Search Results (148)

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

This study evaluates the feasibility and visibility of electric vehicles (EVs) in Egypt, addressing critical research gaps and proposing actionable strategies to drive adoption. Employing a systematic review of academic, governmental, and industry sources, the paper identifies underexplored areas such as rural–urban adoption disparities, lifecycle assessments of EV batteries, and sociocultural barriers, including gender dynamics and entrenched consumer preferences. Its primary contribution is an interdisciplinary framework that integrates technical aspects, such as grid resilience and climate-related battery degradation, with socioeconomic dimensions, providing a holistic overview of EV feasibility in Egypt tailored to Egypt’s context. Key findings reveal infrastructure limitations, inconsistent policy frameworks, and behavioral skepticism as major hurdles, and highlight the untapped potential of renewable energy integration, particularly through synergies between solar PV generation (e.g., Benban Solar Park) and EV charging infrastructure. Recommendations prioritize policy reforms (e.g., tax incentives, streamlined tariffs), solar-powered charging infrastructure expansion, public awareness campaigns, and local EV manufacturing to stimulate economic growth. The study underscores the urgency of stakeholder collaboration to transform EVs into a mainstream solution, positioning Egypt as a regional leader in sustainable mobility and equitable development. Full article

Agrivoltaic (Agri-PV) systems face the critical challenge of balancing photovoltaic energy generation with crop productivity, yet systematic approaches to quantifying the trade-offs between these objectives remain scarce. In this study, we identify nine essential design indicators: panel tilt angle, elevation, photovoltaic coverage ratio, shading factor, land equivalent ratio, photosynthetically active radiation (PAR) utilization, crop yield stability index, water use efficiency, and return on investment. We introduce a novel dual matrix Analytic Hierarchy Process (AHP) to evaluate their relative significance. An international panel of eighteen Agri-PV experts, encompassing academia, industry, and policy, provided pairwise comparisons of these indicators under two objectives: maximizing annual energy yield and sustaining crop output. The high consistency observed in expert responses allowed for the derivation of normalized weight vectors, which form the basis of two Weighted Influence Matrices. Analysis of Total Weighted Influence scores from these matrices reveal distinct priority sets: panel tilt, coverage ratio, and elevation are most influential for energy optimization, while PAR utilization, yield stability, and elevation are prioritized for crop productivity. This methodology translates qualitative expert knowledge into quantitative, actionable guidance, clearly delineating both synergies, such as the mutual benefit of increased elevation for energy and crop outcomes, and trade-offs, exemplified by the negative impact of high photovoltaic coverage on crop yield despite gains in energy output. By offering a transparent, expert-driven decision-support tool, this framework enables practitioners to customize Agri-PV system configurations according to local climatic, agronomic, and economic contexts. Ultimately, this approach advances the optimization of the food energy nexus and supports integrated sustainability outcomes in Agri-PV deployment. Full article

The growing imperative to mitigate climate change and accelerate the shift toward energy sustainability has called for a critical evaluation of heat and electricity generation methods. This article presents a comparative life cycle assessment (LCA) of solar and biogas energy systems on a common basis of 1 kWh of useful energy using SimaPro, the ReCiPe 2016 methodology (both midpoint and endpoint indicators), and cumulative energy demand (CED) analysis. This study is the first to evaluate co-located solar PV, solar thermal compound parabolic concentrator (CPC) and biogas combined heat and power (CHP) systems with in situ data collected under identical climatic and operational conditions. The project costs yield levelized costs of electricity (LCOE) of INR 2.4/kWh for PV, 3.3/kWh for the solar thermal dish and 4.1/kWh for biogas. However, the collaborated findings indicate that neither solar-based systems nor biogas technology uniformly outperform the others; rather, their effectiveness hinges on contextual factors, including resource availability and local policy incentives. These insights will prove critical for policymakers, industry stakeholders, and local communities seeking to develop effective, context-sensitive strategies for sustainable energy deployment, emissions reduction, and robust resource management. Full article

Current–voltage (I–V) characteristics are an important measure of photovoltaic (PV) generators, corresponding to environmental conditions regarding solar irradiance and temperature. The I–V curve tracer is a widely used instrument in power engineering to evaluate system performance and detect fault conditions in PV power systems. Several technologies have been applied to develop the device and trace I–V characteristics, improving accuracy, speed, and portability. Focusing on the outdoor environment, this paper presents an in-depth analysis and comparison of the system design and dynamics to identify the I–V tracing performance based on different power conversion topologies and data acquisition methods. This is a valuable reference for industry and academia to further the technology and promote sustainable power generation. Full article

Tainan’s Cigu, located on Taiwan’s southwestern coast, is a prominent aquaculture hub known for its extensive ponds, tidal flats, and lagoons. This study explored the novel integration of kayaking within aquavoltaic (APV) aquaculture ponds, creating a unique hybrid tourism landscape that merges industrial land use (aquaculture and energy production) with nature-based recreation. We investigated the relationships among facility maintenance and safety professionalism (FM), the perceived value of kayaking training (PV), and green energy and sustainable development recognition (GS) within these APV systems in Cigu, Taiwan. While integrating recreation with renewable energy and aquaculture is an emerging approach to multifunctional land use, the mechanisms influencing visitors’ sustainability perceptions remain underexplored. Using data from 613 kayaking participants and structural equation modeling, we tested a theoretical framework encompassing direct, mediated, and moderated relationships. Our findings reveal that FM significantly influences both PV (β = 0.68, p < 0.001) and GS (β = 0.29, p < 0.001). Furthermore, PV strongly affects GS (β = 0.56, p < 0.001). Importantly, PV partially mediates the relationship between FM and GS, with the indirect effect (0.38) accounting for 57% of the total effect. We also identified significant moderating effects of APV coverage, guide expertise, and operational visibility. Complementary observational data obtained with underwater cameras confirm that non-motorized kayaking causes minimal ecological disturbance to cultured species, exhibiting significantly lower behavioral impacts than motorized alternatives. These findings advance the theoretical understanding of experiential learning in novel technological landscapes and provide evidence-based guidelines for optimizing recreational integration within production environments. Full article

Driven by global climate change and sustainable development, the coordinated development of multiple industries based on photovoltaic energy in the “Desert-Gobi-Wilderness” region has become the key to achieving sustainable development, as well as transforming and upgrading the energy structure. However, the site selection decision for “Photovoltaic +” multi-industry integration, which takes into account economic, social and ecological benefits in a complex ecological environment, is still a key difficulty that restricts the feasibility and scalability of the project. This study first identified and systematically analyzed six “PV +” multi-industry integrations suitable for development in China, including “PV + sand control”, “PV + agriculture”, “PV + agriculture + tourism”, “PV + animal husbandry”, “PV + animal husbandry + tourism”, and “PV + tourism”. Then, a site selection decision framework for “PV +” multi-industry integration consists of three parts. Part 1 establishes a multi-dimensional suitability assessment system that takes into account heterogeneous data from multiple sources. Part 2 uses an integration method based on BWM-CRITIC-TODIM for priority ranking analysis, which first uses a Geographic Information System (GIS) to carry out suitability simulation for the entire region of China—identifying six alternative regions—then uses the interactive and multi-criteria decision-making (MCDM) method to prioritize the alternative areas. Part 3 carries out further sensitivity analyses, scenario analyses, and comparative analyses to verify the dynamics and scientific nature of the site selection decision framework. Finally, this study identifies regions of high suitability for development corresponding to the six multi-industry integrations. The framework is designed to help decision stakeholders achieve precise site selection and benefit optimization for “PV +” multi-industry integration and provides a replicable planning tool for achieving industrial synergy and sustainable development in the “Desert-Gobi-Wilderness” region driven by green energy. Full article

As the global demand for clean and sustainable energy grows, photovoltaics (PVs) have become an important technology in this industry. Thin-film and flexible PV modules offer noticeable advantages for irregular surface mounts and mobile applications. This study investigates the use of four machine learning models to detect different flexible PV module geometries based on power output data. Three identical flexible PV modules were mounted in flat, concave, and convex configurations and connected to batteries via solar chargers. The experimental results showed that all geometries fully charged their batteries within 6–7 h on a sunny day with the flat, concave-, and convex-shaped modules achieving a peak power of 95 W. On a cloudy day, the concave and convex modules recorded peak outputs of 72 W and 65 W, respectively. Simulation results showed that the XGBoost model delivered the best classification performance, showing 93% precision with the flat-mounted module and 98% recall across all geometries. In comparison, the KAN model recorded the lowest precision (78%) with the curved geometries. A calibration analysis on the ML models showed that Random Forest and XGBoost were well calibrated for the flat-mounted module. However, they also showed overconfidence and underconfidence issues with the curved module geometries. Full article

This study investigates the hydrothermal synthesis of calcium silicate hydrate (C-S-H) from photovoltaic (PV) waste glass and carbide sludge as a strategy for resource recovery and sustainable chromium removal from wastewater. Waste-derived precursors were co-ground, blended at controlled Ca/Si molar ratios (0.8, 1.0, 1.2), and hydrothermally treated at 180 °C for 96 h to yield C-S-H with tunable morphology and crystallinity. Comprehensive characterization using XRD, FT-IR, SEM-EDX, and UV-Vis spectroscopy revealed that a Ca/Si ratio of 1.0 produced a well-ordered tobermorite/xonotlite structure with a high surface area and fibrous network, which is optimal for adsorption. Batch adsorption experiments showed that this material achieved rapid and efficient Cr(III) removal, exceeding 90% uptake within 9 h through a combination of surface complexation, ion exchange (Ca²⁺/Na⁺ ↔ Cr³⁺), and precipitation of CaCrO₄ phases. Morphological and structural evolution during adsorption was confirmed by SEM, FT-IR, and XRD, while EDX mapping established the progressive incorporation of Cr into the C-S-H matrix. These findings highlight the viability of upcycling industrial waste into advanced C-S-H sorbents for heavy metal remediation. Further work is recommended to address sorbent regeneration, long-term stability, and application to other contaminants, providing a foundation for circular approaches in advanced wastewater treatment. Full article

Recurrent catastrophic inverter failures significantly undermine the reliability and economic viability of utility-scale photovoltaic (PV) power plants. This paper presents a comprehensive investigation of severe inverter destruction incidents at the Kopli Solar Power Plant, Estonia, by integrating controlled laboratory simulations with extensive field monitoring. Initially, detailed laboratory experiments were conducted to replicate critical DC-side short-circuit scenarios, particularly focusing on negative DC input terminal faults. The results consistently showed these faults rapidly escalating into multi-phase short-circuits and sustained ground-fault arcs due to inadequate internal protection mechanisms, semiconductor breakdown, and delayed relay response. Subsequently, extensive field-based waveform analyses of multiple inverter failure events captured identical fault signatures, thereby conclusively validating laboratory-identified failure mechanisms. Critical vulnerabilities were explicitly identified, including insufficient isolation relay responsiveness, inadequate semiconductor transient ratings, and ineffective internal insulation leading to prolonged arc conditions. Based on the validated findings, the paper proposes targeted inverter design enhancements—particularly advanced DC-side protective schemes, rapid fault-isolation mechanisms, and improved internal insulation practices. Additionally, robust operational and monitoring guidelines are recommended for industry-wide adoption to proactively mitigate future inverter failures. The presented integrated methodological framework and actionable recommendations significantly contribute toward enhancing inverter reliability standards and operational stability within grid-connected photovoltaic installations. Full article

In the context of global decarbonization goals and increasing urban electricity demand, the green transformation of power industry buildings to enhance the utilization of renewable energy represents a significant contribution to sustainable social development. Rooftop photovoltaic (PV) systems can reduce unnecessary radiative heat gain and generate clean electricity to support this transition; however, they also alter the rooftop wind environment. Deploying rooftop PV systems requires well-planned design strategies to optimize renewable energy production while ensuring adequate natural ventilation, particularly for semi-outdoor main transformer rooms where ventilation and heat dissipation are crucial for safe substation operations. This concept was tested at a 220 kV substation in Guangzhou, China, using Computational Fluid Dynamics (CFD) and PVSYST to assess the impact of different rooftop PV systems on natural ventilation and power generation. The analysis showed that while the horizontal PV system achieved the highest energy output, it also resulted in a wind speed reduction of 13.2% or 11.8%. In contrast, the 10° symmetrical PV system offers the most balanced solution, with only a 0.6% decrease in ventilation performance but at the cost of a 13.87% reduction in PV output. The unilateral pitched PV system results in ventilation losses of less than 4%, and the power generation loss is also kept below 4%. However, this configuration may lead to increased wind loads. This approach can be developed into a practical design tool to further support the integration of PV systems in substation green retrofitting projects. Full article

The electrification of industrial processes offers sustainable opportunities for reducing carbon footprints and enhancing energy efficiency in the chemical industry. This paper presents the technical and environmental evaluation (life cycle assessment) of a proposed process for methanol production from the conversion of a conventional process to produce gray hydrogen by SMR technology at a plant located in the Magdalena Medio region of Colombia. The new process incorporates the concept of industrial electrification including a heat pump (HP) system with the use of propane as a working fluid for the distillation and separation system of the water–methanol mixture. The process includes the use of photovoltaic energy (PV) as a thermal supply mechanism for the methanol production process and carbon capture utilization (CCU). The proposed process is compared with a reference methanol production process that uses a dry and wet conversion mechanism. The results obtained using the HYSYS V12.1 simulation software allow identifying a 5% improvement in the performance for methanol production and a reduction in energy consumption of between 30 and 53%, which provides important perspectives on the overall energy efficiency of the process with a significant contribution to the decarbonization (−62%) of the methanol synthesis and production process. Full article

Artificial intelligence (AI) has emerged as a transformative tool for optimizing photovoltaic (PV) systems, enhancing energy efficiency, predictive maintenance, and fault detection. This study presents a systematic literature review and bibliometric analysis to identify the most commonly used AI techniques and their applications in PV systems. The review provides details on the advantages, limitations, and optimal use cases of various review techniques, such as Artificial Neural Networks, Fuzzy Logic, Convolutional Neural Networks, Long-Short Term Memory, Support Vector Machines, Decision Trees, Random Forest, k-Nearest Neighbors, and Particle Swarm Optimization. The findings highlight that maximum power point tracking (MPPT) optimization is the most widely researched AI application, followed by solar power forecasting, parameter estimation, fault detection and classification, and solar radiation forecasting. The bibliometric analysis reveals a growing trend in AI-PV research from 2018 to 2024, with China, the United States, and European countries leading in contributions. Furthermore, a type-2 fuzzy logic system is developed in MATLAB R2023b for automating AI technique selection based on the problem type, offering a practical tool for researchers, industry professionals, and policymakers. The study also discusses the practical implications of adopting AI in PV systems and provides future directions for research. This work serves as a comprehensive reference for advancing AI-driven solar PV technologies, contributing to a more efficient, reliable, and sustainable energy future. Full article

Mining sectors require a continuous and reliable power supply; however, reliance on traditional grid utilities results in high costs and disruptions and increases extreme carbon emission. The Merredin WA sector seeks to resolve critical energy challenges affecting mining operations in Western Australia. Thus, this research proposes an optimal solar PV system with battery storage and backup generation for the mining sector to ensure a stable and cost-effective power supply that reduces harmful environmental effect. A hybrid data-driven long short-term memory (LSTM)-classical optimization framework is designed here, thereby optimizing PV-battery storage operational cost savings and energy usage. The optimization results indicate that approximately 57% of load demand can be fulfilled by the proposed optimal PV system with future cost savings of USD $8627.53 per annum. The optimization method also resulted in the lowest computation time of 1.153 s and highest accuracy 99.247% when compared with other existing algorithms. Furthermore, the integration of renewable energy (RE) technologies within mining operations substantially reduces carbon emissions by 67%, thus contributing to broader global sustainability purposes. The study presents a sustainable and economically viable energy solution for mining operations, setting a precedent for RE adoption in remote and energy-intensive industries. Full article

The development of the building sector to the use of renewable energy, more so in photovoltaic (PV) systems, is a great step toward enhanced environmental sustainability and improved energy efficiency. This study seeks to determine the economic, environmental, and operational effects of integrating a PV system into a Polish production plant for buildings. Case study methodology was followed with the help of actual operating histories and simulation modeling to present the estimates of carbon emission savings, cost savings, and power efficiency. Key findings illustrate that 31.8% of the business’s full-year supply of electricity is through the utilization of solar energy and that it saves as much as 10,366 kg CO₂ of emissions every year. The economic rationale of the system is provided in the form of a 3.6-year payback period against long-term savings of over EUR 128,000 in 26 years. This work also addresses the broader implications of energy storage and management systems on the basis of scalability and reproducibility of intervention at the building construction scale. This study provides evidence towards the requirement of informing decision-making by business managers and policy decisionmakers as a step towards the solution of issues of interest to the utilization of renewable energy at industrial levels towards world agenda harmonization for sustainability and business practice. Full article