Ultra-short-term Wind Power Prediction Based on Dynamical Ensemble Least Square Support Vector Regression
Abstract
For the limitation of least square support vector regression (LSSVR) in modeling the time varying feature of wind power, an ultra-short-term wind power prediction (USTWPP) model based on dynamical ensemble I-SSVR was proposed. Firstly, the off-line LSSVR model library was created by making use of the historical data which were obtained from Numerical Weather Prediction (NWP) and supervisory control and data acquisition (SCADA) system of wind farm. Then, the candidate members of ensemble LSSVR were selected from off-line LSSVR model library dynamically according to the similarity between the NWP of forecasting period and the NWP of training period. The ensemble members were decided by considering the accuracy and diversity. Finally, the weights of ensemble LSSVR members were assigned according to the similarity between the NWP of training and NWP of prediction period. The validity of the dynamical ensemble LSSVR based predictor was verified by predicting the wind power of a wind farm in Hunan Province. Compared with persistence method (P\l). auto regressive integrated moving average (AGIMA). LSSVR. constant weight ensemble LSSVR. and ensemble artificial neural networks (ANN), the dynamical ensemble LSSVR is more accurate, especially when the weather changes severely.
Keywords: ultra-short-term wind power prediction, least square support vector regression, dynamical Ensemble, dynamical time warp, numerical weather prediction
Full Text:
PDFReferences
XUE Yusheng, LEI Xing. XUE Feng, et al. A review on impacts of wind power uncertainties on power systems [J]. Proceedings of the CSEE. 2014. 34(29): 5029-5036. (In Chinese)
KUSIAK Andrew, ZHANG Zijun. Short-horizon prediction of wind power; A data-driven approach [J]. IEEE Transactions on Energy Conversion, 2010, 25(4);
WANG Chengshan, GAO Fei, LI Peng. et al. Control strategy research on low voltage microgrid [J]. Proceedings of the CSEE. 2012. 32(25); 2_8. (In Chinese)
LI Peiqiang. WANG Jifei. LI Xinran. et al. Analysis on the impact of DFKi and DDSG wind plant on the small signal stability of the power system [J]. Journal of Hunan University: Natural Sciences, 2014.41(1): 92-97. (In Chinese)
XUE Yusheng, YU Chen. ZHAO Junhua. et al. A review on shortterm and ultra-short-term wind power prediction [J]. Automation of Electrical Power Systems. 2015,39(6): 141 — 151. (In Chinese)
SHI Jie. DING Zhaohao. LEE Weijen. et al. Hybrid forecating model for very-short term wind power forecasting based on grey relational a-nalysis and wind speed distribution features [J]. IEEE Transactions on Smart Grid, 2014. 5( 1): 521-526
WANG He, HU Zhijian, CHEN Zhen, el al. A hybrid model for wind power forecasting based on ensemble empirical mode decomposition and wavelet neural networksf [J]. Transactions of China Electro technical Society, 2013,28(9); 137—144 . (In Chinese)
ZHOU Songlin. MAO Meiqin. SU Jianhui. Short-term forecasting of wind power and non-parametric confidence interval estimation [J]. Proceedings of the CSEE. 2011. 31(25): 10—16. (In Chinese)
NIMA Amjady. FARSHID Keynia, HAMIDREZA Zareipour. Wind power prediciton by a new forecast engine composed of modified hybrid neural network and enhanced particle swarm optimization [J]. IEEE Transactions on Sustainable Energy, 2011. 2(3) ;265—276.
CHEN Niya. QIAN Zheng. NABNEY Ian T. et al. Wind power forecasts using gaussian processes and numerical weather prediction [J], IEEE Transactions on Power Systems, 2014. 29(2) ;656 — 665.
TAO Jia, ZHANG Hong. ZHU Guorong. et al. Wind power prediction based on technology of advanced phase space reconstruction [JJ] Proceedings of the CSEE. 2011, 31(28) :9—14. (In Chinese)
CHEN Zhibao. DING Jie. ZHOU Hai. et al. A model of very short-term photovoltaic power forecasting based on ground-based cloud images and RBF neural network [JJ. Proceedings of the СSEE. 2015 ,35(3);561 —567. (In Chinese)
Refbacks
- There are currently no refbacks.
