Hour-ahead wind speed and power forecasting using empirical mode decomposition

Energies

Volumn 6, Issue 12, 2013, Pages 6137-6152

  Hong, Ying Yi ^a   Yu, Ti Hsuan ^a   Liu, Ching Yun ^a  

^a CHUNG YUAN CHRISTIAN UNIVERSITY   (Taiwan)

Author keywords

Artificial neural network; Empirical mode decomposition; Intrinsic mode function; Wind power

Indexed keywords

DEEP NEURAL NETWORKS; ELECTRIC LOAD DISPATCHING; ELECTRIC POWER GENERATION; ELECTRIC POWER TRANSMISSION NETWORKS; FORECASTING; FUNCTIONS; NEURAL NETWORKS; SCHEDULING; SIGNAL PROCESSING; SMART POWER GRIDS; SPEED; TURBOGENERATORS; WIND EFFECTS; WIND POWER; WIND TURBINES;

AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; COMPARATIVE STUDIES; CORRELATION COEFFICIENT; EMPIRICAL MODE DECOMPOSITION; INTRINSIC MODE FUNCTIONS; RESIDUAL FUNCTIONS; WIND POWER FORECASTING; WIND SPEED FORECASTING;

WIND;

EID: 84890167160     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en6126137     Document Type: Article

References (23)

1. National Energy Technology Laboratory. The Modern Grid Strategy. Building a Smart Grid Business Case; A Report Developed for the U.S. Department of Energy, Office of Electricity Delivery and Energy Reliability; National Energy Technology Laboratory: Washington, DC, USA, 2009.
2. Hernández, L.; Baladrón, C.; Aguiar, J.M.; Calavia, L.; Carro, B.; Sánchez-Esguevillas, A.; Sanjuán, J.; González, Á.; Lloret, J. Improved short-term load forecasting based on two-stage predictions with artificial neural networks in a microgrid environment. Energies 2013, 6, 4489-4507.
3. Božic, M.; Stojanovic, M.; Stajic, Z.; Tasic, D. A new two-stage approach to short term electrical load forecasting. Energies 2013, 6, 2130-2148.
4. Li, H.Z.; Guo, S.; Zhao, H.R.; Su, C.B.; Wang, B. Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm. Energies 2012, 5, 4430-4445.
5. Fan, G.F.; Qing, S.; Wang, H.; Hong, W.C.; Li, H.J. Support vector regression model based on empirical mode decomposition and auto regression for electric load forecasting. Energies 2013, 6, 1887-1901.
6. Yoo, J.; Hur, K. Load forecast model switching scheme for improved robustness to changes in building energy consumption patterns. Energies 2013, 6, 1329-1343.
7. Wang, F.; Mi, Z.Q.; Su, S.; Zhao, H.S. Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies 2012, 5, 1355-1370.
8. Ernst, B.; Oakleaf, B.; Ahlstrom, M.L.; Lange, M.; Moehrlen, C.; Lange, B.; Focken, U.; Rohrig, K. Predicting the wind. IEEE Power Energies 2007, 5, 78-89.
9. Barbounis, T.G.; Theocharis, J.B.; Alexiadis, M.C.; Dokopoulos, P.S. Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Trans. Energy Convers. 2006, 21, 273-284.
10. Taylor, J.W.; McSharry, P.E.; Roberto, B. Wind power density forecasting using ensemble predictions and time series models. IEEE Trans. Energy Convers. 2009, 24, 775-782.
11. Bhaskar, K.; Singh, S.N. AWNN-assisted wind power forecasting using feed-forward neural network. IEEE Trans. Sustain. Energy 2012, 3, 306-315.
12. Anastasiades, G.; McSharry, P. Quantile forecasting of wind power using variability indices. Energies 2013, 6, 662-695.
13. Zhang, Q.; Lai, K.K.; Niu, D.X.; Wang, Q.; Zhang, X.B. A fuzzy group forecasting model based on least squares support vector machine (LS-SVM) for short-term wind power. Energies 2012, 5, 3329-3346.
14. Catalão, J.P.S.; Pousinho, H.M.I.; Mendes, V.M.F. Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal. IEEE Trans. Sustain. Energy 2011, 2, 50-59.
15. Potter, C.W.; Negnevitsky, M. Very short-term wind forecasting for Tasmanian power generation. IEEE Trans. Power Syst. 2006, 21, 965-972.
16. Amjady, N.; Keynia, F.; Zareipour, H. Wind power prediction by a new forecast engine composed of modified hybrid neural network and enhanced particle swarm optimization. IEEE Trans. Sustain. Energy 2011, 2, 265-276.
17. Khosravi, A.; Nahavandi, S.; Creighton, D. Prediction intervals for short-term wind farm power generation forecasts. IEEE Trans. Sustain. Energy 2013, 4, 602-610.
18. Li, G.; Shi, J.; Zhou, J.Y. Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renew. Energy 2011, 36, 352-359.
19. Amjadya, N.; Keyniaa, F.; Zareipour, H. Short-term wind power forecasting using ridgelet neural network. Electric Power Syst. Res. 2011, 81, 2099-2107.
20. Liu, H.; Tian, H.Q.; Chen, C.; Li, Y.F. A hybrid statistical method to predict wind speed and wind power. Renew. Energy 2010, 35, 1857-1861.
21. Jursa, R.; Rohrig, K. Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models. Int. J. Forecast. 2008, 24, 694-709.
22. Zhou, J.Y.; Shi, J.; Li, G. Fine tuning support vector machines for short-term wind speed forecasting. Energy Convers. Manag. 2011, 52, 1990-1998.
23. Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.; Yen, N.C.; Tung, C.C.; Liu, H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. R. Soc. Lond. A 1998, 454, 903-995.