Ultra-short-term Wind Power Prediction Based on IVMD-KPCA-LSTM
In order to improve the reliability and stability of the power system and cope with the challenges brought by the volatility of wind power,the five climatic factors that restrict wind power generation(wind speed,wind direction,pressure,temperature,humidity)were fully considered.First,IVMD was used to decompose the climate factor sequence to obtain the changes in data signals at different time scales and reduce the non-stationarity of the climate factor sequence;secondly,We used KPCA to extract the key influencing factors of the characteristic sequence,eliminated the correlation and redundancy of the original sequence,and reduced the dimension of the model's input;finally,the LSTM network was used to dynamically model the multi-variable feature sequence to achieve wind power prediction.The Longyuan Electric Power Group's real wind power generation data prediction data set No.2 wind turbine data was used for verification.The experimental results show that the ultra-short-term wind power prediction model based on IVMD-KPCA-LSTM has significantly improved prediction accuracy compared with a single LSTM model.With the improvement,RMSE dropped by 48.6％,MAE dropped by 34.4％,and MAPE dropped by 81.6％.
wind powerimproved VMDkernel principal component analysislong short-term memory network
NETL
NSTL
万方数据
