Generation of representative meteorological years through anomaly-based detection of extreme events
Name
TBPS_A_2499687_O.pdf
Description
Published version
Size
3.75 MB
Format
Adobe PDF
Checksum (MD5)
c53628b1d0b7cbaa4e403a628cf97030
Author(s)
Tarkhan, Nada
Crawley, Drury B
Lawrie, Linda K
Reinhart, Christoph
Date Issued
May 6, 2025
Journal
Journal of Building Performance Simulation
Publisher
Informa UK Limited
Citation
Tarkhan, N., Crawley, D. B., Lawrie, L. K., & Reinhart, C. (2025). Generation of representative meteorological years through anomaly-based detection of extreme events. Journal of Building Performance Simulation, 1–18.
Version
Final published version
Abstract
Typical Meteorological Years (TMYs) have long supported the building sector by integrating local climate into building design for energy, thermal comfort, and peak load assessments. As climates shift, past heat waves and cold spells signal future conditions requiring greater adaptability. This study proposes a new file generation method that preserves TMY properties while embedding extreme events. We combine three anomaly-detection methods—temperature thresholds, Graph Neural Networks (GNNs), and Extreme Value Theory (EVT)—to capture climatic deviations, detect anomalies, and model statistical extremes. An integrated hierarchical method forms the new Representative Meteorological Year (RMY) file. RMY files for six ASHRAE climate-zones consistently capture past extremes, producing worst-case scenarios for key metrics, including peak loads, indoor thermal stress, natural ventilation and outdoor comfort. The largest deviation between the TMY and RMY was a doubling of indoor thermal stress hours across all climates, while average energy use remained aligned, with a deviation of 6%.
MIT Department
Massachusetts Institute of Technology. Building Technology Program
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/19401493.2025.2499687
