A Hybrid Intelligent System to Forecast Solar Energy Production
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Except where otherwise noted, this item's license is described as © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
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A Hybrid Intelligent System to Forecast Solar Energy ProductionDate
2019-08-07Citation
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.
Keywords
Hybrid intelligent system
Clustering
Regression
Neural networks
Solar energy
Renewable energies
Clustering
Regression
Neural networks
Solar energy
Renewable energies
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Rights
© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
ISSN
1879-0755