Generating Stochastic Residential Load Profiles from Smart Meter Data for an Optimal Power Matching at an Aggregate Level
Generating Stochastic Residential Load Profiles from Smart Meter Data for an Optimal Power Matching at an Aggregate Level
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从智能电表数据生成随机住宅负载曲线，以实现总体水平的最佳功率匹配
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发表时间：
2018
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通讯作者：
G. Hug
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作者：
Thierry Zufferey;D. Toffanin;Diren Toprak;Andreas Ulbig;G. Hug
本文提出了一种自适应的方法来模拟住宅负荷配置文件的马尔可夫链的基础上，固有的季节性。这种方法相比，传统的方法，短期的季节性明确建模的过渡矩阵。超过250天的智能电表数据，从几百个家庭的详细评估显示，所提出的方法优于传统的方法，在保留单个负载的统计特性，同时最大限度地减少聚合负载和实际聚合负载数据之间的误差。通过逻辑回归模型实现了实质性的改进，该模型学习马尔可夫转移概率并更好地捕捉中长期的季节性。此外，提出了一种基于最小二乘回归的方法来分配合成配置文件的家庭没有智能电表。结合总功率匹配，用于负载分布生成的自适应方法允许基于部分智能电表数据的精确配电网状态估计。
This paper presents an adaptive approach for modelling residential load profiles based on Markov chains that inherently accounts for seasonality. This approach is compared to a traditional approach where short-term seasonality is explicitly modelled in the transition matrix. A detailed evaluation of over 250 days of smart meter data from a few hundred households shows how the proposed approach outperforms the traditional approach in preserving the statistical properties of individual loads while minimizing the error between the aggregated load and the actual aggregated load data. Substantial improvements are achieved by means of a logistic regression model that learns the Markov transition probabilities and better captures seasonality on a medium-to long-term basis. Furthermore, a method based on least squares regression is proposed for allocating synthetic profiles to households without smart meters. Combined with aggregate power matching, the adaptive approach for load profile generation allows for a precise distribution grid state estimation based on partial smart meter data.