Search Results (96)

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems. Full article

Forecasting future solar power plant production is essential to continue the development of photovoltaic energy and increase its share in the energy mix for a more sustainable future. Accurate solar radiation forecasting greatly improves the balance maintenance between energy supply and demand and grid management performance. This study assesses the influence of input selection on short-term global horizontal irradiance (GHI) forecasting across two contrasting Algerian climates: arid Ghardaïa and coastal Algiers. Eight feature selection methods (Pearson, Spearman, Mutual Information (MI), LASSO, SHAP (GB and RF), and RFE (GB and RF)) are evaluated using a Gradient Boosting model over horizons from one to six hours ahead. Input relevance depends on both the location and forecast horizon. At t+1, MI achieves the best results in Ghardaïa (nMAE = 6.44%), while LASSO performs best in Algiers (nMAE = 10.82%). At t+6, SHAP- and RFE-based methods yield the lowest errors in Ghardaïa (nMAE = 17.17%), and RFE-GB leads in Algiers (nMAE = 28.13%). Although performance gaps between methods remain moderate, relative improvements reach up to 30.28% in Ghardaïa and 12.86% in Algiers. These findings confirm that feature selection significantly enhances accuracy (especially at extended horizons) and suggest that simpler methods such as MI or LASSO can remain effective, depending on the climate context and forecast horizon. Full article

This study aims to assess the carbon footprint for the organization of frozen processed seafood manufacturing plants and propose sustainable strategies for reducing greenhouse gas emissions. Organizational activity data from 2024 were utilized to evaluate the carbon footprint and develop targeted mitigation measures. The findings indicate that Scope 1 emissions amounted to 12,685 tons of CO₂eq, Scope 2 emissions amounted to 15,403 tons of CO₂eq, and Scope 3 emissions amounted to 31,564 tons of CO₂eq. The total greenhouse gas emissions across all three scopes were 59,652 tons of CO₂eq, with additional greenhouse gas emissions recorded at 34,027 tons of CO₂eq. Mitigation measures were considered for activities contributing to at least 10% of emissions in each scope. In Scope 1, the use of R507 refrigerant in the production cooling system accounted for 9907 tons of CO₂eq, representing 78.10% of emissions. In Scope 2, electricity consumption contributed 15,403 tons of CO₂eq, constituting 100% of emissions. In Scope 3, the procurement of surimi (processed fish meat) was responsible for 20,844 tons of CO₂eq, accounting for 66.04% of emissions. Based on these findings, key mitigation strategies were proposed. For Scope 1, reducing emissions involves preventive maintenance of cooling systems to prevent leaks, replacing corroded pipelines, installing shut-off valves, and switching to alternative refrigerants with no greenhouse gas emissions. For Scope 2, energy-saving initiatives include promoting electricity conservation within the organization, maintaining equipment for optimal efficiency, installing energy-saving devices such as variable speed drives (VSD), upgrading to high-efficiency motors, and utilizing renewable energy sources like solar power. For Scope 3, emissions can be minimized by sourcing raw materials from suppliers with certified carbon footprint labels, prioritizing purchases from producers committed to carbon reduction, and selecting suppliers closer to manufacturing sites to reduce transportation-related emissions. Implementing these strategies will contribute to sustainable greenhouse gas emission reductions. Full article

This research aims to calculate the Carbon Footprint for Organization of a plant manufacturing frozen processed seafood and propose strategies to reduce greenhouse gas (GHG) emissions following the Net-Zero Pathway, using 2024 as the baseline year. The findings indicate that Scope 1 emissions amounted to 12,685 tons of CO₂ eq, Scope 2 emissions totaled 15,403 tons of CO₂eq, and Scope 3 emissions reached 31,564 tons of CO₂eq, leading to a combined total of 59,652 tons of CO₂eq across all scopes, with an additional 34,027 tons of CO₂eq from other GHG sources. To achieve net-zero emissions by 2050, annual reductions of 3.46% per category are required. The short-term target for 2028f aims to reduce emissions to 10,929 tons of CO₂eq for Scope 1, 13,270 tons of CO₂eq for Scope 2, and 27,194 tons of CO₂eq for Scope 3, resulting in total emissions of 51,392 tons of CO₂eq. The proposed reduction strategies include optimizing Scope 1 emissions by preventing leaks in R507 refrigerant systems, replacing corroded pipelines, installing shut-off valves, and switching to low-GHG refrigerants. For Scope 2, measures focus on reducing electricity consumption through energy conservation initiatives, carrying out regular machinery maintenance, installing Variable Speed Drives (VSDs), upgrading to high-efficiency motors, and integrating renewable energy sources such as solar power. For Scope 3, emissions from raw material procurement can be minimized by sourcing from certified suppliers with established product carbon footprints, prioritizing carbon reduction labeling, and selecting nearby suppliers to reduce transportation-related emissions. These strategies will support the organization in achieving carbon neutrality and progressing toward the net-zero goal. Full article

The HERMES project investigates the utilization of surplus wind and solar energy to produce renewable fuels such as hydrogen, methane, and methanol for seasonal storage, thereby supporting carbon neutrality and the energy transition. This initiative aims to create a closed-loop, zero-emission energy system with efficiencies of up to 65%, employing a low-pressure (≤30 bar) synthesis process—specifically, sorption-enhanced methanol synthesis—integrated into the power system. Excess renewable electricity is harnessed for chemical synthesis, beginning with electrolysis to generate hydrogen, which is then converted into methanol using CO₂ sourced from a biogas plant. This methanol, biomethane, or a hybrid fuel blend powers a supercritical gas turbine, providing a flexible and reliable energy supply. Optimization analysis indicates that a combined wind and photovoltaic system can meet 62% of electricity demand, while the proposed storage system can handle over 90%. Remarkably, liquid methanol storage requires a compact 313 m³ tank, significantly smaller than storage requirements for hydrogen or methane in gas form. The project entails a total investment of 105 M EUR and annual operation and maintenance costs of 3.1 M EUR, with the levelized cost of electricity expected to decrease by 43% in the short term and 69% in the long term as future investment costs decline. Full article

Photovoltaic systems are among the renewable energy sources with the greatest global impact, driven by technologies that enable real-time monitoring, predictive maintenance, and intelligent integration with the electricity grid. In this context, this paper presents the design and implementation of an embedded Internet of Things (IoT) system to monitor temperature and humidity in photovoltaic systems in the Amazon region. The system was implemented in a photovoltaic solar plant located at the Federal University of Pará and used to monitor parameters such as local humidity and temperature, with the latter being considered at three strategic points: the surface of the photovoltaic module exposed to direct solar radiation, the shaded area of the module, and the ambient temperature. The results obtained showed good performance from the embedded system, with emphasis on the ease of remotely updating the embedded system’s code and centralized visualization of the monitored data in an IoT middleware. The device proved to be resistant to the adverse climatic conditions of the Amazon, allowing the operators and managers of the photovoltaic plant to monitor and visualize the measured variables and to draw up preventive and corrective maintenance strategies. In this way, the embedded system designed and implemented is a valuable tool for the photovoltaic plant’s operators and managers, promoting greater energy efficiency, reducing operating costs and increasing the useful life of the modules. It also contributes to the Sustainable Development Goals (SDGs), such as SDG 7 (Clean and affordable energy) and SDG 13 (Climate action). Full article

Green sustainable energy, especially renewable energy, is gaining huge popularity and is considered a vital energy in addressing energy conservation and global climate change. One of the most significant renewable energy sources in the UAE is solar energy, due to the country’s high solar radiation levels. This paper focuses on advanced technology that integrates parabolic trough mirrors, molten salt storage, and thermoelectric generators (TEGs) to provide a reliable and effective solar system in the UAE. Furthermore, the new system can be manufactured in different sizes suitable for consumption whether in ordinary houses or commercial establishments and businesses. The proposed design theoretically achieves the target electrical energy of 2.067 kWh/day with 90% thermal efficiency, 90.2% optical efficiency, and 8% TEG efficiency that can be elevated to higher values reaching 149% using the liquid-saturated porous medium, ensuring the operation of the system throughout the day. This makes it a suitable solar system in off-grid areas. Moreover, this system is a cost-effective, carbon-free, and day-and-night energy source that can be dispatched on the electric grid like any fossil fuel plant under the proposed method, with less maintenance, thus contributing to the UAE’s renewable energy strategy. Full article

This study compares the technological routes of concrete production in Panama from an environmental perspective, focusing on water, energy, and CO₂ flows per process to identify opportunities for improvement. It addresses a critical gap found in the literature where flow diagrams and production processes are presented as being standardized across concrete plants, offering an in-depth qualitative analysis of resource flows. Data from 20 concrete plants revealed significant variability in resource use and potential environmental impacts due to differences in technology, location, and resource availability. Flow diagrams and similarity dendrograms highlight the similarities and differences in the technological routes. The key findings include variability in water sources and energy consumption patterns, with some utilizing rainwater harvesting and water recycling and most plants relying on grid electricity and diesel. The best practices include the implementation of environmental indicators and water recycling systems. CO₂ injection, already adopted by two plants, shows promise; however, its potential additional energy demands should be assessed. Covering aggregate storage areas for temperature control reduces water spraying needs and could support rainwater harvesting, with opportunities to integrate solar panels. Regular maintenance of concrete trucks also enhances efficiency and reduces environmental impact due to diesel consumption. The study underscores the importance of tailored strategies to improve water and energy efficiency, aligning with national and international initiatives such as “Reduce tu Huella” (Reduce your Footprint) and the 2030 Agenda. These findings provide actionable insights to support the development of a more sustainable concrete industry in Panama and beyond. Full article

Solar photovoltaic (SPV) arrays play a pivotal role in advancing clean and sustainable energy systems, with a worldwide total installed capacity of 1.6 terawatts and annual investments reaching USD 480 billion in 2023. However, climate disaster effects, particularly extremely hot weather events, can compromise the performance and resilience of SPV panels through thermal deterioration and degradation, which may lead to lessened operational life and potential failure. These heatwave-related consequences highlight the need for timely inspection and precise anomaly diagnosis of SPV panels to ensure optimal energy production. This case study focuses on intelligent remote inspection by employing aerial radiometric infrared thermography within a predictive maintenance framework to enhance diagnostic monitoring and early scrutiny capabilities for SPV power plant sites. The proposed methodology leverages pre-trained deep learning (DL) algorithms, enabling a deep transfer learning approach, to test the effectiveness of multiclass classification (or diagnosis) of various thermal anomalies of the SPV panel. This case study adopted a highly imbalanced 6-class thermographic radiometric dataset (floating-point temperature numerical values in degrees Celsius) for training and validating the pre-trained DL predictive classification models and comparing them with a customized convolutional neural network (CNN) ensembled model. The performance metrics demonstrate that among selected pre-trained DL models, the MobileNetV2 exhibits the highest F1 score (0.998) and accuracy (0.998), followed by InceptionV3 and VGG16, which recorded an F1 score of 0.997 and an accuracy of 0.998 in performing the smart inspection of 6-class thermal anomalies, whereas the customized CNN ensembled model achieved both a perfect F1 score (1.000) and accuracy (1.000). Furthermore, to create trust in the intelligent inspection system, we investigated the pre-trained DL predictive classification models using perceptive explainability to display the most discriminative data features, and mathematical-structure-based interpretability to portray multiclass feature clustering. Full article

Maintenance and monitoring of solar photovoltaic (PV) systems are essential for enhancing reliability, extending lifespan, and maintaining efficiency. Some defects in PV cells cannot be detected through output measurements due to the string configuration of interconnected cells. Inspection methods such as thermal imaging, electroluminescence, and photoluminescence are commonly used for fault detection. Among these, thermal imaging is widely adopted for diagnosing PV modules due to its rapid procedure, affordability, and reliability in identifying defects. Similarly, current–voltage (I-V) curve analysis provides valuable insights into the electrical performance of solar cells, offering critical information on potential defects and operational inconsistencies. Different data types can be effectively managed and analyzed using artificial intelligence (AI) algorithms, enabling accurate predictions and automated processing. This paper presents the development of a machine learning algorithm utilizing transfer learning, with thermal imaging and I-V curves as dual and single inputs, to validate its effectiveness in detecting faults in PV cells at King Saud University, Riyadh. Findings demonstrate that integrating thermal images with I-V curve data significantly enhances defect detection by capturing both surface-level and performance-based information, achieving an accuracy and recall of more than 98% for both dual and single inputs. The approach reduces resource requirements while improving fault detection accuracy. With further development, this hybrid method holds the potential to provide a more comprehensive diagnostic solution, improving system performance assessments and enabling the adoption of proactive maintenance strategies, with promising prospects for large-scale solar plant implementation. Full article

Concentrated Solar Power (CSP) plants integrated with Thermal Energy Storage (TES) represent a promising renewable energy source for generating heat and power. Binary molten salt mixtures, commonly referred to as Solar Salts, are utilized as effective heat transfer fluids and storage media due to their thermal stability and favorable thermophysical properties. However, these mixtures pose significant challenges due to their high solidification temperatures, around 240 °C, which can compromise the longevity and reliability of critical system components such as pressure sensors and bellows seal globe valves. Thus, it is essential to characterize their performance, assess their reliability under various conditions, and understand their failure mechanisms, particularly in relation to temperature fluctuations affecting the fluid’s viscosity. This article discusses experimental tests conducted on a pressure sensor and a bellows seal globe valve, both designed for direct contact with molten salts in CSP environments, at the ENEA Casaccia Research Center laboratory in Rome. The methodology for conducting these experimental tests is detailed, and guidelines are outlined to optimize plant operation. The findings provide essential insights for improving component design and maintenance to minimize unplanned plant downtime. They also offer methodologies for installing measurement instruments and electrical heating systems on the components. Full article

This paper presents a systematic review that explores the latest advancements in predictive maintenance methods and cybersecurity for solar panel systems, shedding light on the advantages and challenges of the most recent developments in predictive maintenance techniques for solar plants. Numerous important research studies, reviews, and empirical studies published between 2018 and 2023 are examined. These technologies help in detecting defects, degradation, and anomalies in solar panels by facilitating early intervention and reducing the probability of inverter failures. The analysis also emphasizes how challenging it is to adopt predictive maintenance in the renewable energy industry. Achieving a balance between model complexity and accuracy, dealing with system unpredictability, and adjusting to shifting environmental conditions are among the challenges. It also highlights the Internet of Things (IoT), machine learning (ML), and deep learning (DL), which are all incorporated into solar panel predictive maintenance. By enabling real-time monitoring, data analysis, and anomaly identification, these developments improve the accuracy and effectiveness of maintenance procedures. Full article

The stilt house communities within Ciénaga Grande de Santa Marta (CGSM), Nueva Venecia and Buenavista, have historically lacked access to water under safe conditions. To address this need, a pilot study was implemented, employing two methods to obtain drinking water through non-conventional and sustainable energies: solar distillation and a conventional treatment plant supplied by solar energy. This study involved the local communities and their traditional knowledge at all stages: planning, design, implementation, operation, maintenance, and monitoring. The solar distillers produced a total 9652 L of water, with average yields of 2.8 L m² day⁻¹ and 1.2 L m² day⁻¹ in the villages of Nueva Venecia and Buenavista, respectively. Likewise, the treatment plants reached a total water production of 790,000 L. Both methods produced water following the quality standards for human consumption. This demonstrates the applicability of both methods in using alternative energy to obtain drinking water while considering the environmental and social conditions of the study area, thereby strengthening community self-management to improve access to water. Full article

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model’s performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter’s efficiency compared to the inverter’s power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants. Full article

Photovoltaic energy (PV) is considered one of the pillars of the energy transition. However, this energy source is limited by a power density per unit surface lower than 200 W/m², depending on the latitude of the installation site. Compared to fossil fuels, such low power density opens a sustainability issue for this type of renewable energy in terms of its competition with other land uses, and forces us to consider areas suitable for the installation of photovoltaic arrays other than farmlands. In this frame, floating PV plants, installed in internal water basins or even offshore, are receiving increasing interest. On the other hand, this kind of installation might significantly affect the water ecosystem environment in various ways, such as by the effects of solar shading or of anchorage installation. As a result, monitoring of floating PV (FPV) plants, both during the ex ante site evaluation phase and during the operation of the PV plant itself, is therefore necessary to keep such effects under control. This review aims to examine the technical and academic literature on FPV plant monitoring, focusing on the measurement and discussion of key physico-chemical parameters. This paper also aims to identify the additional monitoring features required for energy assessment of a floating PV system compared to a ground-based PV system. Moreover, due to the intrinsic difficulty in the maintenance operations of PV structures not installed on land, novel approaches have introduced autonomous solutions for monitoring the environmental impacts of FPV systems. Technologies for autonomous mapping and monitoring of water bodies are reviewed and discussed. The extensive technical literature analyzed in this review highlights the current lack of a cohesive framework for monitoring these impacts. This paper concludes that there is a need to establish general guidelines and criteria for standardized water quality monitoring (WQM) and management in relation to FPV systems. Full article