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http://dx.doi.org/10.17661/jkiiect.2021.14.4.307

LSTM-based Power Load Prediction System Design for Store Energy Saving  

Choi, Jongseok (Spartan Software Education Institute, Soongsil University)
Shin, Yongtae (School of Computing, Soongsil University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.14, no.4, 2021 , pp. 307-313 More about this Journal
Abstract
Most of the stores of small business owners are those that use a large number of electrical devices, and in particular, there are many stores that use a cold storage system. In severe cases, there is a lot of power load on the store, which can cause a loss to the assets in the store as the power supply is cut off. Accordingly, in this paper, an LSTM-based power load prediction system was designed to measure the energy demand rate of stores and to save energy. Since it can be used as a data-based power saving system for small and medium-sized stores, it is expected to be used as a data-based power demand prediction system for small businesses in the future, and to be used in the field of preventing damage due to power load.
Keywords
Data Mining; Data Analytics; Smart-Grid; Power Reduction; Store Data;
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