research-article

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SCHMEAR: scalable construction of holistic models for energy analysis from rooftops

Authors:

Thomas R. Dougherty,

Tianyuan Huang,

Rishee K. Jain,

Ram RajagopalAuthors Info & Claims

BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

Pages 111 - 120

https://doi.org/10.1145/3486611.3486666

Published: 17 November 2021 Publication History

Abstract

As the world moves to decarbonize, the built environment commands attention for its intensity of energy consumption. Potential pathways for decarbonizing the built environment can be discovered through the aid of building energy modeling, which helps identify potential retrofit strategies and simulate integration with renewable energy sources. Energy modeling is complicated however, due to compound interactions between building materials, structural design, and urban form. Significant domain knowledge, modeling expertise, and extensive time investment are required for accurate modeling to accommodate this complexity. In this work, we explore the potential of accurately modeling building energy consumption at scale through the application of modern computer vision algorithms. We demonstrate that our computer vision system can accurately predict energy consumption through the extraction of meaningful features contained in satellite imagery. To accomplish this, we introduce a data-collection pipeline and a computer vision architecture to process satellite photos and contextual information from the urban texture. We also demonstrate a method of comparing the relative significance of the automatically extracted features in informing building decarbonization decision making and policy. Our results indicate that this approach reveals valuable insights into the dynamics of building energy consumption on the city scale and enables the rapid analysis of urban energy dynamics with readily available data.

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Cited By

Colverd GBardhan RCullen J(2024)Benchmarking Domestic Energy Consumption using High-Resolution Neighbourhood Energy Data and City Clustering in the UKProceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3671127.3698183(121-131)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3671127.3698183
Correa SPerez GJaramillo PTaneja J(2023)Taking the Long View: Enhancing Learning On Multi-Temporal, High-Resolution, and Disparate Remote Sensing DataProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623722(11-20)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623722

Index Terms

SCHMEAR: scalable construction of holistic models for energy analysis from rooftops
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Modeling and simulation
    1. Model development and analysis
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 2021

388 pages

ISBN:9781450391146

DOI:10.1145/3486611

General Chair:
Xiaofan (Fred) Jiang
Columbia University
,
Program Chairs:
Omprakash Gnawali
University of Houston
,
Zoltan Nagy
The University of Texas Austin

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEnergy: ACM Special Interest Group on Energy Systems and Informatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 November 2021

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National Science Foundation

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BuildSys '21

Sponsor:

SIGEnergy

BuildSys '21: The 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 17 - 18, 2021

Coimbra, Portugal

Acceptance Rates

BuildSys '21 Paper Acceptance Rate 28 of 107 submissions, 26%;

Overall Acceptance Rate 148 of 500 submissions, 30%

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Cited By

Colverd GBardhan RCullen J(2024)Benchmarking Domestic Energy Consumption using High-Resolution Neighbourhood Energy Data and City Clustering in the UKProceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3671127.3698183(121-131)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3671127.3698183
Correa SPerez GJaramillo PTaneja J(2023)Taking the Long View: Enhancing Learning On Multi-Temporal, High-Resolution, and Disparate Remote Sensing DataProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623722(11-20)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623722

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