Skip to main content
Erschienen in: Neural Computing and Applications 2/2014

01.08.2014 | Original Article

Short-term wind power prediction using differential EMD and relevance vector machine

verfasst von: Yan Bao, Hui Wang, Beining Wang

Erschienen in: Neural Computing and Applications | Ausgabe 2/2014

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

As a renewable energy source, wind turbine generators are considered to be important generation alternatives in electric power systems because of their nonexhaustible nature. With the increase in wind power penetration, wind power forecasting is crucially important for integrating wind power in a conventional power grid. In this paper, a short-term wind power output prediction model is presented from raw data of wind farm, and prediction of short-term wind power is implemented using differential empirical mode decomposition (EMD) and relevance vector machine (RVM). The differential EMD method is used to decompose the wind farm power to several detail parts associated with high frequencies [intrinsic mode function (IMF)] and an approximate part associated with low frequencies (r). Then, RVM is used to predict both the IMF components and the r. Finally, the short-term wind farm power is forecasted by summing the RVM-based prediction of both the IMF components and the r. Simulation results have shown that the proposed short-term wind power prediction method has good performance.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Zhu B, Chen M, Wade N, Ran L (2012) A prediction model for wind farm power generation based on fuzzy modeling. Proc Environ Sci 12:122–129CrossRef Zhu B, Chen M, Wade N, Ran L (2012) A prediction model for wind farm power generation based on fuzzy modeling. Proc Environ Sci 12:122–129CrossRef
2.
Zurück zum Zitat Tascikaraoglu A, Uzunoglu M, Vural B (2012) The assessment of the contribution of short-term wind power predictions to the efficiency of stand-alone hybrid systems. Appl Energy 94:156–165CrossRef Tascikaraoglu A, Uzunoglu M, Vural B (2012) The assessment of the contribution of short-term wind power predictions to the efficiency of stand-alone hybrid systems. Appl Energy 94:156–165CrossRef
3.
Zurück zum Zitat An X, Jiang D, Zhao M, Liu C (2012) Short-term prediction of wind power using EMD and chaotic theory. Commun Nonlinear Sci Numer Simul 17:1036–1042CrossRef An X, Jiang D, Zhao M, Liu C (2012) Short-term prediction of wind power using EMD and chaotic theory. Commun Nonlinear Sci Numer Simul 17:1036–1042CrossRef
4.
Zurück zum Zitat Shao Y, Yao X (2012) Cerebellar model controller applied in wind power prediction. Phys Proc 25:2304–2308CrossRef Shao Y, Yao X (2012) Cerebellar model controller applied in wind power prediction. Phys Proc 25:2304–2308CrossRef
5.
Zurück zum Zitat Torres JL, Garcia A, De Blas M, De Francisco A (2005) Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Sol Energy 79:65–77CrossRef Torres JL, Garcia A, De Blas M, De Francisco A (2005) Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Sol Energy 79:65–77CrossRef
6.
Zurück zum Zitat Louka P, Galanis G, Siebert N (2008) Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. J Wind Eng Ind Aerodyn 96:2348–2362CrossRef Louka P, Galanis G, Siebert N (2008) Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. J Wind Eng Ind Aerodyn 96:2348–2362CrossRef
7.
Zurück zum Zitat Taniquchi K, Ichiyanaqi K, Yukita K, Goto Y (2008) Study on forecast of time series of wind velocity for wind power generation by using wide meteorological data. IEEJ Trans Power Energy 128:416–422CrossRef Taniquchi K, Ichiyanaqi K, Yukita K, Goto Y (2008) Study on forecast of time series of wind velocity for wind power generation by using wide meteorological data. IEEJ Trans Power Energy 128:416–422CrossRef
8.
Zurück zum Zitat Carolin Mabel M, Fernandez E (2008) Analysis of wind power generation and prediction using ANN: a case study. Renew Energy 33:986–992CrossRef Carolin Mabel M, Fernandez E (2008) Analysis of wind power generation and prediction using ANN: a case study. Renew Energy 33:986–992CrossRef
9.
Zurück zum Zitat Yesilbudak M, Sagiroglu S, Colak I (2013) A new approach to very short term wind speed prediction using k-nearest neighbor classification. Energy Convers Manag 69:77–86CrossRef Yesilbudak M, Sagiroglu S, Colak I (2013) A new approach to very short term wind speed prediction using k-nearest neighbor classification. Energy Convers Manag 69:77–86CrossRef
10.
Zurück zum Zitat Li L, Liu Y, Yang Y et al. (2013) A physical approach of the short-term wind power prediction based on CFD pre-calculated flow fields. J Hydrodyn 25:56–61CrossRef Li L, Liu Y, Yang Y et al. (2013) A physical approach of the short-term wind power prediction based on CFD pre-calculated flow fields. J Hydrodyn 25:56–61CrossRef
11.
Zurück zum Zitat Zhang W, Wang J, Wang J et al. (2013) Short-term wind speed forecasting based on a hybrid model. Appl Soft Comput 13:3225–3233CrossRef Zhang W, Wang J, Wang J et al. (2013) Short-term wind speed forecasting based on a hybrid model. Appl Soft Comput 13:3225–3233CrossRef
12.
Zurück zum Zitat Peng H, Liu F, Yang X (2013) A hybrid strategy of short term wind power prediction. Renew Energy 50:590–595CrossRef Peng H, Liu F, Yang X (2013) A hybrid strategy of short term wind power prediction. Renew Energy 50:590–595CrossRef
13.
Zurück zum Zitat An X, Jiang D, Liu C, Zhao M (2011) Wind farm power prediction based on wavelet decomposition and chaotic time series. Expert Syst Appl 38:11280–11285CrossRef An X, Jiang D, Liu C, Zhao M (2011) Wind farm power prediction based on wavelet decomposition and chaotic time series. Expert Syst Appl 38:11280–11285CrossRef
14.
Zurück zum Zitat Lei Y, Lin J, He Z, Zuo MJ (2013) A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Signal Process 35:108–126CrossRef Lei Y, Lin J, He Z, Zuo MJ (2013) A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Signal Process 35:108–126CrossRef
15.
Zurück zum Zitat Bhusana P, Chris T (2013) Improving prediction of exchange rates using Differential EMD. Expert Syst Appl 40:377–384CrossRef Bhusana P, Chris T (2013) Improving prediction of exchange rates using Differential EMD. Expert Syst Appl 40:377–384CrossRef
16.
Zurück zum Zitat Flake J, Moon TK, McKee M, Gunther JH (2010) Application of the relevance vector machine to canal flow prediction in the Sevier River Basin. Agric Water Manag 97:208–214CrossRef Flake J, Moon TK, McKee M, Gunther JH (2010) Application of the relevance vector machine to canal flow prediction in the Sevier River Basin. Agric Water Manag 97:208–214CrossRef
17.
Zurück zum Zitat Wong P, Xu Q, Vong C, Wong H (2012) Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine. IEEE Trans Ind Electron 59:1988–2001CrossRef Wong P, Xu Q, Vong C, Wong H (2012) Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine. IEEE Trans Ind Electron 59:1988–2001CrossRef
18.
Zurück zum Zitat Wang Z, Liu L (2012) Sensitivity prediction of sensor based on relevance vector machine. J Inf Comput Sci 9:2589–2597 Wang Z, Liu L (2012) Sensitivity prediction of sensor based on relevance vector machine. J Inf Comput Sci 9:2589–2597
19.
Metadaten
Titel
Short-term wind power prediction using differential EMD and relevance vector machine
verfasst von
Yan Bao
Hui Wang
Beining Wang
Publikationsdatum
01.08.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1482-z

Weitere Artikel der Ausgabe 2/2014

Neural Computing and Applications 2/2014 Zur Ausgabe