Research on Ultra-Short-Term Wind Power Prediction Considering Source Relevance

Wind power forecasting, to a certain extent, will transform the random fluctuation of wind power output into a known situation, which is one of the effective approaches to deal with large-scale wind power integrated into power grid. Due to the use of only historical data and the lack of new information, the accuracy of ultra-short-term wind power prediction (WPP) is still not satisfactory. Therefore, a combined prediction method based on the day-ahead Numerical Weather Prediction (NWP) location technology is proposed. Firstly, the time points with low forecasting accuracy of rolling WPP are approximately located by the NWP information and time windows, and then the hybrid approach combined with neural network and persistence method is presented to predict the future wind power output. The results of the case study show that compared with other classical prediction methods, this method can effectively improve the ultra-short-term prediction accuracy of wind power and verify the effectiveness of the proposed method.

[1]  Hamidreza Zareipour,et al.  A review and discussion of decomposition-based hybrid models for wind energy forecasting applications , 2019, Applied Energy.

[2]  David Infield,et al.  Comparison of advanced non‐parametric models for wind turbine power curves , 2019, IET Renewable Power Generation.

[3]  Omar Noureldeen,et al.  Design of robust intelligent protection technique for large-scale grid-connected wind farm , 2018 .

[4]  Cong Wang,et al.  A new wind power prediction method based on chaotic theory and Bernstein Neural Network , 2016 .

[5]  Xia Hua,et al.  Wind power prediction based on variational mode decomposition multi-frequency combinations , 2018, Journal of Modern Power Systems and Clean Energy.

[6]  Chongqing Kang,et al.  A High-Efficiency Network-Constrained Clustered Unit Commitment Model for Power System Planning Studies , 2019, IEEE Transactions on Power Systems.

[7]  R. Saidur,et al.  Application of support vector machine models for forecasting solar and wind energy resources: A review , 2018, Journal of Cleaner Production.

[8]  Mao Yang,et al.  Ultra-Short-Term Prediction of Photovoltaic Power Based on Periodic Extraction of PV Energy and LSH Algorithm , 2018, IEEE Access.

[9]  Wang Peng,et al.  Ultra short‐term probability prediction of wind power based on LSTM network and condition normal distribution , 2019, Wind Energy.

[10]  Mao Yang,et al.  The impact of wind field spatial heterogeneity and variability on short-term wind power forecast errors , 2019, Journal of Renewable and Sustainable Energy.

[11]  Mao Yang,et al.  Ultra-Short-Term Multistep Wind Power Prediction Based on Improved EMD and Reconstruction Method Using Run-Length Analysis , 2018, IEEE Access.

[12]  Luca Delle Monache,et al.  Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting , 2016 .

[13]  Yu Zhang,et al.  Prediction of Wind Turbine-Grid Interaction Based on a Principal Component Analysis-Long Short Term Memory Model , 2018, Energies.

[14]  Pradipta Kishore Dash,et al.  A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression , 2019, Renewable Energy.

[15]  Kishore Kulat,et al.  A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction , 2019, Energies.

[16]  Shuaishuai Lin,et al.  Short-term wind power prediction based on data mining technology and improved support vector machine method: A case study in Northwest China , 2018, Journal of Cleaner Production.

[17]  Mohsen Vahidzadeh,et al.  Modified Power Curves for Prediction of Power Output of Wind Farms , 2019, Energies.