ダム管理のための機械学習を用いたハイブリッド型流入量予測モデルの検討

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2023/02/01

タイトル(英語): Examine of Hybrid-discharge-prediction Model to Manage Dam by Machine Learning

著者名: 佐藤　江里子（（株）日立製作所 研究開発グループ），山口　悟史（（株）日立製作所 研究開発グループ），楠田　尚史（（株）日立パワーソリューションズ）

著者名(英語): Eriko Sato (Intelligent Information Research Department, Hitachi, Ltd.), Satoshi Yamaguchi (Intelligent Information Research Department, Hitachi, Ltd.), Takashi Kusuda (Hitachi Power Solutions Co., Ltd.)

キーワード: ダム，流入量予測，機械学習，人工知能，気象予報，洪水　　dam，prediction of discharge，machine learning，artificial intelligence，forecast，flood

要約(英語): Recently, as it occurs flood disasters due to climate change, it is important to operate dams. Especially, for hydroelectrical power plants of the dams, it is also necessary to predict the discharge to the dams appropriately at the phase of during usual and flood water for managing power generation and preparing for flood. However, conventional methods to predict the discharge have been developed at each phase of usual and flood water separately. In this report, we developed a hybrid-discharge-prediction model, which is composed by a state discriminator and discharge prediction models with machine learning and a flood simulator. This hybrid-discharge-prediction model can detect the state of discharge and adopt an appropriate discharge prediction model each state of discharge and prediction time. As a result, it was shown that the hybrid-discharge-prediction model can detect 7 states and predict the discharge to the dam in 5 hours at the phase from usual to flood water.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.2 （2023） 特集：エネルギー分野へ適用されたAI・IoT技術

本誌掲載ページ: 133-140 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/143/2/143_133/_article/-char/ja/