A Fused Load Curve Clustering Algorithm Based on Wavelet Transform

The electricity load data recorded by smart meters contain plenty of knowledge that contributes to obtaining load patterns and consumer categories. Generally, the daily load curves are clustered first in order to obtain load patterns of each consumer. However, due to the volume and high dimensions o...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2018-05, Vol.14 (5), p.1856-1865
Main Authors: Jiang, Zigui, Lin, Rongheng, Yang, Fangchun, Wu, Budan
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Clustering
Clustering algorithms
Curve clustering
daily load curve
Datasets
dimensionality reduction
Discrete Wavelet Transform
Discrete wavelet transforms
Electric fuses
Electricity meters
load pattern
Measuring instruments
Signal processing algorithms
smart grid
wavelet transform
Wavelet transforms
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Summary:The electricity load data recorded by smart meters contain plenty of knowledge that contributes to obtaining load patterns and consumer categories. Generally, the daily load curves are clustered first in order to obtain load patterns of each consumer. However, due to the volume and high dimensions of load curves, existing clustering algorithms are not appropriate in this situation. Thus, a fused load curve clustering algorithm based on wavelet transform (FCCWT) is proposed to solve this problem. The algorithm includes two main phases. First, FCCWT applies multilevel discrete wavelet transform (DWT) to convert the daily load curves for dimensionality reduction. Second, it detects clusters at two outputs of the first phase, and then fuses two groups of clusters with a sub-algorithm named cluster fusion to achieve the optimized clusters. FCCWT is implemented on datasets of both China and United States. Their clustering performances are evaluated by diverse validity indices comparing with four typical clustering methods. The experimental results show that FCCWT outperforms other comparison methods. Additionally, case analysis of two datasets are also provided to discuss the significance of load patterns.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2017.2769450