Search Results (201)

The rapid deployment of photovoltaic generation increases uncertainty in power-system operation and strengthens the need for ultra-short-term forecasts with reliable uncertainty estimates. Point-forecasting approaches alone are often insufficient for dispatch and reserve decisions because they do not quantify risk. This study investigates probabilistic forecasting of short-horizon solar generation using quantile regression on a public dataset of solar output and meteorological variables. This study proposes a hybrid attention–convolution model that combines an attention-based encoder to capture long-range temporal dependencies with a causal temporal convolution module that extracts fast local fluctuations using only past information, preventing information leakage. The two representations are fused and decoded jointly across multiple future horizons to produce consistent quantile trajectories. Experiments against representative machine-learning and deep-learning baselines show improved probabilistic accuracy and competitive central forecasts, while illustrating an important sharpness–calibration trade-off relevant to risk-aware grid operation. Key novelties include a multi-horizon quantile formulation at 15 min resolution for one-hour-ahead PV increments, a HybridCNN–STTransformer that fuses causal temporal convolutions with Transformer attention, and a horizon-token decoder that models inter-horizon dependencies to produce consistent multi-step quantile trajectories; reliability/sharpness diagnostics and post hoc calibration are discussed for operational risk-aware use. Full article

This study investigates the dynamics of electricity consumption in Brazil over the past two decades, with a focus on the persistent slowdown in consumption growth observed since 2013. Using segmented regression and interrupted time series (ITS) modeling, the research identifies statistically significant structural breakpoints in national and regional electricity demand. The main novelty of this study lies in the integrated use of segmented regression, ITS, and seasonal SARIMA models to systematically characterize asymmetric and phase-dependent demand behavior rather than to produce short-term forecasts. Seasonal Autoregressive Integrated Moving Average (SARIMA) models reveal that monthly seasonality plays a dominant role in electricity consumption dynamics, with seasonal specifications consistently outperforming non-seasonal alternatives. The results show that Brazil’s electricity demand evolution is best explained by three distinct phases: (i) a stagnation of industrial demand associated with deindustrialization prior to 2013; (ii) an abrupt contraction in commercial and residential demand during the 2014–2016 economic crisis; and (iii) a permanently lower growth trajectory driven by energy efficiency policies under the Brazilian National Electric Energy Conservation Program (PROCEL) and the expansion of solar distributed generation. The findings demonstrate that policy and structural interventions exert gradual, cumulative effects on electricity consumption rather than immediate shifts, providing critical insights for long-term energy planning and policy design in emerging economies. Full article

Horticultural facilities can boost crop yields and quality. However, their structures, costs, and resource efficiency vary significantly. Many facility operators prioritize short-term economic gains at the expense of long-term investments in energy efficiency and environmental management, ultimately leading to increased energy consumption and higher greenhouse gas emissions. A systems-based assessment of tomato production is essential for optimizing resource use. This study integrated emergy analysis (EMA) and life cycle assessment (LCA) to evaluate the sustainability of three tomato production systems: polytunnels, solar greenhouses, and glass greenhouses. The Results demonstrated that polytunnels exhibited the best environmental performance, with the lowest environmental loading ratio (ELR, 19.06) and environmental final index (EFI, 1.62). Solar greenhouses showed the best environmental composite index (ECI), outperforming others in mitigating potential environmental impacts. Glass greenhouses imposed the greatest environmental pressure (ELR, 168.51), primarily due to substantial natural gas consumption and infrastructure investment. Scenario analyses revealed that environmental performance across all systems could be significantly enhanced through shortening transport distance, extending the service life of construction materials, and managing energy use. The maximum reduction potentials for the environmental composite index (ECI)were 23.80% for polytunnels, 18.60% for solar greenhouses, and 19.90% for glass greenhouses. This study confirms that polytunnels are the most environmentally friendly option, and targeted management strategies can effectively steer facility-based agriculture toward a more sustainable trajectory. Full article

The world is increasingly confronted with interconnected challenges such as energy shortages and climate change. Fossil fuels, including coal, oil, and natural gas, remain the dominant global energy sources, yet they are major contributors to greenhouse gas emissions and growing geopolitical instability. In response to energy insecurity and environmental pressures, many countries are expanding their use of renewable energy sources, including hydropower, solar, wind, and geothermal. Hydropower currently generates more electricity than all other renewable technologies combined and is expected to remain the largest source of renewable electricity through the 2030s. This paper analyzes the role of hydropower in national energy transitions by applying innovation diffusion models. Using an innovation diffusion framework, via the Bass Model, we examine the dynamics of hydropower generation across multiple countries and find that this approach effectively captures the mean nonlinear trajectory of most countries. We complete the analysis by evaluating the effect of rainfall on hydropower generation and show that this helps capture the residual variability not modeled by the Bass Model. Full article

This study applies spectral analysis and singular spectrum analysis (SSA) to mean annual runoff of the Dniester River for 1950–2024 to identify dominant periodic components governing the hydrological regime of this transboundary basin shared by Ukraine and Moldova. The novelty lies in a basin-specific integration in the first systematic application of a combined spectral–SSA framework to the Dniester River, enabling consistent characterization of runoff variability and assessment of large-scale natural drivers. Time series from three gauging stations are analysed to develop data-driven runoff models and medium-term forecasts. Four stable groups of periodic variability are identified, with characteristic timescales of approximately 30, 11, 3–5.8, and 2 years, corresponding to major atmospheric–oceanic oscillations (AMO, NAO, PDO, ENSO, QBO) and the 11-year solar cycle. Cross-spectral and coherence analyses reveal a statistically significant relationship between solar activity and river discharge, with an estimated lag of about 2 years. SSA reconstructions explain more than 80% of discharge variance, indicating high model reliability. Forecast comparisons show that spectral methods tend to amplify long-term trends, CNN–LSTM models produce conservative trajectories, while a hybrid ensemble approach provides the most balanced and physically interpretable projections. Ensemble forecasts indicate reduced runoff during 2025–2028, followed by recovery in 2029–2034, supporting long-term water-resources planning and climate adaptation. Full article

Ultra-short-term photovoltaic (PV) power forecasts are vital for secure grid operation as solar penetration rises. We propose a two-stage hybrid framework, WDT–CRMABIL–Fusion. In Stage 1, we apply a three-level discrete wavelet transform to PV power and key meteorological series (shortwave radiation and panel irradiance). We then forecast the approximation and detail sub-series using specialized component predictors: a 1D-CNN with dual residual multi-head attention (feature-wise and time-wise) together with a BiLSTM. In Stage 2, a compact dense fusion network recombines the component forecasts into the final PV power trajectory. We use 5-min data from a PV plant in Milan and evaluate 5-, 10-, and 15-min horizons. The proposed approach outperforms strong baselines (DCC+LSTM, CNN+LSTM, CNN+BiLSTM, CRMABIL direct, and WDT+CRMABIL direct). For the 5-min horizon, it achieves MAE = 1.60 W and RMSE = 4.21 W with $R^2$ = 0.943 and CORR = 0.973, compared with the best benchmark (MAE = 3.87 W; RMSE = 7.89 W). The gains persist across K-means++ weather clusters (rainy/sunny/cloudy) and across seasons. By combining explicit multi-scale decomposition, attention-based sequence learning, and learned fusion, WDT–CRMABIL–Fusion provides accurate and robust ultra-short-term PV forecasts suitable for storage dispatch and reserve scheduling. 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 order to effectively reveal the nonlinear characteristics of a dish concentrating solar thermal power system (DCSTPS) under pulsating wind-induced loads, a fluid simulation model of the DCSTPS was established, and the simulated pulsating winds were developed via the user-defined function (UDF) combined with the autoregressive (AR) model using MATLAB (R2015b). And based on the fluid simulation calculations of the DCSTPS, the time-range data of the relevant wind vibration coefficients under different working conditions were obtained. The research results show the following: (1) When the altitude angle α is 0° or 180° due to the azimuth angle β = 0°, the maximum values of their drag coefficient C_x, lateral force coefficient C_y, and lift coefficient C_z are similar, and the maximum of rolling moment coefficient C_Mx is significantly smaller than the values at the other two angles; the maximum of the pitch moment coefficient C_My and maximum of the azimuth moment coefficient C_Mz are significantly larger than the values of the other two angles. (2) The increase in altitude angle α leads to a reduction in the drag coefficient C_x, an increase in the lift force coefficient C_z, and an increase of the pitch moment C_Mx. Moreover, an improved phase space delay reconstruction method was developed to calculate the delay time, Lyapunov exponent, and Kolmogorov entropy of the DCSTPS, and the research results show that (1) the maximum Lyapunov exponent and Kolmogorov entropy of the DCSTPS are greater than zero under the action of pulsating wind; (2) the action of pulsating wind will cause increases in the maximum Lyapunov exponent and Kolmogorov entropy of the DCSTPS and will accelerate the divergence speed of the DCSTPS trajectory; and (3) the time for the DCSTPS to enter the chaotic state will be shortened, while the time of entering a chaotic state and degree of subsequent chaotic states will be significantly affected by relevant wind vibration coefficients but without regularity. Full article

Nature reserves serve as core spatial units for maintaining regional ecological security and biodiversity. Owing to their high ecosystem integrity, extensive vegetation cover, and low levels of disturbance, they play a crucial role in sustaining ecological processes and ensuring functional stability. Taking the Giant Panda National Park (GPNP), which spans the provinces of Gansu, Sichuan, and Shaanxi in China, as the study region, the vegetation net primary productivity (NPP) during 2001–2023 was simulated using the Carnegie–Ames–Stanford Approach (CASA) model. Spatial and temporal variations in NPP were examined using Moran’s I, Getis-Ord Gi* hotspot analysis, Theil–Sen trend estimation, and the Mann–Kendall test. In addition, the Optimal Parameters-based Geographical Detector (OPGD) model was applied to quantitatively assess the relative contributions of natural and anthropogenic factors to NPP dynamics. The results demonstrated that: (1) The mean annual NPP within the GPNP reached 646.90 gC·m⁻²·yr⁻¹, exhibiting a fluctuating yet generally upward trajectory, with an average growth rate of approximately 0.65 gC·m⁻²·yr⁻¹, reflecting the positive ecological outcomes of national park establishment and ecological restoration projects. (2) NPP exhibits significant spatial heterogeneity, with higher NPP values in the northern, while the central and western regions and some high-altitude areas remain at relatively low levels. Across the four major subregions of the GPNP, the Qinling has the highest mean annual NPP at 758.89 gC·m⁻²·yr⁻¹, whereas the Qionglai–Daxiaoxiangling subregion shows the lowest value at 616.27 gC·m⁻²·yr⁻¹. (3) Optimal NPP occurred under favorable temperature and precipitation conditions combined with relatively high solar radiation. Low elevations, gentle slopes, south facing aspects, and leached soils facilitated productivity accumulation, whereas areas with high elevation and steep slopes exhibited markedly lower productivity. Moderate human disturbance contributed to sustaining and enhancing NPP. (4) Factor detection results indicated that elevation, mean annual temperature, and land use were the dominant drivers of spatial heterogeneity when considering all natural and anthropogenic variables. Their interactions further enhanced explanatory power, particularly the interaction between elevation and climatic factors. Overall, these findings reveal the complex spatiotemporal characteristics and multi-factorial controls of vegetation productivity in the GPNP and provide scientific guidance for strengthening habitat conservation, improving ecological restoration planning, and supporting adaptive vegetation management within the national park systems. Full article

The global temperature increase has posed urgent challenges, with buildings accountable for as much as 40% of CO₂ emissions, and their decarbonization is critical to meet the net-zero target by 2050. Solar photovoltaics present a promising trajectory, especially through building-integrated photovoltaics (BIPVs), where thin-film technologies can be used to replace traditional building materials. This article critically examined the development of thin-film solar cells for BIPVs, including their working mechanisms, material structures, and efficiency improvements in various generations. The discussion underscored that thin-film technologies, including CdTe and CIGS, had noticeably shorter energy payback times between 0.8 and 1.5 years compared to crystalline silicon modules that took 2 to 3 years, thus promising quicker recovery of energy and higher sustainability values. Whereas certain materials posed toxicity and environmental concerns, these were discovered to be surmountable through sound material selection and manufacturing innovation. The conclusions highlighted that the integration of lower material usage, high efficiency potential, and better energy payback performance placed thin-film BIPVs as an extremely viable option for mitigating lifecycle emissions. In summary, the review emphasized the critical role of thin-film solar technologies in making possible the large-scale implementation of BIPVs to drive the world toward net-zero emissions at a faster pace. Full article

This study presents the design, modeling, and multi-platform simulation of NEPTUNE-R, a solar-powered autonomous hydro-robot developed for sustainable water purification and oxygenation. Mechanical design was performed in Fusion 360, trajectory optimization in MATLAB R2024a, and dynamic motion analysis in Roblox Studio, creating a reproducible digital twin environment. The proposed path-planning strategies—Boustrophedon and Archimedean spiral—achieved full surface coverage across various lake geometries, with an average efficiency of 97.4% ± 1.2% and a 12% reduction in energy consumption compared to conventional linear patterns. The integrated Euler-based force model ensured stability and maneuverability under ideal hydrodynamic conditions. The modular architecture of NEPTUNE-R enables scalable implementation of photovoltaic panels and microbubble-based oxygenation systems. The results confirm the feasibility of an accessible, zero-emission platform for aquatic ecosystem restoration and contribute directly to Sustainable Development Goals (SDGs) 6, 7, and 14 by promoting clean water, renewable energy, and life below water. Future work will involve prototype testing and experimental calibration to validate the numerical findings under real environmental conditions. Full article

This study investigates the characteristics, meteorological drivers, and transport pathways of air pollution in Yining City from 2020 to 2024 based on meteorological records and air pollutant monitoring data. An integrated modeling approach combining the Weather Research and Forecasting (WRF) model and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was employed. Results reveal an overall annual decrease in ambient pollutant concentrations in Yining, with PM_2.5 and PM₁₀ consistently below the national secondary standards, In contrast, the O₃ concentration shows a marked yearly increase. Pronounced seasonal variations were identified: the elevated O₃ concentrations in summer were driven by high temperatures and intense solar radiation. The significant increase in PM_2.5 and PM₁₀ concentrations during winter was predominantly attributed to coal-based heating emissions and temperature inversion conditions. Pollutant concentrations were strongly associated with gaseous precursors (e.g., CO and NO₂) and meteorological factors. Higher temperatures and lower relative humidity aggravated O₃ formation, whereas lower temperatures and higher relative humidity favored PM_2.5 pollution. Correlation analysis revealed that NO₂ and CO showed the strongest correlations with PM_2.5 (r = 0.84) and O₃ (r = −0.62), respectively. Backward trajectory analysis revealed that higher pollution levels were associated with air masses originating from the southwest and southeast. Full article

This paper addresses the technical requirements and challenges encountered in the design and development of a customised power electronics board for a stratospheric balloon payload. This board includes power conversion and distribution to critical components (e.g., FPGAs and a ±4 kV power supply), as well as the pressurised enclosure designed to house these components along with other essential electronics. These systems were part of two scientific instruments onboard SUNRISE III, a high-altitude solar observatory launched in July 2024 from ESRANGE (Kiruna, Sweden), with a floating trajectory over the Arctic Circle. The SUNRISE III mission, based on a stratospheric balloon, was carried out by an international consortium of research institutions from Germany, Spain, Japan, and the United States, and in collaboration with NASA’s CSBF and the Swedish Space Corporation. Furthermore, this work presents telemetry data from the pressure sensing system of the electronic unit, as well as voltage and current measurements from the power electronics board outputs. These data were recorded during the floating phase of the mission, up to the balloon’s arrival in northern Canada after a successful week of scientific operations. Full article

Sustainable intensification requires metrics that are able to capture both economic performance and the often-hidden environmental inputs that support agriculture. Emergy analysis (EmA) meets this need by converting all inputs—free environmental flows and purchased goods/services—into a common unit (solar emjoules, sej). We conducted a PRISMA-documented bibliometric review of EmA in agroecosystems (Web of Science + Scopus, 2000–2022) using Bibliometrix and synthesized farm-scale indicators (ELR, EYR, ESI, %R). Our results show output has grown but is concentrated in a few countries (China, Italy and Brazil) and journals, with farm-level assessments dominating over regional and national assessments. Across cases, mixed crop–livestock systems tend to show lower environmental loading (ELR) and higher sustainability (ESI) than crop-only or livestock-only systems. %R is generally modest, indicating continued reliance on non-renewables, with fertilizers (crops) and purchased feed (livestock) identified as recurrent drivers. Thematic mapping reveals well-developed niche clusters but no single motor theme, consistent with the presence of incongruous baselines, transformities and boundaries that limit comparability. We recommend adoption of the 12.1 × 10²⁴ sej yr⁻¹ baseline, transparent transformity reporting and multi-scale designs that link farm diagnostics to basin and national trajectories. Co-reporting with complementary sustainability assessment methods (such as LCA and carbon footprint), along with appropriate UEV resources, would increase its reputation among policymakers while preserving EmA’s systems perspective, converting dispersed case evidence into cumulative knowledge for circular, resilient agroecosystems. Full article

This paper presents a novel methodology for the optimal sizing of solar photovoltaic (PV) systems in distribution networks by determining the monthly optimum tilt and azimuth angles to maximize solar energy capture. Using one year of solar irradiation data, the Grey Wolf Optimizer (GWO) is employed to optimize the tilt and azimuth angles with the objective of maximizing monthly solar insolation. Unlike existing approaches that assume fixed azimuth angles, the proposed method calculates both tilt and azimuth angles for each month, allowing for a more precise alignment with solar trajectories. The optimized orientation parameters are subsequently utilized to determine the optimal number and placement of PV panels, as well as the optimal location and sizing of shunt capacitor (SC) banks, for the IEEE 69-bus distribution system. This optimization is performed under peak load conditions using the GWO, with the objectives of minimizing active power losses, enhancing voltage profile stability, and maximizing PV system penetration. The long-term impact of this approach is assessed through a 20-year energy and economic savings analysis, demonstrating substantial improvements in energy efficiency and cost-effectiveness. Full article