Page 228 - 新能源风力发电技术与自动化技术研究

新能源风力发电技术与自动化技术研究
Research on New Energy Wind Power Generation Technology and Automation Technology

[12] ANDREW K,ZHENG H,SONG Z.Wind farm power prediction:a data-mining ap-
proach[J].Wind Energy,2009,12:275-293.
[13] MATHABA T,XIA X,ZHANG J.Short-term wind power prediction using Least-

Square Support Vector Machines[C]∥Power Engineering Society Conference and
Exposition in Africa(Power Africa).Johannesburg,South Africa,2012:1-6.
[14] TABATABAI M A,EBY W M,NIMEH N,et al.A hybrid model for wind speed
prediction using empirical mode decomposition and artificial neural networks[J].
Renewable Energy,2012,48(6):545-556.

[15] WAN C,XU Z,Pinson P,et al.Probabilistic forecasting of wind power gener-
ation using extreme learning machine[J].IEEE Transactions on Power Sys-
tems,2014,29(3):1033-1044.

[16] CUI M,ZHANG J,FLORITA A R,et al.An optimized swinging door algorithm
for identifying wind ramping events[J].IEEE Transactions on Sustainable Ener-
gy,2015,7(1):150-162.
[17] GREAVES B,COLLINS J,PARKES J,et al.Temporal forecast uncertainty for ramp
events[J].Wind Engineering,2009,33(4).

[18] 崔明建,孙元章,柯德平.基于原子稀疏分解和BP神经网络的风电功率爬坡事件
预测[J].电力系统自动化,2014,38(12):6-11.
[19] TAO Y,CHEN H.A hybrid wind power prediction method[C]∥Power and Energy

Society General Meeting.Boston,USA,2016:1-5.
[20] TAO H.Energy forecasting:past,present and future[J].The International Journal of
Applied Forecasting,2014(32):43-48.
[21] TAO H,PIERRE P,SHU F,et al.Probabilistic energy forecasting:global energy
forecasting competition 2014 and beyond[J].International Journal of Forecast-

ing,2016(32):896-913.
[22] CIGRE WG C1.32.Establishing best practice approaches for developing credible
electricity demand and energy [M].CIGRE,2016.

[23] Felice M D,Alessandri A,Ruti P M. Electricity demand forecasting over Italy: po-
tential benefits using numerical weatherprediction models [J]. Electric Power Sys-
tems Research,2013,104: 71-79.

216