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diyepw: A Python package for Do-It-Yourself EnergyPlus weather file generation

Journal Article · · Journal of Open Source Software
DOI:https://doi.org/10.21105/joss.03313· OSTI ID:1925234
 [1];  [1];  [1];  [1]
  1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
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diyepw allows for quick and easy generation of a set of EnergyPlus weather (EPW) files for a given location over a given historical period. The user can obtain weather files using an open-source, automated workflow by simply specifying the location of interest using the World Meteorological Organization weather station ID number, and specifying a year or set of years for which to generate EPW files. Building energy modelers can use these auto-generated weather files in building performance simulations to represent the actual observed weather conditions in the location(s) of interest, based on meteorological observations obtained from the National Oceanic and Atmospheric Administration's Integrated Surface Database. Because observed weather data are not available for every meteorological variable specified in the EPW format, diyepw starts with a widely-used set of typical meteorological year (TMY) files, using them as the template to generate new EPW files by substituting in the observed values of selected meteorological variables that are known to affect building energy performance. Its output is a weather file or group of weather files that conform to the data standards associated with the EPW format so they can be used with any building performance simulation software employing EnergyPlus as its simulation engine.
Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
1925234
Report Number(s):
PNNL-SA-161402
Journal Information:
Journal of Open Source Software, Journal Name: Journal of Open Source Software Journal Issue: 64 Vol. 6; ISSN 2475-9066
Publisher:
Open Source Initiative - NumFOCUSCopyright Statement
Country of Publication:
United States
Language:
English

References (6)

Simulated building energy demand biases resulting from the use of representative weather stations journal January 2018
Localized Actual Meteorological Year File Creator (LAF): A tool for using locally observed weather data in building energy simulations journal July 2019
Array programming with NumPy journal September 2020
The Integrated Surface Database: Recent Developments and Partnerships journal June 2011
Data Structures for Statistical Computing in Python conference January 2010
xarray: N-D labeled Arrays and Datasets in Python journal April 2017

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Related Subjects

97 MATHEMATICS AND COMPUTING
EnergyPlus
Python
building energy modeling
meteorological data
weather files