Synthetic minority oversampling based machine learning method for urban level building EUI prediction and benchmarking

Xiaoyu Jin, Fu Xiao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Machine learning holds a lot of promise for quickly and correctly assessing building energy performance at urban level. However, due to the lack of data for minority types of buildings, unfavorable results are produced sometimes. Therefore, this study proposes a concise approach to generate enough data for training machine learning models while avoiding overfitting. Superior results are obtained. The importance of variables is analyzed using urban open data sets, which are valuable to data collectors and publishers in decision-making.
Original languageEnglish
Title of host publicationThe 7th Applied Energy Symposium 2021
Subtitle of host publicationLow Carbon Cities and Urban Energy Systems
Number of pages6
DOIs
Publication statusPublished - Sep 2021
EventApplied Energy Symposium 2021: Low carbon cities and urban energy systems - Matsue, Japan
Duration: 4 Sep 20218 Sep 2021

Publication series

NameEnergy Proceedings
Volume16
ISSN (Electronic)2004-2965

Conference

ConferenceApplied Energy Symposium 2021
Country/TerritoryJapan
CityMatsue
Period4/09/218/09/21

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