TY - GEN
T1 - USING MACHINE LEARNING TO PREDICT THE ADOPTION OF BUILDING ELECTRIFICATION TECHNOLOGIES IN US HOUSEHOLDS
AU - Majowicz, Andrew
AU - Odonkor, Philip
N1 - Publisher Copyright:
© 2023 American Society of Mechanical Engineers (ASME). All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - building electrification
KW - load profile
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85178602348&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85178602348&partnerID=8YFLogxK
U2 - 10.1115/DETC2023-116751
DO - 10.1115/DETC2023-116751
M3 - Conference contribution
AN - SCOPUS:85178602348
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 49th Design Automation Conference (DAC)
T2 - ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023
Y2 - 20 August 2023 through 23 August 2023
ER -