A Forecasting Method of Peak-Cut of Power Demand Using LSTM at A Clinic-Reference-Cited by-同舟云学术

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A Forecasting Method of Peak-Cut of Power Demand Using LSTM at A Clinic

Published:2023-08-29 Issue: Volume: Page:
ISSN:
Container-title:2023 12th International Conference on Renewable Energy Research and Applications (ICRERA)
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Author:

Inagata Tomoya¹,Mizuno Yuji²,Matsunaga Keita³,Kurokawa Fujio⁴,Tanaka Masaharu⁴,Matsui Nobumasa⁴

Affiliation:

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

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

3. Nagasaki Institute of Applied Science Nagasaki,Faculty of Engineering,Nagasaki,Japan

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

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10269105/10269319/10269447.pdf?arnumber=10269447

Reference10 articles.

1. A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network

2. A Short-Term Load Demand Forecasting based on the Method of LSTM

3. A Novel Deep Learning Approach for Short and Medium-Term Electrical Load Forecasting Based on Pooling LSTM-CNN Model

4. A Simplified LSTM Neural Networks for One Day-Ahead Solar Power Forecasting

5. Short-Term Wind Power Forecasting Using R-LSTM;devi;Int Journal of Renewable Energy,2021

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