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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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