| Autor: |
Đalto, Mladen, Lončarek, Tomislav, Vašak, Mario, Matuško, Jadranko |
| Jazyk: |
angličtina |
| Rok vydání: |
2015 |
| Předmět: |
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| Popis: |
State of the art results achieved by deep learning methods in various fields suggest potential for improvements in very short-term wind power prediction. Performance of persistence based prediction and shallow neural networks used for wind power forecast have not yet been compared to deep learning methods. Use of large historical weather and SCADA datasets requires computational complexity reduction without sacrificing prediction quality. Issuing improved wind power forecasts for supporting decision-making in regulating reserve management has the advantage of being more cost-effective when compared to other solutions such as increasing backup capacities. Providing forecast uncertainty information further improves decision making capabilities and it is analysed in this paper. We present a comparison of simple persistence based prediction to prediction performance of shallow and deep neural networks. Input variable selection is performed in order to reduce complexity. Partial mutual information and compressing autoencoder approaches are compared. Proposed prediction techniques are tested on the case of on-shore wind farm Danilo in Croatia. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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