2026-06-17T21:56:24Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/82462024-01-09T01:05:28Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Repositorio Institucional de la Universidad de Burgos
author
Basurto Hornillos, Nuño
author
Arroyo Puente, Ángel
author
Vega, Rafael
author
Quintián, Héctor
author
Calvo-Rolle, José Luis
author
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.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Hybrid Intelligent System
Clustering
Regression
Neural networks
Solar Energy
Renewable energies
A Hybrid Intelligent System to forecast solar energy production
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/8246/1/Basurto-cee_2019.pdf
File
MD5
96848439e55ce4bf293da1f5fe066bb8
588224
application/pdf
Basurto-cee_2019.pdf