Open AccessUltra-Short-term Power Prediction of the Wind Farm Based on Multivariate Data Combination
Author(s) -
Yong Jiang,
Zhong Li,
Biao Li,
Cao Xiao,
Jiayuan Zhu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/898/1/012001
Subject(s) - wind power , wind power forecasting , autoregressive integrated moving average , computer science , term (time) , wind speed , power (physics) , electric power system , data mining , meteorology , time series , engineering , machine learning , geography , physics , quantum mechanics , electrical engineering
Accurate wind power prediction is an important way to promote large-scale wind power grid connection. First, to address the abnormal wind farm actual measurement data caused by wind abandonment and power limitation, the DBSCAN method is used to pre-process the wind farm actual measurement data and eliminate the abnormal data. Then, a short-term wind power prediction model with a combination of GA-LSSVM and ARIMA weights is established, and the Lagrange multiplier algorithm is used to obtain the weighted values of each single model in the combined model to further obtain the wind power prediction results. Finally, the effectiveness of the proposed method is verified by arithmetic examples, and the results show that the proposed model and method can effectively improve the prediction accuracy of short-term wind power.