A Hybrid Intelligent System to Forecast Solar Energy Production

Basurto, Nuño; Arroyo, Ángel; Vega-Vega, Rafael A.; Héctor, Quintián; Calvo-Rolle, José Luis

Use this link to cite:

http://hdl.handle.net/2183/35081

A Hybrid Intelligent System to Forecast Solar Energy Production

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Basurto_Nuno_2019_Hybrid_intelligent_system_forecast_solar_energy_production.pdf (648.81 KB)

Identifiers

URI: http://hdl.handle.net/2183/35081

DOI: https://doi.org/10.1016/j.compeleceng.2019.07.023

Publication date

2019-08-07

Authors

Basurto, Nuño

Arroyo, Ángel

Vega-Vega, Rafael A.

Héctor, Quintián

Calvo-Rolle, José Luis

Bibliographic citation

Basurto N, Arroyo Á, Vega R, Quintián H, Calvo-Rolle JL, Herrero Á. A Hybrid Intelligent System to forecast solar energy production. Computers & Electrical Engineering 2019;78:373–87. https://doi.org/10.1016/j.compeleceng.2019.07.023

Abstract

[Abstarct]: 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 energy produced by the panel with an error that was lower than 10-4 in 86% of cases.