Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers

References

1 
IEC TC 1 Electropedia, http://www.electropedia.org/Google Search
2 
Electronic Newspaper, 2017.03.05, Hangeul searches the world’s electronic standards...KATS has registered IEV Korean terms in ‘Elec- trophedia’Google Search
3 
2002, KIEE, Standard dictionary of electrical and electronics termsGoogle Search
4 
2011, KIEE, Dictionary of Smart GridGoogle Search
5 
NIKL, Opendictionary, https://opendict.korean.go.kr/Google Search
6 
IEC TC 1, Instructions for IEV ElectropediaGoogle Search
7 
2016, NIKL, Guidelines for writing terminologiesGoogle Search
8 
Kumar Das Utpal, Soon Tey Kok, Seyedmahmoudian Mehdi, Mekhilef Saad, Yamani Idna Idris Moh, Van Deventer Willem, Horan Bend, Stojcevski Alex, 2018, Forecasting of photovoltaic power generation and model optimization: A review, Renewable and Sustainable Energy Reviews, Vol. 81, pp. 912-928DOI
9 
Zhou Wei, Yang Hongxing, Fang Zhaohong, 2007, A novel model for photovoltaic array performance prediction, Applied Energy, Vol. 84, pp. 1187-1198DOI
10 
Graditi Giorgio, ., Ferlito Sergio, Adinolfi Giovanna, Marco Tina Giuseppe, Ventura Cristina, 2016, Energy yield estimation of thin-film photovoltaic plants by using physical approach and artificial neural networks, Solar Energy, Vol. 130, pp. 232-243DOI
11 
Ogliari Emanuele, Dolara Alberto, Manzolini Giampaolo, Leva Sonia, 2017, Physical and hybrid methods comparison for the day ahead PV output power forecast, Renewable Energy, Vol. 113, pp. 11-21DOI
12 
Alfredo Fernandez-Jimenez L., Munoz-Jimenez Andres, Falces Alberto, Mendoza-Villena Montserrat, Garcia-Garrido Eduardo, M. Lara-Santillan Pedro, Zorzano-Alba Enrique, J. Zorzano-Santamaria Pedro, 2012, Short-term power forecasting system for photovoltaic plants, Renewable Energy, Vol. 44, pp. 311-317DOI
13 
Monteiro Claudio, Santos Tiago, Alfredo Fernandez- Jimenez L., J. Ramirez-Rosado Ignacio, Sonia Terreros-Olarte M., 2013, Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity, Energies, Vol. 6, pp. 2624-2643DOI
14 
Zhang Yue, Beaudin Marc, Taheri Raouf, Zareipour Hamidreza, Wood David, 2015, Day-Ahead Power Output Forecasting for Small-Scale Solar Photovoltaic Electricity Generators, IEEE Transactions on smart grid, Vol. 6, No. 5, pp. 2253-2262DOI
15 
Trapero Juan R., Kourentzes Nikolaos, Martin A., 2015, Short-term solar irradiation forecasting based on Dynamic Harmonic Regression, Energy, Vol. 84, pp. 289-295DOI
16 
Azimi R., Ghayekhloo M., Ghofrani M., 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. 331-344DOI
17 
Jo Sekyung Han HyeonDeok, 2016, The development of the practical photovoltaic power prediction and renewal model, in The Proceedings of the KIEE Summer Annual Conference, pp. 1116-1117Google Search
18 
Chen Changsong, Duan Shanxu, Cai Tao, Liu Bangyin, 2011, Online 24-h solar power forecasting based on weather type classification using artificial neural network, Solar Energy, Vol. 85, pp. 2856-2870DOI
19 
Shi Jie, Lee Wei-Jen, Liu Yongqian, Yang Yongping, Wang Peng, 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. 1064-1069DOI
20 
Pulipaka Subrahmanyam, Kumar Rajneesh, 2016, Power prediction of soiled PV module with neural networks using hybrid data clustering and division techniques, Solar Energy, Vol. 133, pp. 485-500DOI
21 
A. Munshi Amr, A.-R. I. Mohamed Yasser, 2016, Photo- voltaic power pattern clustering based on conventional and swarm clustering methods, Solar Energy, Vol. 124, No. , pp. 39-56DOI
22 
A. Munshi Amr, A.-R. I. Mohamed Yasser, 2017, Com- parisons among Bat algorithms with various objective functions on grouping photovoltaic power patterns, Solar Energy, Vol. 144, pp. 254-266DOI
23 
Yang Xiuyuan, Xua Minglu, Xu Shouchen, Han Xiaojuan, 2017, Day-ahead forecasting of photovoltaic output power with similar cloud space fusion based on incomplete historical data mining, Applied Energy, Vol. 206, pp. 683-696DOI
24 
Sing Lai Chun, Jia Youwei, D. McCulloch Malcolm, Xu Zhao, 2017, Daily Clearness Index Profiles Cluster Analysis for Photovoltaic System, IEEE Transactions on Industrial Informatics, Vol. 13, No. 5, pp. 2322-2332DOI
25 
Liu Luyao, Zhao Yi, Chang Dongliang, Xie Jiyang, Ma Zhanyu, Sun Qie, Yin Hongyi, Wennersten Ronald, 2018, Prediction of short-term PV power output and uncertainty analysis, Applied Energy, Vol. 228, pp. 700-711DOI
26 
Lin Peijie, Peng Zhouning, Lai Yunfeng, Cheng Shuying, Chen Zhicong, Wu Lijun, 2018, Short-term power prediction for photovoltaic power plants using a hybrid improved Kmeans-GRA-Elman model based on multivariate meteorological factors and historical power datasets, Energy Conversion and Management, Vol. 177, pp. 704-717DOI
27 
Wang Qi, Ji Shunxiang, Hu Minqiang, Li Wei, Liu Fusuo, Zhu Ling, 2018, Short-Term Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory, International Journal of Photo- energy, Vol. 2018, No. article id 6973297, pp. 125-144DOI
28 
Xu Zhenlei, Chen Zhicong, Zhou Haifang, Wu Lijun, Lin Peijie, Cheng Shuying, 2019, A short-term photovoltaic power forecasting model based on a radial basis function neural network and similar days, IOP Conf. Series: Earth and Environmental Science, Vol. 227, No. Article ID 022032DOI
29 
Kim Kwang-Deuk, 2013, The development of the short-term predict model for solar power generation, Journal of the Korean Solar Energy Society, Vol. 33, No. 6, pp. 62-69DOI
30 
Chun Gi-young, Han Yong, Song Sung-yoon, Park Joung-hu, 2014, Power estimation for stand-alone PV system using forecast information, in The Proceedings of the KIEE Spring Conference for Society B, Vol. , pp. 228-231Google Search
31 
Lee Keunho, Sonb Heung-gu, Kim Sahm, 2018, A study on solar energy forecasting based on time series models, The Korean Journal of Applied Statistics, Vol. 31, No. 1, pp. 139-153DOI
32 
Marion B., 2008, Comparison of predictive models for Photovoltaic module performance, in Proc. of 33rd IEEE Photovoltaic Specialists Conference, pp. 1-6DOI
33 
Bacher Peder, Madsen Henrik, Aalborg Nielsen Henrik, 2009, Online short-term solar power forecasting, Solar Energy, Vol. 83, pp. 1772-1783DOI
34 
Ding Kun, Ye Zhen, Reindl Thomas, 2012, Comparison of parameterisation models for the estimation of the maximum power output of PV modules, Energy Procedia, Vol. 25, pp. 101-107DOI
35 
G. Kardakos E., C. Alexiadis M., I. Vagropoulos S., K. Simoglou C., N. Biskas P., G. Bakirtzis A., 2013, Application of time series and artificial neural network models in short-term forecasting of PV power generation, in Proc. of 48th IEEE International Universities Power Engineering Conference (UPEC), Vol. , pp. 1-6DOI
36 
Bouzerdoum M., Mellit A., Massi Pavan A., 2013, A hybrid model (SARIMA.SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant, Solar Energy, Vol. 98, pp. 226-235DOI
37 
Li Yanting, Su Yan, Shu Lianjie, 2014, An ARMAX model for forecasting the power output of a grid connected photovoltaic system, Renewable Energy, Vol. 66, pp. 78-89DOI
38 
Chu Yinghao, Urquhart Bryan, M. I. Gohari Seyyed, T. C. Pedro Hugo, Kleissl Jan, F. M. Coimbra Carlos, 2015, Short-term reforecasting of power output from a 48 MWe solar PV plant, Solar Energy, Vol. 112, pp. 68-77DOI
39 
Li Yanting, He Yong, Su Yan, Shu Lianjie, 2015, Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines, Applied Energy, Vol. 180, pp. 392-401DOI
40 
Gurtler a Marc, Paulsen Thomas, 2018, Forecasting performance of time series models on electricity spot markets, International Journal of Energy Sector Management, Vol. 12, No. 4, pp. 617-640DOI
41 
Lee Chang-Sung, Ji Pyeong-Shik, 2015, Development of Daily PV Power Forecasting Models using ELM, The Transactions of the Korean Institute of Electrical Engineers, Vol. 64p, No. 3, pp. 164-168DOI
42 
Lee Kanghuyk, Kim Woo-Je, 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. 175-183Google Search
43 
Lee Hyunjin, 2016, The Development of the predict model for solar power generation based on current temperature data in restricted circumstances, Journal of Digital Contents Society, Vol. 17, No. 3, pp. 157-164DOI
44 
Hong Jeseong, Park Jihoon, Kim Youngchul, 2018, Fault Prediction of Photovoltaic Monitoring System based on Power Generation Prediction Model, Journal of Platform Technology, Vol. 6, No. 2, pp. 19-25Google Search
45 
Mellit Adel, Massi Pavan Alessandro, 2010, A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy, Solar Energy, Vol. 84, pp. 807-821DOI
46 
Mellit A., Massi Pavan A., Lughi V., 2014, Short-term forecasting of power production in a large-scale photovoltaic plant, Solar Energy, Vol. 105, pp. 401-413DOI
47 
Zamo M., Mestre O., Arbogast P., Pannekoucke O., 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. 792-803DOI
48 
Yang Hong-Tzer, Huang Chao-Ming, Huang Yann-Chang, Pai Yi-Shiang, 2014, A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output, IEEE Transactions on Sustainable Energy, Vol. 5, No. 3, pp. 917-926DOI
49 
Pulipaka Subrahmanyam, Kumar Rajneesh, 2016, Modeling of soiled PV module with neural networks and regression using particle size composition, Solar Energy, Vol. 123, pp. 116-126DOI
50 
Leva S., Dolara A., Grimaccia F., Mussetta M., Ogliari E., 2017, Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power, Mathematics and Computers in Simulation, Vol. 131, pp. 88-100DOI
51 
Zhang Weijiang, Dang Hongshe, Simoes Rolando, 2018, A new solar power output prediction based on hybrid forecast engine and decomposition model, ISA Transactions, Vol. 81, pp. 105-120DOI
52 
Yun Da-Eun, Park Hyeong-Dong, Lee Sang-Nam, Kang Yong-Heack, 2017, Empirical analysis on the correlation between mono-crystalline Si photovoltaic module temperature and irradiance and ambient temperature in seoul area, Journal of Korean Society Mineral and Energy Resources Engineers, Vol. 54, No. 2, pp. 139-148Google Search