Predicting Public Health Impacts of Electricity Usage

TL;DR

This paper introduces HealthPredictor, an AI model that links electricity usage to public health impacts, enabling health-informed demand-side energy management to reduce air pollution-related health damages.

Contribution

The paper presents a novel AI framework that integrates fuel mix prediction, air quality modeling, and health impact assessment for public health-aware energy management.

Findings

HealthPredictor outperforms baseline models in predicting health impacts.

Case study shows health benefits of optimized electric vehicle charging.

Datasets and code are publicly available for further research.

Abstract

The electric power sector is a leading source of air pollutant emissions, impacting the public health of nearly every community. Although regulatory measures have reduced air pollutants, fossil fuels remain a significant component of the energy supply, highlighting the need for more advanced demand-side approaches to reduce the public health impacts. To enable health-informed demand-side management, we introduce HealthPredictor, a domain-specific AI model that provides an end-to-end pipeline linking electricity use to public health outcomes. The model comprises three components: a fuel mix predictor that estimates the contribution of different generation sources, an air quality converter that models pollutant emissions and atmospheric dispersion, and a health impact assessor that translates resulting pollutant changes into monetized health damages. Across multiple regions in the United States, our health-driven optimization framework yields substantially lower prediction errors in terms of public health impacts than fuel mix-driven baselines. A case study on electric vehicle charging schedules illustrates the public health gains enabled by our method and the actionable guidance it can offer for health-informed energy management. Overall, this work shows how AI models can be explicitly designed to enable health-informed energy management for advancing public health and broader societal well-being. Our datasets and code are released at: https://github.com/Ren-Research/Health-Impact-Predictor.