A New Training Method for Analyzable Structured Neural Network and Application of Daily Peak Load Forecasting

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  • 構造化ニューラルネットワークの新しい学習法と最大電力需要予測への適用
  • コウゾウカ ニューラル ネットワーク ノ アタラシイ ガクシュウホウ ト サイダイ デンリョク ジュヨウ ヨソク エ ノ テキヨウ

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This paper presents a daily peak load forecasting method using an analyzable structured neural network (ASNN) in order to explain forecasting reasons. In this paper, we propose a new training method for ASNN in order to explain forecasting reason more properly than the conventional training method. ASNN consists of two types of hidden units. One type of hidden units has connecting weights between the hidden units and only one group of related input units. Another one has connecting weights between the hidden units and all input units. The former type of hidden units allows to explain forecasting reasons. The latter type of hidden units ensures the forecasting performance. The proposed training method make the former type of hidden units train only independent relations between the input factors and output, and make the latter type of hidden units train only complicated interactions between input factors. <br>The effectiveness of the proposed neural network is shown using actual daily peak load. ASNN trained by the proposed method can explain forecasting reasons more properly than ASNN trained by the conventional method. Moreover, the proposed neural network can forecast daily peak load more accurately than conventional neural network trained by the back propagation algorithm.

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