Short-Term Wind Power Forecasting: A New Hybrid Model Combined Extreme-Point Symmetric Mode Decomposition, Extreme Learning Machine and Particle Swarm Optimization

被引:33
作者
Zhou, Jianguo [1 ]
Yu, Xuechao [1 ]
Jin, Baoling [1 ]
机构
[1] North China Elect Power Univ, Dept Econ & Management, 689 Huadian Rd, Baoding 071000, Peoples R China
关键词
wind power; hybrid model; extreme-point symmetric mode decomposition; extreme learning machine; particle swarm optimization; FEATURE-SELECTION; NEURAL-NETWORKS; PREDICTION; ENSEMBLE; MULTISTEP; ALGORITHM; WAVELET;
D O I
10.3390/su10093202
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The nonlinear and non-stationary nature of wind power creates a difficult challenge for the stable operation of the power system when it accesses the grid. Improving the prediction accuracy of short-term wind power is beneficial to the power system dispatching department in formulating a power generation plan, reducing the rotation reserve capacity and improving the safety and reliability of the power grid operation. This paper has constructed a new hybrid model, named the ESMD-PSO-ELM model, which combines Extreme-point symmetric mode decomposition (ESMD), Extreme Learning Machine (ELM) and Particle swarm optimization (PSO). Firstly, the ESMD is applied to decompose wind power into several intrinsic mode functions (IMFs) and one residual(R). Then, the PSO-ELM is applied to predict each IMF and R. Finally, the predicted values of these components are assembled into the final forecast value compared with the original wind power. To verify the predictive performance of the proposed model, this paper selects actual wind power data from 1 April 2016 to 30 April 2016 with a total of 2880 observation values located in Yunnan, China for the experimental sample. The MAPE, N-MAE and N-RMSE values of the proposed model are 4.76, 2.23 and 2.70, respectively, and these values are lower than those of the other eight models. The empirical study demonstrates that the proposed model is more robust and accurate in forecasting short-term wind power compared with the other eight models.
引用
收藏
页数:18
相关论文
共 39 条
[1]   A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data [J].
Allen, D. J. ;
Tomlin, A. S. ;
Bale, C. S. E. ;
Skea, A. ;
Vosper, S. ;
Gallani, M. L. .
APPLIED ENERGY, 2017, 208 :1246-1257
[2]   Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization [J].
Chen, Jie ;
Zeng, Guo-Qiang ;
Zhou, Wuneng ;
Du, Wei ;
Lu, Kang-Di .
ENERGY CONVERSION AND MANAGEMENT, 2018, 165 :681-695
[3]  
Colak I, 2015, INT CONF RENEW ENERG, P209, DOI 10.1109/ICRERA.2015.7418697
[4]   Assimilation of SMOS L-band wind speeds: impact on Met Office global NWP and tropical cyclone predictions [J].
Cotton, J. ;
Francis, P. ;
Heming, J. ;
Forsythe, M. ;
Reul, N. ;
Donlon, C. .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2018, 144 (711) :614-629
[5]   Extrapolating wind data at high altitudes with high precision methods for accurate evaluation of wind power density, case study: Center of Iran [J].
Faghani, Gh. R. ;
Ashrafi, Z. Najafian ;
Sedaghat, A. .
ENERGY CONVERSION AND MANAGEMENT, 2018, 157 :317-338
[6]   An improved evolutionary extreme learning machine based on particle swarm optimization [J].
Han, Fei ;
Yao, Hai-Fen ;
Ling, Qing-Hua .
NEUROCOMPUTING, 2013, 116 :87-93
[7]   Wind power forecasting based on principle component phase space reconstruction [J].
Han, Li ;
Romero, Carlos E. ;
Yao, Zheng .
RENEWABLE ENERGY, 2015, 81 :737-744
[8]   Non-parametric hybrid models for wind speed forecasting [J].
Han, Qinkai ;
Meng, Fanman ;
Hu, Tao ;
Chu, Fulei .
ENERGY CONVERSION AND MANAGEMENT, 2017, 148 :554-568
[9]   A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts [J].
Hu, Jianming ;
Wang, Jianzhou ;
Xiao, Liqun .
RENEWABLE ENERGY, 2017, 114 :670-685
[10]   Extreme learning machine: Theory and applications [J].
Huang, Guang-Bin ;
Zhu, Qin-Yu ;
Siew, Chee-Kheong .
NEUROCOMPUTING, 2006, 70 (1-3) :489-501