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

• Published in: Energies (Basel) 2022-05, Vol.15 (9), p.3355
• Main Authors: Xia, Cuihui, Yao, Tandong, Wang, Weicai, Hu, Wentao
• Format: Article
• Language: English
• Subjects: Air conditioning; Algorithms; Blizzards; Carbon; climate; Climate change; Climate effects; Climate models; Cold season; Datasets; Electric power; Electricity; Electricity consumption; Electricity distribution; Emissions; Energy consumption; Extreme weather; heating; Hydrologic cycle; Machine learning; multicollinearity; Population; Precipitation; residential electricity consumption; Residential energy; rural and urban difference; Rural areas; Seasonal variations; Snowfall; Social factors; Socioeconomic factors; Socioeconomics; Urbanization; Variables
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15093355

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