カテゴリ: 論文誌(論文単位)グループ名: 【B】電力・エネルギー部門発行日: 2020/02/01タイトル(英語): PV Output Forecasting by Deep Boltzmann Machines with SS-PPBSO著者名: 小川 彰太（明治大学総合数理学部ネットワークデザイン学科），森 啓之（明治大学総合数理学部ネットワークデザイン学科）著者名(英語): Shota Ogawa (Department of Network Design, School of Interdisciprinary Mathmatical Sciences, Meiji University), Hiroyuki Mori (Department of Network Design, School of Interdisciprinary Mathmatical Sciences, Meiji University)キーワード: 太陽光発電出力予測，深層ニューラルネットワーク，深層ボルツマンマシン，進化的計算，スキャターサーチ，プレデタープレイブレインストーミングオプティミゼーション photovoltaic generation forecasting，deep neural network，deep Boltzmann machine，evolutionary computation，scatter search，predator-prey brain storm optimization要約(英語): This paper proposes an efficient method for photovoltaic (PV) system output forecasting by Deep Boltzmann Machines (DBM) with Scatter Search-Predator-Prey Brain Storm Optimization (SS-PPBSO). DBM plays a key role to extract features of input variables while SS-PPBSO is a new evolutionary computation that combines PPBSO with Scatter Search. In recent years, as renewable energy, PV systems are positively introduced into power network in Japan so that power system operation becomes complicated due to the uncertainty. To overcome this challenge, it is required to forecast PV outputs that are influenced by weather conditions significantly. This paper proposes a new efficient PV output forecasting method with DBM that makes use of SS-PPBSO in learning. The effectiveness of the proposed method is demonstrated for real data of a PV system.本誌: 電気学会論文誌B（電力・エネルギー部門誌） Vol.140 No.2 （2020） 特集：令和元年電力・エネルギー部門大会本誌掲載ページ: 86-93 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/ieejpes/140/2/140_86/_article/-char/ja/
