Comparison of Data Partitioning Method using LSTM for Power Demand at a Clinic-Reference-Cited by-同舟云学术

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Comparison of Data Partitioning Method using LSTM for Power Demand at a Clinic

Published:2024-05-27 Issue: Volume:56 Page:687-692
ISSN:
Container-title:2024 12th International Conference on Smart Grid (icSmartGrid)
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Author:

Inagata Tomoya¹,Mizuno Yuji²,Choi Jiyoung³,Kurokawa Fujio³,Matsui Nobumasa³

Affiliation:

1. Nagasaki Institute of Applied Science,Graduate School of Engineering,Nagasaki,Japan

2. Osaka Electro-Communication University,Dept. Medical Science,Osaka,Japan

3. Nagasaki Institute of Applied Science,Institute for Innovative Science and Technology,Nagasaki,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10577998/10578066/10578199.pdf?arnumber=10578199

Reference18 articles.

1. 2022 Annual Dialysis Data Report, JSDT Renal Data Registry;Hanafusa;Journal of Japanese Society for Dialysis Therapy,2023

2. New Sustainable Operation Method for a Power Grid without an Energy Storage System: A Case Study of a Hospital in Japan

3. Short-Term Load Forecasting Based on Integration of SVR and Stacking

4. Energy management of Renewable Energy Sources based on Support Vector Machine

5. Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

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