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Search Results (243)

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Keywords = high-speed solar wind

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32 pages, 1970 KiB  
Review
A Review of New Technologies in the Design and Application of Wind Turbine Generators
by Pawel Prajzendanc and Christian Kreischer
Energies 2025, 18(15), 4082; https://doi.org/10.3390/en18154082 (registering DOI) - 1 Aug 2025
Viewed by 112
Abstract
The growing global demand for electricity, driven by the development of electromobility, data centers, and smart technologies, necessitates innovative approaches to energy generation. Wind power, as a clean and renewable energy source, plays a pivotal role in the global transition towards low-carbon power [...] Read more.
The growing global demand for electricity, driven by the development of electromobility, data centers, and smart technologies, necessitates innovative approaches to energy generation. Wind power, as a clean and renewable energy source, plays a pivotal role in the global transition towards low-carbon power systems. This paper presents a comprehensive review of generator technologies used in wind turbine applications, ranging from conventional synchronous and asynchronous machines to advanced concepts such as low-speed direct-drive (DD) generators, axial-flux topologies, and superconducting generators utilizing low-temperature superconductors (LTS) and high-temperature superconductors (HTS). The advantages and limitations of each design are discussed in the context of efficiency, weight, reliability, scalability, and suitability for offshore deployment. Special attention is given to HTS-based generator systems, which offer superior power density and reduced losses, along with challenges related to cryogenic cooling and materials engineering. Furthermore, the paper analyzes selected modern generator designs to provide references for enhancing the performance of grid-synchronized hybrid microgrids integrating solar PV, wind, battery energy storage, and HTS-enhanced generators. This review serves as a valuable resource for researchers and engineers developing next-generation wind energy technologies with improved efficiency and integration potential. Full article
(This article belongs to the Special Issue Advancements in Marine Renewable Energy and Hybridization Prospects)
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10 pages, 1640 KiB  
Communication
Investigating the Effects of the Solar Eclipse on the Atmosphere over Land and Oceanic Regions: Observations from Ground Stations and COSMIC2 Data
by Ghouse Basha, M. Venkat Ratnam, Jonathan H. Jiang and Kishore Pangaluru
Atmosphere 2025, 16(7), 872; https://doi.org/10.3390/atmos16070872 - 17 Jul 2025
Viewed by 277
Abstract
The impacts of the solar eclipse that occurred on 8 April 2024 over the United States on various atmospheric parameters are investigated. We analyzed surface and vertical profiles of temperature and humidity to understand how this eclipse affected the atmosphere from the ground [...] Read more.
The impacts of the solar eclipse that occurred on 8 April 2024 over the United States on various atmospheric parameters are investigated. We analyzed surface and vertical profiles of temperature and humidity to understand how this eclipse affected the atmosphere from the ground to the stratosphere. Our findings show a significant response throughout the atmospheric range. The eclipse caused a decrease in shortwave radiation, leading to cooler Earth surfaces and a subsequent drop in surface temperature. This cooling effect also resulted in high relative humidity and lower wind speeds at the surface. Furthermore, GPS radio occultation data from COSMIC-2 revealed a decrease in tropospheric temperature and increase in stratospheric temperature during the eclipse. We also observed a reduction in both the temperature and height of the tropopause. The uniqueness of the present investigations lies in delineating the solar eclipse’s effects on the land and ocean. Our analysis indicates that land regions experienced a more pronounced temperature change compared to ocean regions. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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20 pages, 6173 KiB  
Article
Research on an Energy-Harvesting System Based on the Energy Field of the Environment Surrounding a Photovoltaic Power Plant
by Bin Zhang, Binbin Wang, Hongxi Zhang, Abdelkader Outzourhit, Fouad Belhora, Zoubir El Felsoufi, Jia-Wei Zhang and Jun Gao
Energies 2025, 18(14), 3786; https://doi.org/10.3390/en18143786 - 17 Jul 2025
Viewed by 289
Abstract
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 [...] Read more.
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
(This article belongs to the Section D: Energy Storage and Application)
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19 pages, 3993 KiB  
Article
Optical Monitoring of Particulate Matter: Calibration Approach, Seasonal and Diurnal Dependency, and Impact of Meteorological Vectors
by Salma Zaim, Bouchra Laarabi, Hajar Chamali, Abdelouahed Dahrouch, Asmae Arbaoui, Khalid Rahmani, Abdelfettah Barhdadi and Mouhaydine Tlemçani
Environments 2025, 12(7), 244; https://doi.org/10.3390/environments12070244 - 16 Jul 2025
Viewed by 478
Abstract
The worldwide air pollution situation reveals significant environmental challenges. In addition to being a major contributor to the deterioration of air quality, particulate matter (PM) is also an important factor affecting the performance of solar energy systems given its ability to decrease light [...] Read more.
The worldwide air pollution situation reveals significant environmental challenges. In addition to being a major contributor to the deterioration of air quality, particulate matter (PM) is also an important factor affecting the performance of solar energy systems given its ability to decrease light transmission to solar panels. As part of our research, the present investigation involves monitoring concentrations of PM using a high-performance optical instrument, the in situ calibration protocol of which is described in detail. For the city of Rabat, observations revealed significant variations in concentrations between day and night, with peaks observed around 8 p.m. correlating with high relative humidity and low wind speeds, and the highest levels recorded in February with a monthly average value reaching 75 µm/m3. In addition, an experimental protocol was set up for an analysis of the elemental composition of particles in the same city using SEM/EDS, providing a better understanding of their morphology. To assess the impact of meteorological variables on PM concentrations in two distinct climatic environments, a database from the city of Marrakech for the year 2024 was utilized. Overall, the distribution of PM values during this period did not fluctuate significantly, with a monthly average value not exceeding 45 µm/m3. The random forest method identified the most influential variables on these concentrations, highlighting the strong influence of the type of environment. The findings provide crucial information for the modeling of solar installations’ soiling and for improving understanding of local air quality. Full article
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30 pages, 1501 KiB  
Article
Comprehensive Assessment of PeriodiCT Model for Canopy Temperature Forecasting
by Quanxi Shao, Rose Roche, Hiz Jamali, Chris Nunn, Bangyou Zheng, Huidong Jin, Scott C. Chapman and Michael Bange
Agronomy 2025, 15(7), 1665; https://doi.org/10.3390/agronomy15071665 - 9 Jul 2025
Viewed by 348
Abstract
Canopy temperature is an important indicator of plants’ water status. The so-called PeriodiCT model was developed to forecast canopy temperature using ambient weather variables, providing a powerful tool for planning crop irrigation scheduling. As this model requires observed data in its parameter training [...] Read more.
Canopy temperature is an important indicator of plants’ water status. The so-called PeriodiCT model was developed to forecast canopy temperature using ambient weather variables, providing a powerful tool for planning crop irrigation scheduling. As this model requires observed data in its parameter training before implementing the forecast, it is important to understand the data requirements in the model training such that accurate forecasts are attained. In this work, we conduct a comprehensive assessment of the PeriodiCT model in terms of sample size requirement and predictabilities across sensors in a field and across seasons for the full model and sub-models. The results show that (1) 5 days’ observations are sufficient for the full model and sub-models to achieve very high predictability, with a minimum coefficient of efficiency of 0.844 for the full model and 0.840 for the sub-model using only air temperature. The predictability decreases in the following order: full model, sub-model without radiation S, with air temperature Ta and vapor pressure VP, and with only Ta. The predictions perform reasonably well even when only one day’s observations are used. (2) The predictability into the future is very stable as the prediction steps increase. (3) The predictabilities of the full and sub-models when using a trained model from one sensor for another sensor perform comparatively well, with a minimum coefficient of efficiency of 0.719 for the full model and 0.635 for the sub-model using only air temperature. (4) The predictabilities of the sub-models without solar radiation when using trained models from one season for another season perform comparatively well, with a minimum coefficient of efficiency of 0.866 for the full model and 0.764 for the sub-model using only air temperature, although the cross-season performances are not as good as the cross-sensor performances. The importance of the predictors is in the order of air temperature, vapor pressure, wind speed, and solar radiation, while vapor pressure and wind speed have similar contributions, and solar radiation has only a marginal contribution. Full article
(This article belongs to the Section Water Use and Irrigation)
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18 pages, 2458 KiB  
Article
Co-Optimized Design of Islanded Hybrid Microgrids Using Synergistic AI Techniques: A Case Study for Remote Electrification
by Ramia Ouederni and Innocent E. Davidson
Energies 2025, 18(13), 3456; https://doi.org/10.3390/en18133456 - 1 Jul 2025
Viewed by 466
Abstract
Off-grid and isolated rural communities in developing countries with limited resources require energy supplies for daily residential use and social, economic, and commercial activities. The use of data from space assets and space-based solar power is a feasible solution for addressing ground-based energy [...] Read more.
Off-grid and isolated rural communities in developing countries with limited resources require energy supplies for daily residential use and social, economic, and commercial activities. The use of data from space assets and space-based solar power is a feasible solution for addressing ground-based energy insecurity when harnessed in a hybrid manner. Advances in space solar power systems are recognized to be feasible sources of renewable energy. Their usefulness arises due to advances in satellite and space technology, making valuable space data available for smart grid design in these remote areas. In this case study, an isolated village in Namibia, characterized by high levels of solar irradiation and limited wind availability, is identified. Using NASA data, an autonomous hybrid system incorporating a solar photovoltaic array, a wind turbine, storage batteries, and a backup generator is designed. The local load profile, solar irradiation, and wind speed data were employed to ensure an accurate system model. Using HOMER Pro software V 3.14.2 for system simulation, a more advanced AI optimization was performed utilizing Grey Wolf Optimization and Harris Hawks Optimization, which are two metaheuristic algorithms. The results obtained show that the best performance was obtained with the Grey Wolf Optimization algorithm. This method achieved a minimum energy cost of USD 0.268/kWh. This paper presents the results obtained and demonstrates that advanced optimization techniques can enhance both the hybrid system’s financial cost and energy production efficiency, contributing to a sustainable electricity supply regime in this isolated rural community. Full article
(This article belongs to the Section F2: Distributed Energy System)
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32 pages, 1002 KiB  
Article
A Robust Modeling Analysis of Environmental Factors Influencing the Direct Current, Power, and Voltage of Photovoltaic Systems
by Ali Al Humairi, Hayat El Asri, Zuhair A. Al Hemyari and Peter Jung
Electronics 2025, 14(13), 2647; https://doi.org/10.3390/electronics14132647 - 30 Jun 2025
Viewed by 329
Abstract
Solar photovoltaic technology has become a cornerstone of the renewable energy sector over the last 20 years, yet its efficiency remains sensitive to environmental and operational conditions. This study rigorously analyzes how irradiance, temperature, humidity, wind speed, and soiling affect key electrical outputs—Direct [...] Read more.
Solar photovoltaic technology has become a cornerstone of the renewable energy sector over the last 20 years, yet its efficiency remains sensitive to environmental and operational conditions. This study rigorously analyzes how irradiance, temperature, humidity, wind speed, and soiling affect key electrical outputs—Direct current, power, and voltage—of solar panels using advanced robust regression methods: Ridge Regression, Least Absolute Deviation, and M-Estimation. Our results demonstrate that irradiance is the dominant driver of performance, with Ridge Regression coefficients reaching up to 1193 for power. The M-estimation model achieved high predictive accuracy, with R2 Scores of 0.989 for current (Mean Squared Error = 0.0399) and 0.991 for power (Mean Squared Error ≈ 10,445), indicating strong model reliability. voltage prediction was more challenging but still robust (R2 = 0.836, Mean Squared Error = 49.63). Negative impacts from ambient temperature and humidity were consistently observed across models, while wind speed exhibited a beneficial effect by enhancing cooling and thus improving current and power outputs. Soiling was also identified as a critical factor, significantly reducing power and voltage generation. These findings provide quantifiable evidence of how environmental variables shape solar photovoltaic performance and underscore the importance of environmental monitoring and maintenance strategies to optimize energy yield in operational solar power systems. Full article
(This article belongs to the Special Issue Energy Optimization of Photovoltaic Power Plants)
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16 pages, 3613 KiB  
Article
Temporal and Spatial Dynamics of Dust Storms in Uzbekistan from Meteorological Station Records (2010–2023)
by Natella Rakhmatova, Bakhriddin E. Nishonov, Lyudmila Shardakova, Albina Akhmedova, Alisher Khudoyberdiev, Valeriya Rakhmatova and Dmitry A. Belikov
Atmosphere 2025, 16(7), 782; https://doi.org/10.3390/atmos16070782 - 26 Jun 2025
Viewed by 773
Abstract
This study provides a comprehensive spatiotemporal analysis of sand and dust storms (SDSs) in Uzbekistan using ground-based meteorological data from 2010 to 2023. The results reveal significant spatial heterogeneity in the SDS activity, with the highest frequency of SDS days observed in the [...] Read more.
This study provides a comprehensive spatiotemporal analysis of sand and dust storms (SDSs) in Uzbekistan using ground-based meteorological data from 2010 to 2023. The results reveal significant spatial heterogeneity in the SDS activity, with the highest frequency of SDS days observed in the southern and western regions, including Surkhandarya, Kashkadarya, Bukhara, Khorezm, and Republic of Karakalpakstan. In the most vulnerable areas, such as Karakalpakstan, Surkhandarya, and Kashkadarya, the annual number of SDS days can exceed 80 in certain years, reflecting a high recurrence of extreme dust events in certain climatic zones. About 53% of the SDS events were regional, affecting several stations, while 47% were localized, indicating a combination of large-scale dust transport and localized emissions. Seasonal patterns showed a peak SDS activity between March and August, coinciding with the dry season characterized by elevated temperatures, reduced soil moisture, and intense agricultural activity, all of which contribute to the surface exposure and increased vulnerability. This study found a significant variation in the event duration across regions, with Karakalpakstan and Surkhandarya experiencing the highest proportion of prolonged events due to its orography and persistent southerly wind patterns. Using ERA5 data and a decision tree regressor, the analysis identified the wind direction and mean wind speed as the most influential meteorological factors, followed by the maximum wind speed and soil temperature, with other variables such as solar radiation and soil moisture playing moderate roles. This study highlights the importance of regional wind patterns and geomorphology in SDS formation, with prevailing wind directions from the northwest, west, and south. The integration of the ERA5 reanalysis and machine learning techniques offers significant potential for improving SDS monitoring and studies. Full article
(This article belongs to the Section Meteorology)
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21 pages, 1107 KiB  
Article
Coordinated Scheduling Strategy for Campus Power Grid and Aggregated Electric Vehicles Within the Framework of a Virtual Power Plant
by Xiao Zhou, Cunkai Li, Zhongqi Pan, Tao Liang, Jun Yan, Zhengwei Xu, Xin Wang and Hongbo Zou
Processes 2025, 13(7), 1973; https://doi.org/10.3390/pr13071973 - 23 Jun 2025
Viewed by 437
Abstract
The inherent intermittency and uncertainty of renewable energy generation pose significant challenges to the safe and stable operation of power grids, particularly when power demand does not match renewable energy supply, leading to issues such as wind and solar power curtailment. To effectively [...] Read more.
The inherent intermittency and uncertainty of renewable energy generation pose significant challenges to the safe and stable operation of power grids, particularly when power demand does not match renewable energy supply, leading to issues such as wind and solar power curtailment. To effectively promote the consumption of renewable energy while leveraging electric vehicles (EVs) in virtual power plants (VPPs) as distributed energy storage resources, this paper proposes an ordered scheduling strategy for EVs in campus areas oriented towards renewable energy consumption. Firstly, to address the uncertainty of renewable energy output, this paper uses Conditional Generative Adversarial Network (CGAN) technology to generate a series of typical scenarios. Subsequently, a mathematical model for EV aggregation is established, treating the numerous dispersed EVs within the campus as a collectively controllable resource, laying the foundation for their ordered scheduling. Then, to maximize renewable energy consumption and optimize EV charging scheduling, an improved Particle Swarm Optimization (PSO) algorithm is adopted to solve the problem. Finally, case studies using a real-world testing system demonstrate the feasibility and effectiveness of the proposed method. By introducing a dynamic inertia weight adjustment mechanism and a multi-population cooperative search strategy, the algorithm’s convergence speed and global search capability in solving high-dimensional non-convex optimization problems are significantly improved. Compared with conventional algorithms, the computational efficiency can be increased by up to 54.7%, and economic benefits can be enhanced by 8.6%. Full article
(This article belongs to the Special Issue Applications of Smart Microgrids in Renewable Energy Development)
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21 pages, 2175 KiB  
Article
Performance Ratio Estimation for Building-Integrated Photovoltaics—Thermal and Angular Characterisation
by Ana Marcos-Castro, Carlos Sanz-Saiz, Jesús Polo and Nuria Martín-Chivelet
Appl. Sci. 2025, 15(12), 6579; https://doi.org/10.3390/app15126579 - 11 Jun 2025
Viewed by 514
Abstract
Building-integrated photovoltaics (BIPV) requires tools that improve and facilitate simulating and predicting the system’s output energy. The efficiency of a photovoltaic (PV) system can be determined by the performance ratio (PR), which relates the actual system’s output energy to the theoretical [...] Read more.
Building-integrated photovoltaics (BIPV) requires tools that improve and facilitate simulating and predicting the system’s output energy. The efficiency of a photovoltaic (PV) system can be determined by the performance ratio (PR), which relates the actual system’s output energy to the theoretical output according to the installed power and the solar irradiation, thus accounting for the power losses the PV system undergoes. Among the different parameters affecting PR, module temperature and the angle of incidence of irradiance are the most dependent on the BIPV application due to the varied module positioning. This paper assesses the suitability of several BIPV temperature models and determines the angular losses for any possible module positioning. The proposed methodology is easy to replicate and results in polar heatmap graphs to estimate PR at the desired location as a function of the tilt and azimuth angles of the modules. The calculations require irradiance, ambient temperature, and wind speed data, which can easily be obtained worldwide. Dynamic sky conditions are addressed through filters that smooth out quickly changing input data to avoid high and low peaks. The developed graphs are helpful in the decision-making process for BIPV designs by allowing the designer to estimate the expected PR of the BIPV system for any possible position of the modules on the building envelope, reducing the effect of uncertainties and resulting in more accurate and better predictions of the system’s output energy. The method applied to a BIPV façade in Madrid showed a deviation of less than 3% between the estimated and monitored PRs; the PR values ranged between 0.74 and 0.82, depending on the BIPV application and module position. Full article
(This article belongs to the Special Issue Advances in the Energy Efficiency and Thermal Comfort of Buildings)
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20 pages, 5246 KiB  
Article
Structural Analysis of a Modular High-Concentration PV System Operating at ~1200 Suns
by Taher Maatallah, Mussad Alzahrani, William Cameron, Katie Shanks, Souheil El Alimi, Tapas K. Mallick and Sajid Ali
Machines 2025, 13(6), 468; https://doi.org/10.3390/machines13060468 - 28 May 2025
Viewed by 421
Abstract
The progression of research in concentration photovoltaic systems parallels the advancement of high-efficiency multi-junction solar cells. To translate the theoretical optical framework into practical experimentation, a modular and structurally validated mechanical configuration for a high-concentration photovoltaic (HCPV) system was developed, incorporating boundary conditions [...] Read more.
The progression of research in concentration photovoltaic systems parallels the advancement of high-efficiency multi-junction solar cells. To translate the theoretical optical framework into practical experimentation, a modular and structurally validated mechanical configuration for a high-concentration photovoltaic (HCPV) system was developed, incorporating boundary conditions and ensuring full system integration. The system incorporates a modular mechanical architecture, allowing flexible integration and interchangeability of optical components for experimental configurations. The architecture offers a high degree of mechanical flexibility, providing each optical stage with multiple linear and angular adjustment capabilities to support precision alignment. To ensure tracking precision, the system was coupled with a three-dimensional sun tracker capable of withstanding torques up to 60 Nm and supporting a combined payload of 80 kg, including counterbalance. The integration necessitated implementation of a counterbalance mechanism along with comprehensive static load analysis to ensure alignment stability and mechanical resilience. A reinforced triangular support structure, fabricated from stainless steel, was validated through simulation to maintain deformation below 0.1 mm under stress levels reaching 5 MN/m2, confirming its mechanical robustness and reliability. Windage analysis confirmed that the tracker could safely operate at 15 m/s wind speed for tilt angles of 35° (counter-clockwise) and −5° (clockwise), while operation at a 80° (counter-clockwise) tilt is safe up to 12 m/s, ensuring compliance with local environmental conditions. Overall, the validated system demonstrates structural resilience and modularity, supporting experimental deployment and future scalability. Full article
(This article belongs to the Section Machine Design and Theory)
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25 pages, 4527 KiB  
Article
Balancing Solar Potential and Environmental Risk: A GIS-Based Site-Selection Approach for Concentrated Solar Power in Tibet
by Mingkun Yu, Lei Zhao, Zuliang Chen and Jingyu Wu
Sustainability 2025, 17(11), 4895; https://doi.org/10.3390/su17114895 - 26 May 2025
Viewed by 521
Abstract
The Tibet Autonomous Region presents immense potential for concentrated solar power (CSP) development, driven by its exceptional solar irradiance levels (e.g., a peak DNI exceeding 2100 kWh/m2/day). This positions it as a strategic contributor to China’s 2060 carbon neutrality target and [...] Read more.
The Tibet Autonomous Region presents immense potential for concentrated solar power (CSP) development, driven by its exceptional solar irradiance levels (e.g., a peak DNI exceeding 2100 kWh/m2/day). This positions it as a strategic contributor to China’s 2060 carbon neutrality target and aligns with global energy transition imperatives. However, CSP deployment in this region faces challenges stemming from unique high-altitude geographic characteristics, a complex terrain, and extreme climatic conditions—including pronounced diurnal temperature fluctuations, high wind speeds, and heavy winter snowfall. Additionally, traditional site-selection models inadequately address these region-specific environmental constraints. To address these limitations, an integrated framework combining geographic information systems (GIS) and multi-criteria decision-making (MCDM) is proposed in this study. A localized evaluation system is developed, incorporating four novel high-altitude-specific indicators: the average and maximum wind speed and the average and maximum snow depth. Criteria weights are determined through a hybrid approach integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM), while candidate sites are prioritized using the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) ranking method. The case study results demonstrate that region-specific environmental factors exert a significantly stronger influence on site suitability than traditional solar resource indicators (e.g., direct normal irradiance) under Tibet’s extreme climatic conditions, emphasizing the necessity of localized evaluation frameworks. The proposed methodology not only provides a robust scientific foundation for CSP site selection in high-altitude regions with environmental complexities but also establishes a replicable framework for optimizing multiple trade-offs in renewable energy systems under geographically complex conditions. Full article
(This article belongs to the Section Energy Sustainability)
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22 pages, 2282 KiB  
Article
Enhancement of Photovoltaic Systems Using Plasmonic Technology
by Humam Al-Baidhani, Saif Hasan Abdulnabi and Maher A. R. Sadiq Al-Baghdadi
Processes 2025, 13(5), 1568; https://doi.org/10.3390/pr13051568 - 19 May 2025
Viewed by 461
Abstract
The rise in temperature worldwide, especially in hot regions with extreme weather conditions, has made climate change one of the critical issues that degrades the solar photovoltaic (PV) system performance. In this paper, a new design of solar cells based on plasmonic thin-film [...] Read more.
The rise in temperature worldwide, especially in hot regions with extreme weather conditions, has made climate change one of the critical issues that degrades the solar photovoltaic (PV) system performance. In this paper, a new design of solar cells based on plasmonic thin-film Silver (Ag) technology is introduced. The new design is characterized by enhancing thermal effects, optical power absorption, and output power significantly, thus compensating for the deterioration in the solar cells efficiency when the ambient temperature rises to high levels. The temperature distribution on a PV solar module is determined using a three-dimensional computational fluid dynamics (CFD) model that includes the front glass, crystalline cells, and back sheet. Experimental and analytical results are presented to validate the CFD model. The parameters of temperature distribution, absorbed optical power, and output electrical power are considered to evaluate the device performance during daylight hours in summer. The effects of solar radiation falling on the solar cell, actual temperature of the environment, and wind speed are investigated. The results show that the proposed cells’ temperature is reduced by 1.2 °C thanks to the plasmonic Ag thin-film technology, which leads to enhance 0.48% real value as compared to that in the regular solar cells. Consequently, the absorbed optical power and output electrical power of the new solar cells are improved by 2.344 W and 0.38 W, respectively. Full article
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25 pages, 13809 KiB  
Article
Spatiotemporal Changes of Pine Caterpillar Infestation Risk and the Driving Effect of Habitat Factors in Northeast China
by Jingzheng Zhao, Mingchang Wang, Dong Cai, Linlin Wu, Xue Ji, Qing Ding, Fengyan Wang and Minshui Wang
Remote Sens. 2025, 17(10), 1738; https://doi.org/10.3390/rs17101738 - 16 May 2025
Viewed by 375
Abstract
Pine caterpillar (Dendrolimus) infestations threaten pine forests, causing severe ecological and economic impacts. Identifying the driving factors behind these infestations is essential for effective forest management. This study uses the APCIRD framework combined with an improved random forest model to analyze spatiotemporal changes [...] Read more.
Pine caterpillar (Dendrolimus) infestations threaten pine forests, causing severe ecological and economic impacts. Identifying the driving factors behind these infestations is essential for effective forest management. This study uses the APCIRD framework combined with an improved random forest model to analyze spatiotemporal changes in infestation risk and the driving effects of habitat factors in Northeast China. From 2019 to 2024, we applied SHapley Additive exPlanations (SHAP), frequency analysis, fitting functions, and GeoDetector to quantify the impact of key drivers, such as snow cover and soil, on infestation risk. The findings include (1) the APCIRD framework with the MLP-random forest model (MRF) accurately assesses infestation risks. MRF is composed of MLP and random forest. Between 2019 and 2024, areas with high infestation risk declined, shifting from higher to lower levels, with Eastern Heilongjiang and Southwest Liaoning remaining as key concern areas; (2) snow cover and soil factors are critical to infestation risk, with eight key habitat factors significantly affecting the risk. Their relationships with infestation risk follow complex, non-monotonic quartic and cubic patterns; (3) factors triggering high infestation risks are mostly at low to moderate levels. High-risk areas tend to have low to moderate elevation (<800 m), moderate to high solar radiation and temperature, gentle slopes (<30°), low to moderate evaporation, shallow snow depth (<0.02), moderate snow temperature (266.73–275), low to moderate soil moisture (0.2–0.3), moderate to high soil temperature (276.73–286.92), low to moderate rainfall, moderate wind speed, low leaf area index, high vegetation type, low vegetation cover, low population density, and low surface runoff. Interactions between factors provide a stronger explanation of infestation risk than individual factors. The APCIRD framework, combined with MRF, offers valuable insights for understanding the drivers of pine caterpillar infestations. Full article
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21 pages, 6578 KiB  
Article
Canopy Transpiration Mapping in an Apple Orchard Using High-Resolution Airborne Spectral and Thermal Imagery with Weather Data
by Abhilash K. Chandel, Lav R. Khot, Claudio O. Stöckle, Lee Kalcsits, Steve Mantle, Anura P. Rathnayake and Troy R. Peters
AgriEngineering 2025, 7(5), 154; https://doi.org/10.3390/agriengineering7050154 - 14 May 2025
Viewed by 702
Abstract
Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very [...] Read more.
Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very often selected from sources that do not represent conditions like heterogeneous site-specific conditions. Therefore, a study was conducted to map geospatial ET and transpiration (T) of a high-density modern apple orchard using high-resolution aerial imagery, as well as to quantify the impact of site-specific weather conditions on the estimates. Five campaigns were conducted in the 2020 growing season to acquire small unmanned aerial system (UAS)-based thermal and multispectral imagery data. The imagery and open-field weather data (solar radiation, air temperature, wind speed, relative humidity, and precipitation) inputs were used in a modified energy balance (UASM-1 approach) extracted from the Mapping ET at High Resolution with Internalized Calibration (METRIC) model. Tree trunk water potential measurements were used as reference to evaluate T estimates mapped using the UASM-1 approach. UASM-1-derived T estimates had very strong correlations (Pearson correlation [r]: 0.85) with the ground-reference measurements. Ground reference measurements also had strong agreement with the reference ET calculated using the Penman–Monteith method and in situ weather data (r: 0.89). UASM-1-based ET and T estimates were also similar to conventional Landsat-METRIC (LM) and the standard crop coefficient approaches, respectively, showing correlation in the range of 0.82–0.95 and normalized root mean square differences [RMSD] of 13–16%. UASM-1 was then modified (termed as UASM-2) to ingest a locally calibrated leaf area index function. This modification deviated the components of the energy balance by ~13.5% but not the final T estimates (r: 1, RMSD: 5%). Next, impacts of representative and non-representative weather information were also evaluated on crop water uses estimates. For this, UASM-2 was used to evaluate the effects of weather data inputs acquired from sources near and within the orchard block on T estimates. Minimal variations in T estimates were observed for weather data inputs from open-field stations at 1 and 3 km where correlation coefficients (r) ranged within 0.85–0.97 and RMSD within 3–13% relative to the station at the orchard-center (5 m above ground level). Overall, the results suggest that weather data from within 5 km radius of orchard site, with similar topography and microclimate attributes, when used in conjunction with high-resolution aerial imagery could be useful for reliable apple canopy transpiration estimation for pertinent site-specific irrigation management. Full article
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