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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication7th International Conference on Smart Grid, icSmartGrid 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages46-51
Number of pages6
ISBN (Electronic)9781728148588
DOIs
Publication statusPublished - 2019 Dec
Event7th International Conference on Smart Grid, icSmartGrid 2019 - Newcastle, Australia
Duration: 2019 Dec 92019 Dec 11

Publication series

Name7th International Conference on Smart Grid, icSmartGrid 2019

Conference

Conference7th International Conference on Smart Grid, icSmartGrid 2019
CountryAustralia
CityNewcastle
Period19/12/919/12/11

Keywords

  • Cost minimization
  • demand prediction
  • machine learning
  • operational planning
  • polymer electrolyte fuel cell cogeneration systems

ASJC Scopus subject areas

  • Artificial Intelligence
  • Management Science and Operations Research
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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  • Cite this

    Tsuchiya, Y., Hayashi, Y., Fujimoto, Y., Yoshida, A., & Amano, Y. (2019). Hot Water Demand Prediction Method for Operational Planning of Residential Fuel Cell System. In 7th International Conference on Smart Grid, icSmartGrid 2019 (pp. 46-51). [8990872] (7th International Conference on Smart Grid, icSmartGrid 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icSmartGrid48354.2019.8990872