aridkpi documentation

Climate-resilience KPIs for residential buildings in arid South American climates.

aridkpi is a Python package implementing the 5 core indicators of the KPI Comparison Matrix v1.0, aligned with the IEA EBC Annex 80 framework and extended for arid and semi-arid climates of South America (BWk/BSk Köppen-Geiger).

Note

Designed at INAHE-CONICET Mendoza for the 2026–2029 research plan Towards an integrated climate metric for building performance.

Quick links

What it computes

KPI	Description	Units
`iod`	Indoor Overheating Degree — magnitude of overheating during occupied hours	°C·h
`ccor`	Climate Change Overheating Resistivity — effectiveness of a passive strategy	°C·h/°C
`udh`	Unmet Degree Hours during outage (Passive Survivability)	°C·h
`energy_climate_sensitivity`	ΔE/ΔT — slope of EUI vs T_mean across SSP scenarios	kWh·m⁻²·yr⁻¹/°C
`max_thermal_change_rate`	dT/dt max — peak indoor thermal change rate	°C/h

Quick start

import aridkpi

df = aridkpi.synth.synthetic_dataset(typology="masonry_insulated", days=14, seed=42)
overheating = aridkpi.iod(df["T_in"], T_comf=26.0)
peak_rate = aridkpi.max_thermal_change_rate(df["T_in"])

print(f"IOD = {overheating:.1f} deg-C-h")
print(f"dT/dt max = {peak_rate:.2f} deg-C/h")

Citation

Please cite both the package and the underlying KPI matrix:

@software{barea_aridkpi_2026,
  author    = {Barea Paci, Gustavo Javier},
  title     = {aridkpi: climate-resilience KPIs for residential buildings},
  year      = {2026},
  version   = {0.1.0},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.19986567}
}

Table of contents

Getting started

API reference

API reference

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