Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloOne-day ahead wind speed/power prediction based on polynomial autoregressive model
Anno di pubblicazione2017
Formato-
Autore/iKarakus O.; Kuruoglu E.E.; Altinkaya M.A.
Affiliazioni autoriDepartment of Electrical-Electronics Engineering, Izmir Institute of Technology, Izmir, Turkey; CNR-ISTI Pisa, Italy; Department of Electrical-Electronics Engineering, Izmir Institute of Technology, Izmir, Turkey
Autori CNR e affiliazioni
  • ERCAN ENGIN KURUOGLU
Lingua/e
  • inglese
AbstractWind has been one of the popular renewable energy generation methods in the last decades. Foreknowledge of power to be generated from wind is crucial especially for planning and storing the power. It is evident in various experimental data that wind speed time series has non-linear characteristics. It has been reported in the literature that nonlinear prediction methods such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) perform better than linear autoregressive (AR) and AR moving average models. Polynomial AR (PAR) models, despite being non-linear, are simpler to implement when compared with other non-linear AR models due to their linear-in-the-parameters property. In this study, a PAR model is used for one-day ahead wind speed prediction by using the past hourly average wind speed measurements of Ce?me and Bandon and performance comparison studies between PAR and ANN-ANFIS models are performed. In addition, wind power data which was published for Global Energy Forecasting Competition 2012 has been used to make power predictions. Despite having lower number of model parameters, PAR models outperform all other models for both of the locations in speed predictions as well as in power predictions when the prediction horizon is longer than 12 h.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da1430
Pagine a1439
Pagine totali10
RivistaIET renewable power generation (Print)
Attiva dal 2007
Editore: IET, - Stevenage
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 1752-1416
Titolo chiave: IET renewable power generation (Print)
Titolo proprio: IET renewable power generation. (Print)
Titoli alternativi:
  • Institution of Engineering and Technology renewable power generation (Print)
  • Renewable power generation (Print)
Numero volume della rivista11
Fascicolo della rivista11
DOI10.1049/iet-rpg.2016.0972
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85030127897)
  • ISI Web of Science (WOS) (Codice:000411690700010)
Parole chiaveWind energy, Wind speed forecasting, Polynomial autoregressive models
Link (URL, URI)https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-rpg.2016.0972
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ISTI — Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
Moduli/Attività/Sottoprogetti CNR
  • ICT.P10.012.001 : Elaborazione di segnali e immagini per impieghi diagnostici e interpretazione di immagini multisorgente
Progetti Europei-
Allegati
One-day ahead wind speed/power prediction based on polynomial autoregressive model (documento privato )
Descrizione: published version - Codice PuMa: 2017_A0_061
Tipo documento: application/pdf