Urban Energy Flux: Human Mobility as a Predictor for Spatial Changes

TL;DR

This paper demonstrates that intra-urban human mobility patterns can effectively predict spatial variations in energy demand within cities, offering a new approach to understanding and managing urban energy flux and emissions.

Contribution

It introduces a novel human mobility-based method for predicting urban energy demand, supplementing traditional infrastructure-focused models.

Findings

Human mobility significantly influences urban energy distribution.

Mobility-driven models improve prediction accuracy of energy demand.

Implications for CO2 emission reduction strategies.

Abstract

As a key energy challenge, we urgently require a better understanding of how growing urban populations interact with municipal energy systems and the resulting impact on energy demand across city neighborhoods, which are dense hubs of both consumer population and CO2 emissions. Currently, the physical characteristics of urban infrastructure are the main determinants in predictive modeling of the demand side of energy in our rapidly growing urban areas; overlooking influence related to fluctuating human activities. Here, we show how applying intra-urban human mobility as an indicator for interactions of the population with local energy systems can be translated into spatial imprints to predict the spatial distribution of energy use in urban settings. Our findings establish human mobility as an important element in explaining the spatial structure underlying urban energy flux and demonstrate the utility of a human mobility driven approach for predicting future urban energy demand with implications for CO2 emission strategies.