Estimating treatment effects in demand response
Estimating treatment effects in demand response
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DOI：
10.1109/ssp.2016.7551825
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发表时间：
2016-06
期刊：
影响因子：
--
通讯作者：
Pan Li;Baosen Zhang
中科院分区：
文献类型：
--
作者：
Pan Li;Baosen Zhang
Demand response is designed to motivate electricity customers to modify their loads at critical time periods. Accurately estimating customers response to demand response signals is crucial to the success of these programs. In this paper, we consider signals in demand response programs as a treatment to the customers and estimate the average treatment effect. Specifically, we adopt the linear regression model and derive several consistent linear regression estimators. From both synthetic and real data, we show that including more information about the customers does not always improve estimation accuracy: the interaction between the side information and the demand response signal must be carefully modeled. We then apply the so-called modified covariate method to capture these interactions and show it can strike a balance between having more data and model correctness. Our results are validated using data collected by Pecan Street.
