An Improvement of Power Demand Prediction Method using Weather Information and Machine Learning: A Case of a Clinic in Japan (II)-Reference-Cited by-同舟云学术

An Improvement of Power Demand Prediction Method using Weather Information and Machine Learning: A Case of a Clinic in Japan (II)

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:2023 11th International Conference on Smart Grid (icSmartGrid)
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Author:

Inagata Tomoya¹,Matsunaga Keita²,Mizuno Yuji³,Kurokawa Fujio⁴,Tanaka Masaharu⁴,Matsui Nobumasa⁴

Affiliation:

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

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

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

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

Funder

JSPS KAKENHI

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10169137/10169142/10170940.pdf?arnumber=10170940

Reference20 articles.

1. Deep Learning and Optimization Algorithms Based PV Power Forecast for an Effective Hybrid System Energy Management;ammar;Int Journal of Renewable Energy,2022

2. Comparison of Photovoltaic Production Forecasting Methods;kermia;Int Journal of Renewable Energy,2022

3. Short-Term Demand Prediction Using an Ensemble of Linearly-Constrained Estimators

4. Comparison of Three Methods for a Weather Based Day-Ahead Load Forecasting

5. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

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