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.

中文翻译：

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用于表后负荷/PV 分解的自适应机器学习框架


大量分布式光伏（PV）发电对于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，并且不受公用事业公司直接监控。发电本质上向系统增加了未知的变化负需求，这导致确定总负载时产生额外的不确定性。这种不确定性直接影响系统可靠性、冷负载启动、负载行为建模，从而影响运营成本。因此，有必要创建低复杂度的本地化模型来估计电表后面这些不可见地点的发电量。本文提出了一种自适应机器学习框架，以：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 位置以及未测量的 BTM PV 位置处经过训练的 ML 模型来预测 PV 发电量； c) 使用智能电表或智能变压器测量的估计光伏和净负载来估计每个时间步长的总真实负载； d) 了解具体的负载模式，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，以便：a）机器学习模型可以有效地利用测量的时间依赖性； b) 将测量结果转换到较低维度的空间，以在保持准确性的同时降低复杂性。然后，转换后的测量结果用于训练负载/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。使用两个数据集（来自夏威夷的真实数据集和使用 GridLab-D 中的详细模型的模拟数据集）证明了所提出的框架的功效。 提出了几种测试/训练分割场景，包括 90-10% 分割、一个月、一季和独立于小组的分割，以对所提出的框架进行全面评估。两个数据集的结果表明，所提出的框架可以使用低复杂度方法高精度地估计光伏发电量。准确性结果与较高复杂性模型（例如深度架构）相当，并且与其他研究的 ML 模型相比，RF 在这些特定数据集上提供了卓越的性能。