2026-06-19T12:50:34Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/82462024-01-09T01:05:28Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Basurto Hornillos, Nuño Arroyo Puente, Ángel Vega, Rafael Quintián, Héctor Calvo-Rolle, José Luis Herrero Cosío, Álvaro 2024-01-08T12:45:26Z 2024-01-08T12:45:26Z 2019-09 0045-7906 http://hdl.handle.net/10259/8246 10.1016/j.compeleceng.2019.07.023 There is wide acknowledgement that solar energy is a promising and renewable source of electricity. However, complementary sources are sometimes required, due to its limited capacity, in order to satisfy user demand. A Hybrid Intelligent System (HIS) is proposed in this paper to optimize the range of possible solar energy and power grid combinations. It is designed to predict the energy generated by any given solar thermal system. To do so, the novel HIS is based on local models that implement both supervised learning (artificial neural networks) and unsupervised learning (clustering). These techniques are combined and applied to a realworld installation located in Spain. Alternative models are compared and validated in this case study with data from a whole year. With an optimum parameter fit, the proposed system managed to calculate the solar energyproduced by the panel with an error that was lower than 10-4 in 86% of cases. application/pdf eng Elsevier Computers & Electrical Engineering. 2019, V. 78, p. 373-387 https://doi.org/10.1016/j.compeleceng.2019.07.023 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Hybrid Intelligent System Clustering Regression Neural networks Solar Energy Renewable energies Informática Computer science A Hybrid Intelligent System to forecast solar energy production info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Computers & Electrical Engineering 78 373 387