Solar Energy Advances最新文献_第6页

In-situ PV generation forecasting of utility-scale solar power plants in Ghana: Single and hybrid machine learning approaches 加纳公用事业规模太阳能发电厂的现场光伏发电预测：单一和混合机器学习方法

Solar Energy Advances Pub Date : 2025-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.seja.2025.100124

Mohammed Okoe Alhassan , Samuel Gyamfi , Bernard Aboagye , Felix Amankwah Diawuo , Maxmillian Kwarteng

{"title":"In-situ PV generation forecasting of utility-scale solar power plants in Ghana: Single and hybrid machine learning approaches","authors":"Mohammed Okoe Alhassan , Samuel Gyamfi , Bernard Aboagye , Felix Amankwah Diawuo , Maxmillian Kwarteng","doi":"10.1016/j.seja.2025.100124","DOIUrl":"10.1016/j.seja.2025.100124","url":null,"abstract":"<div><div>The intrinsic intermittency and weather-dependent fluctuations make renewable energy (RE) integration into the electrical grid challenging. Thus, short-to-long term forecasting of power generation of utility-scale photovoltaic (PV) systems is key for reliable operational planning for efficient grid-power management. The study evaluated and compared six machine learning models to predict PV generation based on three positive Pearson correlated input weather features: solar irradiation, PV module temperature and ambient temperature. The evaluated models comprise eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Bayesian Regression (BR), Support Vector Regression (SVR), Linear Regression (LR), and Artificial Neural Network (ANN). Subsequently, hybrid models composed of RF-ANN, RF-XGBoost and XGBoost-ANN stacked from ANN, RF and XGBoost as the best performing models among all the trained predictive models were built to forecast PV generation: one day, one week, two weeks and one month ahead. The models were validated on data from a 50 MWp PV system at Bui, Ghana. Employing performance evaluation metrics including the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The study findings revealed, for both day-ahead and month-ahead forecasts, XGBoost outperformed all the other models. XGBoost-ANN dominated the performance for the week-ahead and two-weeks-ahead forecasts. Comparative with existing works, these findings present practical insights and reliable results with normalized errors deemed acceptable for operational purposes in the scope of RE forecasting. Reinforcing policy frameworks for stakeholders aiming at adopting intelligent solar forecasting technology in smart-grid systems, to enhance operational planning and energy management for grid resilience and reliability.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"5 ","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Industrial solar heat potential in Chile: A technical-economic analysis 智利工业太阳能热潜力：技术经济分析

Solar Energy Advances Pub Date : 2025-01-01 Epub Date: 2024-12-24 DOI: 10.1016/j.seja.2024.100082

Diego Naranjo, Francisco Fuentes, Iván Muñoz

{"title":"Industrial solar heat potential in Chile: A technical-economic analysis","authors":"Diego Naranjo, Francisco Fuentes, Iván Muñoz","doi":"10.1016/j.seja.2024.100082","DOIUrl":"10.1016/j.seja.2024.100082","url":null,"abstract":"<div><div>This paper presents a comprehensive techno-economical potential analysis for integrating solar heat for industrial processes (SHIP) in Chile. Utilizing TRNSYS software for simulation and Python for data analysis, the study evaluates the levelized cost of heat (LCoH) and solar fraction for various industrial sectors across different regions of the country. The findings indicate that regions such as Coquimbo, Antofagasta, and Metropolitan exhibit high solar fractions and competitive LCoH values, particularly in hot water production. The central zone, especially the Metropolitan region, demonstrates significant potential for steam generation, achieving an average solar fraction of 50 % and producing 295 GWh/year of solar thermal energy. Validation with real-world data demonstrated an average error margin of 21 %, underscoring the developed heat load calculation model's reliability. The study concluded that SHIP is a viable solution for reducing energy costs and greenhouse gas emissions in Chile. It has the potential to displace over 3,500 GWh/year of fossil fuel energy in the considered productive sectors. These findings highlight the strategic importance of supporting SHIP implementation through favorable policies and investments.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"5 ","pages":"Article 100082"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and thermal performance investigation of solar-powered water purifying system for Dire Dawa Rural area 迪尔达瓦农村太阳能净水系统设计及热工性能研究

Solar Energy Advances Pub Date : 2025-01-01 Epub Date: 2025-01-04 DOI: 10.1016/j.seja.2025.100088

Arega A. Debu , Kamil D. Adem , Seyoum G. Nigussie

{"title":"Design and thermal performance investigation of solar-powered water purifying system for Dire Dawa Rural area","authors":"Arega A. Debu , Kamil D. Adem , Seyoum G. Nigussie","doi":"10.1016/j.seja.2025.100088","DOIUrl":"10.1016/j.seja.2025.100088","url":null,"abstract":"<div><div>To meet the ongoing demand for fresh water, desalination technologies must be improved to be cleaner, more efficient, and environmentally friendly. Utilizing solar energy for desalination systems provides a rapid fix for the energy shortage as well as an efficient mitigation tool for issues related to global climate change, deforestation, and local economic problems. This study's primary goal is to analyze a solar-assisted water purifying system in the rural area of Dire Dawa with an average per day solar energy of 5 kWh/m2/day. The study was conducted on the lowest solar intensity to determine the parameters of a parabolic dish sunlight collection system: aperture radius of 1 m, deepness of 0.25 m, focal length of 1 m, and receiver diameter of 0.9 m. A clay pot is used as the insulation for condensation water because condensation water is used as a feeder for the receiver and clay is one of the finest insulation materials. The result shows that the absorber heated to its highest surface temperature of 448.3 °C, 0.33 liters of condensed water, and 0.8177 liters of evaporated water were obtained by recycling heat dissipation to heat the water heater. The production efficiency of the collector is obtained as 40.35 %. The water hardness is improved from 479 to 12, pH 6.78 to 7, and total dissolved solids (TDS) from 564 to 94. This study also paves the desalinated water is suitable for industrial and construction works.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"5 ","pages":"Article 100088"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systems 可持续能源系统中易发沙尘地区的机器学习驱动的太阳能预测

Solar Energy Advances Pub Date : 2025-01-01 Epub Date: 2025-05-12 DOI: 10.1016/j.seja.2025.100108

Kadhim Hayawi , Husna Maliakkal , Neethu Venugopal , Thanveer Musthafa Hussain , Gomathi Bhavani Rajagopalan

{"title":"Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systems","authors":"Kadhim Hayawi , Husna Maliakkal , Neethu Venugopal , Thanveer Musthafa Hussain , Gomathi Bhavani Rajagopalan","doi":"10.1016/j.seja.2025.100108","DOIUrl":"10.1016/j.seja.2025.100108","url":null,"abstract":"<div><div>This research focuses on improving solar energy forecasting in dust-affected regions such as the UAE, where frequent dust storms reduce photovoltaic (PV) efficiency by scattering and absorbing sunlight. Many existing models overlook the impact of dust events, leading to inaccurate forecasts during such conditions. To address this, the study develops machine learning models—including LSTM, GRU, and hybrid LSTM-GRU architectures—that incorporate solar, weather, and dust-related features. The models were evaluated across multiple forecasti24 hoursons (1, 6, 12, and 24 hours), demonstrating that including dust-related variables significantly enhances prediction accuracy, particularly for short-term forecasts. Temporal and seasonal analyses revealed that dust events, most frequent in the late afternoon and early spring, correlate with substantial drops in solar power output. The LSTM model consistently outperformed the others, achieving a Mean Absolute Error (MAE) of 0.018034 for a 1-hour horizon when dust features were included. Statistical tests confirmed that dust events significantly affect forecasting accuracy, reinforcing the importance of dust-related features for reliable predictions. This research contributes to optimizing PV power generation in challenging environments, supporting sustainable energy systems and decarbonization efforts. It also offers insights for further model refinement and the inclusion of additional environmental variables.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"5 ","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144223299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Risk assessment, lightning protection, and earthing system design for photovoltaic power plants: A case study of utility-scale solar farm in Iran 光伏电站的风险评估、防雷和接地系统设计：以伊朗公用事业规模太阳能发电场为例

Solar Energy Advances Pub Date : 2025-01-01 Epub Date: 2025-03-03 DOI: 10.1016/j.seja.2025.100098

Mohammad Parhamfar , Reza Naderi , Iman Sadeghkhani

{"title":"Risk assessment, lightning protection, and earthing system design for photovoltaic power plants: A case study of utility-scale solar farm in Iran","authors":"Mohammad Parhamfar , Reza Naderi , Iman Sadeghkhani","doi":"10.1016/j.seja.2025.100098","DOIUrl":"10.1016/j.seja.2025.100098","url":null,"abstract":"<div><div>Photovoltaic (PV) systems play a pivotal role in addressing the growing global demand for sustainable and renewable energy sources, offering a crucial solution to mitigate climate change and reduce dependence on fossil fuels. Due to outdoor installation, PV systems are vulnerable to lightning strikes, which can cause significant damage to the electrical system and pose a safety hazard. Therefore, effective lightning protection measures including the use of surge protective devices, lightning rods, earthing systems, and shielding techniques are crucial to ensure the reliable and safe operation of PV systems. However, the design and implementation of lightning protection system (LPS) continue to be a complex and challenging task for engineers. This paper presents the step-by-step design of an LPS for a large-scale PV power plant located in Iran based on IEC 62305:2010. The procedure includes various aspects of lightning protection including risk assessment, earthing system, and bonding according to the relevant international standards and guidelines. The results show that the non-isolated passive LPS and galvanized earthing system are proper choices for the PV power plant under study. The findings of this paper are of interest to PV system designers, installers, operators, and researchers, as well as to standards organizations, regulatory bodies, and insurance companies involved in the certification and evaluation of PV systems.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"5 ","pages":"Article 100098"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing solar energy production in senegal under future climate scenarios using regional climate models 利用区域气候模型评估塞内加尔在未来气候情景下的太阳能生产

Solar Energy Advances Pub Date : 2025-01-01 Epub Date: 2025-03-04 DOI: 10.1016/j.seja.2025.100101

Aissatou Ndiaye , Dahirou Wane , Cheikh Dione , Amadou Thierno Gaye

{"title":"Assessing solar energy production in senegal under future climate scenarios using regional climate models","authors":"Aissatou Ndiaye , Dahirou Wane , Cheikh Dione , Amadou Thierno Gaye","doi":"10.1016/j.seja.2025.100101","DOIUrl":"10.1016/j.seja.2025.100101","url":null,"abstract":"<div><div>The transition to renewable energy is pivotal for climate change mitigation, yet it entails a greater reliance on weather and climate conditions, impacting energy production from solar plants. Senegal's energy sector is increasingly reliant on solar power, making it essential to assess its long-term viability under changing climate conditions. This study evaluates future solar energy production in Senegal up to 2050, focusing on eight operational solar plants: Bokhol, Sakal, Malicounda, Kahone, Ten Merina, Mekhe, Ndiass, and Kael. The regional climate model (RegCM4) driven by three Global Climate Models (GCMs) from CORDEX-CORE simulations is used and the analysis is conducted under the RCP8.5 scenario. The shortwave solar radiation and ambient air temperature at 2 m from the ERA5 re-analysis provided by the ECMWF are used to evaluate the RegCM4 simulations. Bias correction is applied to enhance the model's accuracy. The validation shows that ERA5 captured the temporal pattern of solar energy production. For the intensity, a minor relative bias averaging 5.6 % over the considered period is noted. Without correction, the model exhibits a relative bias of 10.4 %, which improves to 0.11 % after correction. Additionally, the results show a general decreasing trend in solar energy production over the country. The solar plants are projected to have a decrease in production ranging from -0.43 to -1.14 kWh/year. Policymakers should diversify energy sources, invest in storage solutions, and adopt climate-resilient solar technologies. This study provides insights into the potential impacts of climate change on solar energy generation in Senegal, informing policymakers and stakeholders to optimize power generation and ensure a sustainable energy future.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"5 ","pages":"Article 100101"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solar irradiation comparison of long-span ETFE-covered solar greenhouse 大跨度etfe覆盖日光温室的太阳辐照比较

Solar Energy Advances Pub Date : 2025-01-01 Epub Date: 2025-03-18 DOI: 10.1016/j.seja.2025.100104

Rakhmat Fitranto Aditra , Paolo Beccarelli , James Bean

{"title":"Solar irradiation comparison of long-span ETFE-covered solar greenhouse","authors":"Rakhmat Fitranto Aditra , Paolo Beccarelli , James Bean","doi":"10.1016/j.seja.2025.100104","DOIUrl":"10.1016/j.seja.2025.100104","url":null,"abstract":"<div><div>The solar greenhouse has been utilised to prolong the growing season of agricultural products in the UK. Transparent foil has been utilised as a substitute for glass in solar greenhouses to minimise the shadow produced by the supporting supports. Although simple polytunnels are regularly utilised, long-span and highly curved membrane structures, which are well-known in architectural design, are infrequently employed in solar greenhouses. The long-span membrane structure should cast less shadow by incorporating more structurally efficient curved geometry.</div><div>This study intends to present a solar greenhouse framework featuring three alternatives for long-span membrane designs: (1) pneumatic air-supported structure, (2) tensile membrane structure, and (3) pneumatic cushion structure. Structural analysis and ray-tracing simulations were conducted to examine the impacts of varying sizes of primary structural components and the influence of membrane geometry. The chosen option was also examined to assess its performance across different orientations and lengths.</div><div>The findings indicate that the tensile membrane structure is the most optimum option, with 3.61 % and 4.57 % higher solar irradiance among the three long-span membrane structures. Its horizontal radiation transmission could reach 81 % in both summer and winter. The orientation calculations indicate that an East-West orientated tunnel is optimal for maximising winter sun irradiation in UK. Although the tunnel length enhances average solar irradiation, the long-span design already offers a considerable unshaded region, rendering the impact of additional tunnel length negligible. Further research is required for other plan configurations and for assessing other solar greenhouse factors.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"5 ","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intra-day probabilistic solar irradiation forecasting using infinite hidden Markov model clustering 基于无限隐马尔可夫模型聚类的日内太阳辐射概率预报

Solar Energy Advances Pub Date : 2025-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.seja.2025.100123

Michael Obeng , Emmanuel Asuming Frimpong , Bernard Aboagye

{"title":"Intra-day probabilistic solar irradiation forecasting using infinite hidden Markov model clustering","authors":"Michael Obeng , Emmanuel Asuming Frimpong , Bernard Aboagye","doi":"10.1016/j.seja.2025.100123","DOIUrl":"10.1016/j.seja.2025.100123","url":null,"abstract":"<div><div>Reliable probabilistic forecasts are essential for effectively integrating variable renewable energy (VRE) sources into the grid to provide a reliable and secure electricity supply. We propose the integration of a Bayesian non-parametric (BNP) clustering model, based on the infinite hidden Markov model (iHMM), into a probabilistic forecasting model for short-term solar irradiation forecasting. Due to its flexibility, the iHMM clustering captures the complex non-linear relationships between data points and maintains the temporal dependencies through transition probabilities between states. We define iHMM through a hierarchical Dirichlet process (HDP) realized via a stick-breaking construction to cluster solar irradiation data from six NOAA Surface Radiation Budget Network (SURFRAD) stations. The BNP iHMM model effectively identifies latent patterns, providing insights into the temporal variability of solar irradiation data. The extracted features of the iHMM clustering model are passed into probabilistic forecasting models to enhance model interpretability while maintaining predictive performance. The proposed models improved forecast calibration, mitigated over-dispersion, and achieved 32–37 % improvement in continuous ranked probability skill scores compared to benchmark models.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"5 ","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solar photovoltaic array short circuit fault analysis with machine learning using pre-trained convolutional neural network for feature selection 太阳能光伏阵列短路故障的机器学习分析，使用预训练卷积神经网络进行特征选择

Solar Energy Advances Pub Date : 2025-01-01 Epub Date: 2025-03-23 DOI: 10.1016/j.seja.2025.100103

Tarikua Mekashaw Zenebe , Ole-Morten Midtgård , Steve Völler , Berhane Darsene Dimd

{"title":"Solar photovoltaic array short circuit fault analysis with machine learning using pre-trained convolutional neural network for feature selection","authors":"Tarikua Mekashaw Zenebe , Ole-Morten Midtgård , Steve Völler , Berhane Darsene Dimd","doi":"10.1016/j.seja.2025.100103","DOIUrl":"10.1016/j.seja.2025.100103","url":null,"abstract":"<div><div>Solar photovoltaic (PV) array deployment is rapidly increasing, but faults, particularly short-circuit faults, pose significant reliability and safety challenges. Machine and deep learning techniques have been applied to accurately identify PV short circuit faults using current–voltage (I–V) characteristic curve data; however, traditional machine learning models often require manual feature selection, and deep learning models demand large datasets, which are challenging to obtain. Additionally, shading effects are typically not included as operating conditions. This paper, therefore, proposes a machine learning-based fault detection and classification (FDC) method using pre-trained convolutional neural networks (a type of deep learning) for automatic and efficient feature selection, aiming to maintain high accuracy, fast FDC time and low memory usage while requiring less training data. The training and testing I–V curve data were generated from a PV array modeled in detail in MATLAB/Simulink. Furthermore, faults were simulated under varying irradiance, mismatch levels, and shading effects. The evaluated pre-trained convolutional neural networks include AlexNet, VGG, GoogleNet, ResNet, SqueezeNet, DenseNet, ShuffleNet, and EfficientNet. Among these, EfficientNet paired with a support vector machine demonstrated the best performance, achieving over 95.5 % across all performance metrics, with an FDC time of 4.23 s and a feature selection stage memory usage of only 20 MB. This approach can be integrated into PV system health monitoring to facilitate early FDC, enhancing system lifetime, safety, and reliability.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"5 ","pages":"Article 100103"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Open data in the solar thermal community: Status, barriers, and opportunities 太阳能热社区的开放数据：现状、障碍和机会

Solar Energy Advances Pub Date : 2025-01-01 Epub Date: 2025-07-27 DOI: 10.1016/j.seja.2025.100114

Lukas Emberger , Gireesh Nair , Klaus Lichtenegger , Maria Moser , Thorsten Summ , Thomas Natiesta , Jianhua Fan , Pengcheng Wang , Viktor Unterberger , Philip Ohnewein

引用次数: 0