ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Sustainable Energy Systems
This article is part of the Research TopicNext-Gen Urban Building Energy Modelling: Integrating Sufficiency, Efficiency, Renewables, and Climate ResilienceView all articles
From Tweets to Energy Trends (TwEn2): Social Sensing–Informed Urban Building Energy Modeling
Provisionally accepted
- 1University of Washington, Seattle, United States
- 2Southern Illinois University Carbondale, Carbondale, United States
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Abstract: This study builds on our previous research, "From Tweets to Energy Trends (TwEn)" framework, which demonstrated the potential of social media interactions to inform urban energy predictions through a data-driven approach. Building on this foundation, we introduce TwEn2, a hybrid data-driven and physics-based modeling framework that advances the framework by enabling building-level analysis of urban-scale energy use with greater spatial specificity and enhanced applicability for urban building energy modeling (UBEM). The framework integrates geo-tagged social sensing data from the X platform with the U.S. Department of Energy's prototype building models— focusing on mid-sized and large multifamily residential buildings across New York City's boroughs—and benchmarking records. Tweet activity within building footprints is analyzed as a proxy for occupant presence and behavior, allowing assessment of correlations between human social dynamics and both measured and simulated monthly energy use across electricity, natural gas, and total energy consumption. By incorporating heigh fidelity social-sensing-data into physics-based simulations, TwEn2 improves predictive accuracy and enables occupancy-informed UBEM, validated against empirical benchmarking data. This framework provides a scalable, generalizable tool for urban energy modeling, planning, resilience and sustainability strategies, demonstrating the potential of social media as a real-time indicator of occupant dynamics to support informed energy management in cities.
Keywords: Urban building energy modeling (UBEM), Social media data, Social sensing, Physics-based Energy Simulations, Data-driven Energy Prediction, big data analytics
Received: 19 Aug 2025; Accepted: 14 Nov 2025.
Copyright: © 2025 Abbasabadi and Ashayeri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Narjes Abbasabadi, nabbasab@uw.edu
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