TY - GEN
T1 - Fill Missing Data for Wind Farms Using Long Short-Term Memory Based Recurrent Neural Network
AU - Li, Tie
AU - Tang, Junci
AU - Jiang, Feng
AU - Xu, Xiaopeng
AU - Li, Chunzhu
AU - Bai, Jiawen
AU - Ding, Tao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Due to the uncertainty and volatility of wind energy resources, its large-scale consumption in power gird needs to be based on accurate prediction of output. This puts high demands on the integrity and accuracy of historical wind power data. However, in many wind farms, data loss due to equipment failure or human factors is common, which has a negative impact on wind power forecasting. In this paper, a Long Short-Term Memory (LSTM) strategy is incorporated in the recurrent neural network (RNN) to set up a prediction model and fill the wind power missing data, which behaves better than the traditional RNN methods. The case of this paper uses the historical wind power data of Liaoning Province, which obtains the ideal results, proving the validity of the proposed model and method.
AB - Due to the uncertainty and volatility of wind energy resources, its large-scale consumption in power gird needs to be based on accurate prediction of output. This puts high demands on the integrity and accuracy of historical wind power data. However, in many wind farms, data loss due to equipment failure or human factors is common, which has a negative impact on wind power forecasting. In this paper, a Long Short-Term Memory (LSTM) strategy is incorporated in the recurrent neural network (RNN) to set up a prediction model and fill the wind power missing data, which behaves better than the traditional RNN methods. The case of this paper uses the historical wind power data of Liaoning Province, which obtains the ideal results, proving the validity of the proposed model and method.
KW - data filling
KW - Long Short-term Memory (LSTM)
KW - missing data
KW - Recurrent Neural Network (RNN)
KW - wind power
UR - https://www.scopus.com/pages/publications/85084577173
U2 - 10.1109/CIEEC47146.2019.CIEEC-2019284
DO - 10.1109/CIEEC47146.2019.CIEEC-2019284
M3 - 会议稿件
AN - SCOPUS:85084577173
T3 - Proceedings of 2019 IEEE 3rd International Electrical and Energy Conference, CIEEC 2019
SP - 705
EP - 709
BT - Proceedings of 2019 IEEE 3rd International Electrical and Energy Conference, CIEEC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Electrical and Energy Conference, CIEEC 2019
Y2 - 7 September 2019 through 9 September 2019
ER -