Natural Comfort – a new early stage design tool


BSEG has released publicly Natural Comfort, a new early stage tool that provides design guidance for improving comfort in passive, naturally ventilated commercial buildings in warm and hot climates. The program and the research to develop it are the result of a unique collaboration between members of BSEG and researchers at LabEEE at the Federal University of Santa Catarina in Brazil, through the Fulbright grant research of Adams Rackes.

Natural Comfort estimates the fraction of occupied hours a building is likely to remain comfortable, and also provides precision intervals for the estimate. Comfort is determined by the adaptive model (80% acceptability) of ASHRAE 55-2013, and the tool is appropriate for small to medium commercial buildings in climates that have little to no daytime heating demand (i.e., no central heating system), which includes most of the tropics. Designed to be easy to use, Natural Comfort is implemented as a simple Python routine and requires 38 input parameters, nine of which an included utility program can determine from an .EPW weather file.

The development of Natural Comfort, which is based on support vector regression of ~50,000 EnergyPlus simulations, is described in detail in “Naturally comfortable and sustainable: informed design guidance and performance labeling for passive commercial buildings in hot climates” (Applied Energy, 2016) by Adams Rackes, Ana Paula Melo, and Roberto Lamberts. A case study included in the article demonstrates that modifying a small set of parameters can drastically improve thermal performance and achieve sustainable comfort in hot and warm climates. Natural Comfort is distributed freely for researchers and practitioners to use for quickly assessing and expanding passive comfort opportunities through simple, cost-effective design modifications.

The program can be downloaded directly here or at Research Gate. A full User’s Guide is included to explain how the program works, provide guidance on inputs, and give suggestions for parameters to prioritize based on sensitivity analysis.

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