Article
Energy & Fuels
Jiwon Park, Sung Hyup Hong, Sang Hun Yeon, Byeong Mo Seo, Kwang Ho Lee
Summary: In this study, the prediction performances of regression models and deep learning-based predictive models were compared for hourly insolation prediction. The artificial neural networks (ANN) and long short-term memory (LSTM) models showed reliable predictive performances with CV(RMSE) of 14.0% and 15.8% respectively. The study proposed a direction for future research by utilizing insolation data from previous time-steps and considering variables related to sunrise and sunset for improving predictive performance.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Davide Cannizzaro, Alessandro Aliberti, Lorenzo Bottaccioli, Enrico Macii, Andrea Acquaviva, Edoardo Patti
Summary: This paper introduces a novel methodology for forecasting Global Horizontal Solar Irradiance using machine learning techniques and complex models, incorporating different meteorological information to achieve short- and long-term predictions, and discussing the accuracy of the experimental results.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Mustapha Mukhtar, Ariyo Oluwasanmi, Nasser Yimen, Zhang Qinxiu, Chiagoziem C. Ukwuoma, Benjamin Ezurike, Olusola Bamisile
Summary: This study develops two novel hybrid neural network models for accurate prediction of global solar radiation. Compared with traditional artificial neural network models, the hybrid models show better performance in different countries across Africa. The results of this study are of great significance for finding more accurate methods of solar radiation estimation.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Multidisciplinary
Tugba Ozdemir, Fatma Taher, Babajide O. Ayinde, Jacek M. Zurada, Ozge Tuzun Ozmen
Summary: An experimental study was conducted to predict solar radiation using classical artificial neural network (ANN) and deep learning methods. The results showed that long-short-term memory (LSTM) outperformed ANN in predicting solar radiation. The study paves the way for utilizing renewable energy by leveraging the usage of PV panels.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Multidisciplinary
Omer Ali Karaman, Tuba Tanyildizi Agir, Ismail Arsel
Summary: The study indicates that Extreme Learning Machines (ELM) outperform Artificial Neural Networks (ANN) in solar radiation estimation. Different activation functions were tested, with ELM showing better estimation performance. ELM achieved high accuracy with minimal error in a short amount of time, surpassing ANN.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Energy & Fuels
Aondoyila Kuhe, Victor Terhemba Achirgbenda, Mascot Agada
Summary: This study developed various artificial neural networks for predicting solar radiation in Makurdi, Nigeria. An ensemble of neural networks was used to improve prediction accuracy, achieving better results compared to individual networks.
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
(2021)
Article
Thermodynamics
Omer Ali Karaman
Summary: This paper presents the application of Particle Swarm Optimization (PSO) Algorithm, Artificial Neural Networks (ANNs), and Bagged Tree (BT) methods for forecasting seasonal solar irradiance in Karapinar town Turkey. The study utilizes input data from 2007 to 2020 to estimate solar irradiance as the output value. The BT method yields the lowest statistical error metrics and there is a strong positive linear relationship between air temperature and solar irradiance. The investigation of the impact of utilizing independent inputs on the forecast output has been conducted.
CASE STUDIES IN THERMAL ENGINEERING
(2023)
Article
Thermodynamics
Ali Sohani, Siamak Hoseinzadeh, Saman Samiezadeh, Ivan Verhaert
Summary: An enhanced design for a solar still desalination system was employed to develop artificial neural network (ANN) models, with FF and RBF types identified as the best structures for predicting distillate production and water temperature. Error analysis on data not used for ANN model development showed varying errors in different months.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2022)
Article
Green & Sustainable Science & Technology
Bai Liu, Dazhi Yang, Martin Janos Mayer, Carlos F. M. Coimbra, Jan Kleissl, Merlinde Kay, Wenting Wang, Jamie M. Bright, Xiang'ao Xia, Xin Lv, Dipti Srinivasan, Yan Wu, Hans Georg Beyerj, Gokhan Mert Yagli, Yanbo Shenl
Summary: Current solar forecast verification processes mainly focus on performance comparison of competing methods. However, it is important to evaluate the best method relative to the best-possible performance under specific forecasting situations, and quantify predictability and forecast skill. Unfortunately, there is a lack of literature on the quantification of relative performance of solar irradiance, and few studies on the spatial distributions of predictability and forecast skill. This study quantifies and maps the predictability and forecast skill of solar irradiance in the United States, refutes misconceptions, and revives the formulation of skill score.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Energy & Fuels
Dazhi Yang, Wenting Wang, Jamie M. Bright, Cyril Voyant, Gilles Notton, Gang Zhang, Chao Lyu
Summary: Forecasting global horizontal irradiance up to 12 hours ahead is crucial for solar photovoltaics grid integration. In this study, the ECMWF's HRES model and two NOAA models, namely RAP and HRRR, are validated and compared. Results show that HRES forecasts outperform HRRR and RAP forecasts in terms of accuracy.
Review
Engineering, Multidisciplinary
Mohammad Alhuyi Nazari, Azfarizal Mukhtar, Ahmad Shah Hizam Md Yasir, M. M. Rashidi, Mohammad Hossein Ahmadi, Vojtech Blazek, Lukas Prokop, Stanislav Misak
Summary: Heating and thermal comfort contribute significantly to final energy consumption. Fossil fuels and electrical technologies have been the main sources for heating in buildings so far, but with concerns over the depletion of fossil fuels and environmental issues, renewable energy sources, particularly solar energy, can provide a practical alternative. Intelligent techniques, such as artificial neural networks and support vector machines, have been used to predict the performance of solar heaters with great precision. The function and architecture of the models generated based on these intelligent techniques play a crucial role in their accuracy.
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
(2023)
Article
Engineering, Civil
Elad Dakar, Jose Manuel Fernandez Jaramillo, Isaac Gertman, Roberto Mayerle, Ron Goldman
Summary: We propose a system for predicting the hourly significant wave height at a specific wave measurement station by utilizing an artificial neural network (ANN) composed of two sub-networks. The system incorporates wind forecast data, wave forecast data, and observed data. The ANN system performs better than the existing SWAN wave model in estimating wave heights over 1.5 meters, showing the importance of all input components or just wind and observations. The system's reimplementation in Ashkelon yields smaller improvements due to insufficient training data.
COASTAL ENGINEERING JOURNAL
(2023)
Article
Green & Sustainable Science & Technology
Raj Kumar, Rahul Nadda, Sushil Kumar, Khusmeet Kumar, Asif Afzal, R. K. Abdul Razak, Mohsen Sharifpur
Summary: The study investigated the thermal enhancement characteristics of a solar thermal collector roughened with single arc protrusion ribs. The results show that artificial roughness and jet impingement have a significant impact on the thermo-hydraulic performance of the collector.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Energy & Fuels
Sarvapriya Singh, Siddharth Suman, Santanu Mitra, Manish Kumar
Summary: A hybrid CFD-ANN approach is used to predict the thermo-hydraulic performance of a solar air heater with rotating circular ribs. An optimized ANN model is developed based on CFD simulations, and it shows good agreement with experimental results. The optimized ANN model significantly reduces the computational time compared to CFD simulations.
Review
Environmental Sciences
Masoud Vakili, Seyedeh Akram Salehi
Summary: This review discusses the application of artificial intelligence in solar thermal energy systems, covering various models and methods, and evaluates their accuracy, issues, and challenges. Recommendations for future research are provided.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Environmental Sciences
Patricia Palma, Alexandra Marcha Penha, Maria Helena Novais, Sofia Fialho, Ana Lima, Adriana Catarino, Clarisse Mourinha, Paula Alvarenga, Maksim Iakunin, Goncalo Rodrigues, Miguel Potes, Manuela Morais, Maria Joao Costa, Rui Salgado
Summary: This study explores a new method for analyzing the quality of freshwater sediments in the Guadiana Basin. By using a combination of observational, chemical, and ecotoxicological assays, the most sensitive indicators were identified. The results showed potential contamination with toxic metals, especially in the sediments of the reservoir. Considering climate and geological conditions, the toolbox for comprehensive assessment of sediment quality should include meteorology, land use/cover, granulometry, organic matter content, PTM concentrations, contamination indices, and bioassays.
ENVIRONMENTAL RESEARCH
(2023)
Article
Food Science & Technology
Maria Ines Rouxinol, Maria Rosario Martins, Vanda Salgueiro, Maria Joao Costa, Joao Mota Barroso, Ana Elisa Rato
Summary: Wine quality is affected by climate variations and extreme weather events, which impact grape development and the content of key compounds. This study evaluates the ripening process and phenolic content of Vitis vinifera extracts in a vineyard in Alentejo, Portugal, in two different climatic years. The results show differences in polyphenol compounds between the years, highlighting the importance of monitoring their content during maturation. The reduction in berry size, possibly due to lower rainfall and higher temperatures, leads to higher polyphenolic compounds associated with grape quality.
Article
Meteorology & Atmospheric Sciences
Javier Vaquero-Martinez, Manuel Anton, Maria Joao Costa, Daniele Bortoli, Francisco Navas-Guzman, Lucas Alados-Arboledas
Summary: This study compares integrated water vapor (IWV) data recorded from microwave radiometer (MWR) and sun-photometer (SP) with global navigation satellite system (GNSS) IWV as reference in five mid-latitude sites of Portugal and Spain (2003-2021). Both instruments show very high correlation (R2 between 0.94 and 0.98), but MWR exhibits a wet bias while SP exhibits a dry bias. Mean bias error (MBE) and standard deviation (SD) increase with IWV. The dependence on solar zenith angle (SZA) is also studied, showing slightly larger discrepancies for MWR compared to SP for high SZA values.
ATMOSPHERIC RESEARCH
(2023)
Article
Energy & Fuels
Edgar F. M. Abreu, Christian A. Gueymard, Paulo Canhoto, Maria Joao Costa
Summary: This study evaluates the performance of three state-of-the-art solar radiation models in predicting clear-sky direct normal irradiance (DNI) and global horizontal irradiance (GHI) in Evora, Portugal, by comparing them with high-quality measured irradiance data. The results show that AERONET provides the best DNI estimates, while libRadtran and SMARTS models produce closer estimates to ground-based DNI observations. For GHI, no firm conclusion can be drawn regarding the best data source. Overall, libRadtran/MERRA-2 and SMARTS/AERONET are the best combinations for estimating DNI, and REST2/ AERONET and REST2/MERRA-2 are the best combinations for estimating GHI.
Article
Environmental Sciences
Filippe L. M. Santos, Flavio T. Couto, Susana Saraiva Dias, Nuno de Almeida Ribeiro, Rui Salgado
Summary: This study improves the representation of fuel load and moisture content in vegetation through remote sensing and in-situ data, providing reliable information for wildfire risk assessment and atmosphere fire modeling in Southern Portugal.
REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT
(2023)
Article
Meteorology & Atmospheric Sciences
Catia Campos, Flavio Tiago Couto, Jean-Baptiste Filippi, Roberta Baggio, Rui Salgado
Summary: This study improves the understanding of pyro-convection phenomena by using a fire-atmosphere coupled simulation and investigates the meteorological conditions during multiple mega-fires events in Portugal. Two numerical simulations were performed using the MesoNH model, with the second one coupled with a fire propagation model to study a specific fire. The simulations show the influence of south/southwest winds and subtropical moisture transport on the rapid spread of fires, as well as the formation of a PyroCu cloud within the smoke plume.
ATMOSPHERIC RESEARCH
(2023)
Article
Thermodynamics
J. Garcia Ferrero, R. P. Merchan, M. J. Santos, A. Medina, A. Calvo Hernandez, P. Canhoto, A. Giostri
Summary: This paper conducts a heat transfer analysis on an air volumetric receiver coupled to a parabolic dish for distributed generation. The optical efficiency of the dish collector is computed using ray-tracing software, while the thermal performance of the solar receiver is modeled under steady-state conditions. The results show that the thermal efficiency of the receiver depends on solar irradiance, receiver geometry, materials, and ambient temperature.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Environmental Sciences
Edicle de Souza Fernandes Duarte, Vanda Salgueiro, Maria Joao Costa, Paulo Sergio Lucio, Miguel Potes, Daniele Bortoli, Rui Salgado
Summary: This study analyzed the impact of fire-pollutant-meteorological variables on cardio-respiratory mortality during wildfire season in Portugal. The results showed that the warmest, driest, and most polluted months of the season were associated with an increased risk of cardio-respiratory deaths.
Article
Environmental Sciences
Konstantinos Michailidis, Maria-Elissavet Koukouli, Dimitris Balis, J. Pepijn Veefkind, Martin de Graaf, Lucia Mona, Nikolaos Papagianopoulos, Gesolmina Pappalardo, Ioanna Tsikoudi, Vassilis Amiridis, Eleni Marinou, Anna Gialitaki, Rodanthi-Elisavet Mamouri, Argyro Nisantzi, Daniele Bortoli, Maria Joao Costa, Vanda Salgueiro, Alexandros Papayannis, Maria Mylonaki, Lucas Alados-Arboledas, Salvatore Romano, Maria Rita Perrone, Holger Baars
Summary: The purpose of this study is to investigate the accuracy of the Sentinel-5P TROPOMI instrument in deriving geometric features of aerosol layers in the Mediterranean Basin. Ground-based lidar observations from the EARLINET network were used for validation. The results showed a good correlation between TROPOMI and EARLINET measurements, with TROPOMI consistently providing lower aerosol layer altitudes.
ATMOSPHERIC CHEMISTRY AND PHYSICS
(2023)
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
Computer Science, Artificial Intelligence
Sara Pereira, Paulo Canhoto, Rui Salgado
Summary: This paper presents a method based on artificial neural networks for generating operational direct normal irradiance forecasts using weather and aerosol forecasts. The models show improved accuracy and good agreement with experimental data.
Article
Meteorology & Atmospheric Sciences
Jaisankar Bharath, Tumuluru Venkata Lakshmi Kumar, Vanda Salgueiro, Maria Joao Costa, Rajesh Kumar Mall
Summary: This study compares the global and regional trends of aerosol optical depth (AOD) from model simulations with satellite retrievals and analyzes the intermodel variations. The study finds that the model simulations overestimate AOD in certain regions and shows differences in simulating aerosol size distribution among different models.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2023)
Article
Environmental Sciences
Edicle de Souza Fernandes Duarte, Paulo Sergio Lucio, Maria Joao Costa, Vanda Salgueiro, Rui Salgado, Miguel Potes, Judith J. Hoelzemann, Daniele Bortoli
Summary: This study aims to investigate the seasonal variations of air pollutants and meteorological factors in Portugal and their associations with cardiorespiratory mortality. The results showed that cardiorespiratory mortality rates were significantly higher in winter than in summer, with PM10, PM2.5, CO, and NO2 showing higher concentrations in winter while O-3 showed higher concentrations in spring and summer. The CCA analysis indicated a strong linear correlation between pollutant-meteorological factors and health outcomes.
ENVIRONMENTAL RESEARCH
(2024)
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.