ファジィクラスタリング前処理ANNを用いた電力価格予測モデル

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: 2018/02/01

タイトル(英語): An Electricity Price Forecasting Model with Fuzzy Clustering Preconditioned ANN

著者名: 板羽　智史（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): Satoshi Itaba (Dept. of Network Design, School of Interdisciplinary Mathematical Sciences, Meiji University), Hiroyuki Mori (Dept. of Network Design, School of Interdisciplinary Mathematical Sciences, Meiji University)

キーワード: 電力価格，予測，ニューラルネットワーク，クラスタリング，ファジィ論理，最適化，EPSO　　electricity price，forecasting，artificial neural network，clustering，fuzzy logic，optimization，EPSO

要約(英語): In this paper, a hybrid model of fuzzy clustering and ANN (Artificial Neural Network) is proposed for electricity price forecasting. Due to the complicated behavior of electricity price in power markets, markets players are interested in maximizing profits while minimizing risks. As a result, more accurate models are required to deal with electricity price forecasting. This paper proposes a new method that makes use of fuzzy clustering preconditioned GRBFN (Generalized Radial Basis Function Network) to provide more accurate predicted prices. Fuzzy clustering plays a key role to prevent the number of learning data from decreasing at each cluster. GRBFN is one of efficient ANNs to approximate nonlinear systems. Furthermore, a modified GRBFN model is developed to improve the performance of GRBFN with the use of DA (Deterministic Annealing) clustering for the parameters initialization and EPSO (Evolutionary Particle Swarm Optimization) for optimizing the parameters of GRBFN. The proposed method is successfully applied to real data of ISO New England, USA.

本誌: 電気学会論文誌B（電力・エネルギー部門誌） Vol.138 No.2 （2018） 特集：平成29 年電力・エネルギー部門大会

本誌掲載ページ: 90-98 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/138/2/138_90/_article/-char/ja/