The heterogeneity of thermal sensitivity of residential electricity load between China and USA

Thermal-sensitive electricity usage accounts for the majority of residential electricity use worldwide. Moreover, it has substantial impacts on the power system and the society during the extreme weathers. We propose a novel data-driven method to identify the thermal-sensitive electricity usage and quantify the associated thermal sensitivity directly from widely deployed smart meter data, where the Multivariate Adaptive Regression Splines model provides both the flexibility and interpretability. We apply the proposed method to analyze the thermal sensitivity of individual residential electricity use for two representative urban areas in the United States and China, San Francisco Bay Area and Shanghai, respectively. More than 120,000 households' smart meter data sampled from Bay Area and Shanghai is used for the analysis. To best of our knowledge, we for the first time reveal the heterogeneous patterns of thermal sensitivity of households from USA and China at the individual level. The difference of thermal sensitivity serves as an important factor for personalized techniques and policies towards mitigating the impact of extreme weathers to power systems and improving the buildings' energy efficiency.