Total utility demand prediction system for dwellings based on stochastic processes of actual inhabitants

Abstract

This article describes a new methodology to calculate the likely utility load profiles (energy such as power, natural gas, space heating and cooling, and other thermal requirements, as well as city water) in a dwelling. This calculation takes into account the behavioural 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 heating, ventilating and air conditioning (HVAC) on/off state and the indoor heat generation schedules are varied, time-step by time-step. A data set of time-varying inhabitant behaviour schedules, with a 15-min 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, can be applied to, for example, likely 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 behaviour schedules. In particular, deriving the dynamic state change, of having the HVAC on/off from the inhabitants' schedules, was found to be a significant factor in the maximum cooling and heating loads.

Keywords:

• total utility demand prediction
• high time resolution
• probabilistic inhabitants' behaviour schedule
• probabilistic HVAC turning on/off events
• residential building

Acknowledgements

This research was supported partially by a Grant-in-Aid for Scientific Research by JSPS, awarded to Dr Hagishima (#20686040), and by the Kajima Research Foundation, the JUDANREN Foundation, and the Japan Securities Research Foundation. We express gratitude to these funding sources. Prof. Inoue, Tokyo University of Science, was kind to give us the precious field measurement data. Committee members of ‘A study on pro-energy conservative living style in residential dwellings’ in SHASE provided helpful suggestions to the study. We really appreciate the generous help extended.

Notes

1. In order to validate outputs from the TUD-PS as reported in the former reports (Tanimoto et al. 2008a–c), we have focused mainly on the aggregated time series. Hence, it is a reasonable criticism that we should validate not only averages but also distributions, if claiming that the TUD-PS is a stochastic method to reproduce real data. Having confirmed that the deviation of the inhabitants' behaviour schedules, which provides the basic data for our method, is in agreement with the original publicly available statistical data, and having shown that the average (aggregated) values are reliable, we think it is likely that the TUD-PS can reproduce the real distributions.

2. TAC 2.5% refers to an HVAC capacity with a 2.5% overload risk. This means the HVAC load which potentially allows that the 2.5% of the total amount of operating hours would be less-cooling or less-heating due to insufficient capacity. This particular method of evaluating overload risk was originally proposed by the Technical Advisory Committee of ASHVE (Takeda, 1990), and hence is denoted as the TAC.