摘要
负荷监测是智能电网需求侧管理的重要组成部分，非侵入式负荷监测代表了负荷监测未来的主要方向。近年来，基于字典学习的算法被用到了非侵入式负荷监测中，但是已有的算法都是浅层结构，对负荷特征的表达能力非常有限。为了学习到更细致和更精确的负荷特征表达，本文提出一种基于深度字典学习的非侵入式负荷监测算法。在REDD数据集上的实验结果验证了本文所提出算法的有效性，同时证明了深度字典学习算法相对于浅层字典学习的优势。
Abstract
Load monitoring is the key part of demand-side management in power grid. Non-intrusive load monitoring (NILM)is the most promising method in the field. Recently, dictionary-based method has been proven to be effective for NILM.However, existing approaches are all shallow models, which learn only one layer of dictionary representation. As theresult, the representation ability of existing approaches is limited. In order to learn more robust and accurate loadfeatures, this paper proposes a novel approach based on deep dictionary learning. Extensive experiments on REDD datasetverify the effectiveness of our method. The experimental results also show the advantage of deep models against shallowones.
NETL
NSTL
万方数据