KIEE - The Transactions of the Korean Institute of Electrical Engineers

1

De Leon, 2018, SB-100 California Renewables Portfolio Standard Program: emissions of greenhouse gases, Senate Bill No. 100, Chapter 312, California legislative information

2

Korea Energy Economics Institute, 2013, World Energy Market Insight, Vol. 13-22

3

Korea Energy Economics Institute, 2015, World Energy Market Insight, Vol. 15-29

4

Ministry of Economy, Trade and Industry, 2015, Long-term Energy Supply and Demand Outlook

5

Ministry of Trade, Industry and Energy, 2017, Renewable Energy 3020 initiative

6

Korea Energy Agency, 2018, New and Renewable Energy Statistics 2017

7

Ministry of the Interior and Safety, , LOCALDATA, http://localdata.go.kr

8

Korea Energy Agency, 2019, Rule for Certification Issuing and Trading Market Operation

9

PJM, 2019, Generator Operational Requirements, PJM Manual 14D

10

Muhammad Qamar Raza, Mithulananthan Nadarajah, Chandima Ekanayake, 2016, On recent advances in PV output power forecast, Solar Energy, Vol. 136, pp. 125-144

11

Utpal Kumar Das, Kok Soon Tey, Mehdi Seyedmahmoudian, Saad Mekhilef, Moh Yamani Idna Idris, Willem Van Deventer, Bend Horan, Alex Stojcevski, 2018, Forecasting of photovoltaic power generation and model optimization: A review, Renewable and Sustainable Energy Reviews, Vol. 81, pp. 912-928

12

Korea Power Exchange, 2019, Electricity Market Rule

13

Wei Zhou, Hongxing Yang, Zhaohong Fang, 2007, A novel model for photovoltaic array performance prediction, Applied Energy, Vol. 84, pp. 1187-1198

14

Amr A. Munshi, Yasser A.-R. I. Mohamed, 2016, Photovoltaic power pattern clustering based on conventional and swarm clustering methods, Solar Energy, Vol. 124, pp. 39-56

15

Xiuyuan Yang, Minglu Xua, Shouchen Xu, Xiaojuan Han, 2017, Day-ahead forecasting of photovoltaic output power with similar cloud space fusion based on incomplete historical data mining, Applied Energy, Vol. 206, pp. 683-696

16

Qi Wang, Shunxiang Ji, Minqiang Hu, Wei Li, Fusuo Liu, Ling Zhu, 2018, Short-Term Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory, International Journal of Photoenergy, Vol. 2018, Article ID 6973297, pp. 125-144

17

Yanting Li, Yan Su, Lianjie Shu, 2014, An ARMAX model for forecasting the power output of a grid connected photovoltaic system, Renewable Energy, Vol. 66, pp. 78-89

18

M. Zamo, O. Mestre, P. Arbogast, O. Pannekoucke, 2014, A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: Deterministic forecast of hourly production, Solar Energy, Vol. 105, pp. 792-803

19

Yanting Li, Yong He, Yan Su, Lianjie Shu, 2015, Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines, Applied Energy, Vol. 180, pp. 392-401

20

Marc Gurtler, Thomas Paulsen, 2018, Forecasting performance of time series models on electricity spot markets, International Journal of Energy Sector Management, Vol. 12, No. 4, pp. 617-640

21

Jie Shi, Wei-Jen Lee, Yongqian Liu, Yongping Yang, Peng Wang, 2012, Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines, IEEE Transactions on Industry Applications, Vol. 48, No. 3, pp. 1064-1069

22

M. Bouzerdoum, A. Mellit, A. Massi Pavan, 2013, A hybrid model (SARIMA.SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant, Solar Energy, Vol. 98, pp. 226-235

23

R. Azimi, M. Ghayekhloo, M. Ghofrani, 2016, A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting, Energy Conversion and Management, Vol. 118, pp. 331-344

24

Subrahmanyam Pulipaka, Rajneesh Kumar, 2016, Modeling of soiled PV module with neural networks and regression using particle size composition, Solar Energy, Vol. 123, pp. 116-126

25

S. Leva, A. Dolara, F. Grimaccia, M. Mussetta, E. Ogliari, 2017, Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power, Mathematics and Computers in Simulation, Vol. 131, pp. 88-100

26

Tae-Woo Kim, Jong-Ho Yoon, U-Cheul Shin, 2013, Annual energy yield prediction of PV system depending on the Sim, in Proceedings of the Korean Solar Energy Society Spring Conference, pp. 251-256

27

Chang-Sung Lee, Pyeong-Shik Ji, 2015, Development of daily PV power forecasting models using ELM, The Transactions of the Korean Institute of Electrical Engineers, Vol. 64p, No. 3, pp. 164-168

28

Chan-Hyeok Oh, Joon-Ho Choi, Sang- Yoon, and Seon-Ju Ahn, 2017, Prediction of PV output with available weather information and PV data, in Proceedings of the KIEE Summer Annual Conference, pp. 358-359

29

Kanghuyk Lee, Woo-Je Kim, 2016, Forecasting of 24_hours ahead photovoltaic power output using support vector regression, The Journal of Korean Institute of Information Technology, Vol. 14, No. 3, pp. 175-183

30

Dong-Ha Shin, Jun-Ho Park, Chang-Bok Kim, 2017, Photo- voltaic generation forecasting using weather forecast and predictive sunshine and radiation, Journal of Advanced Navigation Technology, Vol. 21, No. 6, pp. 638-650

31

Jin-Hyuk Jang, Dong-Ha Shin, Chang-Bok Kim, 2018, Forecasting of short term photovoltaic generation by various input model in supervised learning, Journal of Advanced Navigation Technology, Vol. 22, No. 5, pp. 478-484

32

Ye-Ji Lee, Myeong-Jin Ko, Yong-Shik Kim, 2018, Prediction and accuracy analysis of photovoltaic module temperature in summer using data mining techniques, Journal of The Korean Society of Living Environmental System, Vol. 25, No. 3, pp. 269-276

33

Jung-Hoon Yeo, Young-Jin Jeong, Woo-Young Lee, 2018, Solar power plant power prediction by using simulation program, in Proceedings of KIIEE Annual Conference, pp. 68

34

Seung-Beom Nam, 2019, A study on the development of hybrid solar power forecasting based on probability considering spatio-temporal characteristics, Sangmyung University

35

Hanho Kim, Haesung Tak, Hwan-gue Cho, 2019, Design of photovoltaic power generation prediction model with recurrent neural network, Journal of KIISE, Vol. 46, No. 6, pp. 506-514

36

Keunho Lee, Heung-gu Sonb, Sahm Kim, 2018, A study on solar energy forecasting based on time series models, The Korean Journal of Applied Statistics, Vol. 31, No. 1, pp. 139-153

37

J. M. Bates, C. W. J. Granger, 1969, The combination of forecasts, Operational Research Quarterly, Vol. 20, pp. 451-468

38

Jeremy Smith, Kenneth F. Wallis, 2009, A simple explanation of the forecast combination puzzle, Oxford Bulletin of Economics and Statistics, Vol. 71, No. 3, pp. 331-355

39

Clive W. J. Granger, Ramu Ramanathan, 1984, Improved methods of combining forecasts, Journal of Forecasting, Vol. 3, No. 2, pp. 197-204

40

James H. Stock, Mark W. Watson, 1999, Forecasting inflation, Journal of Monetary Economics, Vol. 44, pp. 293-335

41

Chung-ki Min, Arnold Zellner, 1993, Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates, Journal of Econometrics, Vol. 56, pp. 89-118

42

Karol Szafranek, 2019, Bagged neural networks for forecasting Polish (low) inflation, International Journal of Forecasting, Vol. 35, pp. 1042-1059

43

George Sideratos, Andreas Ikonomopoulos, Nikos D. Hatziargyriou, 2020, A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks, Electric Power Systems Research, Vol. 178, 106025

44

Sunoong Hwang, 2018, Forecasting regional recessions in real time using autoregressive probit models and probability forecast combination, Journal of Economics Studies, Vol. 36, No. 1, pp. 77-99

45

Ha-Hyun Jo, et al, 2017, A study for the combination methods of electricity demand forecasting and evaluation methods of model accuracy, R&D Reports of Korea Power Exchange

46

Yi Wang, Ning Zhang, Qixin Chen, Daniel S. Kirschen, Pan Li, Qing Xia, 2018, Data-driven probabilistic net load forecasting with high penetration of behind-the-meter PV, IEEE Transactions on Power Systems, Vol. 33, No. 3, pp. 3255-3264

47

Tomas Landelius, Sandra Andersson, Roger Abrahamsson, 2019, Modelling and forecasting PV production in the absence of behind-the-meter measurements, Progress in Photovoltaics: Research and Applications, DOI: 10.1002/pip.3117

48

Kangping Li, Fei Wanga, Zengqiang Mi, Mahmoud Fotuhi- Firuzabad, Neven Duić, Tieqiang Wang, 2019, Capacity and output power estimation approach of individual behind-the- meter distributed photovoltaic system for demand response baseline estimation, Applied Energy, Vol. 253,113595

49

B. Marion, 2008, Comparison of predictive models for Photovoltaic module performance, in Proc. of 33rd IEEE Photovoltaic Specialists Conference, pp. 1-6

50

Wan-Soo Kim, Ha-Hyun Jo, 2019, PV generation modeling using regional weather factors for dispatch planning, The Transactions of the Korean Institute of Electrical Engineers, Vol. 68, No. 9, pp. 1109-1117

51

Kun Ding, Zhen Ye, Thomas Reindl, 2012, Comparison of parameterisation models for the estimation of the maximum power output of PV modules, Energy Procedia, Vol. 25, pp. 101-107

52

Korea Energy Agency, Procurement notice, http://kpx.or.kr/

53

H. Theil, A. S. Goldberger, 1961, On pure and mixed statistical estimation in economics, International Economic Review, Vol. 2, No. 1, pp. 65-78

54

National Institution of Meteorological Sciences, Renewable energy resources, http://www.greenmap.go.kr

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KIEEThe Transactions ofthe Korean Institute of Electrical Engineers