Feature extraction for enhancing data-driven urban building energy models

Name: Feature extraction for enhancing data-driven urban building energy models
Author: Bolluk, Muhammed Said, Seyis, Senem, Aydoğan, Reyhan

İsim	Feature extraction for enhancing data-driven urban building energy models
Yazar	Bolluk, Muhammed Said, Seyis, Senem, Aydoğan, Reyhan
Basım Tarihi:	2023
Basım Yeri	- European Council on Computing in Construction (EC3)
Konu	Machine learning, Urban building energy demand prediction, Feature extraction
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-070170273-1
Kayıt Numarası	748a34b1-ddbd-4ed7-b3ad-a1ca1d03698a
Lokasyon	Computer Science, Civil Engineering
Tarih	2023
Örnek Metin	Building energy demand assessment plays a crucial role in designing energy-efficient building stocks. However, most studies adopting a data-driven approach feel the deficiency of datasets with building-specific information in building energy consumption estimation. Hence, the research objective of this study is to extract new features within the climate, demographic, and building use type categories and increase the accuracy of a non-parametric regression model that estimates the energy consumption of a building stock in Seattle. The results show that adding new features to the original dataset from the building use type category increased the regression results with a 6.8% less error and a 30.8% higher R2 Score. Therefore, this study shows that building energy consumption estimation can be enhanced via new feature extraction equipped with domain knowledge.
DOI	10.35490/EC3.2023.291

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Feature extraction for enhancing data-driven urban building energy models

Yazar Bolluk, Muhammed Said, Seyis, Senem, Aydoğan, Reyhan

Basım Tarihi 2023

Basım Yeri - European Council on Computing in Construction (EC3)

Konu Machine learning, Urban building energy demand prediction, Feature extraction

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-070170273-1

Kayıt Numarası 748a34b1-ddbd-4ed7-b3ad-a1ca1d03698a

Lokasyon Computer Science, Civil Engineering

Tarih 2023

Örnek Metin Building energy demand assessment plays a crucial role in designing energy-efficient building stocks. However, most studies adopting a data-driven approach feel the deficiency of datasets with building-specific information in building energy consumption estimation. Hence, the research objective of this study is to extract new features within the climate, demographic, and building use type categories and increase the accuracy of a non-parametric regression model that estimates the energy consumption of a building stock in Seattle. The results show that adding new features to the original dataset from the building use type category increased the regression results with a 6.8% less error and a 30.8% higher R2 Score. Therefore, this study shows that building energy consumption estimation can be enhanced via new feature extraction equipped with domain knowledge.

DOI 10.35490/EC3.2023.291