通过智能电表数据无监督识别电动汽车充电负荷，助力电网管理。
Electric Vehicle Identification from Behind Smart Meter Data
- 基于异常检测的无监督方法，无需预先知道充电特征。
- 在澳大利亚维多利亚州家庭数据上，准确识别出有电动车的用户。
- 仅需普通用户用电数据，适合大规模电网应用。
从智能电表记录中识别电动汽车（EV）充电负荷是能源运营商实现电网可靠性智能决策的关键环节。当电动汽车在电表后（BTM）充电时，其用电量被计入用户总负荷，未被配电运营商（DNOs）单独计量。因此，了解电网中电动车的存在至关重要。本文提出一种基于深度时序编码解码（TAE）网络的非侵入式无监督学习方法，仅利用非电动车用户的实际功率消耗数据（这些数据在实践中广泛存在），即可从低频智能电表数据中识别出电动车充电行为。该方法无需事先掌握电动车充电模式。实验在澳大利亚维多利亚州家庭的智能电表数据上进行，TAE模型表现出优越的识别性能。
原文摘要 · Abstract (English)
Electric vehicle (EV) charging loads identification from behind smart meter recordings is an indispensable aspect that enables effective decision-making for energy distributors to reach an informed and intelligent decision about the power grid's reliability. When EV charging happens behind the meter (BTM), the charging occurs on the customer side of the meter, which measures the overall electricity consumption. In other words, the charging of the EV is considered part of the customer's load and not separately measured by the Distribution Network Operators (DNOs). DNOs require complete knowledge about the EV presence in their network. Identifying the EV charging demand is essential to better plan and manage the distribution grid. Unlike supervised methods, this paper addresses the problem of EV charging load identification in a non-nonintrusive manner from low-frequency smart meter using an unsupervised learning approach based on anomaly detection technique. Our approach does not require prior knowledge of EV charging profiles. It only requires real power consumption data of non-EV users, which are abundant in practice. We propose a deep temporal convolution encoding decoding (TAE) network. The TAE is applied to power consumption from smart BTM from Victorian households in Australia, and the TAE shows superior performance in identifying households with EVs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。