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References

1 
T. Mahmoud, Z. Y. Dong, J. Ma, 2018, An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine, Renewable Energy, Vol. 126, pp. 254-269DOI
2 
Bhaskar Kanna, S. N. Singh, 2012, AWNN-Assisted Wind Power Forecasting Using Feed-Forward, IEEE Transactions on Sustainable Energy, Vol. 3, No. 2DOI
3 
X. Qu, X. Kang, C. Zhang, S. Jiang, X. Ma, 2016, Shortterm prediction of wind power based on deep long shortterm memory, IEEE PES Asia-Pacific Power and Energy ConferenceDOI
4 
Xiaoyun Qu, Xiaoning Kang, Chao Zhang, Shuai Jiang, Xiuda Ma, 2016, Short-Term Prediction of Wind Power Based on Deep Long Short-Term Memory, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)DOI
5 
Eyecioglu Onder, Hangun Batuhan, Kayisli Korhan, Yesilbudak Mehmet, 2019, Performance Comparison of Different Machine Learning Algorithms on the Prediction of Wind Turbine Power Generation, 2019 IEEE 8th International Conference on Renewable Energy Research and Applications (ICRERA)DOI
6 
G. Chen, 2019, Research on Wind Power Prediction Method Based on Convolutional Neural Network and Genetic Algorithm, 2019 IEEE Innovative Smart Grid Technologies -Asia (ISGT Asia), Chengdu, China, pp. 3573-3578DOI
7 
M. Liu, P. Qiu, K. Wei, 2019, Research on Wind Speed Prediction of Wind Power System Based on GRU Deep Learning, 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), Changsha, China, pp. 1699-1703DOI
8 
S. Hochreiter, J. Schmidhuber, 1997, Long Short-Term memory, Neural Computation, Vol. 9, No. 8, pp. 1735-1780DOI