A Frequency Domain Approach to Characterize and Analyze Load Profiles

Zhong, Shiyin; Tam, Kwa-Sur

doi:10.1109/tpwrs.2011.2170592

Cited by 29 publications

(11 citation statements)

References 16 publications

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“…The work presented in [10] is the process from Step I to Step II in Fig. 2, while the work presented in this paper is the process from Steps II to Step V. The results presented in [10] do not demonstrate any hierarchical structure but the work presented in this paper demon- strates hierarchy in both the method (Section II) and the results (Section III).…”

Section: Introductionmentioning

confidence: 65%

“…After the DFT transformation of the time-domain load profiles (stage I), the major frequency domain components are identified to form the frequency domain load profile descriptor ( at stage II) [10]: (1) and…”

Section: A Classification Treementioning

confidence: 99%

“…While the work presented in this paper is built upon the work reported in [10], the two papers are significantly different. The work in [10] show the benefits of using frequency domain descriptors to characterize and analyze load profile.…”

Section: Introductionmentioning

confidence: 99%

“…The work in [10] show the benefits of using frequency domain descriptors to characterize and analyze load profile. However, different customer classes can be characterized by frequency domain descriptors that have the same members but some or all of the members exist in different proportions.…”

Section: Introductionmentioning

confidence: 99%

“…While the work presented in [10] is based on daily load data, the work presented in this paper is based on annual load profile which is more difficult to characterize. The work presented in [10] is the process from Step I to Step II in Fig.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Hierarchical Classification of Load Profiles Based on Their Characteristic Attributes in Frequency Domain

Zhong

Tam

2015

IEEE Trans. Power Syst.

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Load profile classification is very important in load forecast, planning and management. Although customers are generally grouped by utilities into residential, commercial classes and respective subclasses, there is a lack of systematic framework that can be used to characterize different classes with signatures that are both human-readable and machine-readable. The work presented in this paper attempts to formulate the theoretical framework for customer classification using the annual load profiles. This paper demonstrates how to extract characteristic attributes in frequency domain (CAFD) and use these CAFDs to formulate a hierarchy of load profiles that can be used as the systematic framework for customer load classification. As signatures for customer classes and subclasses, the CAFDs are obtained by using a data mining method called CART (classification and regression tree). The paper presents a load profile classification test to establish the efficacy of the proposed approach which is significant improvement over current practices that provide mostly qualitative labeling.

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Section: Introductionmentioning

confidence: 65%

Section: A Classification Treementioning

confidence: 99%