Publication
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.
Keywords
Machine Learning , Building Energy Consumption , Carbon Reduction , Urban Sustainability .
Metadata
Disciplines: Artificial Intelligence Technology
Subjects: Machine Learning
Cite This Article
APA Style
Zhang, D. & Zheng, Q. (2025). Machine learning-based building energy consumption prediction and carbon reduction potential assessment in u.s. metropolitan areas. Journal of Industrial Engineering and Applied Science, 3(5), 27-40. https://doi.org/10.70393/6a69656173.333137
Acknowledgments
I would like to extend my sincere gratitude to Seyedzadeh, S., Rahimian, F. P., Glesk, I., and Roper, M. for their comprehensive research on machine learning for estimation of building energy consumption and performance as published in their article titled[9] "Machine learning for estimation of building energy consumption and performance: a review" in Visualization in Engineering (2018). Their systematic review and analytical framework have significantly influenced my understanding of machine learning applications in building energy systems and have provided valuable methodological guidance for my own research in this critical area.
I would like to express my heartfelt appreciation to Robinson, C., Dilkina, B., Hubbs, J., Zhang, W., Guhathakurta, S., Brown, M. A., and Pendyala, R. M. for their innovative study on machine learning approaches for estimating commercial building energy consumption, as published in their article titled[10] "Machine learning approaches for estimating commercial building energy consumption" in Applied Energy (2017). Their comprehensive analysis of machine learning algorithms and predictive modeling approaches for commercial buildings have significantly enhanced my knowledge of energy consumption prediction methodologies and inspired my research framework development in this field.
FUNDING
INSTITUTIONAL REVIEW BOARD STATEMENT
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
INFORMED CONSENT STATEMENT
CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
AUTHOR CONTRIBUTIONS
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