Name
Title | ||
|---|---|---|
Bayesian Optimization of a Hybrid Prediction System for Optimal Wave Energy Estimation Problems. |
Abstract | ||
|---|---|---|
In the last years, Bayesian optimization (BO) has emerged as a practical tool for high-quality parameter selection in prediction systems. BO methods are useful for optimizing black-box objective functions that either lack an analytical expression, or are very expensive to evaluate. In this paper we show how BO can be used to obtain optimal parameters of a prediction system for a problem of wave energy flux prediction. Specifically, we propose the Bayesian optimization of a hybrid Grouping Genetic Algorithm with an Extreme Learning Machine (GGAELM) approach. The system uses data from neighbor stations (usually buoys) in order to predict the wave energy at a goal marine energy facility. The proposed BO methodology has been tested in a real problem involving buoys data in the Western coast of the USA, improving the performance of the GGA-ELM without a BO approach. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59153-7_56 | ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT I |
Keywords | Field | DocType |
Sea waves energy,Prediction system,Bayesian optimization | Mathematical optimization,Extreme learning machine,Computer science,Bayesian optimization,Artificial intelligence,Marine energy,Energy flux,Genetic algorithm,Machine learning,Energy estimation,Prediction system | Conference |
Volume | ISSN | Citations |
10305 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laura Cornejo-Bueno | 1 | 10 | 3.45 |
| Eduardo C. Garrido-Merchán | 2 | 0 | 0.68 |
| Daniel Hernández-Lobato | 3 | 440 | 26.10 |
| Sancho Salcedo-Sanz | 4 | 580 | 71.21 |