Towards a Review of Building Energy Forecast Models

Hannah Daniel, Bharadwaj R.K. Mantha, Borja Garcia 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
    EditorsChao Wang, Yong K. Cho, Fernanda Leite, Amir Behzadan
    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

    Fingerprint

    Learning systems
    Decision trees
    Support vector machines
    Energy conservation
    Electricity
    Genetic algorithms
    Neural networks

    ASJC Scopus subject areas

    • Computer Science(all)
    • Civil and Structural Engineering

    Cite this

    Daniel, H., Mantha, B. R. K., & Garcia de Soto, B. (2019). Towards a Review of Building Energy Forecast Models. In C. Wang, Y. K. Cho, F. Leite, & A. Behzadan (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

    Towards a Review of Building Energy Forecast Models. / Daniel, Hannah; Mantha, Bharadwaj R.K.; Garcia de Soto, Borja.

    Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019. ed. / Chao Wang; Yong K. Cho; Fernanda Leite; Amir Behzadan. American Society of Civil Engineers (ASCE), 2019. p. 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).

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

    Daniel, H, Mantha, BRK & Garcia de Soto, B 2019, Towards a Review of Building Energy Forecast Models. in C Wang, YK Cho, F Leite & A Behzadan (eds), Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019. 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), pp. 74-82, ASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019, Atlanta, United States, 6/17/19. https://doi.org/10.1061/9780784482445.010
    Daniel H, Mantha BRK, Garcia de Soto B. Towards a Review of Building Energy Forecast Models. In Wang C, Cho YK, Leite F, Behzadan A, editors, 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). 2019. p. 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). https://doi.org/10.1061/9780784482445.010
    Daniel, Hannah ; Mantha, Bharadwaj R.K. ; Garcia de Soto, Borja. / Towards a Review of Building Energy Forecast Models. Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019. editor / Chao Wang ; Yong K. Cho ; Fernanda Leite ; Amir Behzadan. American Society of Civil Engineers (ASCE), 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).
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