侵入型家電機器需要モニタリングへのニューロジェネティック学習の適用

• 花田 一麿

  東京農工大学

• 長坂 研

  東京農工大学

書誌事項

タイトル別名

• Neuro-Genetic Method for Intrusive Appliance Load Monitoring
• シンニュウガタ カデン キキ ジュヨウ モニタリング エ ノ ニューロジェネティック ガクシュウ ノ テキヨウ

抄録

Power utilities must understand how customers use their products. This is essential to formulate any long range planning，forecasting or marketing strategies. However，only customer’s service level loads are available. Monitoring/survey analysis of customer usage patterns is the best way to understand how customers use electricity. The key problem is that collection and analysis of customer usage data is very time consuming and very costly. In this　research，we propose a method to estimate the power consumption of appliances used in a household from service level load by Artificial Neural Network （ANN）. However，as selection of optimum parameters of stand-alone ANN and learning weights in ANN is difficult，in this research，we apply Genetic Algorithm （GA） to ANN called as Neuro-Genetic for automatic finding of optimal ANN parameters such as learning rate，momentum，etc . and also optimal weights for better training. As the result，we obtained some findings that are at least as same of that with the trial-and-error methods. Ultimately，we could greatly shorten the learning time. This method is required for monitoring.

収録刊行物

• 電気設備学会誌

  電気設備学会誌 25 (3), 204-213, 2005

  一般社団法人 電気設備学会

キーワード

• 侵入型モニタリング
• 家電機器モニタリング
• ニューラルネットワーク
• 遺伝的アルゴリズム
• ニューロジェネティック

詳細情報

• CRID: 1390282680722216320
• NII論文ID: 10014506808
• NII書誌ID: AN10067173
• DOI: 10.14936/ieiej.25.
• ISSN: 21886946
• ISSN: 09100350
• NDL書誌ID: 7278610
• Web Site: http://id.ndl.go.jp/bib/7278610
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I7278610
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • NDL
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可