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

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.