Article
Green & Sustainable Science & Technology
Jen-Yu Han, Petr Vohnicky
Summary: This study proposes a method for solar irradiance mapping in mid-low latitude regions and emphasizes the importance of site adaptation for improving the accuracy of the model. The findings highlight the significance of site adaptation in creating accurate solar maps and databases.
Article
Energy & Fuels
Quentin Paletta, Guillaume Arbod, Joan Lasenby
Summary: Integrating sky cameras and satellite observations in a machine learning framework improves solar irradiance forecasting, especially in clear-sky conditions, laying the groundwork for future innovative approaches in solar nowcasting.
Article
Energy & Fuels
Luis Eduardo Ordonez Palacios, Victor Bucheli Guerrero, Hugo Ordonez
Summary: Understanding solar energy is crucial for modern societies, and this study built predictive models of solar energy at different altitudes using satellite imagery and solar radiation data. The Random Forest algorithm showed the best performance in the model below 800 m.a.s.l.
Article
Energy & Fuels
A. Carpentieri, D. Folini, M. Wild, L. Vuilleumier, A. Meyer
Summary: To forecast solar resources and photovoltaic power generation, we investigated the accuracy of surface solar radiation (SSR) estimates from the satellite product HelioMont Meteosat, specifically designed for the Alpine region. We compared it with another satellite product, SARAH-2, and examined longer time scales. The results show significant deviations between the satellite SSR estimates and ground-measured SSR, posing challenges for accurate intra-day PV estimates and forecasts.
Article
Automation & Control Systems
Raimondo Gallo, Marco Castangia, Alberto Macii, Enrico Macii, Edoardo Patti, Alessandro Aliberti
Summary: In this study, a deep learning-based approach for solar radiation forecasting using satellite images and meteorological variables is proposed. The ConvLSTM model outperforms the 3D-CNN model for longer forecasting horizons, and the use of raw satellite images improves the prediction accuracy.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Energy & Fuels
Chunlin Huang, Hongrong Shi, Dazhi Yang, Ling Gao, Peng Zhang, Disong Fu, Xiang 'ao Xia, Qixiang Chen, Yuan Yuan, Mengqi Liu, Bo Hu, Kaifeng Lin, Xia Li
Summary: It is widely accepted that modern solar resource assessment relies on acquiring satellite-derived irradiance. This study focuses on developing a region-specific model based on China's latest-generation geostationary satellite FY-4, to estimate global horizontal irradiance (GHI) at a higher resolution. The developed model outperforms the existing Himawari-8 radiation product in resolution and bias, making it vital for solar resource assessment and agricultural-ecological studies.
Article
Environmental Sciences
Jared D. Salinas-Gonzalez, Alejandra Garcia-Hernandez, David Riveros-Rosas, Gamaliel Moreno-Chavez, Luis F. Zarzalejo, Joaquin Alonso-Montesinos, Carlos E. Galvan-Tejada, Alejandro Mauricio-Gonzalez, Adriana E. Gonzalez-Cabrera
Summary: Solar resource assessment is crucial for planning solar energy applications. This study conducted a multivariate analysis in Mexico to assess solar resources and identified 17 optimal locations with a certain degree of solar irradiance homogeneity using cluster analysis.
Article
Environmental Sciences
Fangwen Bao, Kai Huang, Shengbiao Wu
Summary: This study proposes a random forest (RF) model driven by a differential operator for aerosol retrieval from geostationary satellite Himawari-8. The model establishes a linear relationship between aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance enhancement. It achieves simultaneous retrievals over different surfaces and maintains mathematical correlation between spectral AODs and Angstrom Exponents (AE). The proposed method improves the performance of RF in retrieving aerosol properties and offers a new prospect for aerosol remote sensing.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Energy & Fuels
Zhiyuan Si, Ming Yang, Yixiao Yu, Tingting Ding
Summary: This paper proposes a novel method based on satellite images to accurately forecast photovoltaic power by predicting cloud movement and dynamically selecting cloud regions. By utilizing the XGBoost algorithm and considering multiple factors, the accuracy of the forecast can be improved, as demonstrated through testing the effectiveness of the method compared to other benchmarks.
Article
Energy & Fuels
Tao Sun, Ming Shan, Xing Rong, Xudong Yang
Summary: This article proposes a new method using satellite images to estimate the spatial distribution of rural rooftop photovoltaic (PV) power generation potential. By using a revised deep learning network and a calculation method for PV panel potential, accurate spatial distribution information of rooftop PV power generation potential in rural areas can be obtained. The research was applied in a village and a town in northern China, achieving high accuracy.
Article
Environmental Sciences
Irfan Uckan, Kameran Mohammed Khudhur
Summary: This study compares sunshine duration-based models with other meteorological parameter-based models and develops new forecasting models. The results show that models based on other meteorological parameters have better predictions, and the newly proposed models provide more accurate estimates of global solar radiation at different locations.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Ersan Omer Yuzer, Altug Bozkurt
Summary: This study proposes a new deep learning-based solar radiation forecasting model that uses solar radiation data and satellite images. The model accurately predicts solar radiation without the need for any measuring device and achieves more accurate results than traditional models. It can also be used to verify and calibrate global solar radiation measured from the ground in other locations.
AIN SHAMS ENGINEERING JOURNAL
(2023)
Article
Environmental Sciences
Mohamed Zaiani, Abdanour Irbah, Djelloul Djafer, Constantino Listowski, Julien Delanoe, Dimitris Kaskaoutis, Sabrina Belaid Boualit, Fatima Chouireb, Mohamed Mimouni
Summary: The study compares five models of global solar radiation, finding that the ESRA models are the most reliable for estimating the Linke factor with strong correlation. Angstrom turbidity coefficients mostly range between 0.02 and 0.15, peaking in the summer months.
Article
Green & Sustainable Science & Technology
G. Terren-Serrano, M. Martinez-Ramon
Summary: To reduce the costs of uncertain energy generated by photovoltaic systems, researchers introduce a multi-task intra-hour solar forecast to optimize energy dispatch and increase photovoltaic participation. The algorithm estimates cloud motion and forecasts global horizontal irradiance using features extracted from clouds. Through low-cost infrared sky imagers and solar trackers, sky images are acquired for the forecast. The proposed algorithm achieved a 16.48% forecasting skill 8 minutes ahead with a resolution of 1 minute, surpassing previous work. Additionally, multi-task Bayesian learning methods are evaluated and compared for probabilistic forecast.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Economics
Ritika Khurana, Levan Elbakidze, Joshua Hall
Summary: The study found that voter support for pro-solar policies is influenced by factors such as per capita income, pro-environmental ideology, and the composition of industries in the county. It is crucial for policymakers to structure amendments in a way that maximizes voter turnout and minimizes negative impacts on industries where solar energy may not be viewed favorably in order to pave the way for the passage of pro-solar initiatives through referenda.
APPLIED ECONOMICS LETTERS
(2021)
Correction
Environmental Sciences
Annette Hammer, Jan Kuehnert, Kailash Weinreich, Elke Lorenz
Article
Environmental Sciences
Annette Hammer, Jan Kuehnert, Kailash Weinreich, Elke Lorenz
Article
Energy & Fuels
Elena Barykina, Annette Hammer
Article
Energy & Fuels
Gerald M. Lohmann, Annette Hammer, Adam H. Monahan, Thomas Schmidt, Detlev Heinemann
Article
Geochemistry & Geophysics
Yashwant Kashyap, Ankit Bansal, Anil Kumar Sao, Annette Hammer
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2018)
Article
Green & Sustainable Science & Technology
Selmin Ener Rusen, Annette Hammer, Bulent G. Akinoglu
Article
Energy & Fuels
Tanja Behrendt, Jan Kuehnert, Annette Hammer, Elke Lorenz, Jethro Betcke, Detlev Heinemann
Article
Environmental Sciences
Richard Mueller, Tanja Behrendt, Annette Hammer, Axel Kemper
Article
Environmental Sciences
A Hammer, D Heinemann, C Hoyer, R Kuhlemann, E Lorenz, R Müller, HG Beyer
REMOTE SENSING OF ENVIRONMENT
(2003)
Article
Thermodynamics
Yong Cheng, Fukai Song, Lei Fu, Saishuai Dai, Zhiming Yuan, Atilla Incecik
Summary: This paper investigates the accessibility of wave energy absorption by a dual-pontoon floating breakwater integrated with hybrid-type wave energy converters (WECs) and proposes a hydraulic-pneumatic complementary energy extraction method. The performance of the system is validated through experiments and comparative analysis.
Article
Thermodynamics
Jing Gao, Chao Wang, Zhanwu Wang, Jin Lin, Runkai Zhang, Xin Wu, Guangyin Xu, Zhenfeng Wang
Summary: This study aims to establish a new integrated method for biomass cogeneration project site selection, with a focus on the application of the model in Henan Province. By integrating Geographic Information System and Multiple Criterion Decision Making methods, the study conducts site selection in two stages, providing a theoretical reference for the construction of biomass cogeneration projects.
Article
Thermodynamics
Mert Temiz, Ibrahim Dincer
Summary: The current study presents a hybrid small modular nuclear reactor and solar-based system for sustainable communities, integrating floating and bifacial photovoltaic arrays with a small modular reactor. The system efficiently generates power, hydrogen, ammonia, freshwater, and heat for residential, agricultural, and aquaculture facilities. Thermodynamic analysis shows high energy and exergy efficiencies, as well as large-scale ammonia production meeting the needs of metropolitan areas. The hybridization of nuclear and solar technologies offers advantages of reliability, environmental friendliness, and cost efficiency compared to renewable-alone and fossil-based systems.
Editorial Material
Thermodynamics
Wojciech Stanek, Wojciech Adamczyk
Article
Thermodynamics
Desheng Xu, Yanfeng Li, Tianmei Du, Hua Zhong, Youbo Huang, Lei Li, Xiangling Duanmu
Summary: This study investigates the optimization of hybrid mechanical-natural ventilation for smoke control in complex metro stations. The results show that atrium fires are more significantly impacted by outdoor temperature variations compared to concourse/platform fires. The gathered high-temperature smoke inside the atrium can reach up to 900 K under a 5 MW train fire energy release. The findings provide crucial engineering insights into integrating weather data and adaptable ventilation protocols for smoke prevention/mitigation.
Article
Thermodynamics
Da Guo, Heping Xie, Mingzhong Gao, Jianan Li, Zhiqiang He, Ling Chen, Cong Li, Le Zhao, Dingming Wang, Yiwei Zhang, Xin Fang, Guikang Liu, Zhongya Zhou, Lin Dai
Summary: This study proposes a new in-situ pressure-preserved coring tool and elaborates its pressure-preserving mechanism. The experimental and field test results demonstrate that this tool has a high pressure-preservation capability and can maintain a stable pressure in deep wells. This study provides a theoretical framework and design standards for the development of similar technologies.
Article
Thermodynamics
Aolin Lai, Qunwei Wang
Summary: This study assesses the impact of China's de-capacity policy on renewable energy development efficiency (REDE) using the Global-MSBM model and the difference-in-differences method. The findings indicate that the policy significantly enhances REDE, promoting technological advancements and marketization. Moreover, regions with stricter environmental regulations experience a higher impact.
Article
Thermodynamics
Mostafa Ghasemi, Hegazy Rezk
Summary: This study utilizes fuzzy modeling and optimization to enhance the performance of microbial fuel cells (MFCs). By simulating and analyzing experimental data sets, the ideal parameter values for increasing power density, COD elimination, and coulombic efficiency were determined. The results demonstrate that the fuzzy model and optimization methods can significantly improve the performance of MFCs.
Article
Thermodynamics
Zhang Ruan, Lianzhong Huang, Kai Wang, Ranqi Ma, Zhongyi Wang, Rui Zhang, Haoyang Zhao, Cong Wang
Summary: This paper proposes a grey box model for fuel consumption prediction of wing-diesel hybrid vessels based on feature construction. By using both parallel and series grey box modeling methods and six machine learning algorithms, twelve combinations of prediction models are established. A feature construction method based on the aerodynamic performance of the wing and the energy relationship of the hybrid system is introduced. The best combination is obtained by considering the root mean square error, and it shows improved accuracy compared to the white box model. The proposed grey box model can accurately predict the daily fuel consumption of wing-diesel hybrid vessels, contributing to operational optimization and the greenization and decarbonization of the shipping industry.
Article
Thermodynamics
Huayi Chang, Nico Heerink, Junbiao Zhang, Ke He
Summary: This study examines the interaction between off-farm employment decisions between couples and household clean energy consumption in rural China, and finds that two-paycheck households are more likely to consume clean energy. The off-farm employment of women is a key factor driving household clean energy consumption to a higher level, with wage-employed wives having a stronger influence on these decisions than self-employed ones.
Article
Thermodynamics
Hanguan Wen, Xiufeng Liu, Ming Yang, Bo Lei, Xu Cheng, Zhe Chen
Summary: Demand-side management is crucial to smart energy systems. This paper proposes a data-driven approach to understand the relationship between energy consumption patterns and household characteristics for better DSM services. The proposed method uses a clustering algorithm to generate optimal customer groups for DSM and a deep learning model for training. The model can predict the possibility of DSM membership for a given household. The results demonstrate the usefulness of weekly energy consumption data and household socio-demographic information for distinguishing consumer groups and the potential for targeted DSM strategies.
Article
Thermodynamics
Xinglan Hou, Xiuping Zhong, Shuaishuai Nie, Yafei Wang, Guigang Tu, Yingrui Ma, Kunyan Liu, Chen Chen
Summary: This study explores the feasibility of utilizing a multi-level horizontal branch well heat recovery system in the Qiabuqia geothermal field. The research systematically investigates the effects of various engineering parameters on production temperature, establishes mathematical models to describe their relationships, and evaluates the economic viability of the system. The findings demonstrate the significant economic feasibility of the multi-level branch well system.
Article
Thermodynamics
Longxin Zhang, Songtao Wang, Site Hu
Summary: This investigation reveals the influence of tip leakage flow on the modern transonic rotor and finds that the increase of tip clearance size leads to a decline in rotor performance. However, an optimal tip clearance size can extend the rotor's stall margin.
Article
Thermodynamics
Kristian Gjoka, Behzad Rismanchi, Robert H. Crawford
Summary: This paper proposes a framework for assessing the performance of 5GDHC systems and demonstrates it through a case study in a university campus in Melbourne, Australia. The results show that 5GDHC systems are a cost-effective and environmentally viable solution in mild climates, and their successful implementation in Australia can create new market opportunities and potential adoption in other countries with similar climatic conditions.
Article
Thermodynamics
Jianwei Li, Guotai Wang, Panpan Yang, Yongshuang Wen, Leian Zhang, Rujun Song, Chengwei Hou
Summary: This study proposes an orientation-adaptive electromagnetic energy harvester by introducing a rotatable bluff body, which allows for self-regulation to cater for changing wind flow direction. Experimental results show that the output power of the energy harvester can be greatly enhanced with increased rotatory inertia of the rotating bluff body, providing a promising solution for harnessing wind-induced vibration energy.
