Direct interval forecasting of wind power

IEEE Transactions on Power Systems

Volumn 28, Issue 4, 2013, Pages 4877-4878

  Wan, Can ^a   Xu, Zhao ^a   Pinson, Pierre ^b  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

Extreme learning machine; Forecasting; Particle swarm optimization; Prediction interval; Wind power

Indexed keywords

COVERAGE PROBABILITIES; DIRECT OPTIMIZATION; EXTREME LEARNING MACHINE; FORECASTING ERROR; HIGH POTENTIAL; INTERVAL FORECASTING; PREDICTION INTERVAL; PRIOR KNOWLEDGE;

ELECTRIC POWER GENERATION; ELECTRIC UTILITIES; FORECASTING; PARTICLE SWARM OPTIMIZATION (PSO); TIME SERIES ANALYSIS;

WIND POWER;

EID: 84886086025     PISSN: 08858950     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPWRS.2013.2258824     Document Type: Article

References (4)

1. H. A. Nielsen, H. Madsen, and T. S. Nielsen, "Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts," Wind Energy, vol. 9, no. 1-2, pp. 95-108, 2006.
2. P. Pinson and G. Kariniotakis, "Conditional prediction intervals of wind power generation," IEEE Trans. Power Syst., vol. 25, no. 4, pp. 1845-1856, Nov. 2010.
3. G. B. Huang, Q. Y. Zhu, and C. K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, no. 1-3, pp. 489-501, Dec. 2006.
4. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conf. Neural Netw., Perth, Australia, 1995, vol. 4, pp. 1942-1948.