Effect of Climate on Residential Electricity Consumption: A Data-Driven Approach

Quantifying the climatic effect on residential electricity consumption (REC) can provide valuable insights for improving climate–energy damage functions. Our study quantifies of climate REC in Tibet using machine learning algorithm models and model-agnostic interpretation tools feature importance scores partial dependence plots. Results show that contributes about 16.46% to total while socioeconomic factors contribute 83.55%. Precipitation (particularly snowfall) boosts during cold season. The is stronger urban (~25.06%) than rural (~14.79%), particularly September when electricity-aided heating considered optional, as higher incomes amplified response climate. With urbanization income growth, expected more REC. Hence, precipitation should be incorporated climate–REC functions social cost carbon (SCC) estimation, regions vulnerable snowfall blizzards. Herein, we developed a method quantify differentiating between contributions from temperature precipitation, which used facilitate interdisciplinary cross-section analysis earth system science. Moreover, this data-driven model adapted warn against extreme weather induced power outages.