使用机器学习方法量化和预测住宅建筑灵活性
Quantifying and Predicting Residential Building Flexibility Using Machine Learning Methods
AI总结：
针对住宅建筑灵活性量化与预测不足的问题，本文提出功率和能量灵活性指标，并比较多种机器学习模型，发现LSTM在24小时功率灵活性预测中表现最佳。
摘要：
截至2022年，住宅建筑占美国总用电量的很大一部分（35%）。随着越来越多的分布式能源资源安装在建筑中，它们向电网提供灵活性的潜力不断增加。为了利用建筑提供的这种灵活性，聚合商或系统运营商需要量化并预测灵活性。该领域以往的工作主要集中在商业建筑上，对住宅建筑的研究很少。为弥补这一差距，本文首先提出了两个互补的灵活性指标（即功率灵活性和能量灵活性），随后研究了若干主流的基于机器学习的模型，用于在4小时和24小时预测时域下预测住宅建筑随时间变化且偶发的灵活性。长短期记忆（LSTM）模型取得了最佳性能，能够预测最长未来24小时的功率灵活性，平均误差约为0.7 kW。然而，对于能量灵活性，LSTM模型仅对全年运行模式一致的负荷有效，在预测与HVAC系统相关的能量灵活性时面临挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
