Machine Learning-Based Building Energy Consumption Prediction and Carbon Reduction Potential Assessment in U.S. Metropolitan Areas

Authors

  • Daiyang Zhang Georgetown University
  • Qichang Zheng University of Chicago

DOI:

https://doi.org/10.70393/6a69656173.333137

ARK:

https://n2t.net/ark:/40704/JIEAS.v3n5a04

Disciplines:

Artificial Intelligence Technology

Subjects:

Machine Learning

References:

70

Keywords:

Machine Learning, Building Energy Consumption, Carbon Reduction, Urban Sustainability

Abstract

Building energy consumption accounts for 40% of U.S. energy usage, presenting critical challenges for urban sustainability. This paper presents a machine learning framework integrating energy consumption prediction with carbon reduction assessment across five major metropolitan areas. We analyze 50,000+ buildings from 2019-2023, combining meteorological data, building characteristics, and socioeconomic factors to develop predictive models using LSTM networks, Random Forest algorithms, and Support Vector Machines. Our framework introduces a novel carbon assessment indicator system accounting for regional grid emission factors and building-specific operational patterns. Experimental results demonstrate Random Forest algorithms achieve 8.2-12.7% mean absolute percentage error, representing 15-23% improvement over traditional methods. LSTM networks excel for buildings with complex temporal patterns. Carbon assessment reveals reduction potential of 2.8-7.2 million tons CO₂ equivalent annually, with envelope improvements and HVAC upgrades contributing 70% of total potential at implementation costs of $15-85 per ton CO₂. The framework provides scalable prediction capabilities and actionable insights for urban energy policy, supporting evidence-based interventions toward carbon neutrality goals.

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Author Biographies

Daiyang Zhang, Georgetown University

Communication, Culture & Technology, Georgetown University, DC, USA.

Qichang Zheng, University of Chicago

Computational Social Science, University of Chicago, IL, USA.

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Published

2025-10-02

How to Cite

[1]
D. Zhang and Q. Zheng, “Machine Learning-Based Building Energy Consumption Prediction and Carbon Reduction Potential Assessment in U.S. Metropolitan Areas”, Journal of Industrial Engineering & Applied Science, vol. 3, no. 5, pp. 27–40, Oct. 2025.

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