翌日最大電力需要予測における最適なニューラルネットワーク構成の決定法

DOI Web Site Web Site 被引用文献10件 参考文献13件

書誌事項

タイトル別名
  • Selection of the Optimal Neural Network Architecture for Next Peak Load Forecasting
  • ヨクジツ サイ ダイ デンリョク ジュヨウ ヨソク ニ オケル サイテキ ナ

この論文をさがす

抄録

In engineering fields, one of the most important applications of artificial neural networks is a non-linear parametric model that can approximate any continuous input-output relation. Especially, in next day peak load forecating, the ability of approximation of artificial neural networks is very useful. However, an important but difficult problem exits in applying a neural network to next day peak load forecasting. This problem is to estimate the quality of approximation of a neural network. This quality depends on the architecture of the neural network applied, as well as on the complexity of the target relation. In other words, the pmoblem is to determine the optimal number of parameters in the neural network.<br> This paper proposes a new method for the selection of the optimal neural network architecture for next day peak load forecasting. The proposed method is based on a new information criterion. This paper also applies the proposed method to next day peak load forecasting constructed by real data and shows the comparison of error estimation of some models for peak load forecasting.

収録刊行物

被引用文献 (10)*注記

もっと見る

参考文献 (13)*注記

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ