A Hybrid Intelligent System to forecast solar energy production. (September 2019)

• Record Type: Journal Article
• Authors: Basurto, Nuño; Arroyo, Ángel; Vega, Rafael; Quintián, Héctor; Calvo-Rolle, José Luis; Herrero, Álvaro
• Is Part Of: Computers & electrical engineering. Volume 78(2019)
• Page Start: 373
• Page End: 387
• Publication Date: 2019-09
• Subjects: Hybrid Intelligent System -- Clustering, regression -- Neural networks -- Solar energy -- Renewable energies; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.07.023
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: An enhanced Hybrid Intelligent System is proposed to predict the energy generated by a solar thermal system. Supervised (neural networks) and unsupervised learning (clustering) are combined an applied to a real-world case study. Best regression results are obtained when applying a Multilayer Perceptron trained with the Bayesian Regularization and Levenberg-Marquardt algorithms to k-means clustered datasets. Abstract: 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 real-world 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.