Article
Meteorology & Atmospheric Sciences
Zbynek Sokol, Radmila Brozkova, Jana Popova, Gabriela Bobotova, Filip Svabik
Summary: An evaluation of forecasts from the ALADIN numerical weather prediction model was conducted by comparing synthetic and measured Infrared and Water vapour channel brightness temperatures from the geostationary satellite Meteosat Second Generation. The evaluation covered a large part of Europe for both summer and winter months in 2020 and 2021, with verification parameters such as bias, root-mean-square error, correlation coefficient, and Fraction Skill Score. The study aimed to provide insights for model developers based on the relationships between forecasted and measured variables.
ATMOSPHERIC RESEARCH
(2022)
Article
Environmental Sciences
Sheetabh Gaurav, Sebastian Egli, Boris Thies, Joerg Bendix
Summary: In this study, a machine learning-based approach is used to generate a consistent dataset by harmonizing Meteosat First Generation (MFG) and Meteosat Second Generation (MSG) satellite datasets (1991-2020). The synthesized data shows a good match with the original data, with small mean absolute errors. This harmonized dataset can be used to analyze and generate a long-term time series of fog and low stratus (FLS) occurrences.
Article
Meteorology & Atmospheric Sciences
Ziyan Wang, Ming Zhang, Lunche Wang, Wenmin Qin
Summary: The study evaluated monthly surface solar radiation (SSR) trends using the MERRA-2 reanalysis and found that SSR has generally decreased in North America, Northern East Asia, and Oceania, while it has increased in Europe. Changes in cloud cover and aerosols played a significant role in the fluctuations of SSR in different regions.
ATMOSPHERIC RESEARCH
(2022)
Article
Meteorology & Atmospheric Sciences
Buwen Dong, Rowan T. Sutton, Laura J. Wilcox
Summary: Satellite-derived products and reanalyses show a consistent increase in surface solar radiation and a decrease in cloud cover over North America and Europe. Through experiments with an atmospheric general circulation model, it is found that the observed trends are mainly driven by changes in anthropogenic aerosol emissions, sea surface temperature/sea ice extent, and greenhouse gas concentrations. Specifically, the reduction in aerosol emissions has a dominant role in Europe, while SST/SIE play a more important role in North America.
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
Meteorology & Atmospheric Sciences
Vojtech Bliznak, Petr Zacharov, Zbynek Sokol, Petr Pesice, Jindrich Stastka, Pavel Sedlak
Summary: This paper presents a novel method of using satellite-derived cloud cover information to improve road surface temperature (RST) forecasting. By incorporating the extrapolated cloud mask product into the road weather model, the accuracy of RST predictions can be enhanced. The experiments conducted on Czech motorways showed that the new model produced RSTs closer to the observed values compared to the original model, especially during the day and in the 2nd and 3rd forecasted hours.
ATMOSPHERIC RESEARCH
(2023)
Article
Environmental Sciences
Kingsley K. Kumah, Joost C. B. Hoedjes, Noam David, Ben H. P. Maathuis, H. Oliver Gao, Bob Z. Su
Summary: This study investigated a new method using Meteosat Second Generation satellite data to improve rainfall estimates of commercial microwave links, showing that the MSG technique provides more robust estimates compared to conventional techniques, especially for convective rain events with spatial variability.
Article
Meteorology & Atmospheric Sciences
Husi Letu, Run Ma, Takashi Y. Nakajima, Chong Shi, Makiko Hashimoto, Takashi M. Nagao, Anthony J. Baran, Teruyuki Nakajima, Jian Xu, Tianxing Wang, Gegen Tana, Sude Bilige, Huazhe Shang, Liangfu Chen, Dabin Ji, Yonghui Lei, Lesi Wei, Peng Zhang, Jun Li, Lei Li, Yu Zheng, Pradeep Khatri, Jiancheng Shi
Summary: In this study, an optimal algorithm was developed to calculate the compositions of downward solar radiation. The algorithm combines the radiative transfer model with machine learning techniques and considers the effects of aerosols, clouds, and gas components. The algorithm was validated with ground-based data and showed higher accuracy compared to state-of-the-art products. The study also identified significant variations in solar radiation due to the reduction of aerosols and increase of ozone during the COVID-19 period.
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
(2023)
Article
Green & Sustainable Science & Technology
Mario Biencinto, Lourdes Gonzalez, Loreto Valenzuela
Summary: This study aims to analyze the impact of real-time weather forecasting on the simulation of solar thermal electricity plants. A novel method is proposed to assess the uncertainty of forecasting solar thermal electricity production, and variable window lengths are used to simulate the growing uncertainty of forecasts over time.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Energy & Fuels
Grant Buster, Michael Rossol, Galen Maclaurin, Yu Xie, Manajit Sengupta
Summary: A physics-based algorithm was developed to downscale NSRDB data to higher spatiotemporal resolution for use in solar generation models and power system models. The downscaled data showed comparable accuracy to the native NSRDB and was used to generate power production profiles for comparison against high-temporal-resolution photovoltaic plant power generation.
Article
Meteorology & Atmospheric Sciences
Ziyan Wang, Ming Zhang, Huaping Li, Lunche Wang, Wei Gong, Yingying Ma
Summary: In this study, an effective method was used to correct the systematic error of the MERRA-2 reanalysis surface solar radiation (SSR) from 1980 to 2015, reducing its error and successfully reproducing the seasonal and interannual variations of the observed data in most areas. The study also analyzed the influencing factors of SSR, quantifying the contributions of cloud fraction, aerosol optical depth (AOD), and water vapor to the interannual variability of SSR.
Article
Meteorology & Atmospheric Sciences
Husi Letu, Run Ma, Takashi Y. Nakajima, Chong Shi, Makiko Hashimoto, Takashi M. Nagao, Anthony J. Baran, Teruyuki Nakajima, Jian Xu, Tianxing Wang, Gegen Tana, Sude Bilige, Huazhe Shang, Liangfu Chen, Dabin Ji, Yonghui Lei, Lesi Wei, Peng Zhang, Jun Li, Lei Li, Yu Zheng, Pradeep Khatri, Jiancheng Shi
Summary: An optimal algorithm was developed to calculate surface downward solar radiation compositions with high spatial-temporal resolutions. The algorithm combines a radiative transfer model with machine learning techniques and considers the effects of aerosols, clouds, and gases. The results show that the algorithm outperforms state-of-the-art products and reveal significant variations in solar radiation compositions due to COVID-19.
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
(2023)
Article
Meteorology & Atmospheric Sciences
Husi Letu, Run Ma, Takashi Y. Nakajima, Chong Shi, Makiko Hashimoto, Takashi M. Nagao, Anthony J. Baran, Teruyuki Nakajima, Jian Xu, Tianxing Wang, Gegen Tana, Sude Bilige, Huazhe Shang, Liangfu Chen, Dabin Ji, Yonghui Lei, Lesi Wei, Peng Zhang, Jun Li, Lei Li, Yu Zheng, Pradeep Khatri, Jiancheng Shi
Summary: An algorithm was developed to calculate surface downward solar radiation compositions, including PAR, UVA, UVB, and SWR, using a combination of radiative transfer model and machine learning techniques. The algorithm considers various factors such as aerosol types, cloud phases, and gas components. The accuracy of the algorithm is validated and found to be superior to state-of-the-art products. The study also investigates the impact of aerosols, clouds, and gases on solar radiation during the COVID-19 period and identifies significant variations.
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
(2023)
Article
Energy & Fuels
Dragana Nikodinoska, Mathias Kaeso, Felix Muesgens
Summary: Accurate renewable energy feed-in forecasts are crucial for enhancing the efficiency of renewable energy integration, and combining forecasting models can improve accuracy. The dynamic elastic net estimation method can enhance the accuracy of photovoltaic and wind energy forecasts.
Article
Energy & Fuels
S. Bhavsar, R. Pitchumani, M. A. Ortega-Vazquez
Summary: This work presents a novel and efficient method to generate statistically accurate scenarios from probabilistic forecasts and reduce the number of scenarios using unsupervised machine learning, while preserving the statistical properties of the original set. The approach yields statistically equivalent characteristics as a full set with a substantially reduced cardinality and preserves the temporal correlation in time-series data.
Article
Energy & Fuels
Siddharth Sradhasagar, Omkar Subhasish Khuntia, Srikanta Biswal, Sougat Purohit, Amritendu Roy
Summary: In this study, machine learning models were developed to predict the bandgap and its character of double perovskite materials, with LGBMRegressor and XGBClassifier models identified as the best predictors. These models were further employed to predict the bandgap of novel bismuth-based transition metal oxide double perovskites, showing high accuracy, especially in the range of 1.2-1.8 eV.
Article
Energy & Fuels
Wei Shuai, Haoran Xu, Baoyang Luo, Yihui Huang, Dong Chen, Peiwang Zhu, Gang Xiao
Summary: In this study, a hybrid model based on numerical simulation and deep learning is proposed for the optimization and operation of solar receivers. By applying the model to different application scenarios and considering multiple performance objectives, small errors are achieved and optimal structure parameters and heliostat scales are identified. This approach is not only applicable to gas turbines but also heating systems.
Article
Energy & Fuels
Mubashar Ali, Zunaira Bibi, M. W. Younis, Muhammad Mubashir, Muqaddas Iqbal, Muhammad Usman Ali, Muhammad Asif Iqbal
Summary: This study investigates the structural, mechanical, and optoelectronic properties of the BaCuF3 fluoroperovskite using the first-principles modelling approach. The stability and characteristics of different cubic structures of BaCuF3 are evaluated, and the alpha-BaCuF3 and beta-BaCuF3 compounds are found to be mechanically stable with favorable optical properties for solar cells and high-frequency UV applications.
Article
Energy & Fuels
Dong Le Khac, Shahariar Chowdhury, Asmaa Soheil Najm, Montri Luengchavanon, Araa mebdir Holi, Mohammad Shah Jamal, Chin Hua Chia, Kuaanan Techato, Vidhya Selvanathan
Summary: A novel recycling system is proposed in this study to decompose and reclaim the constituent materials of organic-inorganic perovskite solar cells (PSCs). By utilizing a one-step solution process extraction approach, the chemical composition of each layer is successfully preserved, enabling their potential reuse. The proposed recycling technique helps mitigate pollution risks, minimize waste generation, and reduce recycling costs.
Article
Energy & Fuels
Peijie Lin, Feng Guo, Xiaoyang Lu, Qianying Zheng, Shuying Cheng, Yaohai Lin, Zhicong Chen, Lijun Wu, Zhuang Qian
Summary: This paper proposes an open-set fault diagnosis model for PV arrays based on 1D VoVNet-SVDD. The model accurately diagnoses various types of faults and is capable of identifying unknown fault types.