Tackling extreme urban heat: a machine learning approach to assess the impacts of climate change and the efficacy of climate adaptation strategies in urban microclimates
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ID: 281671
2024
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Abstract
As urbanization and climate change progress, urban heat becomes a priority
for climate adaptation efforts. High temperatures concentrated in urban heat
can drive increased risk of heat-related death and illness as well as increased
energy demand for cooling. However, estimating the effects of urban heat is an
ongoing field of research typically burdened by an imprecise description of the
built environment, significant computational cost, and a lack of
high-resolution estimates of the impacts of climate change. Here, we present
open-source, computationally efficient machine learning methods that can
improve the accuracy of urban temperature estimates when compared to historical
reanalysis data. These models are applied to residential buildings in Los
Angeles, and we compare the energy benefits of heat mitigation strategies to
the impacts of climate change. We find that cooling demand is likely to
increase substantially through midcentury, but engineered high-albedo surfaces
could lessen this increase by more than 50%. The corresponding increase in
heating demand complicates this narrative, but total annual energy use from
combined heating and cooling with electric heat pumps in the Los Angeles urban
climate is shown to benefit from the engineered cooling strategies under both
current and future climates.
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| Authors | Grant Buster; Jordan Cox; Brandon N. Benton; Ryan N. King |
| Journal | arXiv |
| Year | 2024 |
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