Name
Abstract | ||
|---|---|---|
Ensembles turn out to be excellent wind power prediction methods. But the space of algorithms and parameters of supervised learning ensembles is large. For an efficient optimization and tuning of ensembles, we propose to employ evolutionary multi-objective optimization methods in this work. NSGA-II is a classic optimization algorithm based on non-dominated sorting and maximization of the crowding distance and has successfully been applied in various applications in the past. The experimental part of the paper shows how NSGA-II tunes SVR ensembles, random forests, and heterogenous ensembles. The study demonstrates that the proposed approach evolves an attractive set of ensembles for a practitioner yielding numerous compromises of prediction accuracy and runtime. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-50947-1_9 | DATA ANALYTICS FOR RENEWABLE ENERGY INTEGRATION (DARE 2016) |
DocType | Volume | ISSN |
Conference | 10097 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin Heinermann | 1 | 0 | 0.34 |
| Jörg Lässig | 2 | 175 | 22.53 |
| Oliver Kramer | 3 | 304 | 38.42 |