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.

该研究提出了两种互补的灵活性测量指标，并使用主流的基于机器学习的模型预测住宅建筑物在四小时和 24 小时预测时段内的时间变化和间歇性灵活性。长短期记忆（LSTM）模型在功率灵活性方面表现最佳，可以在 24 小时内准确预测功率灵活性，平均误差约为 0.7 千瓦。然而，在能源灵活性方面，LSTM 模型只能成功预测具有一致运行模式的负荷，并且在预测与暖通空调系统相关的能源灵活性时面临挑战。