Deep Learningによる院内負荷予測モデルを活用した異常時における院内分散電源のエネルギーマネジメント

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: 2020/02/01

タイトル(英語): Power Management for Hospital Combined Distributed Power with Load Prediction Using Deep Learning in Islanded Operation Mode

著者名: 水野　裕志（大阪電気通信大学），田中　義人（長崎総合科学大学），黒川　不二雄（長崎総合科学大学），松井　信正（長崎総合科学大学）

著者名(英語): Yuji Mizuno (Osaka Electro-Communication University), Yoshito Tanaka (Nagasaki Institute of Applied Science), Fujio Kurokawa (Nagasaki Institute of Applied Science), Nobumasa Matsui (Nagasaki Institute of Applied Science)

キーワード: 病院，単独運転，非常電源，ディープラーニング，負荷予測，電源エミュレータ　　hospital，islanded operation mode，emergency power supply，deep learning，load prediction，power emulator

要約(英語): The purpose of this paper is to propose a power management method in a hospital for a combination of emergency generators (EGs) with photovoltaic power generation (PV). The power balance of the grid not only influences the droop control for the generator but also the output fluctuations of the PV. Frequency control and a load control by a load prediction are necessary for the system grid combined with EGs and PV in an islanded operation mode. When the PV system is installed in the grid, the EG system should distribute power to small generators, the reason is because when the EG is too large, the power balance cannot be maintained to stabilize the system frequency in all power ranges. Since the distributed generation system needs the demand for each generator, it is important to predict the load. This paper proposes a new method for power energy management for stabilization with the islanded operation mode in a hospital power grid with load prediction using deep learning. The proposed method can realize operation by using a power emulator with the hospital power grid model. The verification of results show that the power emulator is effective in the energy management strategies.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.140 No.2 （2020）　特集：エネルギーデータを対象としたIoT，AI活用技術

本誌掲載ページ: 156-163 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/140/2/140_156/_article/-char/ja/