用机器学习提升城市热环境预测精度，评估降温策略效果
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
- 构建开源高效机器学习模型，提升城市温度预测准确率
- 中等世纪前冷却需求将大幅上升，高反射率材料可减少超50%增长
- 结合电热泵系统，降温策略在当前与未来气候下均降低全年能耗
随着城市化和气候变化推进，城市热岛效应成为气候适应的关键挑战。高温集中区域加剧热相关疾病与死亡风险，并增加制冷能耗。但现有研究常因建筑环境描述不精确、计算成本高及缺乏高分辨率气候变化影响估计而受限。本文提出开源、计算高效的机器学习方法，相比历史再分析数据显著提升城市温度预测精度。模型应用于洛杉矶住宅建筑，比较降温策略与气候变化的影响。结果显示，至2050年前冷却需求将显著上升，但人工高反照率表面可使其增幅减少逾50%。尽管供暖需求相应增加，但在洛杉矶城市气候下，采用电热泵的冷暖联合能耗仍因工程降温策略而降低，无论当前或未来气候条件。
原文摘要 · Abstract (English)
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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