Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States
Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States
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赢家不是守护者：在美国使用机器学习来描述需求响应中的家庭参与度、收益和能源模式
DOI：
10.1016/j.erss.2020.101595
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发表时间：
2020
影响因子：
6.7
通讯作者：
J. Zuboy
中科院分区：
文献类型：
--
作者：
Annika Todd;C. Spurlock;Ling Jin;Peter A. Cappers;S. Borgeson;Dan;Fredman;J. Zuboy
需求响应程序可以帮助公用事业公司管理快速发展的电网，但这些程序受制于人类行为的复杂性。本文探索了一种揭示家庭异质性的新方法。我们使用一种名为条件推理树(c-tree)的机器学习方法，根据通过智能电表收集的家庭能源行为特征对家庭进行分类，并探索这如何转化为他们对灾难恢复计划的现实世界响应中的异质性。使用随机对照试验的数据，我们对该计划在每个家庭组内造成的能源使用变化进行了估计。我们的结果表明，与通过几种传统分割方法获得的差值相比，c-树方法根据家庭的能源使用特征来区分家庭，从而增加了家庭组之间的入学率差额和临界峰值减少。因此，c-tree分析能够针对主要的潜在能源节约者进行最有针对性的定位，并可以最大限度地提高家庭招募到灾难恢复计划的成本效益。我们的结果还提供了对家庭能源行为特征之间的关系的新见解--例如峰值能源使用和“结构性赢利”(在不改变能源使用行为的情况下在灾难恢复计划下节省资金的能力)--与家庭关于参加灾难恢复计划和减少能源消耗的决定之间的关系。我们的研究还证明了智能电表数据与机器学习和计量经济学方法相结合的潜力，可以为公用事业公司、项目实施者、研究人员和其他利益相关者提供重要价值。
Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. We use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their real-world response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics – such as peak energy use and “structural winningness” (the ability to save money under a DR program without changing energy-use behaviors) – and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.