A machine learning framework to assess the impact of the COVID-19 pandemic on electricity consumption patterns

Abstract

Energy self-sufficiency and resilience are essential elements for long-term urban sustainability. Energy supply in many cities in the United States has been highly dependent on fossil fuels and excessive energy consumption, both at home and outside (i.e., residential and non-residential energy consumption). In this work, we identify electricity consumption spatial trends and profiles across neighborhoods in the city of Chicago using a self-organizing map (SOM) machine learning method as a main tool. Toward this goal, we use an anonymous dataset provided by Commonwealth Edison (ComEd; Chicago's main electricity provider), which includes electricity consumption data of individual residential and non-residential accounts in Chicago. We aggregate electricity consumption to the zip codes level, apply machine learning clustering methods to categorize electricity consumption patterns, and finally couple it with land use variables to find spatial interrelations. The output demonstrates how specific profiles of electricity consumption emerge across zip codes, which is the first step for developing tailored policies to lower energy consumption. The result of this study can help engineers, urban planners, and policymakers develop actionable strategies for electricity consumption management.