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References

1 
Korea Power Exchange, Dec. 2017, The 8th Basic Plan for Long-term Electricity Supply and DemandGoogle Search
2 
2017, Renewable Energy 3020 Implementation Plan, KOREA Energy AgencyGoogle Search
3 
Yeong-ju Kim, Min-a Jeong, Nam-rye Son, 2017, Forecasting of Short-term Wind Power Generation Based on SVR Using Characteristics of Wind Direction and Wind Speed, The Journal of Korean Institute of Communications and Information Sciences, Vol. 42, No. 05DOI
4 
Soo-Hyun Park, Sahm Kim, 2016, A study on short-term wind power forecasting using time series models, The Korean Journal of Applied Statistics, Vol. 29, No. 7DOI
5 
B. G. Brown, W.K. Richard, H.M. Allan, 1984, Time series models to simulate and forecast wind speed and wind power, Journal of Climate and Applied MeteorologyDOI
6 
D. C. Hill, D. McMillan, K. R. Bell, D. Ineld, 2012, Application of auto-regressive models to UK wind speed data for power system impact studies, IEEE Transactions on Sustainable EnergyDOI
7 
Minseok Kim, Seunghwan Jung, Jonggeun Kim, Hansoo Lee, Baekcheon Kim, Sungshin Kim, 2019, A Study on Artificial Neural Network-based Solar Radiation Forecasting for Efficient Solar Photovoltaic System, Journal of Korean Institute of Intelligent Systems, Vol. 29, No. 6Google Search
8 
Se Yoon Kim, Sung-ho Kim, 2011, Study on the Prediction of wind Power Generation Based on Artificial Neural Network, Journal of Institute of Control, Robotics and Systems, Vol. 17, No. 11DOI
9 
Kanna Bhaskar, S. N. Singh, 2012, AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network, IEEE Transactions on Sustainable Energy, Vol. 3, No. 2DOI
10 
Yongqian Liu, Ying Sun, David Infield, Yu Zhao, Shuang Han, Jie Yan, 2017, A Hybrid Forecasting Method for Wind Power Ramp Based on Orthogonal Test and Support Vector Machine (OT-SVM), IEEE Transactions on Sustainable Energy, Vol. 8, No. 2DOI
11 
Umit Cali, Vinayak Sharma, 2019, Short-term wind power forecasting using long-short term memory based recurrent neural network model and variable selection, International Journal of Smart Grid and Clean EnergyDOI
12 
S. Gangwar, V. Bali, A. Kumar, 2019, Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM, EAI Endorsed Transactions on Scalable Information SystemsDOI
13 
Qu Xiaoyun, Kang Xiaoning, Zhang Chao, Jiang Shuai, Ma Xiuda, 2016, Short-Term Prediction of Wind Power Based on Deep Long Short-Term Memory, in 2016 IEEE PES Asia-Pacific Power and Energy ConferenceDOI
14 
G. Chen, L. Li, Z. Zhang, S. Li, 2020, Short-Term Wind Speed Forecasting With Principle-Subordinate Predictor Based on Conv-LSTM and Improved BPNN, IEEE AccessDOI
15 
Mahdi Khodayar, Jianhui Wang, 2019, Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting, IEEE Transactions on Sustainable Energy, Vol. 10, No. 2DOI
16 
Renshu Wang, Bin Chen, Bingqian Liu, Huoduan Lin, Wuxiao Chen, 2019, The Application Research of Deep Learning Based Method for Short-term Wind Speed Forecasting, in IEEE 4th Advanced Information Technology, Electronic and Automation Control ConferenceDOI
17 
Byeong-Chan Oh, Sung-Yul Kim, 2019, Development of SVR based Short-term Load Forecasting Algorithm, The Transaction of the Korean Institute of Electrical Engineers, Vol. 68, No. 2Google Search
18 
Bowen Zhou, Xiangjin Ma, Yanhong Luo, Dongsheng Yang, 2019, Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation, IEEE Access, Vol. 7DOI