Typical and Extreme (Heatwave) Future Weather Files for Building Energy Simulations: Case Studies for Turin and Bolzano, Italy, and De Bilt, Netherlands

This dataset contains future weather files for building energy simulations for Turin and Bolzano, Italy, and De Bilt, Netherlands. The weather data is provided in EPW format, which is commonly used as input for the EnergyPlus simulation engine. These files were generated using two primary downscaling methods: statistical and dynamical downscaling.

For statistical downscaling, two main approaches were employed:

• The Morphing method using the CCWeatherGen tool(Version 1.9).
• Statistical techniques implemented via Meteonorm software(Version 8.2.0).

We followed the Annexe 80 methodology for dynamical downscaling, as referenced in the research article "Extreme heat: improved bias-adjusted weather data for building performance simulation". The dynamical downscaling utilized data derived from regional climate models (RCMs) under the Representative Concentration Pathway (RCP) 8.5 scenario, representing a high greenhouse gas emissions pathway.

The dataset includes both Typical Meteorological Year (TMY) weather files and Heatwave (HW) scenarios for analyzing building performance under extreme conditions. Files are organized according to the following naming convention:

[Type of Downscaling]_[City Name]_[Data Type]_[Year]

• Type of Downscaling:
  • CC: CCWeatherGen (morphing method)
  • MET: Meteonorm (statistical method)
  • RCM: Dynamical downscaling with regional climate models
• City Name: Turin, Bolzano, or De Bilt
• Data Type: TMY (Typical Meteorological Year) or HW (Heatwave scenario)
• Year:
  • 2020: Current climate
  • 2050: Mid-future climate projection
  • 2080: Far-future climate projection

These weather files provide hourly climate data tailored for building energy simulations, accounting for various future climate scenarios. The data was bias-corrected using historical observations and processed to match the input requirements of the EnergyPlus engine.

This dataset is valuable for researchers and practitioners interested in assessing building energy performance under typical and extreme future climate conditions.