approach by examining the physical mechanisms of heat transfer within beach soils, including conduction, convection, and radiation, and how they are modulated by factors such as soil texture, moisture, and solar radiation. The analysis highlights that while GHGs increase air temperature through the greenhouse effect, they do not directly alter incoming solar radiation, the principal driver of subsurface temperature. Furthermore, increased air temperature enhances evaporation and soil drying, reducing thermal conductivity and potentially lowering heat penetration into nesting depths. Consequently, air or SST proxies can misrepresent the actual thermal environment of marine turtle nests, leading to inaccurate or even reverse projections of sex ratios under climate change. A mechanistic approach integrating soil heat dynamics and solar radiation is therefore essential for realistic assessments of TSD responses and conservation planning in a warming world. Full article

The internal temperature of a vehicle is influenced by various factors such as the external environment (temperature, solar radiation, and humidity) and the air conditioning habits of the driver. Even when the air conditioning system is set to a specific temperature, the internal temperature can vary depending on the time, weather, and driver’s manipulation of the system. In this study, we developed and evaluated a deep learning-based vehicle cabin temperature prediction system using CAN (Controller Area Network) data collected from the vehicle and temperature data from thermometers installed on the roof and seats of an electric vehicle (EV). The models used in the temperature prediction system were evaluated by applying various deep learning architectures that consider the characteristics of time series data, and their accuracy was measured using the mean absolute percentage error (MAPE) metric. Additionally, a low-pass filter was applied to the prediction results, which reduced the MAPE from 4.2798% to 4.1433%, indicating an improvement in prediction accuracy. Among the deep learning models, the model with the highest performance achieved an MAPE of 3.5287%, corresponding to an approximate error of 0.88 °C at an actual temperature of 25 °C. The results of this study contribute significantly to enhancing the accuracy and reliability of EV interior temperature predictions, enabling more precise simulations, and improving the thermal comfort and energy efficiency of EVs. The proposed temperature-prediction system is expected to contribute to the comfort of EV users and overall performance of vehicles, thereby strengthening the role of EVs as a sustainable means of transportation. Full article

An accurate estimate of sun irradiance is important for solar energy management in urban areas with complicated atmospheric conditions. The urban solar irradiance (USI) can be predictively researched with a variety of models; however, basing this entirely on one model often leads to other important conditions being omitted. A hybrid framework is suggested in this study, integrating the Perez–Ineichen PI model with a Deep Neural Network (DNN) model for predicting USI in Wuhan, China. The PI model predicts clear-sky irradiance labels based on atmospheric parameters normalized against the National Solar Radiation Database for greater accuracy. The model is trained on the Clear Sky Index with real-time atmospheric parameters gained from ground station measurements and satellite images. Following correlation analysis using bands from Sentinel-2 to find suitable bands for the model, the algorithm was prepared for atmospheric parameters, including cloud cover, aerosol concentration, and surface reflectance, all of which impact solar radiation. The architecture incorporates attention methods for important atmospheric parameters and skip connections for greater training stability. Results from the Deep Neural Network-Selected bands (DNN-S) and Deep Neural Network-All bands (DNN-A) models gave different performances, with the DNN-S model yielding better accuracy with a RMSE of 69.49 W/m² clear-sky, 87.60 W/m² cloudy-sky, and 72.57 W/m² all-sky. The results were validated using hyperspectral imagery, along with cloud mask, solar area, and surface albedo-derived products, confirming that the USI estimates are supported by the high precision and consistency of Sentinel-2-derived irradiance estimates. Full article

In the context of global climate change, quantifying the coupling relationship between forest growth status and soil moisture (SM) remains crucial for predicting ecosystem functional changes. However, systematic analysis of their dynamic coupling mechanisms is currently lacking. To address this gap, we systematically quantified the sensitivity of forest growth status to soil moisture and revealed its spatiotemporal patterns and driving mechanisms based on NDVI, LAI, and GPP data during the growing season of Chinese forests from 2001 to 2020. Results demonstrate that Chinese forests are experiencing a significant “soil moisture desensitization” process (|r| trend < 0, p < 0.05). Although forest growth status increased significantly (p < 0.05), sensitivity to soil moisture declined across 71% of forest areas, with only 26% showing increasing trends. Moreover, the area of soil moisture deficit regions (r > 0, p < 0.05) contracted sharply from 27% to 5%, while surplus regions (r < 0, p < 0.05) expanded by 7%. Attribution analysis reveals that solar radiation (~35%), precipitation (~25%), and atmospheric CO₂ concentration (~17%) represent the dominant factors driving sensitivity trends. This study provides new insights into hydrological response mechanisms of Chinese forests and holds important implications for optimizing ecological management and climate adaptation strategies. Full article

Gallium nitride (GaN) has emerged as one of the most promising wide-bandgap semiconductors for next-generation space photovoltaics. In contrast to conventional III–V compounds such as GaAs and InP, which are highly efficient under terrestrial conditions but suffer from radiation-induced degradation and thermal instability, GaN offers an exceptional combination of intrinsic material properties ideally suited for harsh orbital environments. Its wide bandgap, high thermal conductivity, and strong chemical stability contribute to superior resistance against high-energy protons, electrons, and atomic oxygen, while minimizing thermal fatigue under repeated cycling between extreme temperatures. Recent progress in epitaxial growth—spanning metal–organic chemical vapor deposition, molecular beam epitaxy, hydride vapor phase epitaxy, and atomic layer deposition—has enabled unprecedented control over film quality, defect densities, and heterointerface sharpness. At the device level, InGaN/GaN heterostructures, multiple quantum wells, and tandem architectures demonstrate outstanding potential for spectrum-tailored solar energy conversion, with modeling studies predicting efficiencies exceeding 40% under AM0 illumination. In this review article, the current state of knowledge on GaN materials and device architectures for space photovoltaics has been summarized, with emphasis placed on recent progress and persisting challenges. Particular focus has been given to defect management, doping strategies, and bandgap engineering approaches, which define the roadmap toward scalable and radiation-hardened GaN-based solar cells. With sustained interdisciplinary advances, GaN is anticipated to complement or even supersede traditional III–V photovoltaics in space, enabling lighter, more durable, and radiation-hard power systems for long-duration missions beyond Earth’s magnetosphere. Full article

To elucidate the long-term spatiotemporal patterns and key drivers factors, this study employed a meta-analysis of data from soil containing Potentially Toxic Elements (Cd, As, Cr, Hg, and Pb) in Chinese farmland soils from 2003 to 2025. The geoaccumulation index, the potential ecological risk index, and standard deviation ellipses were used to assess the spatiotemporal evolution of heavy metal accumulation and ecological risk, while the Random forest–SHapley Additive exPlanations (RF-SHAP) method was employed to identify driving mechanisms. At the national scale, Cd and Hg are significantly enriched relative to the background values, whereas As, Cr, and Pb remained at relatively low levels, with enrichment ranked as Cd > Hg > Pb > Cr > As. Cd and Hg indicated mild pollution, but the Sichuan Basin emerged as a hotspot, where Cd reached moderate pollution and showed strong ecological risk, and Hg also exhibited high ecological risk. Over the past two decades, the contamination center shifted from coastal to southwestern inland regions, with an expanded and more dispersed distribution. Since 2017, Cd and Hg pollution levels have stabilized, suggesting that the aggravating trend has been preliminarily curbed. Industrial waste and wastewater discharge, irrigation and fertilization were identified as the primary anthropogenic factors of soil heavy metal accumulation, while climatic factors (temperature, precipitation, and solar radiation) and soil physicochemical properties (pH, clay content, and organic matter) played fundamental roles in spatial distribution and accumulation. Our findings call for targeted predictive research and policies to manage heavy metal risks and preserve farmland sustainability in a changing climate. Full article

Generative adversarial networks (GANs) can rapidly predict urban environmental performance. However, most existing studies focus on a single target and lack cross-performance comparisons under unified conditions. Under unified urban-form inputs and training settings, this study employs the conditional adversarial model pix2pix to predict four targets—the Universal Thermal Climate Index (UTCI), annual global solar radiation (Rad), sunshine duration (SolarH), and near-surface wind speed (Wind)—and establishes a closed-loop evaluation framework spanning pixel, structural/region-level, cross-task synergy, complexity, and efficiency. The results show that (1) the overall ranking in accuracy and structural consistency is SolarH ≈ Rad > UTCI > Wind; (2) per-epoch times are similar, whereas convergence epochs differ markedly, indicating that total time is primarily governed by convergence difficulty; (3) structurally, Rad/SolarH perform better on hot-region overlap and edge alignment, whereas Wind exhibits larger errors at corners and canyons; (4) in terms of learnability, texture variation explains errors far better than edge count; and (5) cross-task synergy is higher in low-value regions than in high-value regions, with Wind clearly decoupled from the other targets. The distinctive contribution lies in a unified, reproducible evaluation framework, together with learnability mechanisms and applicability bounds, providing fast and reliable evidence for performance-oriented planning and design. Full article

Accurate solar radiation forecasting with numerical weather prediction (NWP) is critical for optimizing photovoltaic power generation. This study evaluates short-term (<36 h) performance of the Weather Research and Forecasting model (WRF-Solar) and its ensemble version (WRF-Solar EPS) for global horizontal irradiance (GHI) and direct horizontal irradiance (DIR) over East Asia (December 2019–November 2020) against geostationary satellite retrievals. Both models effectively capture GHI spatial patterns but exhibit systematic overestimation (biases: 17.27–17.68 W/m²), with peak errors in northwest China and the North China Plain. Temporal mismatches between bias (maximum in winter-spring) and RMSE/MAE (maximum in summer) may indicate seasonal variability in error signatures dominated by aerosols and clouds. For DIR, regional biases prevail: overestimation in the Tibetan Plateau and northwest China, and underestimation in southern China and Indo-China Peninsula. Errors (RMSE and MAE) are larger than for GHI, with peaks in southeast and northwest China, likely linked to poor cloud–aerosol simulations. WRF-Solar EPS shows no significant bias reduction but modest RMSE/MAE improvements in summer–autumn, particularly in southeast China, indicating limited enhancement of short-term predictive stability. Both WRF-Solar and WRF-Solar EPS require further refinements in cloud–aerosol parameterizations to mitigate systematic errors over East Asia in future applications. Full article