Paper Abstract and Keywords |
Presentation |
2016-09-30 13:30
Power Control for Smart Home Based on Solar Power Prediction Using Machine Learning Shun Muraoka, Go Hasegawa, Kazuhiro Matsuda, Morito Matsuoka (Osaka Univ.), Yoshiki Makino, Yasuo Tan (JAIST) NS2016-86 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this paper, we proposed a power management algorithm based on the photovoltaic power predicted by a machine learning.
The power generation model of the solar cell was built by using a set of measured values of the actual solar cell located in the smart home in JAIST.
The future generation value was predicted with the weather forecast by using the model as the test data.
The Root Mean Square Error(RMSE) of total photovoltaic power generation using test data reached around 2.9 [kWh]. The standard deviation also reached around 2.0 [kWh].
The electric power flow between the system power and the smart home was managed for the cost function, including $mathrm{CO_{2}}$ emission and power consumption, to be minimum.
Eventually, around 82% $mathrm{CO_{2}}$ emission was demonstrated to be decreased by using the algorithm.
We also apply the algorithm for the electric bill as the cost function with Feed-in Tariff system.
As a result, 17% of the electric bill was demonstrated to be lower than that without the algorithm. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Machine Learning / Photovoltaic Power Generation / Smart Home / Electric Power Control / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 116, no. 230, NS2016-86, pp. 67-72, Sept. 2016. |
Paper # |
NS2016-86 |
Date of Issue |
2016-09-22 (NS) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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NS2016-86 |
Conference Information |
Committee |
NS CS IN NV |
Conference Date |
2016-09-29 - 2016-09-30 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Tohoku Univ. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Post IP networking, Next Generation Network (NGN)/New Generation Network (NWGN), Contingency Plan/BCP, Network Coding/Network Algorithms, Session Management (SIP/IMS), Internetworking/Standardization, Network configuration, etc. |
Paper Information |
Registration To |
NS |
Conference Code |
2016-09-NS-CS-IN-NV |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Power Control for Smart Home Based on Solar Power Prediction Using Machine Learning |
Sub Title (in English) |
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Keyword(1) |
Machine Learning |
Keyword(2) |
Photovoltaic Power Generation |
Keyword(3) |
Smart Home |
Keyword(4) |
Electric Power Control |
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1st Author's Name |
Shun Muraoka |
1st Author's Affiliation |
Osaka University (Osaka Univ.) |
2nd Author's Name |
Go Hasegawa |
2nd Author's Affiliation |
Osaka University (Osaka Univ.) |
3rd Author's Name |
Kazuhiro Matsuda |
3rd Author's Affiliation |
Osaka University (Osaka Univ.) |
4th Author's Name |
Morito Matsuoka |
4th Author's Affiliation |
Osaka University (Osaka Univ.) |
5th Author's Name |
Yoshiki Makino |
5th Author's Affiliation |
Japan Advanced Institute of Science and Technology (JAIST) |
6th Author's Name |
Yasuo Tan |
6th Author's Affiliation |
Japan Advanced Institute of Science and Technology (JAIST) |
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Speaker |
Author-1 |
Date Time |
2016-09-30 13:30:00 |
Presentation Time |
25 minutes |
Registration for |
NS |
Paper # |
NS2016-86 |
Volume (vol) |
vol.116 |
Number (no) |
no.230 |
Page |
pp.67-72 |
#Pages |
6 |
Date of Issue |
2016-09-22 (NS) |
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