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

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.