Total utility demand prediction based on probabilistically generated behaviroal schedules of actural inhabitants

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

Abstract

This paper describes a new methodology in calculating accurately the time series utility loads (energy, power, city water, hot water, etc.) in a dwelling. This calculation takes into account the behavioral variations of the dwelling inhabitants. The proposed method contains a procedure for cooling load calculations based on a series of Monte Carlo simulations where the HVAC on/off state and the indoor heat generation schedules are varied, time-step by time-step. A data set of time-varying inhabitant behavior schedules, with a 15 minute resolution, generated by the authors in previous studies and validated by a comparison analysis to several field measurement data sets, was integrated into the model. The established model, which is called the Total Utility Demand Prediction System (TUD-PS) can be applied to, for example, accurate estimation of an integrated space maximum requirement, such as the total load of a building or an urban area. In a series of numerical experiments, huge discrepancies were found between the conventional results and those considering the time-varying inhabitant behavior schedules. In particular, deriving the dynamic state change, of having the HVAC on/off from the inhabitant's schedules, was found to be a significant factor in the maximum cooling and heating loads.

Original languageEnglish
Title of host publicationIBPSA 2009 - International Building Performance Simulation Association 2009
Pages521-528
Number of pages8
Publication statusPublished - 2009
Event11th International IBPSA Conference - Building Simulation 2009, BS 2009 - Glasgow, United Kingdom
Duration: Jul 27 2007Jul 30 2007

Other

Other11th International IBPSA Conference - Building Simulation 2009, BS 2009
CountryUnited Kingdom
CityGlasgow
Period7/27/077/30/07

All Science Journal Classification (ASJC) codes

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

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