2026-06-09T20:40:50Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/82492024-01-09T01:05:38Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Repositorio Institucional de la Universidad de Burgos
author
Arroyo Puente, Ángel
author
Basurto Hornillos, Nuño
author
Casado Vara, Roberto Carlos
author
Timiraos, Míriam
author
Calvo-Rolle, José Luis
2024-01-08T13:25:10Z
2024-01-08T13:25:10Z
2024-01
0925-2312
http://hdl.handle.net/10259/8249
10.1016/j.neucom.2023.126997
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.
eng
Atribución 4.0 Internacional
Regression
Neural network
Solar Energy
Renewable energy
Clustering
A Hybrid Intelligent Modeling approach for predicting the solar thermal panel energy production
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/8249/1/Arroyo-neurocomputing_2024.pdf
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Arroyo-neurocomputing_2024.pdf