Eager: Predicting Electricity Demand And Indoor Air Quality During Heat Waves


Heat waves can have substantial impacts on human health, especially for individuals in densely populated locations. This project will collect air quality and power usage data from households in urban settings and connect this data to a multi-level climate model. The core scientific merit of the work is to integrate real data and climate models to predict the vulnerability of certain population segments. The research team will work with the Housing Authority of the City of Elizabeth, NJ, who seek to understand how the poorest urban households can better cope with an increasing number of heat waves. This award is part of the Smart and Connected Communities program at NSF. The research team plans to add buildings and their occupants to the multi-level climate-to-humans modeling framework developed under a previous award. The modeling framework will link occupant responses to heat stress to changes in indoor pollutants and electricity consumption. Sensors will be deployed to measure air quality and power usage. The researchers expect to be able to predict how adaptable different populations segments are to heat waves as a function of personal, building-level, and locational characteristics, thereby identifying thresholds beyond which climate-related stresses become human health disasters.
Effective start/end date8/1/167/31/18


  • National Science Foundation (NSF)


Air quality
Climate models
Data structures
Hot Temperature