Solar Panel Identification Via Deep Semi-Supervised Learning and Deep One-Class Classification
Solar Panel Identification Via Deep Semi-Supervised Learning and Deep One-Class Classification
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DOI：
10.1109/tpwrs.2021.3125613
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发表时间：
2022-07
影响因子：
6.6
通讯作者：
Elizabeth Cook;Shuman Luo;Yang Weng
中科院分区：
文献类型：
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作者：
Elizabeth Cook;Shuman Luo;Yang Weng
随着住宅光伏（PV）系统的安装持续快速增长，公用事业公司需要确定这些新组件的位置，以管理非常规的双向电力流，并保持对配电网的可持续管理。但是，历史记录是不可靠的，不断重新评估活跃的住宅光伏位置是资源密集型的。为了解决这些问题，我们建议在基于标记数据的机器学习设置中对太阳检测问题进行建模，例如，监督学习。然而，大多数实用程序面临的挑战是有限的标签或仅针对一种类型的用户的标签。因此，我们设计了新的基于自编码器的半监督学习和一类分类方法，极大地改善了人类行为和太阳行为的非线性数据表示。所提出的方法不仅在基于公开可用数据集的合成数据上进行了测试和验证，而且还在公用事业合作伙伴的实际数据上进行了测试和验证。数值结果表明，该方法具有较好的检测精度，为配电网分布式能源的管理奠定了基础。
As residential photovoltaic (PV) system installations continue to increase rapidly, utilities need to identify the locations of these new components to manage the unconventional two-way power flow and maintain sustainable management of distribution grids. But, historical records are unreliable and constant re-assessment of active residential PV locations is resource-intensive. To resolve these issues, we propose to model the solar detection problem in a machine learning setup based on labeled data, e.g., supervised learning. However, the challenge for most utilities is limited labels or labels on only one type of users. Therefore, we design new semi-supervised learning and one-class classification methods based on autoencoders, which greatly improve the nonlinear data representation of human behavior and solar behavior. The proposed methods have been tested and validated not only on synthetic data based on a publicly available data set but also on real-world data from utility partners. The numerical results show robust detection accuracy, laying down the foundation for managing distributed energy resources in distribution grids.