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Accurate Solar Radiation Site Adaptation: Harnessing Satellite Data and in Situ Measurements

https://doi.org/10.2139/ssrn.4755087
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Evaluation the efficiency of a parametric model based on MODIS data for solar radiation estimation in comparison with some empirical models
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Machine Learning: Algorithms, Real-World Applications and Research Directions
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Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning
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Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system
inferredcites →https://doi.org/10.1016/j.buildenv.2023.110822openalex-extracted
A review and guide on selecting and optimizing machine learning algorithms for daylight prediction
inferredcites →https://doi.org/10.1016/j.egyai.2023.100331openalex-extracted
Site adaptation with machine learning for a Northern Europe gridded global solar irradiance product
inferredcites →https://doi.org/10.1016/j.enconman.2021.113960openalex-extracted
A review on global solar radiation prediction with machine learning models in a comprehensive perspective
inferredcites →https://doi.org/10.1016/j.jag.2018.08.018openalex-extracted
A satellite stand-alone procedure for deriving net radiation by using SEVIRI and MODIS products
inferredcites →https://doi.org/10.1016/j.measurement.2021.109802openalex-extracted
Estimation of daily global solar radiation based on MODIS satellite measurements: The case study of Adrar region (Algeria)
inferredcites →https://doi.org/10.1016/j.renene.2020.11.089openalex-extracted
Machine learning for site-adaptation and solar radiation forecasting
inferredcites →https://doi.org/10.1016/j.renene.2022.06.043openalex-extracted
Site adaptation of global horizontal irradiance from the Copernicus Atmospheric Monitoring Service for radiation using supervised machine learning techniques
inferredcites →https://doi.org/10.1016/j.renene.2023.119671openalex-extracted
Global horizontal irradiance prediction model considering the effect of aerosol optical depth based on the Informer model
inferredcites →https://doi.org/10.1016/j.rser.2018.03.003openalex-extracted
The National Solar Radiation Data Base (NSRDB)
inferredcites →https://doi.org/10.1016/j.rser.2020.110114openalex-extracted
Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison
inferredcites →https://doi.org/10.1016/j.seta.2021.101764openalex-extracted
A case study in the tropical region to evaluate univariate imputation methods for solar irradiance data with different weather types
inferredcites →https://doi.org/10.1016/j.solener.2015.03.015openalex-extracted
A support vector machine–firefly algorithm-based model for global solar radiation prediction
inferredcites →https://doi.org/10.1016/j.solener.2016.03.001openalex-extracted
Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets
inferredcites →https://doi.org/10.1016/j.solener.2020.03.040openalex-extracted
Benchmarking on improvement and site-adaptation techniques for modeled solar radiation datasets
inferredcites →https://doi.org/10.1016/j.solener.2023.04.037openalex-extracted
Improvement of satellite-derived surface solar irradiance estimations using spatio-temporal extrapolation with statistical learning
inferredcites →https://doi.org/10.1109/tla.2023.10251811openalex-extracted
Benchmarking of modeled solar irradiation data in Uruguay at a daily time scale
inferredcites →https://doi.org/10.3390/en15239010openalex-extracted
Site-Adaptation for Correcting Satellite-Derived Solar Irradiance: Performance Comparison between Various Regressive and Distribution Mapping Techniques for Application in Daejeon, South Korea
inferredcites →https://doi.org/10.3390/rs12132127openalex-extracted
Site-Adaptation of Modeled Solar Radiation Data: The SiteAdapt Procedure
inferredcites →https://doi.org/10.3390/rs14215496openalex-extracted
An Empirical Correction Model for Remote Sensing Data of Global Horizontal Irradiance in High-Cloudiness-Index Locations
inferredcites →https://doi.org/10.46855/2020.05.07.01.48.541802openalex-extracted
Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning
inferredcites →https://doi.org/10.5194/gmd-7-1247-2014openalex-extracted
Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
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