Examination of empirical and Machine Learning methods for regression of missing or invalid solar radiation data using routine meteorological data as predictors

Examination of empirical and Machine Learning methods for regression of missing or invalid solar radiation data using routine meteorological data as predictors

Sensors are prone to malfunction, leading to blank or erroneous measurements that cannot be ignored in most practical applications. Therefore, data users are always looking for efficient methods to substitute missing values with accurate estimations. Traditionally, empirical methods have been used f...

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Bibliographic Details
Main Authors: Konstantinos X Soulis, Evangelos E Nikitakis, Aikaterini N Katsogiannou, Dionissios P Kalivas
Format: Article
Language:English
Published: AIMS Press 2024-12-01
Series:AIMS Geosciences
Subjects:
machine learning
solar radiation
recurrent neural networks
random forest
empirical methods
regression
Online Access:https://www.aimspress.com/article/doi/10.3934/geosci.2024044
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https://www.aimspress.com/article/doi/10.3934/geosci.2024044

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