An Adaptive Machine Learning Framework for Behind-the-Meter Load/PV Disaggregation
An Adaptive Machine Learning Framework for Behind-the-Meter Load/PV Disaggregation
复制标题
用于表后负荷/PV 分解的自适应机器学习框架
DOI：
10.1109/tii.2021.3060898
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发表时间：
2021
影响因子：
12.3
通讯作者：
A. Gebremedhin
中科院分区：
文献类型：
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作者：
Ramyar Saeedi;Sajan K. Sadanandan;A. Srivastava;Kevin L. Davies;A. Gebremedhin
大量的分布式光伏（PV）发电对配电系统运营商来说是“不可见的”，因为它位于客户场所的电表后面，不受公用事业公司的直接监控。发电本质上增加了一个未知的变化的负需求的系统，这导致额外的不确定性，在确定总负荷。这种不确定性直接影响系统可靠性、冷负荷拾取、负荷行为建模，从而影响运行成本。因此，必须创建低复杂度的本地化模型，用于估计电表后面这些不可见站点的发电量。本文提出了一种自适应机器学习框架：a）使用天气数据和最少数量的BTM PV发电测量传感器进行学习，B）使用天气、PV位置和未测量的BTM PV位置处的训练ML模型预测PV发电，c）使用估计的PV和由智能电表或智能Transformer测量的净负载来估计每个时间步长的总真实负载;以及d）学习特定的负载模式，最终适应本地化模型。所提出的框架的核心思想是转换数据，使得：a）机器学习模型可以有效地利用测量的时间依赖性;以及B）测量被转换到较低维度的空间中，以在保持准确性的同时降低复杂性。然后，变换后的测量值用于训练用于负载/PV分解的机器学习模型。研究的机器学习模型包括线性回归，决策树，随机森林（RF）和多层感知器。该框架的有效性证明使用两个数据集，一个真实的数据集从夏威夷和模拟数据集使用详细的模型在GridLab-D。几个测试/培训分裂的情况下，包括90-10%的分裂，一个月，一个赛季，和面板独立的分裂提供了一个全面的评估建议的框架。两个数据集上的结果表明，所提出的框架可以使用低复杂度的方法以高精度估计光伏发电量。准确度结果与更高复杂度的模型（例如，深度架构），并且发现RF与所研究的其他ML模型相比，对这些特定数据集提供了上级性能。
A significant amount of distributed photovoltaic (PV) generation is “invisible” to distribution system operators since it is behind the meter on customer premises and not directly monitored by the utility. The generation essentially adds an unknown varying negative demand to the system, which causes additional uncertainty in determining the total load. This uncertainty directly impacts system reliability, cold load pickup, load behavior modeling, and hence cost of operation. Thus, it is essential to create low-complexity localized models for estimating power generation from these invisible sites behind the meters. This article proposes an adaptive machine learning framework to: a) learn using weather data and a minimal number of BTM PV generation measurement sensors, b) forecast PV generation using weather, location of PV, and trained ML model at location for unmeasured BTM PV; c) use estimated PV and net load measured by smart meter or smart transformer to estimate total true load at each time step; and d) learn the specific load patterns eventually to adapt localized models. The proposed framework's core idea is to transform the data such that: a) the machine learning model can effectively utilize the time dependency of measurements; and b) the measurements are transformed into a lower dimensional space to reduce complexity while maintaining accuracy. The transformed measurements are then used to train the machine learning models for load/PV disaggregation. Machine learning models investigated include linear regression, decision tree, random forest (RF), and multilayer perceptron. The proposed framework's efficacy is demonstrated using two datasets, a real dataset from Hawaii and a simulated dataset using detailed models in GridLab-D. Several test/training split scenarios, including 90–10% split, one-month-out, one-season-out, and panel-independent split are presented to provide a thorough evaluation of the proposed framework. Results on both datasets show that the proposed framework can estimate PV generation with high accuracy using low-complexity methods. The accuracy results are comparable to higher complexity models (e.g., deep architectures), and RF is found to provide superior performance with these specific datasets compared to the other ML models investigated.