| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Keine Angabe |
| Titel: | Two machine learning approaches for short-term wind speed time-series prediction |
| Autor/-in: | Ak, Ronay Fink, Olga Zio, Enrico |
| DOI: | 10.1109/TNNLS.2015.2418739 |
| Erschienen in: | IEEE Transactions on Neural Networks and Learning Systems |
| Band(Heft): | 27 |
| Heft: | 8 |
| Seite(n): | 1734 |
| Seiten bis: | 1747 |
| Erscheinungsdatum: | 2016 |
| Verlag / Hrsg. Institution: | IEEE |
| ISSN: | 2162-237X 2162-2388 |
| Sprache: | Englisch |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/13904 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
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Ak, R., Fink, O., & Zio, E. (2016). Two machine learning approaches for short-term wind speed time-series prediction. IEEE Transactions on Neural Networks and Learning Systems, 27(8), 1734–1747. https://doi.org/10.1109/TNNLS.2015.2418739
Ak, R., Fink, O. and Zio, E. (2016) ‘Two machine learning approaches for short-term wind speed time-series prediction’, IEEE Transactions on Neural Networks and Learning Systems, 27(8), pp. 1734–1747. Available at: https://doi.org/10.1109/TNNLS.2015.2418739.
R. Ak, O. Fink, and E. Zio, “Two machine learning approaches for short-term wind speed time-series prediction,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 8, pp. 1734–1747, 2016, doi: 10.1109/TNNLS.2015.2418739.
AK, Ronay, Olga FINK und Enrico ZIO, 2016. Two machine learning approaches for short-term wind speed time-series prediction. IEEE Transactions on Neural Networks and Learning Systems. 2016. Bd. 27, Nr. 8, S. 1734–1747. DOI 10.1109/TNNLS.2015.2418739
Ak, Ronay, Olga Fink, and Enrico Zio. 2016. “Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.” IEEE Transactions on Neural Networks and Learning Systems 27 (8): 1734–47. https://doi.org/10.1109/TNNLS.2015.2418739.
Ak, Ronay, et al. “Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 8, 2016, pp. 1734–47, https://doi.org/10.1109/TNNLS.2015.2418739.
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