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http://dx.doi.org/10.7316/KHNES.2022.33.5.591

Deep Learning Based Electricity Demand Prediction and Power Grid Operation according to Urbanization Rate and Industrial Differences  

KIM, KAYOUNG (Department of Climate and Energy Systems Engineering, Ewha Womans University)
LEE, SANGHUN (Department of Climate and Energy Systems Engineering, Ewha Womans University)
Publication Information
Transactions of the Korean hydrogen and new energy society / v.33, no.5, 2022 , pp. 591-597 More about this Journal
Abstract
Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.
Keywords
Artificial Intelligence; Deep learning; Power grid; Urbanization;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 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.   DOI
2 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.   DOI
3 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.   DOI
4 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.   DOI
5 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.   DOI
6 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.   DOI
7 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.   DOI
8 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.   DOI
9 U. Bureau, "CPH21, United States summary", 2012. Retrieved from https://www.census.gov.
10 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.   DOI
11 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.   DOI
12 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.   DOI
13 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.   DOI
14 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.   DOI
15 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.   DOI
16 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.   DOI
17 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.   DOI
18 PJM, "Data Miner 2", PJM. Retrieved from http://dataminer2.pjm.com/list.