Dataset and notebook for: Metrological setup for accurate determination of passive radiative cooling power

Dataset and notebook related to: Metrological setup for accurate determination of passive radiative cooling power This archive contains a Jupyter notebook used to generate the characterization figures and uncertainty maps shown in Figures 2 and 3 of the associated paper. Contents uncertainty_map.ipynb — analysis notebook data/ntc_thermopile_data.dat — data read from the NTCs and thermopile Compared experimental setups The table below summarizes the main characteristics and measurement assumptions of the experimental rigs considered in the analysis.When the original references did not specify whether reported uncertainties corresponded to a coverage factor of k = 1 or k = 2, a coverage factor of k = 2 is assumed. Identifier Reference Collector area (m²) Temperature measurement Flow-rate measurement Weighting factor α Ambient temperature uncertainty Jia2024_Energy Jia et al., Energy (2024), DOI: 10.1016/j.energy.2024.130907 0.06 Two Pt100 sensors (in/out), ±0.05 K Constant ±1 % (Dijiang flow meter) 0.5 ±0.6 K (WatchDog 2550) Zhao2019_AppliedEnergy Zhao et al., Applied Energy (2019), DOI: 10.1016/j.apenergy.2019.113432 0.99 Two temperature sensors, ±0.05 K (model not specified) Constant ±2 % (model not specified) 0.5 ±0.5 K (model not specified) Aili2019_ECM Aili et al., Energy Conversion and Management (2019), DOI: 10.1016/j.enconman.2019.03.006 1.35 Type-K thermocouples (in/out), ±0.3 Absolute uncertainty ±0.02 L/min (MJE-100-1G) 0.5 ±0.3 K (Type-K) Goldstein2017_NatEnergy Goldstein et al., Nature Energy (2017), DOI: 10.1038/nenergy.2017.143 0.34 4-wire RTD sensors (in/out), ±0.3 K Estimated constant ±1 % (assumed) 0.5 ±0.3 K (model not specified) Wang2026_ECM Wang et al., Energy Conversion and Management (2026), DOI: 10.1016/j.enconman.2026.121062 1.00 4-wire Pt1000 + acquisition unit, ±0.06 K Constant ±1.5 % (rotameter) 0.5 ±0.3 K (automatic weather station) This work - 0.23 Direct ΔT measurement via thermopile, ±0.0124 K Calibrated polynomial model, max ±0.28 % 0.25 ±0.30 K (MAXIMET GMX501) Requirements The notebook uses only standard scientific Python packages: numpy pandas matplotlib A minimal requirements.txt is included for convenience.