Modeling Urban Electricity Demand and Spatial Fairness Using Machine Learning: Evidence from New York City

Zhiyi Xu

doi:10.54254/2755-2721/2025.LD26306

Applied and Computational EngineeringOpen access

Modeling Urban Electricity Demand and Spatial Fairness Using Machine Learning: Evidence from New York City

Research Article

Open Access

Modeling Urban Electricity Demand and Spatial Fairness Using Machine Learning: Evidence from New York City

Zhiyi Xu ^1*

¹ Applied Urban Science and Informatics, New York University, New York, USA

^*Corresponding author: zx1981@nyu.edu

Published on 26 August 2025

ACE Vol.179

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-184-6

ISBN (Online): 978-1-80590-129-7

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Abstract

Understanding electricity consumption at a fine-grained spatial level is vital for equitable infrastructure planning in cities. This study analyzes electricity usage patterns across New York City using multiple Machine Learning models, including time series forecasting with Prophet, classification with Random Forest, and regression with ensemble models. This study examine 2021–2024 monthly electricity consumption data at the borough and neighborhood level to identify high-demand zones, assess prediction accuracy, and evaluate spatial disparities in energy allocation. Using a combination of Gini coefficients, model residuals, and geospatial visualization, the study reveals significant inequalities in model performance and projected load trends. These findings underscore the importance of integrating fairness diagnostics into urban energy modeling, even when using standard public datasets and minimal input features.

Keywords:

Energy Justice, Resource Allocation, Spatial Inequality, Machine Learning, New York City

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References

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[5]. Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45. https: //doi.org/10.1080/00031305.2017.1380080

Cite this article

Xu,Z. (2025). Modeling Urban Electricity Demand and Spatial Fairness Using Machine Learning: Evidence from New York City. Applied and Computational Engineering,179,56-67.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-184-6(Print) / 978-1-80590-129-7(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 17 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.179

ISSN: 2755-2721(Print) / 2755-273X(Online)

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