Hot Water Demand Prediction Method for Operational Planning of Residential Fuel Cell System

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

This study proposes a hot water demand prediction method for operational planning of polymer electrolyte fuel cell cogeneration systems (PEFC-CGSs). PEFC-CGSs provide hot water by utilizing waste heat produced in the electricity generation process. An optimal operational plan according to household demand leads to further energy saving. Therefore, operational planning methods based on household demand prediction have received intense focus. In particular, the prediction of the amount of hot water demand is important for efficient operation. The authors have attempted to improve the hot water prediction method based on multivariate random forest (MRF), which uses the average of many decision trees' outputs as the prediction result. However, some experimental results show that a prediction strategy based on averaging the outputs of decision trees does not always lead to the best solution. In this study, the authors propose a novel prediction method utilizing the quantile of the estimation results derived in MRF. By setting the appropriate quantile, we can evade the demand underestimation, which has a higher negative impact on operational efficiency than overestimation. The usefulness of the proposed approach is evaluated via numerical simulations using real-world demand data.

本文言語English
ホスト出版物のタイトル7th International Conference on Smart Grid, icSmartGrid 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ46-51
ページ数6
ISBN(電子版)9781728148588
DOI
出版ステータスPublished - 2019 12
イベント7th International Conference on Smart Grid, icSmartGrid 2019 - Newcastle, Australia
継続期間: 2019 12 92019 12 11

出版物シリーズ

名前7th International Conference on Smart Grid, icSmartGrid 2019

Conference

Conference7th International Conference on Smart Grid, icSmartGrid 2019
国/地域Australia
CityNewcastle
Period19/12/919/12/11

ASJC Scopus subject areas

  • 人工知能
  • 経営科学およびオペレーションズ リサーチ
  • エネルギー工学および電力技術
  • 再生可能エネルギー、持続可能性、環境
  • 電子工学および電気工学

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