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

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Chinese explanation / 中文解读

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Original abstract

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.

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