Search Results (142)

In recent years, solar energy has emerged as a pillar of sustainable development. However, maintaining panel efficiency under extreme environmental conditions remains a persistent hurdle. This study introduces an automated defect detection pipeline that leverages deep learning and computer vision to identify five standard anomaly classes: Non-Defective, Dust, Defective, Physical Damage, and Snow on photovoltaic surfaces. To build a robust foundation, a heterogeneous dataset of 8973 images was sourced from public repositories and standardized into a uniform labeling scheme. This dataset was then expanded through an aggressive augmentation strategy, including flips, rotations, zooms, and noise injections. A YOLOv11-based model was trained and fine-tuned using both fixed and adaptive learning rate schedules, achieving a mAP@0.5 of 85% and accuracy, recall, and F1-score above 95% when evaluated across diverse lighting and dust scenarios. The optimized model is integrated into an interactive dashboard that processes live camera streams, issues real-time alerts upon defect detection, and supports proactive maintenance scheduling. Comparative evaluations highlight the superiority of this approach over manual inspections and earlier YOLO versions in both precision and inference speed, making it well suited for deployment on edge devices. Automating visual inspection not only reduces labor costs and operational downtime but also enhances the longevity of solar installations. By offering a scalable solution for continuous monitoring, this work contributes to improving the reliability and cost-effectiveness of large-scale solar energy systems. Full article

Rapid urban growth poses distinct energy and environmental challenges in various regions of the world. This study evaluated the technical and economic feasibility of a grid-connected photovoltaic system in Santo Domingo Tehuantepec, Oaxaca, Mexico, using Homer Pro software, version 3.14.2, to simulate realistic scenarios. The analysis incorporated local climate data, residential load profiles, and updated economic parameters for 2024. System optimization resulted in an installed capacity of 173 kW of solar panels and 113 kW of inverters, yielding a levelized cost of energy (LCOE) of MXN 1.43/kWh, a return on investment (ROI) of 5.3%, an internal rate of return (IRR) of 8%, and a simple payback period of 10 years. The projected annual energy output was 281,175 kWh, covering 36% of the local energy demand. These results highlight the potential for integrating renewable energy into urban contexts, offering significant economic and environmental benefits. The integration of public policy with urban planning can enhance energy resilience and sustainability in intermediate cities. This study also supports the application of tools such as Homer Pro in designing energy solutions tailored to local conditions and contributes to a fair and decentralized energy transition. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

Space layout design has been recognized as a key technical challenge in achieving low-energy and low-carbon rural houses. Adjustment of room location can influence building energy performance and is subject to both meteorological and design parameters. To elucidate the impact of these parameters on the energy saving potential of room relocation (ESR), this study investigated rural houses in Northwest China using dynamic simulations to compare the relative energy saving rates (RES) associated with three types of single heated room location changes: from the west side to the middle (WM), from the east side to the middle (EM), and from the west side to the east side (WE). Simulations were conducted across different climate regions (Lhasa, Xi’an, Tuotuohe, and Altay) and design parameters, including exterior wall U-value, building orientation (BO), building height (BH), and window-to-wall ratio (WWR). Additionally, the maximum differences in energy consumption (MD) among six layouts with multiple heated rooms were assessed. The results demonstrated that ESR varied significantly with room relocation. The ranges of RES_WM, RES_EM, and RES_WE were −7.89% to 13.20%, −7.82% to 10.25%, and −2.29% to 3.36%, respectively. The MD values ranged from 2.42% to 15.01%. For single heated rooms, including direct normal irradiance (I_dn), the difference between east and west solar-air temperature (△T_sa), outdoor dry bulb temperature (T_e), exterior wall heat transfer coefficient (U), and WWR significantly influenced RES_WM and RES_EM. The ranking of the factor contributions was U > △T_sa > I_dn > T_e > WWR for RES_WM and U > I_dn > △T_sa > T_e > WWR for RES_EM. In the case of RES_WE, I_dn, △T_sa, T_e, exterior wall U value, and BO had significant effects, ranking I_dn > △T_sa > T_e > BO > U. For MD, the key influencing factors were I_dn, △T_sa, T_e, exterior wall U value, and WWR, which were ranked as I_dn > △T_sa > U > T_e > WWR. The effects of design parameters on ESR varied under different climatic conditions. In high-temperature regions, the exterior wall U-value had a stronger influence on the ESR of WE. In regions with larger |△T_sa|, BO exerted a more pronounced effect on the ESR of WE. In regions characterized by high temperatures and radiation, WWR and BH significantly influenced the ESR of WM and EM. Similarly, in these regions, WWR and BH exhibited a greater impact on MD. Finally, among the meteorological parameters, I_dn and △T_sa were significantly correlated with ESR (p < 0.01). These findings provide a valuable reference for the energy-efficient layout design of rural houses in Northwest China and cold regions and support the future development of intelligent and automated rural residential spatial layout design. Full article

Pyrolysis of oily sludge (OS) faces two significant challenges, dehydration in emulsion and coke formation, which cause extra energy consumption. Targeting energy saving, this paper first reported on solar photothermal dehydration and confined catalytic pyrolysis of OS using a single material. A wood sponge loaded with carbon dots (CM-CDs) can generate heat by absorbing solar energy and promote rapid phase separation and water transport via capillary action of oil–water emulsion in OS under sunlight. Almost all free water in OS with varied content can be removed after 3 h. Hydrocarbons entered the internal space of CM-CDs instead of contacting with soil minerals, contributed to the subsequent confined catalytic pyrolysis, led to a reduction in Ea (35.61 kJ/mol), inhibited coking and caking, and yielded higher oil recovery efficiency. In addition, CDs can form hotspots to enhance pyrolytic behaviors in local regions. When the ratio of OS to CM-CDs reached 10:0.6, the recovery rate of the oil fraction through combined pyrolysis was as high as 89%, which was 17% higher than that of OS pyrolysis alone. This discovery provides a new way to solve the bottleneck problems of OS pyrolysis in the industry. Full article

With the rapid development of energy transformation, the proportion of new energy is increasing, and the efficient trading mechanism of multi-microgrids can realize energy sharing to improve the consumption rate of new energy. A distributed multi-microgrid cooperative energy sharing strategy is proposed based on Nash bargaining. Firstly, by comprehensively considering the adjustable heat-to-electrical ratio, ladder-type positive and negative carbon trading, peak–valley electricity price and demand response, a multi-microgrid system with wind–solar-storage-load and combined heat and power is constructed. Then, a multi-microgrid cooperative game optimization framework is established based on Nash bargaining, and the complex nonlinear problem is decomposed into two stages to be solved. In the first stage, the cost minimization problem of multi-microgrids is solved based on the alternating direction multiplier method to maximize consumption rate and protect privacy. In the second stage, through the established contribution quantification model, Nash bargaining theory is used to fairly distribute the benefits of cooperation. The simulation results of three typical microgrids verify that the proposed strategy has good convergence properties and computational efficiency. Compared with the independent operation, the proposed strategy reduces the cost by 41% and the carbon emission by 18,490 kg, thus realizing low-carbon operation and optimal economic dispatch. Meanwhile, the power supply pressure of the main grid is reduced through energy interaction, thus improving the utilization rate of renewable energy. Full article

Evaluating the renewable energy potential, such as that of solar photovoltaics (PV), is important for developing renewable energy policies. This study investigated rooftop solar PV potential in Thailand based on open-source geographic information system (GIS) building footprints, solar PV power output, and the most recent land use data (2022). GIS-based overlay analysis, buffering, fishnet modeling, and spatial join operations were applied to assess rooftop availability across various building types, taking into account PV module installation parameters and optimal panel orientation. Economic feasibility and sensitivity analyses were conducted using standard economic metrics, including net present value (NPV), internal rate of return (IRR), payback period, and benefit–cost ratio (BCR). The findings showed a total rooftop solar PV power generation potential of 50.32 TWh/year, equivalent to 25.5% of Thailand’s total electricity demand in 2022. The Central region contributed the highest potential (19.59 TWh/year, 38.94%), followed by the Northeastern (10.49 TWh/year, 20.84%), Eastern (8.16 TWh/year, 16.22%), Northern (8.09 TWh/year, 16.09%), and Southern regions (3.99 TWh/year, 7.92%). Both commercial and industrial sectors reflect the financial viability of rooftop PV installations and significantly contribute to the overall energy output. These results demonstrate the importance of incorporating rooftop solar PV in renewable energy policy development in regions with similar data infrastructure, particularly the availability of detailed and standardized land use data for building type classification. Full article

Photovoltaic panels are pivotal in transforming solar irradiance into electricity, making them a key technology in renewable energy. Despite their potential, the distribution of photovoltaic systems in Romania remains sparse, requiring advanced data analytics for effective management, particularly in addressing the intermittent nature of photovoltaic energy. This study investigates the predictive capabilities of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures for forecasting hourly photovoltaic energy production in Romania. The results indicate that CNN models significantly outperform LSTM models, with 77% of CNNs achieving an R² of 0.9 or higher compared to only 13% for LSTMs. The best-performing CNN model reached an R² of 0.9913 with a mean absolute error (MAE) of 9.74, while the top LSTM model achieved an R² of 0.9880 and an MAE of 12.57. The rapid convergence of the CNN model to stable error rates illustrates its superior generalization capabilities. Moreover, the model’s ability to accurately predict photovoltaic production over a two-day timeframe, which is not included in the testing dataset, confirms its robustness. This research highlights the critical role of accurate energy forecasting in optimizing the integration of photovoltaic energy into Romania’s power grid, thereby supporting sustainable energy management strategies in line with the European Union’s climate goals. Through this methodology, we aim to enhance the operational safety and efficiency of photovoltaic systems, facilitating their large-scale adoption and ultimately contributing to the fight against climate change. Full article

Parabolic trough collector (PTC) systems, often deployed in arid regions, are vulnerable to dust accumulation (soiling), which reduces mirror reflectivity and energy output. This study presents a physically based soiling forecast algorithm (SFA) designed to estimate soiling levels. The model was calibrated and validated using three meteorological data sources—numerical forecasts (YR), METAR observations, and on-site measurements—from a PTC facility in Limassol, Cyprus. Field campaigns covered dry, rainy, and red-rain conditions. The model demonstrated robust performance, particularly under dry summer conditions, with normalized root mean square errors (NRMSE) below 1%. Sedimentation emerged as the dominant soiling mechanism, while the contributions of impaction and Brownian motion varied according to site-specific environmental conditions. Under dry deposition conditions, the reflectivity change rate during spring and autumn was approximately twice that of summer, indicating a need for more frequent cleaning during transitional seasons. A red-rain event resulted in a pronounced drop in reflectivity, showcasing the model’s ability to capture abrupt soiling dynamics associated with extreme weather episodes. The proposed SFA offers a practical, adaptable tool for reducing soiling-related losses and supporting seasonally adjusted maintenance strategies for solar thermal systems. Full article

Photovoltaic thermal (PVT) hybrid systems offer a promising approach to maximizing solar energy utilization by combining electricity generation with thermal energy recovery. This study presents an experimental evaluation of a commercially available PVT panel, focusing on its thermal performance under varying inlet temperatures and flow rates. The work addresses a gap in the literature regarding the real-world behavior of integrated systems, particularly in residential settings where space constraints and energy efficiency are crucial. Experimental tests were conducted at three mass flow rates and five inlet water temperatures, demonstrating that lower inlet temperatures and higher flow rates consistently improve thermal efficiency. The best-performing condition was achieved at 0.012 kg/s and 10 °C. These findings deepen our understanding of the panel’s thermal behavior and confirm its suitability for practical applications. The experimental platform developed in this study also enables standardized PVT testing under controlled conditions, supporting consistent evaluation across different settings and contributing to global optimization efforts for hybrid solar technologies. Full article

Predicted natural ventilation (NV) often diverges from actual performance in dwellings. This discrepancy arises in part because most design tools do not account for how occupants actually operate windows. This study aims to determine how window geometry and orientation should be adjusted when occupant behavior is considered. Survey data from 150 Melbourne residents were converted into two window-operation schedules: Same Behavior (SB), representing average patterns, and Probable Behavior (PB), capturing stochastic responses to comfort, privacy, and climate. Both schedules were embedded in EnergyPlus and applied to over 200 annual simulations across five window-design stories that varied orientations, placements, and window-to-wall ratios (WWRs). Each story was tested across two living room wall dimensions (7 m and 4.5 m) and evaluated for air-change rate per hour (ACH) and solar gains. PB increased annual ACH by 5–12% over SB, with the greatest uplift in north-facing cross-ventilated layouts on the wider wall. Integrating probabilistic occupant behavior into window design remarkably improves NV effectiveness, with peak summer ACH reaching 4.8, indicating high ventilation rates that support thermal comfort and improved IAQ without mechanical assistance. These results highlight the potential of occupant-responsive window configurations to reduce reliance on mechanical cooling and enhance indoor air quality (IAQ). This study contributes a replicable occupant-centered workflow and ready-to-apply design rules for Australian temperate climates, adapted to different climate zones. Future research will extend the method to different climates, housing types, and user profiles and will integrate smart-sensor feedback, adaptive glazing, and hybrid ventilation strategies through multi-objective optimization. Full article

The transition to renewable energy is critical to enhance Puerto Rico’s energy resilience and reduce dependence on imported fossil fuels. Rooftop photovoltaic (PV) systems provide a scalable opportunity to meet these objectives. This study evaluates the potential of rooftop PV systems across Puerto Rico using the National Renewable Energy Laboratory’s (NREL) PV Rooftop Database, processing detailed roof surface data to estimate installed capacity, energy generation, Levelized Cost of Electricity (LCOE), and solar resource potential at municipal and barrio levels. Findings reveal high solar rooftop capacity in urban neighborhoods, with areas like Sabana Abajo and Hato Tejas each exceeding 450 GWh/year in potential generation. Solar rooftop resource values peak at 3.67 kWh/kW in coastal areas, with LCOE values (0.071–0.215 USD/kWh) below current electricity rates. All municipalities demonstrate technical potential to meet their electricity demand with rooftop PV system alone. This research contributes through (1) developing Puerto Rico’s first comprehensive solar rooftop potential map; (2) providing unprecedented barrio-level analysis; (3) introducing a methodology for estimating missing post-disaster consumption data; and (4) integrating technical, economic, and equity indicators to inform energy policy. These findings demonstrate the importance of rooftop solar in achieving renewable energy goals and provide an understanding of spatial energy inequalities. Full article

This study investigates a hybrid renewable energy system combining the municipal solid waste (MSW) gasification and solar photovoltaic (PV) for electricity generation in Lobito, Angola. A fixed-bed downdraft gasifier was selected for MSW gasification, where the thermal decomposition of waste under controlled air flow produces syngas rich in CO and H₂. The syngas is treated to remove contaminants before powering a combined cycle. The PV system was designed for optimal energy generation, considering local solar radiation and shading effects. Simulation tools, including Aspen Plus v11.0, PVsyst v8, and HOMER Pro software 3.16.2, were used for modeling and optimization. The hybrid system generates 62 GWh/year of electricity, with the gasifier contributing 42 GWh/year, and the PV system contributing 20 GWh/year. This total energy output, sufficient to power 1186 households, demonstrates an integration mechanism that mitigates the intermittency of solar energy through continuous MSW gasification. However, the system lacks surplus electricity for green hydrogen production, given the region’s energy deficit. Economically, the system achieves a Levelized Cost of Energy of 0.1792 USD/kWh and a payback period of 16 years. This extended payback period is mainly due to the hydrogen production system, which has a low production rate and is not economically viable. When excluding H₂ production, the payback period is reduced to 11 years, making the hybrid system more attractive. Environmental benefits include a reduction in CO₂ emissions of 42,000 t/year from MSW gasification and 395 t/year from PV production, while also addressing waste management challenges. This study highlights the mechanisms behind hybrid system operation, emphasizing its role in reducing energy poverty, improving public health, and promoting sustainable development in Angola. Full article

This study investigates the potential of low-cost, naturally available porous materials (PoMs), gravel, marble, flint, and sandstone, as thermal management for photovoltaic (PV) panels. Experiments were conducted in a controlled environment at a solar energy laboratory, where variables such as solar irradiance, ambient temperature, air velocity, and water flow were carefully regulated. A solar simulator delivering a constant irradiance of 1250 W/m² was used to replicate solar conditions throughout each 3 h trial. The test setup involved polycrystalline PV panels (30 W rated) fitted with cooling channels filled with PoMs of varying porosities (0.35–0.48), evaluated across water flow rates ranging from 1 to 4 L/min. Experimental results showed that PoM cooling significantly outperformed both water-only and passive cooling. Among all the materials tested, sandstone with a porosity of 0.35 and a flow rate of 2.0 L/min demonstrated the highest cooling performance, reducing the panel surface temperature by 58.08% (from 87.7 °C to 36.77 °C), enhancing electrical efficiency by 57.87% (from 4.13% to 6.52%), and increasing power output by 57.81% (from 12.42 W to 19.6 W) compared to the uncooled panel. The enhanced heat transfer (HT) was attributed to improved conductive and convective interactions facilitated by lower porosity and optimal fluid velocity. Furthermore, the cooling system improved I–V characteristics by stabilizing short-circuit current and enhancing open-circuit voltage. Comparative analysis revealed material-dependent efficacy—sandstone > flint > marble > gravel—attributed to thermal conductivity gradients (sandstone: 5 W/m·K vs. gravel: 1.19 W/m·K). The configuration with 0.35 porosity and a 2.0 L/min flow rate proved to be the most effective, offering an optimal balance between thermal performance and resource usage, with an 8–10% efficiency gain over standard water cooling. This study highlights 2.0 L/min as the ideal flow rate, as higher rates lead to increased water usage without significant cooling improvements. Additionally, lower porosity (0.35) enhances convective heat transfer, contributing to improved thermal performance while maintaining energy efficiency. Full article

This study investigates the influence of input values for building energy model parameters on simulation results, with the aim of improving the reliability and sustainability of energy performance assessments. Dynamic simulations were conducted in TRNSYS for three theoretical multi-residential buildings, varying parameters such as referent model dimensions, infiltration rates, envelope thermophysical properties, and interior thermal capacitance. The case study, based in Slovenia, demonstrates that glazing-related parameters, particularly the solar heat gain coefficient (g-value), exert the most significant influence—reducing the g-value from 0.62 to 0.22 decreased simulated heating (q_H,nd) and cooling (q_C,nd) demands by 25% and 95%, respectively. In contrast, referent dimensions for modeled floor area proved least influential. For Building III (BSF = 0.36), dimensional variations altered results by less than ±1%, whereas, for Building I (BSF = 0.62), variations reached up to ±20%. In general, lower shape factors yield more robust energy models that are less sensitive to input deviations. These findings are critical for promoting resource-efficient simulation practices and ensuring that energy modeling contributes effectively to sustainable building design. Understanding which inputs warrant detailed attention supports more targeted and meaningful simulation workflows, enabling more accurate and impactful strategies for building energy efficiency and long-term environmental performance. Full article