Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/8249
Título
A Hybrid Intelligent Modeling approach for predicting the solar thermal panel energy production
Autor
Publicado en
Neurocomputing. 2024, V. 565, 126997
Editorial
Elsevier
Fecha de publicación
2024-01
ISSN
0925-2312
DOI
10.1016/j.neucom.2023.126997
Resumen
There is no doubt that the European Union is undergoing an ecological transition, with renewable energies
accounting for an increasing share of energy consumption in the Member States. In Spain, solar energy is one of these rapidly expanding renewable sources. This study analyzes the solar energy production of a panel in the Spanish region of Galicia. It has been demonstrated that the solar energy produced by this panel can be predicted using a hybrid stepwise system. The missing value imputation is a key step in the process. This involves combining regression and clustering techniques on different subdivisions of the complete dataset, starting with a smaller and less complete dataset and performing appropriate imputations to create a larger and more complete collection. Finally, the dataset is divided into more relevant subsets for regression analysis to calculate the amount of solar energy generated. The imputing missing values using an Artificial Neural Network resulted in a more valid dataset for further processing than eliminating rows with corrupted or empty values. Also, properly applying clustering techniques gives better results than working on the whole dataset.
Palabras clave
Regression
Neural network
Solar Energy
Renewable energy
Clustering
Materia
Informática
Computer science
Versión del editor
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