Energy Consumption Prediction of Residential Buildings Using Machine Learning: A Study on Energy Benchmarking Datasets of Selected Cities Across the United States

Milad Parvaneh, Abolfazl Seyrfar, Ali Movahedi, Hossein Ataei, Khuong Le Nguyen, Sybil Derrible

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

1 Citation (Scopus)

Abstract

Energy consumption around the globe has been rising for many decades. A significant portion of this consumption occurs in residential buildings. Developing reliable methods to understand and predict energy use is essential in the global effort to become more sustainable. Many cities across the U.S. have mandatory energy benchmarking programs requiring large buildings to track and report their energy use. These openly available datasets have encouraged many researchers to study energy use and develop energy use prediction models. In this study, we employ Extreme Gradient Boosting, Random Forest, and Artificial Neural Network as three common Machine Learning methods to predict building energy use in eight U.S. metropolitan areas. By examining the models’ performance, we also evaluate and compare the datasets provided by the benchmarking programs and we investigate whether the openly available datasets provide adequate input variables for energy use prediction. Based on the results, suggestions are provided to enhance the datasets and further improve building energy use research.

Original languageEnglish
Title of host publicationCIGOS 2021, Emerging Technologies and Applications for Green Infrastructure - Proceedings of the 6th International Conference on Geotechnics, Civil Engineering and Structures
EditorsCuong Ha-Minh, Anh Minh Tang, Tinh Quoc Bui, Xuan Hong Vu, Dat Vu Khoa Huynh
Place of PublicationSingapore
PublisherSpringer
Pages197-205
Number of pages9
ISBN (Electronic)9789811671609
ISBN (Print)9789811671593
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event6th International Conference on Geotechnics, Civil Engineering and Structures, CIGOS 2021 - Hạ Long Bay, Viet Nam
Duration: 28 Oct 202129 Oct 2021

Publication series

NameLecture Notes in Civil Engineering
Volume203
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference6th International Conference on Geotechnics, Civil Engineering and Structures, CIGOS 2021
Country/TerritoryViet Nam
CityHạ Long Bay
Period28/10/2129/10/21

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