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Tai-Yeon Ku

As seen in: MDPI
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Articles

Exploring the Preference for Discrete over Continuous Reinforcement Learning in Energy Storage Arbitrage

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By Jaeik Jeong, Tai-Yeon Ku, Wan-Ki Park
| MDPI
1. Introduction Energy arbitrage is a key approach for optimizing energy storage system (ESS) usage, especially with the rise of renewable energy sources [1]. By buying electricity when prices are low and selling it when prices are high, energy arbitrage balances supply and demand, stabilizes the grid, and maximizes profits. Various storage technologies, such as battery storage, hydrogen storage, and thermal storage, can be used for energy arbitrage [2,3].
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Denoising Masked Autoencoder-Based Missing Imputation within Constrained Environments for Electric Load Data

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By Jaeik Jeong, Tai-Yeon Ku, Wan-Ki Park
| MDPI
1. Introduction The increasing global demand for energy has amplified the need for smart grid technologies to enhance the energy efficiency in various settings, including the residential, commercial, and industrial sectors. Specifically, the utilization of data technologies such as machine learning has become essential to achieve energy efficiency improvements in complex energy systems [1].
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