Quantifying and Predicting Residential Building Flexibility Using
  Machine Learning Methods
Patrick Salter Qiuhua Huang Paulo Cesar Tabares-Velasco
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, this paper first
proposes two complementary flexibility metrics (i.e., power and energy
flexibility) and then investigates several mainstream machine learning-based
models for predicting the time-variant and sporadic flexibility of residential
buildings at four-hour and 24-hour forecast horizons. The
long-short-term-memory (LSTM) model achieves the best performance and can
predict power flexibility for up to 24 hours ahead with the average error
around 0.7 kW. However, for energy flexibility, the LSTM model is only
successful for loads with consistent operational patterns throughout the year
and faces challenges when predicting energy flexibility associated with HVAC
systems.