A methodology for peak energy requirement considering actual variation of occupants' behavior schedules

A novel methodology to accurately estimate the cooling demand in residential units is proposed, as a means of providing a better assessment of urban heat-island effects attributable to the use of residential air-conditioning units. The methodology integrates probabilistic variations in occupant behavior, which is shown to be a significant factor in estimated residential cooling requirements. The methodology consists of two key features. The first is an algorithm that generates short-term events that are likely to occur in a residential context, based on published data on occupant behavior. The second is a Monte Carlo approach to cooling load calculations based on stochastic variations in these short-term events and in the consequent likelihood of switching air-conditioning on or off.