Quantifying and Predicting Residential Building Flexibility Using Machine Learning Methods

arXiv:2403.01669v1 Announce Type: new
Abstract: Residential buildings account for a significant portion (35\%) of the total electricity consumption in the U.S. as of 2022. As more distributed energy resources are installed in buildings, their potential to provide flexibility to the grid increases. To tap into that flexibility provided by buildings, aggregators or system operators need to quantify and forecast flexibility. Previous works in this area primarily focused on commercial buildings, with little work on residential buildings. To address the gap, …

abstract arxiv building buildings consumption cs.lg cs.sy distributed eess.sy electricity energy flexibility grid machine machine learning resources total type