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Paper Abstract and Keywords
Presentation 2021-11-27 14:10
Studies of maximum electricity forecasting model including electricity market price -- Time series analysis with extra regressors added --
Hiroyuki Ogura (Nihon Univ.), Shunsuke Managi (Kyushu Univ.) SWIM2021-27
Abstract (in Japanese) (See Japanese page) 
(in English) As one of the solutions to the difficult problem of achieving both stable electricity supply and decarbonization, improving the forecasting accuracy of daily maximum electricity demand is a necessary condition. By improving the forecasting accuracy, it is possible to adjust the supply and demand when electricity is tight. In this paper, we constructed a daily maximum electricity demand forecasting model using electricity supply and demand data, meteorological data, and wholesale electricity market transaction price data in the TEPCO area since April 2016. First, we constructed a multiple regression forecasting model and statistically analyzed the correlation coefficient and regression coefficient for each explanatory variable (regression factor) including the wholesale electricity market price. Next, we added "date" to the explanatory variables and constructed a time-series forecasting model that considered the time-series data as the multiplication of the periodic functions of "weekly" and "annual" and the long-term change "trend". As a result, we improved the accuracy of forecasting the daily maximum electricity demand.
Keyword (in Japanese) (See Japanese page) 
(in English) electricity deregulation / electricity trading market / decarbonization society / demand forecasting / regression analysis / time series analysis / machine learning / explainability  
Reference Info. IEICE Tech. Rep., vol. 121, no. 274, SWIM2021-27, pp. 7-14, Nov. 2021.
Paper # SWIM2021-27 
Date of Issue 2021-11-20 (SWIM) 
ISSN Online edition: ISSN 2432-6380
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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Conference Information
Committee SWIM  
Conference Date 2021-11-27 - 2021-11-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SWIM 
Conference Code 2021-11-SWIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Studies of maximum electricity forecasting model including electricity market price 
Sub Title (in English) Time series analysis with extra regressors added 
Keyword(1) electricity deregulation  
Keyword(2) electricity trading market  
Keyword(3) decarbonization society  
Keyword(4) demand forecasting  
Keyword(5) regression analysis  
Keyword(6) time series analysis  
Keyword(7) machine learning  
Keyword(8) explainability  
1st Author's Name Hiroyuki Ogura  
1st Author's Affiliation Nihon University (Nihon Univ.)
2nd Author's Name Shunsuke Managi  
2nd Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2021-11-27 14:10:00 
Presentation Time 45 minutes 
Registration for SWIM 
Paper # SWIM2021-27 
Volume (vol) vol.121 
Number (no) no.274 
Page pp.7-14 
Date of Issue 2021-11-20 (SWIM) 

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