Future Weather Files (under Climate Change Scenarios) to Support Building Energy Simulation

Published: 19-10-2020| Version 2 | DOI: 10.17632/rnmxfv6pyn.2
Bruno Lee,
Mirata Hosseini


The future weather files are synthesized based on the methodology presented in the research project on the impact of climate change on building energy performance. These weather files (in EPW format) are intended to be used for building energy simulation and performance evaluation. The team provides here future weather files for 30 years (from 2020 to 2049) for four Representative Concentration Pathways (RCP2.6, 4.5, 6.0, and 8.5) used in the Fifth IPCC (Intergovernmental Panel on Climate Change) Assessment. Due to limited resources, Canada - Montreal (WMO 716270) is the first set of data available. Weather files for other locations and scenarios will gradually be added. If you have any pressing needs, you are welcome to contact the team to explore the possibility. The future weather files are synthesized based on a workflow developed by the team. A full explanation is presented in our article. The following is a brief description of the workflow: The effect of climate change is based on the General Circulation Models (GCM). First, a bias-correction technique, known as the quantile-quantile method, is applied to remove the bias in the data in order to adopt GCMs to a specific location. A hybrid classification-regression model is used to downscale the bias-corrected GCM data to synthesize future weather data at an hourly resolution for building energy simulation. The proposed workflow enables users to use a set of observed weather data by finding similar patterns rather than artificially generating data. However, in cases where the future GCM data show temperatures outside the range of the observed data, a trained regression model is applied to synthesize hourly weather data. If readers are interested in the topic, please browse through related articles of the team: Generating future weather files under climate change scenarios to support building energy simulation — a machine learning approach https://doi.org/10.1016/j.enbuild.2020.110543 A systematic approach in constructing typical meteorological year weather files using machine learning https://doi.org/10.1016/j.enbuild.2020.110375 Cooling and heating energy performance of a building with a variety of roof designs; the effects of future weather data in a cold climate https://doi.org/10.1016/j.jobe.2018.02.001 Energy performance of cool roofs under the impact of actual weather data https://doi.org/10.1016/j.enbuild.2017.04.006 Version History: Version 1: WMO 716270, RCP 4.5 only Version 2: WMO 716270, RCP 2.6, 6.0, 8.5 added Disclaimer: The weather files provided here are synthesized based on the novel workflow developed by the research team with limitations and based on assumptions. A full description of the workflow is presented in our articles. We do not make any warranties about the completeness, reliability, and accuracy of the data. Any use of the data is strictly at your own risk, and we will not be liable for any losses and damages in connection with the use.