Article
Energy & Fuels
Francisco J. Rodriguez-Benitez, Miguel Lopez-Cuesta, Clara Arbizu-Barrena, Maria M. Fernandez-Leon, Miguel A. Pamos-Urena, Joaquin Tovar-Pescador, Francisco J. Santos-Alamillos, David Pozo-Vazquez
Summary: This study proposes and evaluates methods for extending the forecasting horizon of all-sky imager (ASI)-based solar radiation nowcasts and improving the temporal resolution and latency of satellite-imagery-derived solar nowcasts. The results suggest that the use of ASI-based models provide low benefits compared to satellite-based models for point solar radiation nowcasting, and recommend the use of a simple smart persistence algorithm in combination with a low-resolution satellite nowcasting model based on the frequency of occurrence of different sky types in the study area.
Article
Environmental Sciences
Miguel Lopez-Cuesta, Ricardo Aler-Mur, Ines Maria Galvan-Leon, Francisco Javier Rodriguez-Benitez, Antonio David Pozo-Vazquez
Summary: Accurate solar radiation nowcasting models are critical for integrating solar energy. This study explored the benefits of blending multiple models and found that the general approach and the random forest model provided improved forecasts.
Article
Green & Sustainable Science & Technology
Stavros-Andreas Logothetis, Vasileios Salamalikis, Stefan Wilbert, Jan Remund, Luis F. Zarzalejo, Yu Xie, Bijan Nouri, Evangelos Ntavelis, Julien Nou, Niels Hendrikx, Lennard Visser, Manajit Sengupta, Mario Po, Remi Chauvin, Stephane Grieu, Niklas Blum, Wilfried van Sark, Andreas Kazantzidis
Summary: Accurate solar short-term forecasts are crucial for optimal utilization of solar energy systems. This study evaluated the performance of four all-sky imagers in forecasting global horizontal irradiance (GHI), and found that these imagers were able to accurately forecast GHI under different sky conditions, outperforming persistence models.
Article
Energy & Fuels
Ioannis-Panagiotis Raptis, Stelios Kazadzis, Ilias Fountoulakis, Kyriakoula Papachristopoulou, Dimitra Kouklaki, Basil E. Psiloglou, Andreas Kazantzidis, Charilaos Benetatos, Nikolaos Papadimitriou, Kostas Eleftheratos
Summary: In this study, the spectrally integrated outputs of the SENSE system for solar irradiance nowcasting during the ASPIRE campaign in Athens, Greece were evaluated. The GHI estimations were more accurate than the DNI estimations, and the accuracy improved when comparing larger time intervals. However, discrepancies in cloud conditions, AOD, and precipitable water vapor between ground-based retrievals and satellite estimations affected the results, with the visibility of the solar disc being the main source of error.
Article
Energy & Fuels
Remember Samu, Satya Girdhar Bhujun, Martina Calais, G. M. Shafiullah, Moayed Moghbel, Md Asaduzzaman Shoeb, Bijan Nouri
Summary: The rapid increase in solar PV integration into electricity networks presents technical challenges. This study evaluates the performance of an all-sky imager-based nowcasting system in Perth, Western Australia, and proposes a simplified classification method for irradiance variability.
Article
Energy & Fuels
Sergiu-Mihai Hategan, Nicoleta Stefu, Marius Paulescu
Summary: Solar resource forecasting is important for smart grid management. This study proposes a novel ensemble model that combines statistical extrapolation, machine learning, and all-sky imagery. The results show that different methods perform best at different forecast horizons, with the all-sky-imagery-based model performing well for longer horizons.
Article
Energy & Fuels
Shaojian Song, Zijun Yang, HuiHwang Goh, Qingbao Huang, Gang Li
Summary: This study proposes a real-time global horizontal irradiance (GHI) forecasting method based on sky images, using artificial intelligence and convolutional neural network models. By combining the local cloud cover as a numerical feature with the cloud feature in the sky image, the forecasting accuracy of GHI on cloudy days can be significantly enhanced. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods in GHI forecasting.
Article
Energy & Fuels
Alfredo Nespoli, Alessandro Niccolai, Emanuele Ogliari, Giovanni Perego, Elena Collino, Dario Ronzio
Summary: This study presents a new model and method to detect clouds that may affect the output power of solar panels in real time, and proposes a novel procedure for predicting the clearness sky index in a short period of time in a specific geographical area.
Article
Energy & Fuels
Ronewa Collen Nemalili, Lordwell Jhamba, Joseph Kiprono Kirui, Caston Sigauke
Summary: Challenges in utilizing fossil fuels for energy generation lead to the need for renewable energy sources. This study focuses on modeling and nowcasting the optimal tilt angle for solar energy harnessing using historical data from a radiometric station in South Africa. The study compared random forest (RF), K-nearest neighbors (KNN), and long short-term memory (LSTM) models for nowcasting the optimum tilt angle, with gradient boosting (GB) as the benchmark model. The results showed that LSTM had the best performance, followed by RF and GB, while KNN performed the worst.
Article
Environmental Sciences
Pedro C. Valdelomar, Jose L. Gomez-Amo, Caterina Peris-Ferrus, Francesco Scarlatti, Maria Pilar Utrillas
Summary: The study proposes a methodological approach using HDR images from a sky-camera to provide accurate and calibrated measurements of sky radiance and broadband solar irradiance. Through detailed instrumental characterization and extensive validation, the results demonstrate high reliability and great potential for accurate measurements of sky radiance and solar radiation components using sky-cameras.
Article
Energy & Fuels
Dilantha Haputhanthri, Daswin De Silva, Seppo Sierla, Damminda Alahakoon, Rashmika Nawaratne, Andrew Jennings, Valeriy Vyatkin
Summary: The rapid penetration of photovoltaic generation increases the need for virtual power plants, which require accurate short-term generation forecasts. A novel multimodal approach for short-term solar irradiance nowcasting is proposed, improving forecast accuracy through cloud detection and sun localization.
FRONTIERS IN ENERGY RESEARCH
(2021)
Article
Meteorology & Atmospheric Sciences
Chunlin Huang, Hongrong Shi, Ling Gao, Mengqi Liu, Qixiang Chen, Disong Fu, Shu Wang, Yuan Yuan, Xiang'ao Xia
Summary: In this study, a system for estimating and nowcasting surface solar irradiance (SSI) using the FY-4A satellite was established. The system combines a hybrid estimation method and a cloud motion vector prediction model. Evaluation results based on measurements from a radiation station in China show that the system has high accuracy in estimating and forecasting global horizontal irradiance (GHI) and direct normal irradiance (DNI).
ADVANCES IN ATMOSPHERIC SCIENCES
(2022)
Article
Astronomy & Astrophysics
G. M. Perugini, S. C. Marsden, I. A. Waite, S. Jeffers, N. Piskunov, N. Shaw, D. M. Burton, M. W. Mengel, J. E. Hughes, E. M. Hebrard
Summary: The study of a young G-type star HIP 89829 reveals stable spot features and poloidal magnetic fields, indicating its status as an active young solar-type star with near-solid-body rotation. The star shows stable spot latitudes and unusual magnetic features considering its rapid rotation period of 0.57 days.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
(2021)
Article
Astronomy & Astrophysics
Jiayi Liu, Xudong Sun, Peter W. Schuck, Sarah A. Jaeggli, Brian T. Welsch, Carlos Quintero Noda
Summary: This study investigates the Doppler shift characteristics in the central penumbral light bridge of a solar active region and finds a correlation with the cosine of the magnetic inclination. The observations indicate that a new velocity estimator can better recover the Doppler signal. The findings have important implications for understanding the magnetic structure and flow-driving mechanism of sunspots.
ASTROPHYSICAL JOURNAL
(2023)
Article
Energy & Fuels
Takahiro Takamatsu, Hideaki Ohtake, Takashi Oozeki, Tosiyuki Nakaegawa, Yuki Honda, Masahiro Kazumori
Summary: This study aims to investigate the impact of EPS on GHI prediction and develop a machine learning model that effectively utilizes EPS. The results show that the machine learning model can improve average accuracy while reducing maximum prediction error by MEPS, and it is useful for improving the systematic error of MEPS.
Article
Meteorology & Atmospheric Sciences
Domenico Cimini, Carmine Serio, Guido Masiello, Pietro Mastro, Elisabetta Ricciardelli, Francesco Di Paola, Salvatore Larosa, Donatello Gallucci, Tim Hultberg, Thomas August, Filomena Romano
Summary: This article introduces the ComboCloud project, funded by EUMETSAT, which aims to develop algorithms for retrieving cloud microphysical properties from the synergy of microwave and infrared observations. The study shows significant improvements in the retrieval capabilities of cloud properties through the synergy of microwave and infrared observations, using simulated data.
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
(2023)
Article
Green & Sustainable Science & Technology
Alessio Castorrini, Sabrina Gentile, Edoardo Geraldi, Aldo Bonfiglioli
Summary: This study investigates the improvement of wind resource prediction accuracy through the combination of mesoscale numerical weather prediction and high fidelity computational fluid dynamics. Different model setups and approaches are tested to obtain realistic wind profiles for predicting wind turbine performance and loads.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Environmental Sciences
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Francesco Di Paola, Saverio Teodosio Nilo, Elisabetta Ricciardelli, Ermann Ripepi, Filomena Romano
Summary: This work presents an algorithm based on a neural network for cloud detection using spectral observations from spaceborne microwave radiometers. The algorithm distinguishes clear sky versus ice and liquid clouds and has been validated using spectral observations from AMSU-A and MHS sounders. The results show the high accuracy of the NN algorithm in detecting clear, ice, and liquid cloud conditions.
Review
Meteorology & Atmospheric Sciences
Simone Kotthaus, Juan Antonio Bravo-Aranda, Martine Collaud Coen, Juan Luis Guerrero-Rascado, Maria Joao Costa, Domenico Cimini, Ewan J. O'Connor, Maxime Hervo, Lucas Alados-Arboledas, Maria Jimenez-Portaz, Lucia Mona, Dominique Ruffieux, Anthony Illingworth, Martial Haeffelin
Summary: The atmospheric boundary layer (ABL) is the volume of air adjacent to the Earth's surface for the dilution of heat, moisture, and trace substances. Knowledge on the variations in the heights of the ABL and its sub-layers is still limited, but recent advances in ground-based remote-sensing technology have made continuous profiling of the ABL possible. This review summarizes the capabilities of various instruments for ABL monitoring and provides an overview of retrieval methods for detecting ABL sub-layer heights.
ATMOSPHERIC MEASUREMENT TECHNIQUES
(2023)
Article
Geochemistry & Geophysics
Elisabetta Ricciardelli, Francesco Di Paola, Domenico Cimini, Salvatore Larosa, Pietro Mastro, Guido Masiello, Carmine Serio, Tim Hultberg, Thomas August, Filomena Romano
Summary: This study develops an algorithm using the infrared atmospheric sounding interferometer-new generation (IASI-NG) to detect optically thin cirrus. The algorithm utilizes a feedforward neural network approach to identify thin cirrus misidentified as clear sky by a previous cloud detection algorithm. The validation of the algorithm shows that IASI-NG outperforms IASI in terms of probability of detection, bias, and accuracy.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)