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首页> 外文期刊>IEEE transactions on industrial informatics >An Adaptive Machine Learning Framework for Behind-the-Meter Load/PV Disaggregation
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An Adaptive Machine Learning Framework for Behind-the-Meter Load/PV Disaggregation

机译：用于米后面的加载/光伏分类的自适应机器学习框架

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摘要

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.
机译：分布式光伏（PV）发电的显著量是“看不见”的配电系统运营商，因为它是在客户端的计量器之后和实用程序不能直接监控。产生本质上增加了一个未知变负的需求到系统中，这会导致额外的不确定性在确定总负载。直接这种不确定性影响系统的可靠性，冷负荷启动，负载行为建模，因此成本操作。因此，有必要从背后米这些看不见的网站估计发电创造低复杂度的局部模型。本文提出了一种自适应机器学习框架于：a）学习使用利用天气预报光伏发电天气数据和BTM光伏发电测量传感器的最小数目，b）中，PV的位置，并且在未测量BTM PV位置训练ML模型; c）中使用估计的PV和由智能仪表或估计在每个时间步总真实负载智能变压器测量的净负荷;和d）了解具体负载模式最终局部模型适应。所提出的框架的核心思想是转换数据，使得：1）机器学习模型可以有效利用的测量时间的关系;和b）测量被变换成低维空间，以降低复杂性，同时保持精度。将转化的测量值随后用于训练机器学习模型对负载/ PV分解。调查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。所提出的框架的有效性是使用两个数据集，从夏威夷一个真正的数据集，并使用详细的模型GridLab-d仿真的数据集证实。几个测试/培训拆分方案，包括90-10％分割，一个个月了，一个赛季了，和面板独立分体都提供建议的框架进行全面评估。两个数据集的结果表明，该框架可以使用低复杂度的方法估计与高精度光伏发电。精度的结果是相当更高的复杂性模型（例如，深架构），和RF被发现提供具有这些特定数据集的处理相比，所研究的其它ML模型。

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