期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 12, 页码 8057-8065出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3061470
关键词
Clustering algorithms; Dimensionality reduction; Principal component analysis; Informatics; Standards; Task analysis; Covariance matrices; Clustering framework; clustering validation; dimensionality reduction; electric demand profiles
类别
资金
- ADAPT Centre for Digital Content Technology under the SFI Research Centres Programme [13/RC/2106]
- European Regional Development Fund
This article introduces a novel scheme to objectively validate and compare the clustering results of residential electric demand profiles, considering all steps prior to the clustering algorithm. Compared to traditional clustering validity indices, the proposed scheme provides better, unbiased, and uniform recommendations.
Large-scale deployment of smart meters has made it possible to collect sufficient and high-resolution data of residential electric demand profiles. Clustering analysis of these profiles is important to further analyze and comment on electricity consumption patterns. Although many clustering techniques have been proposed in the literature over the years, it is often noticed that different techniques fit best for different datasets. To identify the most suitable technique, standard clustering validity indices are often used. These indices focus primarily on the intrinsic characteristics of the clustering results. Moreover, different indices often give conflicting recommendations, which can only be clarified with heuristics about the dataset and/or the expected cluster structures-information that is rarely available in practical situations. This article presents a novel scheme to validate and compare the clustering results objectively. Additionally, the proposed scheme considers all the steps prior to the clustering algorithm, including the preprocessing and dimensionality reduction steps, in order to provide recommendations over the complete framework. Accordingly, the proposed strategy is shown to provide better, unbiased, and uniform recommendations as compared to the standard clustering validity indices.
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