Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A Novel Graph Neural Network for Zone-Level Urban-Scale Building Energy Use Estimation
Date
2023-11-15
Author
Halaçll, Eren Gökberk
Canll, Ilkim
Işeri, Orçun Koral
Yavuz, Feyza
Akgül, Çaǧla Meral
Kalkan, Sinan
Gürsel Dino, İpek
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
84
views
0
downloads
Cite This
Buildings are highly responsible for total energy consumption in cities; therefore, accurate estimation of building energy consumption is essential for developing energy-efficient strategies on an urban scale. Data-driven urban building energy models can predict energy use with high precision and low computational cost. In recent years, machine learning, especially neural networks, emerged as the prominent method for predicting energy load for buildings. These models typically use different input features on building form, occupancy and operation. However, they remain inadequate in capturing the complex inter-dependencies (i.e., heat transfer) between units in multi-zone buildings, as they do not have an explicit representation of the neighborhood relations between zones. The precision of data-driven models can be improved using Graph neural networks (GNN) that can capture the underlying relationships and dependencies between different building elements. In this paper we propose a novel GNN model that surpasses the current state-of-art methods for the prediction of zone-level heating energy use. We applied the methodology in a residential neighborhood consisting of 5866 buildings and 64462 zones. We use zone-level features regarding their geometry, material thermal characteristics, internal loads as node features, inter-zone parameters as edge features (total area and U value of the adjacent surfaces) and weather parameters. The results showed that our proposed model provides improvements over alternative approaches for precise prediction of urban building energy consumption.
Subject Keywords
Graph Neural Network
,
Urban Building Energy Modeling
,
Urban-scale Building Energy Consumption
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85179519284&origin=inward
https://hdl.handle.net/11511/108016
DOI
https://doi.org/10.1145/3600100.3623747
Conference Name
10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2023
Collections
Department of Computer Engineering, Conference / Seminar
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
E. G. Halaçll et al., “A Novel Graph Neural Network for Zone-Level Urban-Scale Building Energy Use Estimation,” presented at the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2023, İstanbul, Türkiye, 2023, Accessed: 00, 2024. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85179519284&origin=inward.