Search Results (1,909)

Photovoltaic Systems have been a long-standing challenge to integrate with electrical Power Grids due to the randomness of solar irradiance. Deep Learning (DL) has potential to forecast solar irradiance; however, black-box DL models typically do not offer interpretation, nor can they easily distinguish between deterministic astronomical cycles, and random meteorological variability. The objective of this study was to develop and apply a new Physics-Aware Deep Learning Framework that identifies and utilizes physical attributes of solar irradiance via Fourier-based signal decomposition. The proposed method decomposes the time-series into polynomial trend, Fourier-based seasonal component and stochastic residual, each of which are processed within different neural network paths. A wide variety of architectures were tested (Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN)), at multiple historical window sizes and forecast horizons on a diverse dataset from a three-year span. All of the architectures tested demonstrated improved accuracy and robustness when using the physics aware decomposition as opposed to all other methods. Of the architectures tested, the GRU architecture was the most accurate and performed well in terms of overall evaluation. The GRU model had an RMSE of 78.63 W/m² and an $R^2$ value of 0.9281 for 15 min ahead forecasting. Additionally, the Fourier-based methodology was able to reduce the maximum absolute error by approximately 15% to 20%, depending upon the architecture used, and therefore it provided a way to reduce the impact of the larger errors in forecasting during periods of unstable weather. Overall, this framework represents a viable option for both physically interpretive and computationally efficient real-time solar forecasting that provides a bridge between Physical Modeling and Data-Driven Intelligence. Full article

Amidst the ongoing global energy crisis, environmental deterioration, and the exacerbation of climate change, the development of renewable energy, particularly solar energy, has become a central topic in the global energy transition. This study investigates a solar photovoltaic thermal (PVT) heat pump system that utilizes an expanded honeycomb-channel PVT module to enhance the comprehensive utilization efficiency of solar energy. A simulation platform for the solar PVT heat pump system was established using Aspen Plus software (V12), and the system’s performance impact mechanisms and engineering applications were researched. The results indicate that solar irradiance and the circulating water temperature within the PVT module are the primary factors affecting system performance: for every 100 W/m² increase in solar irradiance, the coefficient of performance for heating (COP_h) increases by 13.7%, the thermoelectric comprehensive performance coefficient (COP_co) increases by 14.9%, and the electrical efficiency of the PVT array decreases by 0.05%; for every 1 °C increase in circulating water temperature, the COP_h and COP_co increase by 11.8% and 12.3%, respectively, and the electrical efficiency of the PVT array decreases by 0.03%. In practical application, the system achieves an annual heating capacity of 24,000 GJ and electricity generation of 1.1 million kWh, with average annual COP_h and COP_co values of 5.30 and 7.60, respectively. The Life Cycle Cost (LCC) is 13.2% lower than that of the air-source heat pump system, the dynamic investment payback period is 4–6 years, and the annual carbon emissions are reduced by 94.6%, demonstrating significant economic and environmental benefits. This research provides an effective solution for the efficient and comprehensive utilization of solar energy, utilizing the low-global-warming-potential refrigerant R290, and is particularly suitable for combined heat and power applications in regions with high solar irradiance. Full article

This article presents a methodology for assessing the ecosystem external costs linked to land-use changes caused by utility-scale photovoltaic systems using a regionalized life cycle approach. The core scientific challenge is to integrate a typically non-site-specific method—life cycle assessment—with a site-specific evaluation of ecosystem services affected by land-use changes. The methodology does not model specific agricultural practices. The approach is applied to three configurations of solar-tracking photovoltaic plants installed on arable land: ground-mounted photovoltaics, elevated agrivoltaics, and spaced agrivoltaics. For each configuration, the external costs or benefits per megawatt-hour (MWh) produced are estimated, allowing a comparative life cycle analysis. The findings show that the elevated agrivoltaic system is the only configuration resulting in a net loss of ecosystem service value, albeit marginal (−0.2 EUR/MWh). In contrast, the ground-mounted system yields a net benefit (approximately 1 EUR/MWh), followed by spaced agrivoltaics (0.1 EUR/MWh). These outcomes are mainly driven by the construction and operational phases, while the impacts from component production, transport, and end-of-life stages are significantly lower. The methodology offers a replicable framework for integrating the monetary evaluation of ecosystem services into life cycle assessments of land-intensive renewable energy systems. Full article

The increasing penetration of variable renewable energy sources intensifies grid imbalance and challenges the reliability of small-scale power systems. This study addresses these challenges by developing and analyzing a fully integrated hybrid energy system that combines biogas upgrading to biomethane, photovoltaic (PV) generation, hydrogen production via alkaline electrolysis, hydrogen storage, and a gas-steam combined cycle (CCGT). The system is designed to supply uninterrupted electricity to a small municipality of approximately 4500 inhabitants under predominantly self-sufficient operating conditions. The methodology integrates high-resolution, full-year electricity demand and solar resource data with detailed process-based simulations performed using Aspen Plus, Aspen HYSYS, and PVGIS-SARAH3 meteorological inputs. Surplus PV electricity is converted into hydrogen and stored, while upgraded biomethane provides dispatchable backup during periods of low solar availability. The gas-steam combined cycle enables flexible and efficient electricity generation, with hydrogen blending supporting dynamic turbine operation and further reducing fossil fuel dependency. The results indicate that a 10 MW PV installation coupled with a 2.9 MW CCGT unit and a hydrogen storage capacity of 550 kg is sufficient to ensure year-round power balance. During winter months, system operation is sustained entirely by biomethane, while in high-solar periods hydrogen production and storage enhance operational flexibility. Compared to a conventional grid-based electricity supply, the proposed system enables near-complete elimination of operational CO₂ emissions, achieving an annual reduction of approximately 8800 tCO₂, corresponding to a reduction of about 93%. The key novelty of this work lies in the simultaneous and process-level integration of biogas, hydrogen, photovoltaic generation, energy storage, and a gas-steam combined cycle within a single operational framework, an approach that has not been comprehensively addressed in the recent literature. The findings demonstrate that such integrated hybrid systems can provide dispatchable, low-carbon electricity for small communities, offering a scalable pathway toward resilient and decentralized energy systems. Full article

Residential space heating in Northern Europe requires long-duration thermal storage to align summer solar gains with winter heating demand. This study investigates a compact sand-based seasonal thermal energy storage integrated with flat-plate solar collectors for an A+ class single-family house in Kaunas, Lithuania. An iterative co-design couples collector sizing with the seasonal charging target and a 3D COMSOL Multiphysics model of a 300 m³ sand-filled, phenolic foam-insulated system, with a 1D conjugate model of a copper pipe heat-exchanger network. The system was charged from March to September and discharged from October to February under measured-weather boundary conditions across three consecutive annual cycles. During the first year, the storage supplied the entire winter heating demand, though 35.2% of the input energy was lost through conduction, resulting in an end-of-cycle average sand temperature slightly below the initial state. In subsequent years, both the peak sand temperature and the residual end-of-cycle temperature increased by 3.7 °C and 3.2 °C, respectively, by the third year, indicating cumulative thermal recovery and improved retention. Meanwhile, the peak conductive losses rate decreased by 98 W, and cumulative annual losses decreased by 781.4 kWh in the third year, with an average annual reduction of 4.15%. These results highlight the progressive self-conditioning of the surrounding soil and demonstrate that a low-cost, sand-based storage system can sustain a complete seasonal heating supply with declining losses, offering a robust and scalable approach for residential building heating applications. 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

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

As China’s urbanization rate continues to rise, the scale of high-rise residences also grows, emerging as one of the main sources of building energy consumption and carbon emissions. It is therefore crucial to conduct energy-efficient design tailored to local climate and resource endowments during the schematic design phase. At the same time, consideration should also be given to its impact on economic efficiency and environmental comfort, so as to achieve synergistic optimization of energy, carbon emissions, and economic and environmental performance. This paper focuses on typical high-rise residences in three cities across China’s northwestern region, each with distinct climatic conditions and solar energy resources. The optimization objectives include building energy consumption intensity (BEI), useful daylight illuminance (UDI), life cycle carbon emissions (LCCO2), and life cycle cost (LCC). The optimization variables include 13 design parameters: building orientation, window–wall ratio, horizontal overhang sun visor length, bedroom width and depth, insulation layer thickness of the non-transparent building envelope, and window type. First, a parametric model of a high-rise residence was created on the Rhino–Grasshopper platform. Through LHS sample extraction, performance simulation, and calculation, a sample dataset was generated that included objective values and design parameter values. Secondly, an SVM prediction model was constructed based on the sample data, which was used as the fitness function of MOPSO to construct a multi-objective optimization model for high-rise residences in different cities. Through iterative operations, the Pareto optimal solution set was obtained, followed by an analysis of the optimization potential of objective performances and the sensitivity of design parameters across different cities. Furthermore, the TOPSIS multi-attribute decision-making method was adopted to screen optimal design patterns for high-rise residences that meet different requirements. After verifying the objective balance of the comprehensive optimal design patterns, the influence of climate differences on objective values and design parameter values was explored, and parametric models of the final design schemes were generated. The results indicate that differences in climatic conditions and solar energy resources can affect the optimal objective values and design variable settings for typical high-rise residences. This paper proposes a building optimization design framework that integrates parametric design, machine learning, and multi-objective optimization, and that explores the impact of climate differences on optimization results, providing a reference for determining design parameters for climate-adaptive high-rise residences. Full article

Strawberry is widely cultivated due to its short growth cycle, high yield, and significant profits. In high-latitude cold regions, the planting area of overwintering strawberry has expanded rapidly in recent years. However, although daytime temperatures inside solar greenhouses rise quickly with solar radiation, plants are frequently subjected to persistent nocturnal low-temperature stress (nocturnal temperature below 10 °C). This stress restricts photosynthesis, delays growth, and markedly reduces yield. Therefore, accurately evaluating the tolerance of strawberry varieties to low nocturnal temperatures is crucial for unheated overwintering production in cold regions. This study selected Snow White, Benihoppe, and Kaorino as experimental materials for overwintering cultivation trials in a typical cold-region solar greenhouse. We measured and analyzed growth and development, photosynthetic characteristics, phenological traits, and fruit yield. Based on photosynthetic physiology and phenotypic traits, we constructed the Photosynthesis–Fluorescence Index (PFI), the Production–Phenotype Index (PPI), and the Nocturnal Cold Tolerance Index (NCTI). The results showed that Kaorino exhibited significantly higher values in all three indices compared with Benihoppe and Snow White. After exposure to low night temperatures, Kaorino exhibited rapid photosynthetic induction, strong maintenance of PSII activity, vigorous growth, early maturation, and high yield. Moreover, all three composite indices were strongly and positively correlated with total yield (R² > 0.97), demonstrating their effectiveness in distinguishing the nocturnal low-temperature tolerance of strawberry cultivars. These composite indices provide a scientifically robust method for selecting suitable cultivars for unheated overwinter strawberry production in high-latitude cold regions. Full article

Photocatalysis driven by the visible light of solar energy has received considerable attention in the field of environmental remediation and clean energy production. In this work, monomeric sulfonated palladium phthalocyanine (PdPcS) was encapsulated in zeolitic imidazolate frameworks-8 (ZIF-8) crystals (denoted PdPcS@ZIF-8) through electrostatic interaction in the ammonia system, while their photocatalytic activity was well-maintained together with the structural regularity of ZIF-8 crystals. For comparison, a PdPcS/ZIF-8 sample was obtained from the traditional impregnation method. The ¹³C NMR and UV-DRS spectra confirmed the difference between PdPcS@ZIF-8 and PdPcS/ZIF-8 in terms of the chemical environment effect for PdPcS. Under visible light, the optimal PdPcS@ZIF-8 catalyst achieved complete degradation of 0.1 mM bisphenol A in 120 min. It also exhibited excellent stability, retaining 81.5% activity after four cycles, far outperforming the impregnated sample (32.5%) due to effective encapsulation preventing PdPcS leaching. This versatile one-step synthetic strategy is expected to be useful for designing novel macromolecules@MOF composite materials. Full article

Freshwater scarcity is increasing day by day and has already reached a threatening level, especially in remotely populated areas. One of the technological solutions to this rising concern could be the use of the solar-based humidification–dehumidification (SHDH) method for water desalination. This technology is a promising solution but has challenges such as solar intermittency. This challenge can be solved by integrating SHDH with the phase change material as a solar energy storage medium. Therefore, a novel nano-enhanced binary eutectic phase change material (NEPCM) was developed in this project. PCM consisting of 70 wt.% stearic acid (ST) and 30 wt.% suberic acid (SBU) with a varying concentration of silicon carbide (SiC) nanoparticles (NPs) (0.1 to 3 wt.%) was synthesized specifically considering the need of SHDH application. The systematic thermophysical characterization was conducted to investigate their energy storage capacity, thermal durability, and performance consistency over repeated cycles. DSC analysis revealed that the addition of SiC NPs preserved the thermal stability of the NEPCM, while the phase transition temperature remained nearly unchanged with a variation of less than 0.74%. The value of latent heat is inversely related to the nanoparticle concentration, i.e., from 142.75 kJ/kg for the base PCM to 131.24 kJ/kg at 3 wt.% loading. This corresponds to reductions in latent heat ranging between 0.98% and 8.06%. The FTIR measurement confirms that no chemical reactions or no new functional groups were formed. All original functional groups of ST and SBU remained intact, showing that incorporating the SiC NP to the PCM lead to physical interactions (e.g., hydrogen bonding or surface adsorption). The TGA analysis showed that the SiC NPs in the NEPCM act as supporting material, and its nano-doping enhanced the final degradation temperature and thermal stability. There was negligible change in thermal conductivity for nanoparticle loadings of 0.1% and 0.4%; however, it increased progressively by 5.2%, 10.8%, 23.12%, and 25.8% at nanoparticle loadings of 0.7%, 1%, 2%, and 3%, respectively, at 25 °C. Thermal reliability was analyzed through a DSC thermal cycling test which confirmed the suitability of the material for the desired applications. Full article

The development of integrated renewable energy and high-density thermal energy storage systems has been fueled by the need for environmentally friendly heating and cooling in buildings. In this paper, MiniStor, a hybrid thermochemical and phase-change material storage system, is presented. It is equipped with a heat pump, advanced electronics-enabled control, photovoltaic–thermal panels, and flat-plate solar collectors. To optimize energy flows, regulate charging and discharging cycles, and maintain operational stability under fluctuating solar irradiance and building loads, the system utilizes state-of-the-art power electronics, variable-frequency drives and modular multi-level converters. The hybrid storage is safely, reliably, and efficiently integrated with building HVAC requirements owing to a multi-layer control architecture that is implemented via Internet of Things and SCADA platforms that allow for real-time monitoring, predictive operation, and fault detection. Data from the MiniStor prototype demonstrate effective thermal–electrical coordination, controlled energy consumption, and high responsiveness to dynamic environmental and demand conditions. The findings highlight the vital role that digital control, modern electronics, and Internet of Things-enabled supervision play in connecting small, high-density thermal storage and renewable energy generation. This strategy demonstrates the promise of electronics-driven integration for next-generation renewable energy solutions and provides a scalable route toward intelligent, robust, and effective building energy systems. Full article

In this study, the technical, economic, and environmental performances of a Linear Fresnel Reflector (LFR) integrated with an Organic Rankine Cycle (ORC), designed with a non-storage approach, and a monocrystalline photovoltaic (PV) system were comparatively evaluated in meeting a building’s 10 kW electricity demand. Solar-based electricity generation systems play a critical role in reducing carbon emissions and increasing energy self-sufficiency in buildings, yet small-scale, storage-free LFR-ORC applications remain relatively underexplored compared to PV systems. The optimal areas for both systems were determined using the P₁–P₂ methodology. The electricity generation of the LFR-ORC system was calculated based on experimentally measured thermal power output and ORC efficiency, while the production of the PV system was determined using panel area, efficiency, and measured solar irradiation data. System performance was assessed through self-consumption and self-sufficiency ratios, and the economic analysis included life cycle savings (LCS), payback period, and levelized cost of electricity (LCOE). The results indicate that the PV system is more advantageous economically, with an optimal payback of 4.93 years and lower LCOE of 0.053 €/kWh when the economically optimal panel area is considered. On the other hand, the LFR-ORC system exhibits up to 35% lower life-cycle CO₂ emissions compared to grid electricity under grid-connected operation (15.86 tons CO₂-eq for the standalone LFR-ORC system versus 50.57 tons CO₂-eq for PV over 25-year lifetime), thus providing superiority in terms of environmental sustainability. In this context, the study presents an engineering-based approach for the technical, economic, and environmental assessment of small-scale, non-storage solar energy systems in line with the United Nations Sustainable Development Goals (SDG 7: Affordable and Clean Energy and SDG 13: Climate Action) and contributes to the existing literature. Full article

Against the backdrop of global energy transition and increasingly severe environmental conditions, developing clean and efficient energy systems has become crucial. This study aims to investigate a solar tower receiver tri-generation (STRT) system combining supercritical CO₂ (S-CO₂) Brayton cycle and organic Rankine cycle (ORC), with the objective of achieving the production of electricity, hydrogen, and oxygen. The modeling of the STRT system is completed by using Ebsilon, and the performance of the STRT system is analyzed. The results show that the output power and efficiency of the S-CO₂ Brayton cycle are 62.29 MW and 48.3%, respectively. The net power and efficiency of ORC are 8.02 MW and 16.35%. The hydrogen and oxygen production rates of the STRT system are 183.8 kg·h⁻¹ and 1470.4 kg·h⁻¹, respectively. The STRT system shows stable and effective operation performance throughout the year. Through the exergy analysis, the exergy losses and exergy efficiencies of different components of the STRT system are obtained. The solar tower has the largest exergy loss (218.85 MW) and the lowest exergy efficiency (63%). The levelized electricity cost and the levelized hydrogen cost of the STRT system are 0.0788 USD·kWh⁻¹ and 2.97 USD·kg⁻¹ with a recovery period of 8.05 years, which reveal the economic competitiveness of the STRT system. Full article

Decarbonizing industry, improving urban sustainability, and expanding clean energy use are key global priorities. This study presents a techno-economic analysis (TEA) and life-cycle assessment (LCA) of green hydrogen (GH₂) production via water electrolysis for low-carbon applications in the Central Asian region, with Uzbekistan considered as a representative case study. Solar PV and wind power are used as renewable electricity sources for a 44 MW electrolyzer. The assessment also incorporates recent advances in alkaline water electrolyzers (AWE) enhanced with metal–organic framework (MOF) materials, reflecting improvements in efficiency and hydrogen output. The LCA, performed using SimaPro, evaluates the global warming potential (GWP) across the full hydrogen production chain. Results show that the MOF-enhanced AWE system achieves a lower levelized cost of hydrogen (LCOH) at 5.18 $/kg H₂, compared with 5.90 $/kg H₂ for conventional AWE, with electricity procurement remaining the dominant cost driver. Environmentally, green hydrogen pathways reduce GWP by 80–83% relative to steam methane reforming (SMR), with AWE–MOF delivering the lowest footprint at 1.97 kg CO₂/kg H₂. In transport applications, fuel cell vehicles powered by hydrogen derived from AWE–MOF emit 89% less CO₂ per 100 km than diesel vehicles and 83% less than using SMR-based hydrogen, demonstrating the substantial climate benefits of advanced electrolysis. Overall, the findings confirm that MOF-integrated AWE offers a strong balance of economic viability and environmental performance. The study highlights green hydrogen’s strategic role in the Central Asian region, represented by Uzbekistan’s energy transition, and provides evidence-based insights for guiding low-carbon hydrogen deployment. Full article