UC Berkeley

We developed pythermalcomfort, a Python package that allows users to calculate the most common thermal comfort indices in compliance with the main thermal comfort standards. For example, pythermalcomfort can be used to calculate: whole body thermal comfort indices (e.g., Predicted Mean Vote, adaptive models, Standard Equivalent Temperature), local discomfort, clothing insulation, and psychrometric properties of air. All pythermalcomfort functions have been validated against the reference tables provided in the corresponding thermal comfort standards. We have developed documentation, examples and tutorial videos to guide users on how to use our package. pythermalcomfort allows researchers and professionals to accurately perform complex thermal comfort calculations without the need of re-writing the programming code. With Python being among the most widely utilized programming languages and pythermalcomfort being the only Python library which includes a comprehensive list of thermal comfort functions, we believe that pythermalcomfort will have a significant impact on both the research and industrial communities.