Review
Engineering, Chemical
Nur Liyana Mohd Jailani, Jeeva Kumaran Dhanasegaran, Gamal Alkawsi, Ammar Ahmed Alkahtani, Chen Chai Phing, Yahia Baashar, Luiz Fernando Capretz, Ali Q. Al-Shetwi, Sieh Kiong Tiong
Summary: Solar energy is a significant renewable energy source that can meet global energy needs while reducing global warming. Accurate forecasting of renewable energy output is crucial for grid reliability and sustainability, as well as reducing risk and expense in energy markets and systems.
Review
Thermodynamics
Javier Almorox, Cyril Voyant, Nadjem Bailek, Alban Kuriqi, J. A. Arnaldo
Summary: This study reviewed Total Solar Irradiance (TSI) and Solar Constant (SC), reevaluated them, and developed new seasonal radiation coefficient sets. It recommended using a TSI value of 1361 W m(-2) in solar radiation models for accuracy, highlighting the importance of local and seasonal calibration coefficients.
Article
Energy & Fuels
Ewa Chodakowska, Joanicjusz Nazarko, Lukasz Nazarko, Hesham S. S. Rabayah, Raed M. M. Abendeh, Rami Alawneh
Summary: This study applies ARIMA models to predict seasonal solar radiation in different climatic conditions, evaluates the performance and prediction capacity of the models, and develops solar radiation forecasting models using data from Jordan and Poland. The research findings demonstrate that ARIMA models are suitable for solar radiation forecasting and can support the stable long-term integration of renewable energy.
Article
Green & Sustainable Science & Technology
Vateanui Sansine, Pascal Ortega, Daniel Hissel, Marania Hopuare
Summary: Solar-power-generation forecasting tools are essential for microgrid stability, operation, and planning. In this study, a particle swarm optimization algorithm combined with three stand-alone models was used for solar irradiance prediction, and compared with other stand-alone models. The experimental results showed that PSO-LSTM had the best accuracy for day-ahead solar irradiance forecasting.
Article
Thermodynamics
Yunxiao Chen, Mingliang Bai, Yilan Zhang, Jinfu Liu, Daren Yu
Summary: This paper proposes an approach to proactively select input variables based on the information gain factor for improving the accuracy of solar irradiance forecast. The feasibility of this method is validated through experiments, and the suitability of the information gain factor is compared with Pearson correlations.
Article
Green & Sustainable Science & Technology
Haoran Wen, Yang Du, Xiaoyang Chen, Eng Gee Lim, Huiqing Wen, Ke Yan
Summary: This article proposes a novel generative approach for regional solar forecasting, which creates solar irradiance maps (SIMs) and predicts PV power output. The method demonstrates comparable accuracy in solar irradiance forecasting and better predictions in PV power generation. It has the potential to assist solar energy assessment and power system control in highly-penetrated regions.
Article
Computer Science, Software Engineering
Mantosh Kumar, Kumari Namrata, Neha Kumari
Summary: This paper investigates the prediction of solar irradiation components using deep learning and compares its performance with classical machine learning models. The results show that the convolutional neural network model outperforms all other models in terms of accuracy and computational time.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Chemistry, Multidisciplinary
Domingos S. de O. Jr Jr Santos, Paulo S. G. de Mattos Neto, Joao F. L. de Oliveira, Hugo Valadares Siqueira, Tathiana Mikamura Barchi, Aranildo R. Lima, Francisco Madeiro, Douglas A. P. Dantas, Attilio Converti, Alex C. Pereira, Jose Bione de Melo Filho, Manoel H. N. Marinho
Summary: Solar irradiance forecasting is crucial for renewable energy generation, as it enhances the planning and operation of photovoltaic systems. Traditional single models may underperform due to inappropriate selection, misspecification, or random fluctuations. This research proposes a heterogeneous ensemble dynamic selection model that outperforms single models in terms of accuracy.
APPLIED SCIENCES-BASEL
(2022)
Article
Green & Sustainable Science & Technology
Ankit Bhatt, Weerakorn Ongsakul, Nimal M. Madhu, Jai Govind Singh
Summary: This study proposes three deep learning models for solar irradiance forecasting and finds that the deep hybrid model consisting of a convolutional neural network and long short term memory outperforms others in multistep forecasting. The results suggest that the proposed deep hybrid LSTM-CNN model is a reliable alternative for very short-term solar irradiance prediction due to its high predictive accuracy.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Green & Sustainable Science & Technology
Xiaoqiao Huang, Qiong Li, Yonghang Tai, Zaiqing Chen, Jun Zhang, Junsheng Shi, Bixuan Gao, Wuming Liu
Summary: Due to the integration of photovoltaic solar systems into power networks, accurate prediction of solar irradiance is crucial for electric energy planning and management. This study proposes a novel multivariate hybrid deep neural model named WPD-CNN-LSTM-MLP for 1-hour ahead solar irradiance forecasting. The model combines WPD, CNN, LSTM, and MLP with multi-variable inputs, achieving more accurate results compared to traditional models.
Review
Chemistry, Multidisciplinary
Abbas Mohammed Assaf, Habibollah Haron, Haza Nuzly Abdull Hamed, Fuad A. Ghaleb, Sultan Noman Qasem, Abdullah M. Albarrak
Summary: This paper provides a review of deep learning-based solar irradiance forecasting models. It finds that these models perform better than conventional models in solar forecasting applications, especially when combined with techniques that enhance feature extraction. Additionally, the use of data augmentation techniques is useful for improving deep learning performance.
APPLIED SCIENCES-BASEL
(2023)
Article
Energy & Fuels
Weijia Liu, Yangang Liu, Xin Zhou, Yu Xie, Yongxiang Han, Shinjae Yoo, Manajit Sengupta
Summary: This study developed a hierarchy of four new physics-informed persistence models to simultaneously forecast global horizontal irradiance, direct normal irradiance, and diffuse horizontal irradiance. The new models generally outperformed simple and smart persistence models, improving forecast accuracy from 1.25 h to 6 h lead times, with forecast errors highly related to the error and temporal variability of the assumed cloud predictor. The best model for forecasting different radiative components can be explained by the relationship between solar irradiances and cloud properties.
Article
Chemistry, Multidisciplinary
Jiri Pospichal, Martin Kubovcik, Iveta Dirgova Luptakova
Summary: This paper extends the attention mechanism of the Transformer deep neural network model and combines spatiotemporal properties in solar irradiance prediction. The predicted results are included in the input data and achieve better results than competing methods.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Physical
Syed Altan Haider, Muhammad Sajid, Saeed Iqbal
Summary: This study forecasts the solar hydrogen production potential in the Islamabad region using machine learning methods, demonstrating significant potential for green energy production in the area.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2021)
Article
Energy & Fuels
Diego J. Pedregal, Juan R. Trapero
Summary: This paper presents an efficient and easy-to-implement method for Global Horizontal Irradiation forecasts on an hourly basis for a medium-term horizon, which outperforms other well-known alternative methods such as recurrent neural networks in longer horizons due to the inclusion of the yearly cycle.
Article
Anthropology
Grazyna Liczbinska, Marek Brabec, Janusz Piontek, Robert M. Malina
Summary: This study aimed to investigate the relationship between socioeconomic factors and the age of menarche among Polish women. The findings revealed that economic crises and war conditions had a stronger impact on the age of menarche among women from lower socioeconomic backgrounds, and the differences in menarche age between the lowest and highest social groups were reduced among women born after World War II.
AMERICAN JOURNAL OF HUMAN BIOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Fernando Marmolejo-Ramos, Mauricio Tejo, Marek Brabec, Jakub Kuzilek, Srecko Joksimovic, Vitomir Kovanovic, Jorge Gonzalez, Thomas Kneib, Peter Buehlmann, Lucas Kook, Guillermo Briseno-Sanchez, Raydonal Ospina
Summary: The advancement in technology has enabled the collection of large amounts of data across various research fields. Learning analytics (LA)/educational data mining utilizes unsupervised machine learning (ML) algorithms to analyze the vast amount of unstructured observational data captured from educational settings. Generalized additive models for location, scale, and shape (GAMLSS) is a supervised statistical learning framework that offers flexibility and power in modeling the parameters of response variables based on explanatory variables. This article provides an overview of GAMLSS in comparison to other ML techniques, highlighting its potential for causal regularization. The article illustrates its application using a data set from the field of LA.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2023)
Article
Engineering, Mechanical
Eder Batista Tchawou Tchuisseu, Pavel Prochazka, Mohammed Lamine Mekhalfia, Robert Hodbod', Dusan Maturkanic, Pete Russhard, Marek Brabec
Summary: This study proposes a statistical BTT method based on the minimization of statistical variables to determine the placement of probes, in order to obtain the highest quality data and the best accuracy for vibration parameters. The results show that irregular probe arrangements can estimate amplitudes more accurately.
JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES
(2023)
Letter
Endocrinology & Metabolism
Jan Broz, Marek Brabec, Pavlina Krollova, Lucia Fackovcova, Juraj Michalec
Article
Biodiversity Conservation
Radek Bace, Jenyk Hofmeister, Lucie Vitkova, Marek Brabec, Kresimir Begovic, Vojtech Cada, Pavel Janda, Daniel Kozak, Martin Mikolas, Thomas A. Nagel, Jakob Pavlin, Ruffy Rodrigo, Ondrej Vostarek, Miroslav Svoboda
Summary: Natural disturbances can change forest habitat quality, but it is uncertain how this change will be affected by increasing extent and intensity of disturbances under climate change. To understand this, we studied habitat quality in European primary Norway spruce forests using a space-for-time substitution approach. We found that post-disturbance habitat succession has a U-shaped response on plot-scale habitat quality, with greater decline in quality as disturbance severity increases.
JOURNAL OF APPLIED ECOLOGY
(2023)
Article
Biotechnology & Applied Microbiology
Jirina Szakova, Hana Stiborova, Filip Mercl, Niguss Solomon Hailegnaw, Miloslav Lhotka, Tatyana Derevyankina, Chandra Sekhar Paul, Altyn Taisheva, Marek Brabec, Pavel Tlustos
Summary: This study compared the effects of woodchip biochar and bone char on soil nutrient balance and found that soil properties were more important than the characteristics of biochar and bone char in determining element mobility.
JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY
(2023)
Article
Physics, Multidisciplinary
Viorel Badescu
Summary: The maximum work content factor K(U,i)(+) of incoming thermal radiation energy depends on various factors such as temperatures, solid angles, and spectral properties. The formula derived in this study provides a more accurate estimation compared to existing formulas. The work extraction capability of thermal power plants depends on absorptance, emittance, and radiation concentration. Solar thermal power plants based on plane blackbody collectors are not efficient under unconcentrated solar radiation.
Article
Multidisciplinary Sciences
Grazyna Liczbinska, Marek Brabec, Rajesh Gautam, Jyoti Jhariya, Arun Kumar
Summary: This study investigates the trend of age at marriage among Scheduled Castes women in two Indian states and finds that the increase in age at marriage is related to women's education, poverty alleviation, and the implementation of social programs.
Article
Environmental Sciences
Michal Tuser, Marek Brabec, Helge Balk, Vladislav Drastik, Jan Kubecka, Jaroslava Frouzova
Summary: Fish body orientation affects the size estimation using hydroacoustic signals. We measured acoustic signals from tethered fish in different dorsal positions and found that the dorsal aspect influenced the shapes of the amplitude echo envelopes. We also discovered that echo lengths approximately 15 dB below the amplitude maximum could be used to determine the fish dorsal aspect and facilitate the conversion between acoustic target strength and true fish length.
Article
Cell Biology
Zdenek Wurst, Barbora Bircak Kuchtova, Jan Kremen, Anastasiya Lahutsina, Ibrahim Ibrahim, Jaroslav Tintera, Ales Bartos, Marek Brabec, Tanya Rai, Petr Zach, Vladimir Musil, Nicoletta Olympiou, Jana Mrzilkova
Summary: The volume reduction of gray matter structures is accompanied by an asymmetric increase in white matter fibers in Alzheimer's disease patients. This study used diffusion tensor imaging to investigate white matter structure changes in the motor basal ganglia in Alzheimer's disease patients. Measurements were taken in ten patients and ten healthy controls, revealing a decrease in the number of tracts and general fractional anisotropy in the right caudate nucleus of Alzheimer's disease patients. An increase in the left and right putamen was observed. Furthermore, a decrease in structural volume was observed in the left and right putamen.
Article
Clinical Neurology
Klara Brozova, Juraj Michalec, Marek Brabec, Petra Borilova, Pavel Kohout, Jan Broz
Summary: This study investigates the impact of the ketogenic diet on glycemia in non-diabetic patients with refractory epilepsy. The results show that the initiation of the diet increases the risk of hypoglycemia, highlighting the importance of monitoring blood glucose levels during the treatment.
Article
Health Care Sciences & Services
Martina Vlasakova, Jan Muzik, Anna Holubova, Dominik Fiala, Eirik Arsand, Jana Urbanova, Denisa Janickova Zdarskaka, Marek Brabec, Jan Broz
Summary: The Diani telemedicine system was found to be beneficial for patients with type 1 diabetes mellitus, resulting in improved disease management and increased understanding of the impact of measured values on disease management. Its use had a positive effect on the HbA(1c) level.
JMIR FORMATIVE RESEARCH
(2023)
Article
Thermodynamics
Marius Paulescu, Robert Blaga, Ciprian Dughir, Nicoleta Stefu, Andreea Sabadus, Delia Calinoiu, Viorel Badescu
Summary: This paper introduces an upgraded version of the PV2-state model for intra-hour photovoltaic power forecasting. The model incorporates real-time adjustments to the estimated clear-sky PV power or the transmittance of clouds. It demonstrates notable performance particularly under challenging conditions, with the overall model performance ranging between 10 and 20%.
Article
Green & Sustainable Science & Technology
Qahtan A. Abed, Dhafer M. Hachim, Adrian Ciocanea, Viorel Badescu
Summary: This study examines the relative contribution of three different regions in an unglazed transpired collector (UTC) to the increase in air temperature: the front of the plate, the back of the plate, and the inner surface of the holes. Using a hybrid approach that combines experimental results and computational fluid dynamics simulations, the research finds that under no-wind conditions, the majority of the heat received by the air comes from the front of the plate, followed by the back of the plate, and the inner part of the holes. The influence of wind speed on these contributions is significant, with higher wind speeds resulting in increased heat from the front of the plate and decreased heat from the back of the plate.
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
(2023)
Article
Environmental Sciences
Iva Hunova, Marek Brabec, Marek Maly
Summary: This study aims to explore the vertical distribution of ground-level ozone concentrations and examine the O-3 concentration gradients at different heights. Daily mean O-3 concentrations measured at the Kosetice station during 2015-2021 are analyzed using the GAM approach. The study finds significant variations in the O-3 concentration gradients at different height ranges, with the highest dynamics observed between 2 and 8 meters and differences in seasonal and annual aspects.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
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.
