用条件变分自编码器模拟电价对电能消耗曲线的影响
Simulating Tariff Impact in Electrical Energy Consumption Profiles with Conditional Variational Autoencoders
摘要：
家庭用电的有效需求响应（DR）计划的实施将受益于能够模拟不同电价计划影响的数据驱动方法。本文提出了一种新的方法，基于条件变分自编码器（CVAE）生成，从电价配置文件结合外源天气和日历变量，每日消费配置文件的消费者划分在不同的集群。首先，根据消费者的消费行为和价格反应性，将大量的消费者聚集到集群中。聚类方法基于因果关系模型，该模型衡量特定关税对消费水平的影响。然后，使用CVAE为每个集群生成每日电能消耗曲线。这种非参数的方法相比，半参数数据生成器的基础上广义可加模型，并使用先验知识的能源消耗。在公开数据集上的实验表明，该方法在生成原始数据的平均值时表现出与半参数方法相当的性能。这种新方法的主要贡献是能够在生成的消费概况中再现反弹和副作用。事实上，在一个时间窗口内实施特别电费也可能影响这一时间窗口之外的消费。另一个贡献是聚类方法根据消费者的日常消费情况和对关税变化的弹性对消费者进行细分。这两个结果相结合是非常相关的系统运营商，零售商和能源监管机构的未来DR政策的事前测试。
英文摘要：
The implementation of efficient demand response (DR) programs for household electricity consumption would benefit from data-driven methods capable of simulating the impact of different tariffs schemes. This paper proposes a novel method based on conditional variational autoencoders (CVAE) to generate, from an electricity tariff profile combined with exogenous weather and calendar variables, daily consumption profiles of consumers segmented in different clusters. First, a large set of consumers is gathered into clusters according to their consumption behavior and price-responsiveness. The clustering method is based on a causality model that measures the effect of a specific tariff on the consumption level. Then, daily electrical energy consumption profiles are generated for each cluster with CVAE. This non-parametric approach is compared to a semi-parametric data generator based on generalized additive models and that uses prior knowledge of energy consumption. Experiments in a publicly available data set show that, the proposed method presents comparable performance to the semi-parametric one when it comes to generating the average value of the original data. The main contribution from this new method is the capacity to reproduce rebound and side effects in the generated consumption profiles. Indeed, the application of a special electricity tariff over a time window may also affect consumption outside this time window. Another contribution is that the clustering approach segments consumers according to their daily consumption profile and elasticity to tariff changes. These two results combined are very relevant for an ex-ante testing of future DR policies by system operators, retailers and energy regulators.
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。
