USING MACHINE LEARNING TO PREDICT THE ADOPTION OF BUILDING ELECTRIFICATION TECHNOLOGIES IN US HOUSEHOLDS

Andrew Majowicz, Philip Odonkor

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    This paper explores the use of machine learning to predict the adoption of building electrification technologies within US households. This is important due to the increasing prevalence of building electrification as a pathway to addressing climate change, which inadvertently poses a threat to the energy resilience of households during power outages. A non-intrusive, data-driven means of predicting the level of technology adoption can help guide mitigation and adaptation strategies aimed at minimizing the risks vulnerable households may face when power outages are compounded by extreme weather events. This study develops machine learning models based on the energy consumption dynamics of US households to predict the presence of critical electric appliances, including furnace, water heater, induction stove, cooling system, and solar panels. The models are trained using a large dataset of building end-use load consumption for buildings located in New Jersey. The results show that the models are reasonably accurate in predicting the presence of appliances in homes, although there is still significant potential for improvement in model accuracy.

    Original languageEnglish
    Title of host publication49th Design Automation Conference (DAC)
    ISBN (Electronic)9780791887301
    DOIs
    StatePublished - 2023
    EventASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023 - Boston, United States
    Duration: 20 Aug 202323 Aug 2023

    Publication series

    NameProceedings of the ASME Design Engineering Technical Conference
    Volume3A

    Conference

    ConferenceASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023
    Country/TerritoryUnited States
    CityBoston
    Period20/08/2323/08/23

    Keywords

    • building electrification
    • load profile
    • machine learning

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