COMPARATIVE MACHINE LEARNING AND DEEP LEARNING STUDY OF ENERGY PREDICTIONS IN URBAN AND RURAL BUILDINGS

Sharon Coffee; James O’donnell; Cathal Hoare

doi:10.35490/EC3.2025.326

COMPARATIVE MACHINE LEARNING AND DEEP LEARNING STUDY OF ENERGY PREDICTIONS IN URBAN AND RURAL BUILDINGS

Sharon Coffee
, James O’donnell
, Cathal Hoare

School of Engineering

University College Dublin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The European Union’s (EU) energy targets highlight the importance of retrofitting older buildings to reduce carbon emissions.-However, many rural properties remain in the lowest energy rating categories, complicating retrofitting efforts. Urban buildings dominate Energy Performance Certificates (EPC) models, while rural structures require tailored approaches due to their diversity and lower energy performance. This research compares machine and deep learning models to address gaps in predictive accuracy and scalability in retrofitting simulations. The methodology predicts EPC ratings based on renovation policies and improves regional segmentation and archetype classifications. These strategies offer insights for rural residential buildings aligned with EU energy efficiency standards.

Original language	English
Title of host publication	Proceedings of the 2025 European Conference on Computing in Construction and 42nd International CIB W78 Conference on Information Technology in Construction, 2025
Editors	Ekaterina Petrova, Marijana Srećković, Pedro Meda, Ranjith K. Soman, Daniel Hall, Jakob Beetz, Jenn McArthur
Publisher	European Council on Computing in Construction (EC3)
ISBN (Print)	9789083451312
DOIs	https://doi.org/10.35490/EC3.2025.326
Publication status	Published - 2025
Event	European Conference on Computing in Construction, EC3 2025 and 42nd International CIB W78 Conference on IT in Construction, 2025 - Porto, Portugal Duration: 14 Jul 2025 → 17 Jul 2025

Publication series

Name	Proceedings of the European Conference on Computing in Construction
ISSN (Electronic)	2684-1150

Conference

Conference	European Conference on Computing in Construction, EC3 2025 and 42nd International CIB W78 Conference on IT in Construction, 2025
Country/Territory	Portugal
City	Porto
Period	14/07/25 → 17/07/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

building energy modeling
deep learning
Energy Performance Certificates
machine learning
retrofitting simulation

Access to Document

10.35490/EC3.2025.326

Cite this

Coffee, S., O’donnell, J., & Hoare, C. (2025). COMPARATIVE MACHINE LEARNING AND DEEP LEARNING STUDY OF ENERGY PREDICTIONS IN URBAN AND RURAL BUILDINGS. In E. Petrova, M. Srećković, P. Meda, R. K. Soman, D. Hall, J. Beetz, & J. McArthur (Eds.), Proceedings of the 2025 European Conference on Computing in Construction and 42nd International CIB W78 Conference on Information Technology in Construction, 2025 (Proceedings of the European Conference on Computing in Construction). European Council on Computing in Construction (EC3). https://doi.org/10.35490/EC3.2025.326

Coffee, Sharon ; O’donnell, James ; Hoare, Cathal. / COMPARATIVE MACHINE LEARNING AND DEEP LEARNING STUDY OF ENERGY PREDICTIONS IN URBAN AND RURAL BUILDINGS. Proceedings of the 2025 European Conference on Computing in Construction and 42nd International CIB W78 Conference on Information Technology in Construction, 2025. editor / Ekaterina Petrova ; Marijana Srećković ; Pedro Meda ; Ranjith K. Soman ; Daniel Hall ; Jakob Beetz ; Jenn McArthur. European Council on Computing in Construction (EC3), 2025. (Proceedings of the European Conference on Computing in Construction).

@inproceedings{e7f1f66dfe5a481aac5acfb594f2ed50,

title = "COMPARATIVE MACHINE LEARNING AND DEEP LEARNING STUDY OF ENERGY PREDICTIONS IN URBAN AND RURAL BUILDINGS",

abstract = "The European Union{\textquoteright}s (EU) energy targets highlight the importance of retrofitting older buildings to reduce carbon emissions.-However, many rural properties remain in the lowest energy rating categories, complicating retrofitting efforts. Urban buildings dominate Energy Performance Certificates (EPC) models, while rural structures require tailored approaches due to their diversity and lower energy performance. This research compares machine and deep learning models to address gaps in predictive accuracy and scalability in retrofitting simulations. The methodology predicts EPC ratings based on renovation policies and improves regional segmentation and archetype classifications. These strategies offer insights for rural residential buildings aligned with EU energy efficiency standards.",

keywords = "building energy modeling, deep learning, Energy Performance Certificates, machine learning, retrofitting simulation",

author = "Sharon Coffee and James O{\textquoteright}donnell and Cathal Hoare",

note = "Publisher Copyright: {\textcopyright} 2025, European Council on Computing in Construction (EC3). All rights reserved.; European Conference on Computing in Construction, EC3 2025 and 42nd International CIB W78 Conference on IT in Construction, 2025 ; Conference date: 14-07-2025 Through 17-07-2025",

year = "2025",

doi = "10.35490/EC3.2025.326",

language = "English",

isbn = "9789083451312",

series = "Proceedings of the European Conference on Computing in Construction",

publisher = "European Council on Computing in Construction (EC3)",

editor = "Ekaterina Petrova and Marijana Sre{\'c}kovi{\'c} and Pedro Meda and Soman, \{Ranjith K.\} and Daniel Hall and Jakob Beetz and Jenn McArthur",

booktitle = "Proceedings of the 2025 European Conference on Computing in Construction and 42nd International CIB W78 Conference on Information Technology in Construction, 2025",

}

Coffee, S, O’donnell, J & Hoare, C 2025, COMPARATIVE MACHINE LEARNING AND DEEP LEARNING STUDY OF ENERGY PREDICTIONS IN URBAN AND RURAL BUILDINGS. in E Petrova, M Srećković, P Meda, RK Soman, D Hall, J Beetz & J McArthur (eds), Proceedings of the 2025 European Conference on Computing in Construction and 42nd International CIB W78 Conference on Information Technology in Construction, 2025. Proceedings of the European Conference on Computing in Construction, European Council on Computing in Construction (EC3), European Conference on Computing in Construction, EC3 2025 and 42nd International CIB W78 Conference on IT in Construction, 2025, Porto, Portugal, 14/07/25. https://doi.org/10.35490/EC3.2025.326

COMPARATIVE MACHINE LEARNING AND DEEP LEARNING STUDY OF ENERGY PREDICTIONS IN URBAN AND RURAL BUILDINGS. / Coffee, Sharon; O’donnell, James; Hoare, Cathal.
Proceedings of the 2025 European Conference on Computing in Construction and 42nd International CIB W78 Conference on Information Technology in Construction, 2025. ed. / Ekaterina Petrova; Marijana Srećković; Pedro Meda; Ranjith K. Soman; Daniel Hall; Jakob Beetz; Jenn McArthur. European Council on Computing in Construction (EC3), 2025. (Proceedings of the European Conference on Computing in Construction).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - COMPARATIVE MACHINE LEARNING AND DEEP LEARNING STUDY OF ENERGY PREDICTIONS IN URBAN AND RURAL BUILDINGS

AU - Coffee, Sharon

AU - O’donnell, James

AU - Hoare, Cathal

PY - 2025

Y1 - 2025

N2 - The European Union’s (EU) energy targets highlight the importance of retrofitting older buildings to reduce carbon emissions.-However, many rural properties remain in the lowest energy rating categories, complicating retrofitting efforts. Urban buildings dominate Energy Performance Certificates (EPC) models, while rural structures require tailored approaches due to their diversity and lower energy performance. This research compares machine and deep learning models to address gaps in predictive accuracy and scalability in retrofitting simulations. The methodology predicts EPC ratings based on renovation policies and improves regional segmentation and archetype classifications. These strategies offer insights for rural residential buildings aligned with EU energy efficiency standards.

AB - The European Union’s (EU) energy targets highlight the importance of retrofitting older buildings to reduce carbon emissions.-However, many rural properties remain in the lowest energy rating categories, complicating retrofitting efforts. Urban buildings dominate Energy Performance Certificates (EPC) models, while rural structures require tailored approaches due to their diversity and lower energy performance. This research compares machine and deep learning models to address gaps in predictive accuracy and scalability in retrofitting simulations. The methodology predicts EPC ratings based on renovation policies and improves regional segmentation and archetype classifications. These strategies offer insights for rural residential buildings aligned with EU energy efficiency standards.

KW - building energy modeling

KW - deep learning

KW - Energy Performance Certificates

KW - machine learning

KW - retrofitting simulation

UR - https://www.scopus.com/pages/publications/105029481367

U2 - 10.35490/EC3.2025.326

DO - 10.35490/EC3.2025.326

M3 - Conference contribution

AN - SCOPUS:105029481367

SN - 9789083451312

T3 - Proceedings of the European Conference on Computing in Construction

BT - Proceedings of the 2025 European Conference on Computing in Construction and 42nd International CIB W78 Conference on Information Technology in Construction, 2025

A2 - Petrova, Ekaterina

A2 - Srećković, Marijana

A2 - Meda, Pedro

A2 - Soman, Ranjith K.

A2 - Hall, Daniel

A2 - Beetz, Jakob

A2 - McArthur, Jenn

PB - European Council on Computing in Construction (EC3)

T2 - European Conference on Computing in Construction, EC3 2025 and 42nd International CIB W78 Conference on IT in Construction, 2025

Y2 - 14 July 2025 through 17 July 2025

ER -

Coffee S, O’donnell J, Hoare C. COMPARATIVE MACHINE LEARNING AND DEEP LEARNING STUDY OF ENERGY PREDICTIONS IN URBAN AND RURAL BUILDINGS. In Petrova E, Srećković M, Meda P, Soman RK, Hall D, Beetz J, McArthur J, editors, Proceedings of the 2025 European Conference on Computing in Construction and 42nd International CIB W78 Conference on Information Technology in Construction, 2025. European Council on Computing in Construction (EC3). 2025. (Proceedings of the European Conference on Computing in Construction). doi: 10.35490/EC3.2025.326

COMPARATIVE MACHINE LEARNING AND DEEP LEARNING STUDY OF ENERGY PREDICTIONS IN URBAN AND RURAL BUILDINGS

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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