Simplified prediction tool for peak occupancy rate in office buildings

This paper presents a simplified tool for predicting peak occupancy rates b ased on the number of rooms and the associated average occupancy in an offi ce building. Twelve months of time-labeled occupancy data were obtained for 99 office and office service rooms, taken every 5 min using the facilities management computer at the Foothills Laboratory of the National Center for Atmospheric Research (NCAR) in Boulder, CO. Subsets were defined within the 99 room set by two different methods, then used to calculate data triads of set size, average occupancy and peak occup ancy values. From this subset data, a prediction tool was developed, with s et size and average occupancy as inputs and monthly peak occupancy for dail y and hourly periods as output. The predictions were compared with data fro m NCAR with very good agreement for daily and annual averages, and with dat a from an Energy Center of Wisconsin research project, with fair agreement.