KIEE - The Transactions of the Korean Institute of Electrical Engineers

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KEPCO, 2018, Technology of Distributed Energy Resource connection with distribution system: Guideline

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Renewable Energy 3020 Implementation Plan, 2017, Ministry of Trade, Industry and Energy

3

R. J. Hyndman, G. Athanasopoulos, 2018, Forecasting: Principles and Practice, 2nd edition, OTexts

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M. N. Akhter, S. Mekhilef, H. Mokhlis, N. M. Shah, 2019, Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques, IET Renewable Power Generation, Vol. 13, No. 7, pp. 1009-1023

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U. K. Das, K. S. Tey, M. Seyedmahmoudian, S. Mekhilef, M.Y.I. Idris, W.V. Deventer, B. Horan, A. Stojcevski, 2018, Forecasting of photovoltaic power generation and model, optimization: A review. Renew. Sustain. Energy Rev., Vol. 81, pp. 912-928

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MH Alsharif, MK Younes, J. Kim, 2019, Time Series ARIMA Model for Prediction of Daily and Monthly Average Global, Solar Radiation: The Case Study of Seoul, South Korea Symmetry, Vol. 11, pp. 240-257

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S.B. Boualit, A. Mellit, 14–17 November 2016, SARIMA-SVM hybrid model for the prediction of daily global solar radiation time series, In Proceedings of the 2016 International Renewable and Sustainable Energy Conference (IRSEC) IEEE, Marrakesh Morocco, pp. 712-717

8

Sahm Kim, 2017, A study on solar irradiance forecasting with weather variables, The Korean Journal of Applied Statistics

9

B. Oryani, Y. Koo, S. Rezania, 2020, Structural Vector Autoregressive Approach to Evaluate the Impact of Electricity Generation Mix on Economic Growth and CO2 Emissions in Iran, Energies, Vol. 13, No. 4268

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Y. Li, June 2014, An ARMAX model for forecasting the power output of a grid connected photovoltaic system, Renewable Energy, Vol. 66, pp. 78-89

11

S. Bae, 2018, Research Trends of Photovoltaic Generation Forecasting, KIEE: World of electric, Vol. 67, No. 12, pp. 16-25

12

A. Almadhor, 2018, Performance prediction of distributed PV generation systems using Artificial Neural Networks (ANN) and Mesh Networks, International Conference on Smart Grid, Vol. nagasaki, No. japan, pp. 88-91

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M. N. Rahman, A. Esmailpour, 2015, An Efficient Electricity Generation Forecasting System Using Artificial Neural Network Approach with Big Data, IEEE First International Conference on Big Data Computing Service and Applications, Redwood City, Vol. ca, pp. 213-217

14

Gwon-Yoon Lee, Sang-Boo Lee, 2018, Universal Prediction System Realization Using RNN, Journal of KII, Vol. 16, No. 10, pp. 11-20

15

Juan Ospina, Newaz Alvi, Omar Faruque M., 2019, Forecasting of PV plant output using hybrid wavelet-based LSTM- DNN structure model, IET Renewable Power Generation, Vol. 13, No. 7, pp. 1087-1095

16

S. Tomonobu, Y. Atsushi, U. Naomitsu, F. Toshihisa, 2006, Application of Recurrent Neural Network to Long-Term- Ahead Generating Power Forecasting for Wind Power Generator, IEEE Power Systems Conference and Exposition, pp. 1260-1265

17

Jungin Lee, 2019, Technology trends of Renewable energy generation forecasting based on ICT, The Journal of The Korean Institute of Communication Sciences, Vol. 36, No. 11, pp. 3-8

18

K. W. Kim, G. S. Jang, S. M. Lim, I. K. Ahn, J. Park, H. C. Oh, 2020, GRU-based Activity Recognition from Early-stage Motion, The Institute of Electronics and Information Engineers, pp. 2016-2019

19

Yong-jin Jung, Kyoung-woo Cho, Jong-sung Lee, Chang-heon Oh, 2019, Particulate Matter (PM10) Concentration Prediction Model using GRU, The Korea Institute of Information and Communication Engineering, Vol. 23, No. 2, pp. 644-646

20

W. Li, T. Logenthiran, W. L. Woo, 2019, Multi-GRU prediction system for electricity generation's planning and operation, in IET Generation, Transmission & Distribution, Vol. 13, No. 9, pp. 1630-1637

21

Ju-Hwan Ham, Sung-Yul Kim, 2020, A Study on the Comparison of Kernel Functions Appropriate to the SVR-based Power Demand Prediction Algorithms, The Korean Institute of Electrical Engineers, Vol. workshop, pp. 158-159

22

Gwon-Sun Mun, 1997, Understaing of VAR model, KOSTAT, Vol. research of statistics analysis, No. , pp. 23-56

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Jae Hyun Yoo, 1997, Gaussian Process Model based Reinforcement Learning, Journal of Institute of Control, Robotics and Systems, Vol. 25, No. 8, pp. 746-751

24

Junho Song, Seungwook Yoon, Kanggu Park, Euiseok Hwang, 2017, Hybrid Day-ahead Prediction of Power Consumption based on Linear Prediction and Gaussian Process with Atypical Residual of Meteorological Information, KIEE, pp. 33-35

25

A. G. Abo-Khalil, D. Lee, 2007, SVR-based Wind Speed Estimation for Power Control of Wind Energy Generation System, 2007 Power Conversion Conference, Vol. nagoya, pp. 1431-1436

26

J. Yan, K. Li, E. Bai, A. Foley, 2015, Special condition wind power forecasting based on Gaussian Process and similar historical data, 2015 IEEE Power & Energy Society General Meeting, Vol. denver, pp. 1-5

27

N. Chen, Z. Qian, I. T. Nabney, X. Meng, march 2014, Wind Power Forecasts Using Gaussian Processes and Numerical Weather Prediction, in IEEE Transactions on Power Systems, Vol. 29, No. 2, pp. 656-665

28

Kuster Corentin, 2017, Electric load forecasting models: A critical systematic review, Sustainable Cities and Socierty

KIEEThe Transactions of
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The Transactions of the Korean Institute of Electrical Engineers

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KIEEThe Transactions ofthe Korean Institute of Electrical Engineers