Estimation of one-minute direct normal irradiance using a deep neural network for five climate zones

[1]  Dazhi Yang.  Estimating 1-min beam and diffuse irradiance from the global irradiance: A review and an extensive worldwide comparison of latest separation models at 126 stations , 2022, Renewable and Sustainable Energy Reviews.

[2]  Tahsin Koroglu,et al.  Estimation of hourly global solar radiation using artificial neural network in Adana province, Turkey , 2021, Journal of Thermal Engineering.

[3]  Chang Ki Kim,et al.  Analysis of minute-scale variability for enhanced separation of direct and diffuse solar irradiance components using machine learning algorithms , 2021, Energy.

[4]  Qian Yu,et al.  Estimating half-hourly solar radiation over the Continental United States using GOES-16 data with iterative random forest , 2021 .

[5]  Zahraa E. Mohamed,et al.  Potential of Machine Learning Based Support Vector Regression for Solar Radiation Prediction , 2021, Comput. J..

[6]  N. M. Mubarak,et al.  Recent progress in solar water heaters and solar collectors: A comprehensive review , 2021 .

[7]  V. Chandramohan,et al.  Applications of solar energy based drying technologies in various industries – A review , 2021 .

[8]  Yanfeng Liu,et al.  A review on global solar radiation prediction with machine learning models in a comprehensive perspective , 2021 .

[9]  Abdellah Mechaqrane,et al.  Estimation of daily global solar radiation using empirical and machine-learning methods: A case study of five Moroccan locations , 2021, Sustainable Materials and Technologies.

[10]  B. Mathew,et al.  A review of recent advances in humidification and dehumidification desalination technologies using solar energy , 2021 .

[11]  Eusebio Ventura-Ramos,et al.  Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review , 2021, Applied Sciences.

[12]  Hasan Yildizhan,et al.  Application of machine learning for solar radiation modeling , 2021, Theoretical and Applied Climatology.

[13]  S. Dehuri,et al.  DecisionTree for Classification and Regression: A State-of-the Art Review , 2020, Informatica.

[14]  J. J. Rodríguez,et al.  Integrated Aquaculture Recirculation System (IARS) Supported by Solar Energy as a Circular Economy Alternative for Resilient Communities in Arid/Semi-Arid Zones in Southern South America: A Case Study in the Camarones Town , 2020, Water.

[15]  Mirata Hosseini,et al.  A systematic approach in constructing typical meteorological year weather files using machine learning , 2020 .

[16]  Tanveer Ahmad,et al.  A critical review of comparative global historical energy consumption and future demand: The story told so far , 2020 .

[17]  S. Sabetghadam,et al.  Estimation of global solar radiation data based on satellite-derived atmospheric parameters over the urban area of Mashhad, Iran , 2020, Environmental Science and Pollution Research.

[18]  Amir Mosavi,et al.  Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction , 2020, Entropy.

[19]  José Reinaldo da Silva Cabral de Moraes,et al.  Köppen-Geiger and Camargo climate classifications for the Midwest of Brasil , 2020, Theoretical and Applied Climatology.

[20]  Tzu-Tsung Wong,et al.  Reliable Accuracy Estimates from k-Fold Cross Validation , 2020, IEEE Transactions on Knowledge and Data Engineering.

[21]  Enrique López Droguett,et al.  Operation scheduling in a solar thermal system: A reinforcement learning-based framework , 2020 .

[22]  Li Li,et al.  Köppen-Geiger climate classification adjustment of the BRL diffuse irradiation model for Australian locations , 2020 .

[23]  Volker H. Hoffmann,et al.  Challenges and solution technologies for the integration of variable renewable energy sources—a review , 2020, Renewable Energy.

[24]  Gonzalo Martínez-Muñoz,et al.  A comparative analysis of gradient boosting algorithms , 2019, Artificial Intelligence Review.

[25]  Jesús Ferrero Bermejo,et al.  Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants , 2019, Energies.

[26]  M. J. Costa,et al.  Prediction of diffuse horizontal irradiance using a new climate zone model , 2019, Renewable and Sustainable Energy Reviews.

[27]  Dimitris Papamichail,et al.  Solar radiation estimation methods using ANN and empirical models , 2019, Comput. Electron. Agric..

[28]  John Boland,et al.  Satellite-augmented diffuse solar radiation separation models , 2019, Journal of Renewable and Sustainable Energy.

[29]  Eugenia Paulescu,et al.  A simple and reliable empirical model with two predictors for estimating 1-minute diffuse fraction , 2019, Solar Energy.

[30]  M. Abd Elaziz,et al.  Modeling of solar energy systems using artificial neural network: A comprehensive review , 2019, Solar Energy.

[31]  E. Bellos,et al.  Development of empirical models for estimation of global solar radiation exergy in India , 2019, Journal of Cleaner Production.

[32]  Wenmin Qin,et al.  Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods , 2019, Energies.

[33]  Chi Wang,et al.  Machine Learning Approach for Solar Wind Categorization , 2018, Earth and Space Science.

[34]  J. Boland,et al.  Resolution of the cloud enhancement problem for one-minute diffuse radiation prediction , 2018, Renewable Energy.

[35]  Xinhua Xue,et al.  Prediction of daily diffuse solar radiation using artificial neural networks , 2017 .

[36]  Muhammed A. Hassan,et al.  Exploring the potential of tree-based ensemble methods in solar radiation modeling , 2017 .

[37]  John Boland,et al.  Assessment of solar radiation components in Brazil using the BRL model , 2017 .

[38]  Andreas Holzinger,et al.  Introduction to MAchine Learning & Knowledge Extraction (MAKE) , 2017, Mach. Learn. Knowl. Extr..

[39]  Ricardo Aler,et al.  Improving the separation of direct and diffuse solar radiation components using machine learning by gradient boosting , 2017 .

[40]  Soteris A. Kalogirou,et al.  Machine learning methods for solar radiation forecasting: A review , 2017 .

[41]  Alexandros G. Charalambides,et al.  Computations of diffuse fraction of global irradiance: Part 2 – Neural Networks , 2016 .

[42]  Alexandros G. Charalambides,et al.  Computations of diffuse fraction of global irradiance: Part 1 – Analytical modelling , 2016 .

[43]  Carlo Renno,et al.  ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building , 2016 .

[44]  Silvia Helena Modenese Gorla da Silva,et al.  Assessment of ANN and SVM models for estimating normal direct irradiation (Hb) , 2016 .

[45]  Por Lip Yee,et al.  Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model , 2016 .

[46]  J. A. Ruiz-Arias,et al.  Extensive worldwide validation and climate sensitivity analysis of direct irradiance predictions from 1-min global irradiance , 2016 .

[47]  David Pozo-Vázquez,et al.  An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery , 2015 .

[48]  Nicholas A. Engerer.  Minute resolution estimates of the diffuse fraction of global irradiance for southeastern Australia , 2015 .

[49]  Christian A. Gueymard,et al.  A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects , 2014 .

[50]  Amit Kumar Yadav,et al.  Solar radiation prediction using Artificial Neural Network techniques: A review , 2014 .

[51]  N. D. Kaushika,et al.  Artificial neural network model based on interrelationship of direct, diffuse and global solar radiations , 2014 .

[52]  John Boland,et al.  Modelling of diffuse solar fraction with multiple predictors , 2010 .

[53]  S. C. Kaushik,et al.  Assessment of diffuse solar energy under general sky condition using artificial neural network , 2009 .

[54]  P. Ineichen.  A broadband simplified version of the Solis clear sky model , 2008 .

[55]  John Boland,et al.  Models of diffuse solar radiation , 2008 .

[56]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[57]  Hamdy K. Elminir,et al.  Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models , 2007 .

[58]  S. Alam,et al.  Computation of beam solar radiation at normal incidence using artificial neural network , 2006 .

[59]  Marija Zlata Boznar,et al.  Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique , 2004 .

[60]  Gabriel López,et al.  Selection of input parameters to model direct solar irradiance by using artificial neural networks , 2004 .

[61]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[62]  Jouko Lampinen,et al.  Bayesian approach for neural networks--review and case studies , 2001, Neural Networks.

[63]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[64]  J. Boland,et al.  Assessing one-minute diffuse fraction models based on worldwide climate features , 2021 .

[65]  R. Alkama,et al.  Assessment of climate change in Algeria from 1951 to 2098 using the Köppen–Geiger climate classification scheme , 2018, Climate Dynamics.

[66]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[67]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[68]  W. Beckman,et al.  Diffuse fraction correlations , 1990 .

[69]  John H. Holland,et al.  Genetic Algorithms and Adaptation , 1984 .

[70]  J. Duffie,et al.  Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation , 1982 .

[71]  J. Orgill,et al.  Correlation equation for hourly diffuse radiation on a horizontal surface , 1976 .