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 language | English |
|---|---|
| Title of host publication | IBPSA 2009 - International Building Performance Simulation Association 2009 |
| Pages | 521-528 |
| Number of pages | 8 |
| Publication status | Published - 2009 |
| Event | 11th International IBPSA Conference - Building Simulation 2009, BS 2009 - Glasgow, United Kingdom Duration: Jul 27 2007 → Jul 30 2007 |
Other
| Other | 11th International IBPSA Conference - Building Simulation 2009, BS 2009 |
|---|---|
| Country | United Kingdom |
| City | Glasgow |
| Period | 7/27/07 → 7/30/07 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Building and Construction
- Architecture
- Modelling and Simulation
Cite this
Total utility demand prediction based on probabilistically generated behaviroal schedules of actural inhabitants. / Tanimoto, Jun; Hagishima, Aya.
IBPSA 2009 - International Building Performance Simulation Association 2009. 2009. p. 521-528.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Total utility demand prediction based on probabilistically generated behaviroal schedules of actural inhabitants
AU - Tanimoto, Jun
AU - Hagishima, Aya
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:84870186780
SP - 521
EP - 528
BT - IBPSA 2009 - International Building Performance Simulation Association 2009
ER -