Data-driven residential building energy consumption prediction for supporting multiscale sustainability assessment

Lufan Wang, Nora M. El-Gohary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sustainability assessment has drawn much attention worldwide. However, current sustainability assessment models/systems are usually focused on a single scale, which does not allow for capturing the interdependencies across the different scales. Also, existing sustainability assessment models/systems typically require high levels of data collection and complex measurements, which is usually costly and time-consuming. To address these gaps, the authors propose a data-driven multiscale sustainability assessment and prediction (DM-SAP) framework, which (1) focuses on the building, neighborhood, and city scales, and (2) takes a data-driven approach that relies on machine learning. The machine learning approach focuses on two primary objectives: (1) learning from history (previous building, neighborhood, and city data) to predict and assess sustainability metrics (e.g., energy consumption) in an efficient and reliable manner, and (2) feature selection to identify the minimum amount of data that would be sufficient for reliable assessment. This paper focuses on presenting the feature selection and the machine learning-based model development for predicting residential building energy consumption for supporting multiscale sustainability assessment. The LASSO algorithm was used for feature selection. Three machine learning algorithms were implemented and tested. The prediction performance was evaluated in terms of coefficient of variation. The 2009 Residential Energy Consumption Survey (RECS) by the U.S. Energy Information Administration (EIA) was utilized for model training and testing. The testing results showed reasonable prediction performance.

Original languageEnglish (US)
Title of host publicationComputing in Civil Engineering 2017
Subtitle of host publicationSmart Safety, Sustainability and Resilience - Selected Papers from the ASCE International Workshop on Computing in Civil Engineering 2017
EditorsKen-Yu Lin, Ken-Yu Lin, Nora El-Gohary, Nora El-Gohary, Pingbo Tang, Pingbo Tang
PublisherAmerican Society of Civil Engineers
Pages324-332
Number of pages9
ISBN (Electronic)9780784480823, 9780784480847
DOIs
StatePublished - Jan 1 2017
Event2017 ASCE International Workshop on Computing in Civil Engineering, IWCCE 2017 - Seattle, United States
Duration: Jun 25 2017Jun 27 2017

Publication series

NameCongress on Computing in Civil Engineering, Proceedings
Volume0

Other

Other2017 ASCE International Workshop on Computing in Civil Engineering, IWCCE 2017
CountryUnited States
CitySeattle
Period6/25/176/27/17

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications

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  • Cite this

    Wang, L., & El-Gohary, N. M. (2017). Data-driven residential building energy consumption prediction for supporting multiscale sustainability assessment. In K-Y. Lin, K-Y. Lin, N. El-Gohary, N. El-Gohary, P. Tang, & P. Tang (Eds.), Computing in Civil Engineering 2017: Smart Safety, Sustainability and Resilience - Selected Papers from the ASCE International Workshop on Computing in Civil Engineering 2017 (pp. 324-332). (Congress on Computing in Civil Engineering, Proceedings; Vol. 0). American Society of Civil Engineers. https://doi.org/10.1061/9780784480823.039