BSOを用いた学習によるLASSO-GRBFNの電力価格予測法

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): A LASSO-GRBFN-based Method for Electricity Price Forecasting with BSO

著者名: 三輪　陸人（明治大学先端数理科学研究科ネットワークデザイン専攻），森　啓之（明治大学先端数理科学研究科ネットワークデザイン専攻）

著者名(英語): Rikuto Miwa (Department of Network Design, Meiji University), Hiroyuki Mori (Department of Network Design, Meiji University)

キーワード: 電力価格予測，ニューラルネットワーク，GRBFN，LASSO，Brain Storm Optimization　　electricity price forecasting，artificial neural network，GRBFN，LASSO，Brain Storm Optimization

要約(英語): This paper proposes a new method for one-step ahead electricity price forecasting. It is based on GRBFN (Generalized Radial Basis Function Network) that is an extension of RBFN of Artificial Neural Network (ANN). GRBFN has advantage over RBFN that the Gaussian function parameters are evaluated by the learning process. The conventional ANN methods consider overfitting with the weight decay method that corresponds to the L2 norm of weights between neurons, but there is still room for improvement. According to the idea of the sparse modeling, this paper proposes the use of Least Absolute Shrinkage and Selection Operator (LASSO) to improve the model performance. Also, this paper presents BSO of evolutionary computation to evaluate the cost function with the term of the L1 norm. That is because the conventional methods with the gradient do not work for the L1 norm. The proposed method is successfully applied to real data of ISO New England in USA.

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: 125-132 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_125/_article/-char/ja/