GC13C-0799 – Assessing the Optimality of ASHRAE Climate Zones using High Resolution Meteorological Data Sets


Pierre Dens Fils
University of Connecticut
Jitendra Kumar
Oak Ridge National Laboratory
Nathan Collier
Oak Ridge National Laboratory
Forrest M. Hoffman (forrest at climatemodeling dot org)
Oak Ridge National Laboratory
Min Xu
Oak Ridge National Laboratory
Whitney Forbes
University of Tennessee


Integrated Human-Earth Systems Modeling for Vulnerability and Risk Assessment, Adaptation, and Resilience II Posters
Monday, December 11, 2017 13:40–18:00
New Orleans Ernest N. Morial Convention Center – Poster Hall D–F


Energy consumed by built infrastructure constitutes a significant fraction of the nation’s energy budget. According to 2015 US Energy Information Agency report, 41% of the energy used in the US was going to residential and commercial buildings. Additional research has shown that 32% of commercial building energy goes into heating and cooling the building. The American National Standards Institute and the American Society of Heating Refrigerating and Air-Conditioning Engineers Standard 90.1 provides climate zones for current state-of-practice since heating and cooling demands are strongly influenced by spatio-temporal weather variations. For this reason, we have been assessing the optimality of the climate zones using high resolution daily climate data from NASA’s DAYMET database. We analyzed time series of meteorological data sets for all ASHRAE climate zones between 1980–2016 inclusively. We computed the mean, standard deviation, and other statistics for a set of meteorological variables (solar radiation, maximum and minimum temperature)within each zone. By plotting all the zonal statistics, we analyzed patterns and trends in those data over the past 36 years. We compared the means of each zone to its standard deviation to determine the range of spatial variability that exist within each zone. If the band around the mean is too large, it indicates that regions in the zone experience a wide range of weather conditions and perhaps a common set of building design guidelines would lead to a non-optimal energy consumption scenario. In this study we have observed a strong variation in the different climate zones. Some have shown consistent patterns in the past 36 years, indicating that the zone was well constructed, while others have greatly deviated from their mean indicating that the zone needs to be reconstructed. We also looked at redesigning the climate zones based on high resolution climate data. We are using building simulations models like EnergyPlus to develop optimal energy guidelines for each climate zone and quantify potential energy savings that can be realized by redesigning climate zones using state-of-the art data sets.

Forrest M. Hoffman (forrest at climatemodeling dot org)