Real-Time Energy Disaggregation at Substations With Behind-the-Meter Solar Generation
Real-Time Energy Disaggregation at Substations With Behind-the-Meter Solar Generation
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DOI：
10.1109/tpwrs.2020.3035639
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发表时间：
2020-11
影响因子：
6.6
通讯作者：
Wenting Li;Ming Yi;Meng Wang;Yishen Wang;Di Shi;Zhiwei Wang
中科院分区：
文献类型：
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作者：
Wenting Li;Ming Yi;Meng Wang;Yishen Wang;Di Shi;Zhiwei Wang
变电站（ED）的能量分解是具有挑战性的，因为测量大多是在多种类型的负载上汇总的，而某些负载（例如幕后太阳能）的存在是运营商不知道的与传统的无模型方法相比，方法从记录的历史数据集中学习所有负载的模式，这些方法需要对每种培训的纯度测量或每个培训样本的完整标签，我们的方法可以从部分标记的总体数据中提取负载模式，因此，可以更适用于实用的场景和新的动态。学习问题，添加了列符号和不一致的调节术语，以识别未标记的负载并在实时分解中学习独特的模式，我们的方法解决了一个改进的稀疏分解问题，其中一个分解了聚合的测量值，作为某些代表性记录的测量结果与已知的分类阶段有关。
Energy Disaggregation at substations (EDS) is challenging because measurements are mostly aggregated over multiple types of loads, and the existence of some loads such as behind-the-meter solar is unknown to the operator. This paper for the first time addresses this so-called “partial labels” issue in energy disaggregation and develops a model-free EDS method to separate individual loads, including BTM solar, from the total energy consumption in real-time. Our approach learns the patterns of all loads offline from recorded historical datasets with partial labels. Compared with conventional model-free methods that require either pure measurements of each load for training or full labels of each training sample, our method can extract load patterns from partially labeled aggregated data and thus, is more applicable to practical scenarios and alleviates the annotation burden for the operator. Specifically, we propose to solve a new dictionary learning problem, where column-sparsity and incoherence regularization terms are added to identify unlabeled loads and learn distinctive patterns of each load. In real-time disaggregation, our approach solves an improved sparse decomposition problem where one decomposes the aggregated measurements as a linear combination of some representative recorded measurements with known disaggregation learned in the offline stage. Numerical experiments are reported to validate our method.