Satellite-guided, AI-enhanced framework for assessing UHI-driven cooling and heating loads
Michael Lehning
Quantifying how urban heat islands (UHIs) influence regional electricity consumption remains challenging because station-based indicators and prescribed heating/cooling degree-day (HDD/CDD) thresholds often fail to capture intra-urban thermal heterogeneity and nonlinear demand responses during extremes. This study addresses these limitations by combining machine learning with MODIS thermal remote sensing to reconstruct gap-free daily land surface temperature (LST) fields across Local Climate Zon