TY - GEN
T1 - Wind power generation prediction based on LSTM
AU - Zhang, Jinxia
AU - Jiang, Xuru
AU - Chen, Xin
AU - Li, Xiaojing
AU - Guo, Dong
AU - Cui, Lixin
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/4/12
Y1 - 2019/4/12
N2 - In recent years, with the increasing proportion of wind power generation, the impact of wind power generation on grid security is also growing. This makes the prediction accuracy of wind power generation higher and higher. This paper utilizes the LSTM model of the deep learning domain to predict wind power generation. Besides, Auto Encoder is employed to reduce the data dimension, improve the generalization ability of the model, and shorten the training time. Simulation experiments show that the LSTM model has better prediction accuracy than other machine learning model such as SVM.
AB - In recent years, with the increasing proportion of wind power generation, the impact of wind power generation on grid security is also growing. This makes the prediction accuracy of wind power generation higher and higher. This paper utilizes the LSTM model of the deep learning domain to predict wind power generation. Besides, Auto Encoder is employed to reduce the data dimension, improve the generalization ability of the model, and shorten the training time. Simulation experiments show that the LSTM model has better prediction accuracy than other machine learning model such as SVM.
KW - Auto encoder
KW - Deep learning
KW - Long short-term memory
UR - https://www.scopus.com/pages/publications/85068827637
U2 - 10.1145/3325730.3325735
DO - 10.1145/3325730.3325735
M3 - Conference contribution
AN - SCOPUS:85068827637
T3 - ACM International Conference Proceeding Series
SP - 85
EP - 89
BT - ICMAI 2019 - Proceedings of 2019 4th International Conference on Mathematics and Artificial Intelligence
PB - Association for Computing Machinery
T2 - 4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019
Y2 - 12 April 2019 through 15 April 2019
ER -