Towards a Review of Building Energy Forecast Models

Hannah Daniel, Bharadwaj R.K. Mantha, Borja García De Soto

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

Abstract

This paper presents a critical review of the state-of-the-art data-driven machine learning methods utilized for building energy forecast. Specifically, it offers a look into the advantages and disadvantages of four widely adopted machine learning methods: artificial neural networks, support vector machines, genetic algorithms, and decision trees. Based on the performance of these methods explored in previous studies, recommendations of application are provided for different categories such as building type (e.g., residential), forecasting method (e.g., long-term), and building energy (e.g., electricity). Some of the main identified research gaps include the lack of studies dedicated to long-term energy forecasts and inability to successfully incorporate occupant behavior into the models. This review also highlights the potential and prospects of hybrid models as avenues of growth in the domain of building energy forecast. Further research efforts in these areas of study can reap future benefits by promoting energy conservation thereby reducing the ecological footprint.

Original languageEnglish (US)
Title of host publicationComputing in Civil Engineering 2019
Subtitle of host publicationSmart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
EditorsYong K. Cho, Fernanda Leite, Amir Behzadan, Chao Wang
PublisherAmerican Society of Civil Engineers (ASCE)
Pages74-82
Number of pages9
ISBN (Electronic)9780784482445
DOIs
StatePublished - Jan 1 2019
EventASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019 - Atlanta, United States
Duration: Jun 17 2019Jun 19 2019

Publication series

NameComputing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019

Conference

ConferenceASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019
CountryUnited States
CityAtlanta
Period6/17/196/19/19

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ASJC Scopus subject areas

  • Computer Science(all)
  • Civil and Structural Engineering

Cite this

Daniel, H., Mantha, B. R. K., & Soto, B. G. D. (2019). Towards a Review of Building Energy Forecast Models. In Y. K. Cho, F. Leite, A. Behzadan, & C. Wang (Eds.), Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (pp. 74-82). (Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019). American Society of Civil Engineers (ASCE). https://doi.org/10.1061/9780784482445.010