Simulation of energy performance of a residential building through the year 2100 and the workflow to integrate evolving climate projections with historical hourly meteorological data

Building energy simulations have traditionally relied on historical weather files that do not account for climate change. This study presents an open-source, automated Python workflow to generate future hourly weather files compatible with EnergyPlus for building energy and renewable energy simulations across North America through 2100. The methodology combines quality-controlled historical data (CWEEDS, GHCNh, NSRDB) with bias-corrected NA-CORDEX projections using Quantile Delta Mapping and analogue-based temporal disaggregation. A machine learning step is introduced to decompose solar radiation for EPW compliance. The pipeline produces ready-to-use CSV and EPW files, enabling direct assessment of hourly building performance and renewable energy potential under future climate scenarios. Validation indicates plausible warming (e.g. +3°C–6°C in Winnipeg under RCP8.5) and consistent photovoltaic simulation results. The open-source Python code is made publicly available.