Search Results (3,277)

Reliable rooftop photovoltaic planning requires design-stage energy predictions to be verified against actual system performance. The novelty of this study is the integration of a multi-year assessment of agreement with PV*SOL design estimates with an independent assessment of normalized productivity, interannual variability, seasonality, and meteorological effects for several rooftop systems operating under the same regional conditions. PV*SOL, a commercial photovoltaic simulation software used to estimate system energy production during the design stage, was evaluated using three years (2023–2025) of monitored data from three rooftop photovoltaic (PV) systems (17.60–75.40 kWp) in Rzeszów, south-eastern Poland. The analysis comprised 108 installation-month observations and included final yield, capacity factor, annual prediction errors, seasonal variability, Pearson correlations, and hierarchical regression. Mean annual final yield ranged from 907.4 to 966.2 kWh/kWp, while annual deviations from PV*SOL design estimates ranged from −0.80% to +7.41%. Monthly final yield was strongly associated with solar irradiation, and the final hierarchical regression model explained 96.4% of its variability. The results indicate that PV*SOL provides a useful annual design reference, but operational monitoring and local benchmark data remain essential for reliable performance assessment. The study supports United Nations Sustainable Development Goal 7 (Affordable and Clean Energy) by improving the evidence base for rooftop photovoltaic planning and monitoring. Full article

Background: The optical losses in the form of reflections, parasitic absorption, and scattering limit photovoltaic efficiency. This review examines plasma-assisted surface engineering as an effective tool for improving light management in solar cells. Plasma-based methods, including etching, oxidation, deposition, and texturing, enable precise control of surface morphology and chemistry, lowering reflectance, enhancing light trapping, and passivating defects. Methods: In contrast to wet-chemical or high-temperature processes, plasma processes are dry, low-temperature, scalable, and can be used with silicon, perovskite, thin-film, and organic solar cells, as well as tandem structures. The fundamentals of optical losses are described, along with the principles of radio-frequency (RF), inductively coupled plasma (ICP), microwave, and atmospheric plasma systems and their distinctive advantages for controlling ion and reactive-species generation. Key applications reviewed include black-silicon texturing by ICP reactive-ion etching (ICP-RIE), anti-reflective/passivation coatings by plasma-enhanced chemical vapor deposition (PECVD), and interface activation by atmospheric plasma. Results: Among performance improvements are a reflectance of less than 2%, a photocurrent increase of 10–20%, and longer carrier lifetime. Conclusions: The advantages of plasma compared to lithography and sol–gel processes are in the precision and affordability of the method. The difficulties include damage caused by the processing, uniformity over extensive areas, and environmental stress resistance. Future directions rely on low-temperature plasmas for flexible PV, machine-learning-guided process optimization, and hybrid plasma–laser systems. This synthesis of otherwise fragmented studies is intended to support the implementation of plasma-based methods in next-generation, high-efficiency, and sustainable solar production. Full article

Visible-light photovoltaic (PV) inspection is affected by periodic grid-line backgrounds, low-contrast cracks, irregular crack topology, and the extreme aspect ratios of slender defects. To address these difficulties, a frequency-domain hybrid deformation and geometry-regression YOLO network, termed FHDG-YOLO, is proposed in this study. The method introduces frequency-domain dynamic decoupled convolution to attenuate periodic background responses, incorporates a high-resolution P2 detection head and efficient multi-scale attention to retain and recalibrate shallow spatial details, embeds DCNv2 to adapt convolutional sampling to irregular defect boundaries, and replaces the original regression loss with MicroShape-IoU for geometry-sensitive localization. Experiments are conducted on a reorganized two-class visible-light PV dataset containing 6493 images, comprising 6262 screened public images and 231 field images collected by the authors. On the 1300-image validation split, FHDG-YOLO obtains a Precision of 0.954, Recall of 0.943, mAP@0.5 of 0.971, and mAP@0.5:0.95 of 0.861. Compared with YOLOv8n, mAP@0.5 and mAP@0.5:0.95 increase by 3.6 and 6.9 percentage points, respectively. On the held-out 649-image test split, the corresponding mAP values are 0.970 and 0.860, compared with 0.931 and 0.785 for YOLOv8n. Under the original four-class Panel Solar validation protocol, FHDG-YOLO obtains mAP@0.5 and mAP@0.5:0.95 values of 0.954 and 0.843, compared with 0.929 and 0.780 for YOLOv8n. Full article

Hydrogen is acknowledged as a clean and sustainable energy source due to the increasing demand for renewable energy sources. Photoelectrochemical (PEC) water splitting presents a viable approach for directly producing hydrogen from solar energy with negligible implications for the environment. However, regardless of the intensive studies over several years, a major hurdle to translating impressive laboratory-scale efficiency into robust, dependable, large-scale production of hydrogen is increasing competition from quickly advancing photovoltaic (PV)–based electrolysis technology. In parallel, Z-scheme or S-scheme artificial leaf catalyst systems mimicking photosynthesis are gaining ground in the research community. The performance and reliability of photo-electrocatalytic large-scale hydrogen production should be evaluated via pilot-scale and field studies, along with life cycle and economic studies. In the present manuscript, a comprehensive overview of technologies related to scalability is presented, with a focus on semiconductor materials and reactor design. In conclusion, problems and opportunities for future research on large-scale production technologies are presented. Full article

Lhasa combines abundant solar resources with a high heating demand and low winter solar altitude, making residential morphology simultaneously important to operational energy use and roof–façade photovoltaic (PV) generation. Based on a residential-area inventory covering more than 80 residential areas and 15 typical slab-type blocks, this study developed locally constrained parametric prototypes and generated 2186 valid physics-based samples. Thirteen morphological predictors were retained for surrogate modelling, followed by sunlight-constrained bi-objective optimization at floor area ratio (FAR) = 1.5, 1.8, and 2.2. On the independent test set, the selected energy and PV surrogate models achieved R² values of 0.999 and 0.989, respectively. The Pareto fronts at FAR = 1.5 and 1.8 included near-zero annual net-energy solutions, whereas FAR = 2.2 retained a minimum deficit of 0.682 × 10⁶ kWh. Low-energy solutions generally favoured a lower site coverage, continuous elongated slabs, and a greater mean height, whereas high-generation solutions favoured larger building footprints, a lower height, and controlled shading. As FAR increased, façade PV made a larger contribution, but this was insufficient to offset the decline in the rooftop supply and the rise in total energy demand. Energy-balance-oriented solutions generally concentrated the building orientation within 5–15° and the building depth within 14.0–15.5 m, with the density and height requiring a joint adjustment across FAR scenarios. The results provide an interpretable basis for the early-stage morphology screening and planning control of slab-type residential development in Lhasa. Full article

Photovoltaic (PV) output degradation is difficult to detect early using outdoor operating data because it can overlap with normal output variations caused by solar irradiance and module temperature. This study evaluated PV output degradation detectability using irradiance-condition-specific relative-error distributions constructed from 1-min electrical and meteorological data from a 3 kW PV testbed. Irradiance-condition classes were defined using K_D and POP_D to reflect the daily irradiance levels and variability. For each class, a class-specific reference distribution was constructed from the normal-state relative-error distribution using Gaussian or kernel density estimation (KDE)-based density models according to goodness-of-fit. Output degradation conditions were simulated by reducing the measured power by 10–50%, and the separability between normal and output degradation conditions was evaluated using the negative log-likelihood (NLL) and right-tail (RT) scores. Performance was analyzed using the area under the precision–recall curve (AUC-PR), area under the receiver operating characteristic curve (ROC-AUC), recall, and F1 score. The results showed that 10–20% degradation was highly detectable under high-irradiance and low-variability conditions, whereas 40–50% degradation was required under low-irradiance or highly variable conditions. The RT score improved the threshold-based detection performance in some variable classes by reflecting the directional rightward shift caused by output degradation. These findings indicate that PV degradation detection should use irradiance-condition-specific reference distributions and directional degradation information, rather than a single reference distribution. Full article

Offshore floating photovoltaics (OFPVs) can expand solar generation without terrestrial land-use conflicts, but marine structures, installations, and operations remain cost-intensive. This study develops a reproducible deterministic and probabilistic levelized cost of electricity (LCOE) framework for the modular 0.5 MW PV-BOS platform. The methodological contribution is the consistent integration of component-level CAPEX and OPEX ranges, route-specific logistics evidence, lifetime degradation, financing uncertainty, and scenario-dependent cost modes within one model. The deterministic calculations were reproduced using initial CAPEX at year 0 and annual energy degradation expressed as (1 − d)^(t−1). For a 30-year long-term scenario, the optimized deterministic LCOE is 85.56 USD/MWh. A 100,000-trial Monte Carlo analysis gives a mean of 119.42 USD/MWh and P10/P50/P90 values of 91.64/117.11/150.23 USD/MWh under the explicitly defined baseline distributions. The route-specific Vigo–Valencia comparison shows a 58.7% reduction for the transport-and-port-assembly subtotal, but this ratio is not interpreted as a universal full T&I saving. The capacity factor is the dominant LCOE driver, followed by CAPEX. The results support modular logistics as a potentially important cost-reduction mechanism while showing that bankability depends on site-specific energy-yield, metocean design, availability, financing, and O&M validation. Full article

Photovoltaic (PV) deployment is expanding rapidly, but the climate and economic value of identical generation varies with electricity-system and market conditions. This study examines whether avoided-emission rankings align with project-level financial valuation when physical project characteristics are held constant. A documented 1.101 MWp PV project, with expected annual generation of 1592.6 MWh/year, was assessed counterfactually across seven regional electricity groupings, with Brazil as the reference case. The framework integrates generation-weighted emission factors, avoided-emission accounting, discounted cash flow, carbon-abatement costs, deterministic sensitivity analysis, Monte Carlo simulation, and exploratory Pearson and Spearman correlations with exact permutation inference. To address the physical variability of the reference asset, an auxiliary hourly PV simulation covering ten complete years (2016–2025) was additionally performed using solar irradiance, ambient temperature, and wind-speed data. The modeled annual generation averaged 1472.4 MWh/year, with a coefficient of variation of 1.44%, indicating limited interannual variability over the evaluated decade. Financial indicators were evaluated over a 20-year horizon, whereas lifetime avoided emissions and carbon-abatement costs were assessed over a 25-year technical lifetime. The Arab League presented the highest avoided emissions (1005 tCO₂/year; 6.1 times Brazil) but the weakest base-case financial result: a net abatement cost of approximately USD 7/tCO₂ and a 70.1% probability of positive NPV. The European Union achieved the highest NPV, a net abatement cost of approximately −USD 255/tCO₂, and a 100% probability of positive NPV despite lower mitigation. An inverse association between emission factor and electricity tariff further characterized this mismatch (Spearman’s $\rho =-0.786$; exact permutation $p=0.048$). The results show that physical PV production, mitigation potential, and project-level financial valuation represent distinct but complementary dimensions that should be assessed jointly when comparing PV deployment contexts. Full article

The intermittent nature of solar energy requires precise capacity and battery sizing for reliable microgrid design. Extending beyond traditional annual photovoltaic maps, we introduce a methodology for high-resolution capacity sizing surfaces that integrate hourly solar irradiance and battery storage estimation from averaged meteorological data, demonstrated here for Winnipeg, Canada. Leveraging pvlib Python 3.13 library, we simulate performance across all module orientations using photovoltaic modules with 22.5% efficiency and lithium-ion batteries assuming 92% round-trip efficiency, defining core metrics—Capacity ratio, Storage ratio, Excess ratio, and Battery cycling ratio, including Battery charging and discharging C-rates—while incorporating system losses. The analysis further embeds hourly unmet load and average battery state of charge. Unlike conventional PV-yield tools and single-configuration microgrid studies, the present method combines site-specific hourly meteorological data, full tilt–azimuth evaluation, battery dispatch, and explicit compliance-linked PV and storage sizing. These intuitive capacity sizing surfaces reveal how hourly modeling resolves diurnal and seasonal structure that monthly-average tools omit; a sensitivity analysis bounds the residual effect of inter-annual variability removed by climatological averaging, enhancing reliability and battery longevity under variable target standard deviation of compliance ranging from $\sigma =5/8$ to $\sigma =3$. Open-access capacity sizing surfaces for Canada-wide at 564 stations and an open-source Python library that reproduces the methodology for any hourly meteorological dataset world-wide are provided to easily size solar PV and battery systems. Full article

Accurate parameter estimation of photovoltaic (PV) models is essential for performance evaluation, efficiency enhancement, and reliable energy forecasting in solar energy systems. However, the nonlinear, multimodal, and implicit nature of the current–voltage (I–V) relationship makes this task challenging for conventional optimization methods, which often suffer from premature convergence and sensitivity to initial conditions. In this study, a modified variant of the Artificial Circulatory System Algorithm, termed Dynamic Elite Cooperative ACSA (DEC-ACSA), is proposed for estimating the unknown parameters of the Single-Diode Model (SDM). The proposed approach extends the original ACSA by incorporating dynamic population grouping, elite-guided cooperative interaction, and directional elite refinement, thereby aiming to improve convergence stability and the utilization of high-quality population information. The objective is to minimize the residual root mean square error (RMSE) of the implicit SDM equation using measured I–V data from four established benchmarks: the RTC France solar cell and the PWP201, STM6-40/36, and STP6-120/36 PV modules. The performance of DEC-ACSA is evaluated against the original ACSA, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Henry Gas Solubility Optimization (HGSO) over 30 independent runs under an equal budget of 50,100 function evaluations. The DEC-ACSA configuration selected on RTC France was retained unchanged for the three additional module benchmarks. DEC-ACSA achieved mean residual RMSE values of 1.2514 × 10⁻³, 2.656 × 10⁻³, 2.647 × 10⁻³, and 1.8108 × 10⁻² for RTC France, PWP201, STM6-40/36, and STP6-120/36, respectively, while consistently reducing run-to-run variability relative to ACSA. Holm-corrected tests showed no significant difference from PSO on RTC France and PWP201, whereas significant differences from all comparison algorithms were observed on STM6-40/36 and STP6-120/36. I–V reconstruction further confirmed close agreement with the measured data across all four PV systems. Full article

The growing penetration of centralized and distributed solar photovoltaic (PV) generation has introduced significant operational challenges in power systems, most notably the “duck curve” phenomenon, which is characterized by steep net load ramp-up at dusk. This issue has become increasingly evident worldwide, including in Brazil, where solar PV capacity is expanding rapidly. In this context, this paper proposes optimization-based strategies for dispatching Battery Energy Storage Systems (BESS) to smooth the duck curve, using real data from the state of Minas Gerais, Brazil. Three distinct optimization objectives are investigated: load derivative compensation, variable generation compensation, and average load tracking. Energetic and electric analyses were performed using the real data. For the evaluated Minas Gerais case study, both analyses indicate that the BESS operation based on load derivative compensation provides the most effective reduction in load ramps among the three strategies investigated. Full article

The rapid global expansion of solar photovoltaic (PV) capacity has increased the operational need for accurate generation forecasting to support grid balancing, dispatch, and market participation. Artificial intelligence (AI) and machine learning (ML) methods now dominate this research area, but the resulting literature is large and methodologically fragmented, making it difficult to establish which methods are used, what data they require, and where the principal gaps lie. This paper combines a bibliometric analysis of 3111 records retrieved from the Web of Science Core Collection (2010–2026) with a technical synthesis of 27 highly cited studies published from 2022 onward, combining the most highly cited works with targeted additions from 2024–2025 covering specific methodological gaps. The bibliometric analysis shows exponential growth in annual output, from three publications in 2010 to 609 in 2025, with keyword evolution tracing a clear methodological trajectory from classical and fuzzy-logic approaches, through shallow and deep neural networks, to transformer- and attention-based architectures since 2023. The technical synthesis finds that classical machine learning remains competitive for day-ahead forecasting with well-structured numerical weather prediction inputs, that convolutional neural network–long short-term memory (CNN-LSTM) hybrids dominate the deep-learning literature, and that graph-based and transformer architectures address multi-site and multi-horizon forecasting, respectively. A comparison of reported results shows that absolute error metrics are not directly comparable across studies due to heterogeneous datasets, metrics, temporal resolutions, and climates, although relative improvements within controlled comparisons are directionally consistent. Seven research gaps are identified, including the absence of standardized benchmarks, limited public dataset availability, weak cross-region generalization, and underdeveloped uncertainty quantification. Full article

Convective fruit drying is energy-intensive and a major greenhouse gas (GHG) source in the agri-food sector. Existing carbon footprint (CF) studies are fragmented, separating emissions accounting from economic performance and quality verification, limiting their usefulness for small and medium-sized enterprises (SMEs) seeking practical decarbonization actions. This study proposes an integrated, verification-oriented framework linking organizational carbon accounting, process engineering, and product quality within a unified decision platform. The framework combines (i) GHG quantification (ISO 14064-1:2018 and ISO 14067:2018); (ii) techno-economic modeling via process simulation; (iii) experimental validation of quality attributes (moisture, water activity, color, texture); and (iv) sensitivity analysis. The framework was applied to four energy configurations: baseline and three decarbonization scenarios (solar thermal, photovoltaic (PV), and combined). The baseline footprint was 139,090 kg CO₂e (±11.43%). Emission reductions ranged from 15.05% to 34.73%, with the combined solar-PV configuration achieving the highest mitigation while maintaining commercial quality. Emission intensity dropped from 1.19 to 0.78 kg CO₂e per functional unit. Economic performance showed payback periods of 5.78–10.54 years and IRRs of 15.8–24.2%. This framework provides a transferable tool for SMEs to prioritize decarbonization strategies based on integrated environmental, economic, and quality criteria. Full article

Economic evaluation of Battery Energy Storage Systems (BESSs) integrated with solar photovoltaic (PV) systems requires the consideration of financial performance, operational behavior, battery degradation, reliability, and uncertainty. This PRISMA-based systematic review synthesizes mathematical and decision-making approaches relevant to Fuzzy Soft Set-based PV–BESS evaluation. Searches in Scopus, ScienceDirect, SpringerLink, and Taylor & Francis Online identified 789 records published between 2015 and 2026, and 74 studies were included. All studies were coded by theme, method, and economic and technical criteria, while 15 representative studies were analyzed at the equation level. Six model families were identified: techno-economic valuation, multi-objective planning, operational energy management, reliability assessment, fuzzy and multi-criteria decision-making, and degradation-aware optimization or control. Most studies appeared in 2024–2026 (57/74; 77.0%), while keyword analysis highlighted decision making and battery storage. Common formulations included NPV, NPC, LCOE, IRR, payback period, market revenue, battery capacity fade, equivalent full cycles, degradation cost, LPSP, LOLE, EENS, Monte Carlo simulation, and fuzzy ranking methods. These components remain fragmented across separate frameworks. Fuzzy Soft Set is therefore positioned as a potential parameterized decision-support layer for integrating heterogeneous outputs, subject to further theoretical development and empirical validation. This direction is relevant to Sustainable Development Goal 7 (Affordable and Clean Energy). Full article

Artificial intelligence (AI) is transforming photovoltaic (PV) systems from passive generators into self-forecasting, self-diagnosing, and self-optimizing energy assets. This review synthesizes 170 Scopus-indexed documents published between 2020 and 2026 across six technical domains: solar resource and PV power forecasting; intelligent monitoring, fault diagnosis, and cybersecurity; AI-driven control and design optimization; smart grid integration and real-time energy management; AI-assisted PV materials, devices, and manufacturing; and cross-sectoral smart PV applications. Quantitative synthesis shows that hybrid deep learning forecasters reduce root mean square error by 31.9–43.9% relative to persistence and single-model baselines. Attention-based architectures achieve mean absolute percentage errors of up to 5%. Machine learning classifiers achieve fault detection accuracies of 92.3–99.4% with protection response times below 100 ms, enabling predictive maintenance at the fleet scale. Reinforcement learning energy management lowers electricity costs by 20–55%, reduces peak demand by 13–31.5%, and raises PV self-sufficiency from 71.5% to 89.7%. AI-optimized thermal and material interventions deliver efficiency gains of up to 22.2%, and indoor perovskite devices exceed 40% conversion efficiency. Reported performance metrics are derived from heterogeneous datasets, horizons, and baselines; they are therefore synthesized as indicative ranges rather than directly comparable benchmarks. Persistent barriers include data scarcity, model opacity, cybersecurity vulnerabilities, edge deployment constraints, and energy justice concerns. These barriers are mapped to research directions in explainable AI, federated and transfer learning, digital twins, and blockchain-enabled energy markets. A roadmap to 2040 consolidates the findings and charts the transition toward high-efficiency, resilient, and sustainable solar energy conversion. Full article