Search Results (1,867)

Accurate plant-level photovoltaic (PV) power estimation is important for performance monitoring, model benchmarking, and grid-integration studies. In bifacial horizontal single-axis tracking (HSAT) systems, this task is complicated by the coupled effects of front-side irradiance, rear-side irradiance, tracker position, and module temperature. This study proposes an algorithmic framework for same-time-step AC power estimation in a bifacial HSAT PV plant using field measurements of irradiance, tracker angle, module temperature, and inverter active power. The framework is not intended as an operational forecasting model because future irradiance and weather conditions are not predicted; instead, it evaluates how compact physics-based structure, interpretable nonlinear learning, and ensemble learning estimate measured AC power under nominal operating conditions. An empirical rear-to-front irradiance relationship was derived using solar-elevation bins and incorporated into a compact physics-based benchmark. This benchmark was compared with an additive Explainable Boosting Machine (EBM) and a Random Forest (RF) on a common test subset of 3916 observations. The physics-based model achieved an RMSE of 19.6 kW, an R² of 0.72, and an NRMSE of 0.38. The EBM improved these values to 17.09 kW, 0.786, and 0.334, respectively, while the RF achieved 15.96 kW, 0.814, and 0.312. Chronological validation showed weaker and more variable performance than randomized validation, indicating that temporal generalization remains challenging. Overall, the results support the use of interpretable PV-domain-guided learning as a transparent intermediate approach between compact physics-based modeling and more flexible ensemble regression. Full article

Cryptomeria japonica (Japanese cedar) is extensively planted as windbreaks in Jeju, Korea, producing highly allergenic pollen that significantly affects local populations. This study analyzed 10-year trends of airborne C. japonica pollen concentrations and their relationship with meteorological factors in Jeju to provide essential data for allergy management and climate adaptation strategies. Daily airborne pollen sampling was conducted using Burkard traps from 2015 to 2024 at a monitoring site in Jeju. Meteorological data, including temperature, wind speed, relative humidity, precipitation, solar radiation, and cloud amount, were obtained from the Korea Meteorological Administration. Temporal trends were analyzed using linear regression and the Mann–Kendall test, while correlations between pollen parameters and meteorological variables were calculated using Spearman’s correlation coefficients. Over the 10-year period, annual pollen integral (APIn) and peak concentrations showed statistically significant increasing trends. Pollen season start dates demonstrated a tendency toward earlier occurrence. Season onset was strongly negatively correlated with pre-season temperatures in January and February. January solar radiation showed positive correlations with both season end and period duration. C. japonica pollen concentrations in Jeju demonstrate significant increasing trends with earlier seasonal onset, primarily driven by pre-season warming in January and February. These changes may lead to prolonged allergen exposure periods, necessitating enhanced public health preparedness and adaptation of clinical management strategies for allergic populations. Full article

Changes in vegetation phenology affect ecosystem carbon uptake and water use, thereby regulating water use efficiency (WUE). However, in alpine ecosystems of the Qinghai-Tibetan Plateau (QTP), uncertainty remains regarding the phenological information characterized by different remote-sensing data sources and its associations with WUE. Using solar-induced chlorophyll fluorescence (SIF) and MODIS normalized difference vegetation index (NDVI) data from 2001 to 2018, we derived the start of growth (SOG) and end of growth (EOG) using multiple phenology extraction methods. WUE was calculated using gross primary productivity (GPP) and evapotranspiration (ET) data. We then employed trend analysis, statistical modeling, and a machine learning interpretive framework to systematically evaluate spatiotemporal differences in phenology derived from SIF and NDVI and their associations with WUE. The results showed that: (1) WUE generally increased across the QTP at approximately 0.15 g C m⁻² mm⁻¹ decade⁻¹, with significant increases mainly in the central-eastern and southeastern regions. Both NDVI- and SIF-derived SOG advanced at rates of −1.08 and −1.14 doy decade⁻¹, respectively. In contrast, EOG showed clear data source divergence: EOG_NDVI was delayed by 0.62 doy decade⁻¹, whereas EOG_SIF advanced by −0.48 doy decade⁻¹. SOG_SIF occurred on average 6.6 days later than SOG_NDVI, EOG differences were larger, with EOG_SIF occurring 17.2 days earlier than EOG_NDVI on average. Trend consistency was also higher for SOG than for EOG, whereas opposite EOG trends accounted for 25.3%. (2) After accounting for climatic covariates, SIF- and NDVI-derived phenological indicators showed distinct model-based associations with WUE, but their explanatory contributions were generally weaker than those of key climatic variables. (3) GAM results further showed that SOG was generally negatively associated with standardized WUE in both phenological datasets, whereas the EOG–WUE partial association differed between SIF and NDVI, with positive associations for EOG_SIF and negative associations for EOG_NDVI. This study highlights the differences between SIF- and NDVI-derived phenological indicators and their model-based associations with WUE, providing complementary remote-sensing information for interpreting vegetation phenology and WUE dynamics on the QTP. Full article

Temperate broadleaved forests support diverse arthropod communities, but canopy-dwelling insects in European lime (Tilia cordata Mill.) stands are still poorly known. We surveyed light-attracted canopy insects in six T. cordata Genetic Conservation Units and related protected stands across Lithuania. One modified, solar-powered UV light trap was installed in the canopy (10–15 m) at each site and operated twice per month from June to August in 2023 and 2024. We used diversity metrics, similarity indices, multiple regression, and non-metric multidimensional scaling (NMDS) together with PERMANOVA to examine the structure of insect communities and assess the influence of environmental factors. In total, 6031 individuals representing 295 insect species were recorded, with higher abundance, species richness and Shannon diversity in 2024 than in 2023. Across both years and all sites, Shannon H diversity index ranged from 3.21 to 3.92. Sørensen indices indicated moderate species similarity among sites and distinct species composition at the Ukmergė genetic reserve. The 20 most abundant taxa comprised over 60% of all individuals, and dominance structure changed markedly between years: Serica brunnea dominated in 2023 but was nearly absent in 2024. Regression revealed a significant positive effect of air temperature on insect abundance (about a 31% increase per 1 °C), while precipitation had no significant effect on insect abundance. NMDS and PERMANOVA showed strong spatial structuring, with sites explaining most of the variation, and weaker but significant temporal and site-by-year effects. Overall, insect diversity metrics showed non-significant correlations with T. cordata genetic diversity parameters. Results demonstrate that mature T. cordata forest stands are important reservoirs of canopy insect diversity and highlight pronounced spatial heterogeneity, interannual dynamics, and temperature sensitivity of canopy assemblages in Lithuanian forests. Full article

Accurate modeling of topside ionospheric electron density is essential for improving GNSS positioning and understanding upper-atmosphere dynamics. A new four-dimensional (spatial and temporal) topside electron density model is developed using global GNSS radio occultation data within an L2-regularized artificial neural network framework. The model combines both empirical and physical variables, including geomagnetic coordinates, temporal parameters, solar flux ($F_{10.7}$), geomagnetic activity index ($Kp$), and key ionospheric parameters (NmF2 and hmF2). To support the modeling framework, two sub-models are first constructed to estimate NmF2 and hmF2 when direct measurements are unavailable. The full model is trained using COSMIC-1 data and evaluated against independent datasets, including COSMIC-1, GRACE, and incoherent scatter radar (ISR). The results show that the proposed sub-models reduce relative errors by 4.5% for hmF2 and 11.0% for NmF2 compared with IRI-2016. For the full topside $N_e$ modeling, the proposed approach achieves improvements of 35%, 36%, and 53% relative to IRI-2016 when evaluated against COSMIC-1, GRACE, and ISR datasets, respectively. A systematic analysis of input variables further indicates that both physical drivers and ionospheric structural parameters play essential roles in determining model performance. The new model incorporated with NmF2 and hmF2 sub-models still achieves a 16% improvement over IRI-2016 based on ISR data. In addition to statistical improvements, the model reproduces key ionospheric features, including the equatorial ionization anomaly (EIA) and the midlatitude summer nighttime anomaly (MSNA), under different solar activity conditions. These results demonstrate that the proposed model captures not only the statistical variability but also the underlying physical behavior of the topside ionosphere. Full article

Two semiflexible piezoelectric composite plate structures were developed, incorporating 1 × 9 and 2 × 9 arrays of PZT elements mounted on brass discs and mechanically secured by pop rivets within a thin plastic foil spacer positioned between two copper-clad PCB layers. This configuration provides reliable electrical contact, adequate mechanical compliance, and efficient conversion of mechanical vibration energy into electrical energy. In addition, a multifunctional thermoelectric device was realized, consisting of four cubic modules arranged around a rectangular tube and enabling both handheld operation and coupling to hot or cold surfaces. Each cube is equipped with optimized finned heat sinks and integrates four thermoelectric elements on each face. Experimental results show that each cube generates approximately 6 mW, when handheld and with icy water injected into the central tube, demonstrating its suitability as a compact and versatile thermal energy harvester. Under low-light conditions, a solar panel is supplemented by this hybrid piezoelectric–thermoelectric energy harvesting system that combines the output of a piezoelectric composite plate with the dual outputs of a thermoelectric device using an electronically isolated summing block to ensure source decoupling. Energy storage and management are implemented using a capacitor buffer for the piezoelectric device, two voltage boosters for the thermoelectric outputs, and an automatic ultra-low-power pulse width modulation buck regulator for charging supercapacitors at 5 V. Full article

Vegetation change in cryosphere-affected mountain basins reflects interacting climate and human pressures but their relative influence remains uncertain in the Upper Indus Basin. The novelty of this study is the integration of satellite vegetation, climate variables, human pressure indicators, residual attribution and diagnostic validation in a data-scarce high-mountain basin. We evaluated growing-season Normalized Difference Vegetation Index dynamics and associated drivers from 2001 to 2023 using trend analysis, correlation, Random Forest diagnostics, Sentinel 2 validation, and residual trend analysis. The results showed widespread greening across 96.59% of the basin, with stronger improvement in the lower and central areas. Significant greening covered 69.94% of the basin, while only 1.55% showed significant browning. Precipitation and temperature were predominantly positive drivers of vegetation change, whereas potential evapotranspiration and solar radiation were mostly negative. Soil moisture played a strong regulatory role along elevation gradients. Residual trend analysis provided approximate and method-dependent estimates of the possible anthropogenic influence on vegetation change at 73.09% and climatic drivers at 26.91% rather than direct causal decomposition. These values are approximate and method-dependent estimates, not direct causal decomposition. The findings highlight human-related greening in lower valleys and climate-controlled vegetation responses in high-mountain areas. Full article

The escalating global demand for renewable energy has positioned solar photovoltaics (PV) as a critical technology for achieving net-zero emissions. However, PV efficiency is strictly limited by thermal degradation, where elevated operating temperatures significantly reduce power output and accelerate material aging. This review systematically evaluates the integration of advanced phase change materials (PCMs) as a passive thermal management solution. We analyze the transition from material-level innovations—including nano-enhanced PCMs, 3D conductive frameworks, and shape-stabilization—to system-level hybrid architectures such as liquid—PCM, heat pipe-fin, and thermoelectric generator (TEG) integrations. Synthesis of recent empirical data (2024–2026) demonstrates that optimized PCM composites can achieve PV temperature reductions of up to 32 °C and electrical efficiency enhancements exceeding 19%. Furthermore, techno-economic assessments reveal that these systems can reduce the levelized cost of energy (LCOE) by 5–15% and achieve energy payback times as short as 1.5 years. Finally, this paper identifies critical research gaps in long-term outdoor durability, AI-driven predictive modeling, and sustainable bio-based encapsulation, providing a strategic roadmap for the commercialization of next-generation solar thermal management systems. Full article

Climate change is a global pressing concern that has affected all sectors, including the operations of Small and Medium Entreprises (SMEs) in developing countries, including Malawi. This has negatively affected their economies of scale and exacerbated the SMEs’ growth constraints. Nonetheless, renewable efficient energy (REE) systems, including solar and biogas, could help in building resilience to sustain their performance. In line with this, the study examined the factors that enhance the adoption of renewable efficient energies and constructed their resilience indices. Our study was grounded in the Diffusion of Innovation Theory and the Sustainable Livelihoods Framework. These theories contextualised the study and guided the selection of variables to estimate an Endogenous Switching Regression (ESR) econometric model, alongside estimating the absorptive, adaptive and transformative individual indices for 699 SMEs, using the 2019 Malawi Household Integrated Survey data. The results initially suggests that factors such as access to credit, being male, access to education, access to capital sources, a large profit share, bridging social capital and location among others, have a positive effect in influencing the adoption of renewable efficient systems. We simulated the adoption results and found that SMEs that adopts REE increase their resilience with an Average Treatment Effect of 0.117 and through the subsidy policy effect vulnerable SMEs that later adopt REE would shift their resilience by 0.169. Furthermore, the study found that transformative capacity plays the most important role in building long-term resilience for the SMEs. The study calls for policies, including establishing urban centres where SMEs can access information regarding REE and improving access to formal safety nets and capital sources beyond loan provisions. Full article

Institutional shuttle fleets with fixed routes and predictable terminal parking are well-suited to charging photovoltaic–battery energy storage system (PV–BESS) charging for sustainable campus mobility. However, siting and sizing are often solved numerically without identifying the physical constraints that determine the optimum. This study develops a sustainability-oriented framework for converting a 10-van diesel shuttle fleet at Rajamangala University of Technology Lanna into an electric fleet supported by distributed PV–BESS charging stations. A centralized one-station layout is compared with a distributed two-station layout, and a closed-form active-constraint sizing rule is derived using Karush–Kuhn–Tucker (KKT) analysis. Results show that the distributed configuration eliminates dead-run travel and provides higher lifecycle value than the centralized case. KKT analysis identifies two binding constraints: the PV rooftop-area limit and the BESS one-day autonomy requirement. Under base-case assumptions, the transition achieves positive lifecycle value and substantial CO₂ reduction relative to the diesel baseline. Monte Carlo analysis confirms financial robustness within the uncertainty ranges, while deterministic stress tests show sensitivity to diesel prices, PV electricity credit values, discount rate, and fleet utilization. The framework provides an interpretable decision-support method for institutional fleet electrification in solar-rich campus settings, contributing to SDGs 7, 11, and 13 through clean-energy adoption, sustainable transportation, and CO₂-emission reduction. Full article

Accurate photovoltaic power forecasting is essential for the reliable integration of solar energy into the electrical grid. This work presents a high-resolution dataset and acquisition framework that integrates electrical measurements, environmental variables, and solar position data into a unified relational database, suitable for PV power prediction across all temporal horizons. Using this dataset, we focus on very-short-term forecasting and propose a comprehensive forecasting framework that combines wavelet-based feature extraction with advanced deep learning techniques. The framework is evaluated across forecasting horizons from 5 to 30 min, achieving nMAE values between 0.73% and 4.64%, nRMSE between 1.65% and 7.98%, and PICP ranging from 62.4% to 74.7%. Robustness is assessed by simulating realistic cloud-induced perturbations in the input data. A hybrid approach that combines the deep learning model with a gradient boosting regressor to correct residual errors reduces the overall nMAE from 4.72% to 3.89% and nRMSE from 9.52% to 6.83%, effectively mitigating large errors caused by abrupt power fluctuations. These results demonstrate the framework’s ability to provide accurate and reliable probabilistic forecasts under both standard and perturbed conditions, offering a solid foundation for future PV prediction research and practical applications. Full article

Renewable energy sources, such as solar thermal and photovoltaic, geothermal, biomass, hydropower, and wind, offer significant sustainability advantages. Yet the sector still faces difficulties in several areas that tend to reduce the efficiency of these new energy forms. Some of these challenges include inconsistent electricity supply, the diffuse nature of renewable energy sources, which makes them difficult to exploit, and the inconsistent and unpredictable nature of electricity supply, which has repercussions for renewable energy markets. Although Industry 4.0 is inherently energy-intensive, its positive contribution to renewable energy systems may outweigh its costs. Consequently, this study conducts a scoping review on the role of digital technologies in renewable energy systems. It focuses on open-access conference papers, journal articles, and book chapters published between 2020 and 2026, selected from scientific platforms and databases such as IEEE Xplore, ScienceDirect, SpringerLink, and Scopus. A multi-stage screening process and a summary sheet for a set of 89 selected articles were produced to extract the necessary information. The results show that Industry 4.0 influences renewable energy systems at the design and installation stage in predictive maintenance, efficient management, and energy security. Meanwhile, Industry 4.0 in renewable energy systems still faces negative externalities that can be categorised as political, financial, infrastructural, environmental, human, security, and technological. To address these challenges, which tend to become entangled in cycles of negative reinforcement, the paper suggests defining standardised, clear, strict, and stable frameworks at the political, legal, regulatory, and environmental levels to overcome most challenges associated with the digital transformation of renewable energy. The study also recommends flexible, inclusive strategic planning that accounts for the digital maturity of the renewable energy system. From these perspectives, the study contributes to the literature by addressing the role of Industry 4.0 technologies in renewable energy systems from a strategic and coordinated perspective, from both human and technological standpoints. It also offers managerial and policy implications by supporting innovation in renewable energy systems on the one hand and contributing to policy and regulatory decision-making that favour their growth on the other. Full article

The efficiency of perovskite solar cells has increased to a certified value of 27% over the past decade, benefiting from the superior properties of metal halide perovskite materials. However, their long-term operational stability is still far inferior to that of commercial crystalline silicon solar cells. A key source of this instability is field-driven ion migration in vertical architectures, along with the consequent degradation at the absorber–electrode interfaces. Compared with the widely investigated vertical structures, back-contacted (BC) perovskite solar cells—wherein both electrodes are positioned on the same side of the absorber—offer a unique route to suppress interfacial ion migration and thereby enhance long-term device stability. These advantages are especially pronounced when combined with single-crystal perovskites, which possess low charge trap densities, long carrier diffusion lengths, and high bulk ion migration barriers. Unfortunately, only a handful of research groups have participated in the development of single-crystal BC perovskite solar cells; thus, the advancement of this area lags far behind that of its vertical counterpart. Therefore, a review that discusses the recent developments and challenges of single-crystal BC perovskite solar cells is urgently required to provide guidelines for this emerging field. In this progress report, we first introduce the main growth methods of single-crystal wafers compatible with BC architectures, followed by an outline of the developmental history of BC perovskite solar cells. Finally, the core bottlenecks facing single-crystal BC devices and corresponding optimization strategies are discussed in detail. Full article

One to four million annual premature deaths are associated with household air pollution. This indoor pollution is mainly generated by traditional biomass cookstoves. Thus, solar cooking can significantly reduce this toll. Its proliferation would also mitigate deforestation pressures. Additionally, for developing countries, it would alleviate the fuel collection workload, mainly borne by women responsible for fuel collection. Electric cooking provides a clean and controllable alternative to thermal cookers for indoor food preparation, sterilization and heating. This study presents a minimal, off-grid photovoltaic solar cooker that operates without batteries and power electronics. Such a cooker constitutes a low-cost and high-reliability solution for electrically decentralized locations. The system encompassing the cooker is conceived as an accessible entry point for household-level photovoltaic (PV) adoption. So, it offers the potential to catalyze the uptake of clean-energy technologies and to support sustainable development. The proposed design dissipates PV power into heat using commercial positive temperature coefficient (PTC) resistors operating near their Curie temperature. A simplified theoretical model is formulated to easily estimate the thermal power and heat-transfer conductances required for achieving cooking temperatures. An instrumented prototype allows for characterizing the transient temperature evolution during controlled heating and cooling experiments in the laboratory, facilitating development in an initial step avoiding the PV panel. The results demonstrate that the minimal PV configuration is technically feasible, robust, and compatible with low-resource settings. This encourages its adoption in communities experiencing energy poverty. Full article

Renewable energy is a central component of global sustainable energy development, with solar energy experiencing substantial growth over recent decades. Solar power is widely regarded as one of the most accessible routes to clean energy generation. However, high upfront costs remain a major barrier to adoption. Many potential users are reluctant to invest in solar photovoltaic (PV) systems because of the longer payback period. To address this financial constraint, a range of business models has been developed. This study used a systematic literature review to examine existing and emerging business models for promoting Solar PV solutions. The review included peer-reviewed journal articles published in English from 2020 to 2026. In total, 39 articles were critically evaluated considering their characteristics. Nine potential business models were identified, several of which are commonly used internationally and have shown positive results that could also be applied in the UK. Importantly, Community Energy Models have shown success in Europe, Sub-Saharan and Asian regions. This has been widely supported by the government due to sustainability and climate change targets. The UK has set their target to achieve net-zero in greenhouse gas emissions by 2050. Beyond financial barriers, reliance on weather conditions and the mismatch between energy demand and supply remain substantial barriers to wider solar PV deployment. Full article