E-viri
Recenzirano
-
Yu, Chuanjin; Li, Yongle; Bao, Yulong; Tang, Haojun; Zhai, Guanghao
Energy conversion and management, 12/2018, Letnik: 178Journal Article
•A novel prediction framework is proposed.•Three new hybrid models based on the framework are put forward.•Compared to normal methods, the proposed models yield a better prediction accuracy. In this paper, a novel framework for wind speed forecasting is proposed. In the new prediction framework, wavelet transform is firstly adopted to decompose original wind speed history into several sub-series. Then, for low-frequency sub-series, recurrent neural networks are used to extract deeper features, which are fed into suitable machine learning methods for predicting, while others are still predicted by normal methods. Meanwhile, three new hybrid models are established, where support vector machine is taken as the predictor, and the standard recurrent neural network and its variant version: long short term memory neural networks and gated recurrent unit neural networks are employed to extract the deeper features. Four experiments from the real world are conducted through the proposed methods as well as normal algorithms. The results demonstrate that the three new proposed hybrid models based on the novel framework yield more accurate predictions.
Vnos na polico
Trajna povezava
- URL:
Faktor vpliva
Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Baze podatkov, v katerih je revija indeksirana
Ime baze podatkov | Področje | Leto |
---|
Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
---|
Vir: Osebne bibliografije
in: SICRIS
To gradivo vam je dostopno v celotnem besedilu. Če kljub temu želite naročiti gradivo, kliknite gumb Nadaljuj.