Name
Abstract | ||
|---|---|---|
•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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compeleceng.2019.07.023 | Computers & Electrical Engineering |
Keywords | Field | DocType |
Hybrid Intelligent System,Clustering, regression,Neural networks,Solar energy,Renewable energies | Renewable energy,Electricity,Computer science,Solar energy,Supervised learning,Real-time computing,Unsupervised learning,Hybrid intelligent system,Artificial neural network,Cluster analysis | Journal |
Volume | ISSN | Citations |
78 | 0045-7906 | 1 |
PageRank | References | Authors |
0.36 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nuño Basurto | 1 | 2 | 2.75 |
| Ángel Arroyo | 2 | 1 | 0.36 |
| Rafael Vega Vega | 3 | 1 | 1.04 |
| Héctor Quintián | 4 | 1 | 1.38 |
| José Luís Calvo-Rolle | 5 | 175 | 41.67 |
| Álvaro Herrero | 6 | 3 | 1.75 |