2026-09-06T02:41:33Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/82492024-01-09T01:05:38Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

00925njm 22002777a 4500 dc Arroyo Puente, Ángel author Basurto Hornillos, Nuño author Casado Vara, Roberto Carlos author Timiraos, Míriam author Calvo-Rolle, José Luis author 2024-01 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. 0925-2312 http://hdl.handle.net/10259/8249 10.1016/j.neucom.2023.126997 Regression Neural network Solar Energy Renewable energy Clustering A Hybrid Intelligent Modeling approach for predicting the solar thermal panel energy production