Validation of methodology for utility demand prediction considering actual variations in inhabitant behaviour schedules

Jun Tanimoto, Aya Hagishima, Hiroki Sagara

研究成果: ジャーナルへの寄稿記事

26 引用 (Scopus)

抄録

A data set of myriad and time-varying inhabitant behaviour schedules with a 15-min time resolution, generated by the authors in a previous study, is validated through a comparison analysis. The key idea of generating a set of raw schedule data from the restricted statistical information is called the ‘generate and kill’ concept, which is commonly used in the fields of artificial intelligence and multi-agent simulation. In the present study, we show three comparisons. The first and second compare the estimated demand with a time series of measured utility demand. These comparisons indicate that the generated data and the algorithm, as described by the authors, have the required robustness. Another comparison between the estimate and the annually averaged daily water demand of a residential area, consisting of 9327 residences, also shows an acceptable consistency.

元の言語英語
ページ(範囲)31-42
ページ数12
ジャーナルJournal of Building Performance Simulation
1
発行部数1
DOI
出版物ステータス出版済み - 1 1 2008

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Artificial intelligence
Time series
Schedule
Methodology
Prediction
Water
Multi-agent Simulation
Artificial Intelligence
Time-varying
Robustness
Demand
Estimate

All Science Journal Classification (ASJC) codes

  • Architecture
  • Modelling and Simulation
  • Building and Construction
  • Computer Science Applications

これを引用

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