참고문헌 (18)

1. L. BerrangFord, R. Biesbroek, J. D. Ford, A. Lesnikowski, A. Tanabe, F. M. Wang, C. Chen, A. Hsu, J. J. Hellmann, P. Pringle, M. Grecequet, J. C. Amado, S. Huq, S. Lwasa, and S. J. Heymann, "Tracking global climate change adaptation among governments", Nat. Clim. Change, Vol. 9, No. 6, 2019, pp. 440-449, doi: https://doi.org/10.1038/s4155801904900. 

   

2. J. W. Ahn, "The significance of longterm perception on renewable energy and climate change", Trans Korean Hydrogen New Energy Soc, Vol. 29, No. 1, 2018, pp. 117-123, doi: https://doi.org/10.7316/KHNES.2018.29.1.117. 

   원문보기 상세보기

3. A. Robbins, "How to understand the results of the climate change summit: Conference of Parties21 (COP21) Paris 2015", Journal of Public Health Policy, Vol. 37, No. 2, 2016, pp. 129-132, doi: https://doi.org/10.1057/jphp.2015.47. 

   상세보기

4. R. Gross, M. Leach, and A. Bauen, "Progress in renewable energy", Environ. Int., Vol. 29, No. 1, 2003, pp. 105-122, doi: https://doi.org/10.1016/S01604120(02)001307. 

   상세보기

5. H. Lee and S. Lee, "Economic analysis on hydrogen pipeline infrastructure establishment scenarios: case study of South Korea", Energies, Vol. 15, No. 18, 2022, pp. 6824, doi: https://doi.org/10.3390/en15186824. 

   상세보기

6. Y. H. Jang, S. Lee, H. Y. Shin, and J. Bae, "Development and evaluation of a 3cell stack of metalbased solid oxide fuel cells fabricated via a sinterjoining method for auxiliary power unit applications", Int. J. Hydrogen Energy, Vol. 43, No. 33, 2018, pp. 1621516229, doi: https://doi.org/10.1016/j.ijhydene.2018.06.141. 

   상세보기

7. S. Lee, T. Kim, G. Han, S. Kang, Y. S. Yoo, S. Y. Jeon, and J. Bae, "Comparative energetic studies on liquid organic hydrogen carrier: a net energy analysis", Renewable Sustainable Energy Rev., Vol. 150, 2021, pp. 111447, doi: https://doi.org/10.1016/j.rser.2021.111447. 

   상세보기

8. S. Ali and C. M. Jang, "Field testing and performance evaluation of 1.5 kW Darrieus wind turbine", Trans Korean Hydrogen New Energy Soc, Vol. 30, No. 6, 2019, pp. 608-613, doi: https://doi.org/10.7316/KHNES.2019.30.6.608. 

   원문보기 상세보기

9. J. Kong and J. Jung, "Development of incentive model for photovoltaic generators to participate in a dayahead electricity market in South Korea", 2019 IEEE Innovative Smart Grid TechnologiesAsia (ISGT Asia), 2019, pp. 2898-2902, doi: https://doi.org/10.1109/ISGTAsia.2019.8881082. 

   

10. M. Q. Raza and A. Khosravi, "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings", Renewable Sustainable Energy Rev., Vol. 50, No. 2015, pp. 1352-1372, doi: https://doi.org/10.1016/j.rser.2015.04.065. 

    상세보기

11. G. Han, S. Lee, J. Lee, K. Lee, and J. Bae, "Deeplearning and reinforcementlearningbased profitable strategy of agrid-level energy storage system for the smart grid", J. Energy Storage, Vol. 41, No. 2021, pp. 102868, doi: https://doi.org/10.1016/j.est.2021.102868. 

    상세보기

12. E. Choi, S. Cho, and D. K. Kim, "Power demand forecasting using long shortterm memory (LSTM) deeplearning model for monitoring energy sustainability", Sustainability, Vol. 12, No. 3, 2020, pp. 1109, doi: https://doi.org/10.3390/su12031109. 

    상세보기

13. A. L. Klingler and L. Teichtmann, "Impacts of a fore cast-based operation strategy for gridconnected PV storage systems on profitability and the energy system", Sol. Energy, Vol. 158, 2017, pp. 861868, doi: https://doi.org/10.1016/j.solener.2017.10.052. 

    상세보기

14. C. Wan, J. Zhao, Y. Song, Z. Xu, J. Lin, and Z. Hu, "Photovoltaic and solar power forecasting for smart grid energy management", CSEE J. Power and Energy Syst., Vol. 1, No. 4, 2015, pp. 3846, doi: https://doi.org/10.17775/CSEEJPES.2015.00046. 

    

15. Y. Zhang, T. Huang, and E. F. Bompard, "Big data analytics in smart grids: a review", Energy Inf., Vol. 1, No. 1, 2018, pp. 124, doi: https://doi.org/10.1186/s4216201800075. 

    

16. PJM, "Data Miner 2", PJM. Retrieved from http://dataminer2.pjm.com/list. 

17. U. Bureau, "CPH21, United States summary", 2012. Retrieved from https://www.census.gov. 

18. A. Gasparin, S. Lukovic, and C. Alippi, "Deep learning for time series forecasting: the electric load case", CAAI Trans. Intell. Technol., Vol. 7, No. 1, 2022, pp. 125, doi: https://doi.org/10.1049/cit2.12060. 

    상세보기