Load Profile Extraction by Mean-Shift Clustering with Sample Pearson Correlation Coefficient Distance

Nakyoung Kim, Sangdon Park, Joohyung Lee and Jun Kyun Choi
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Nakyoung Kim: Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
Sangdon Park: Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
Joohyung Lee: Department of Software, Gachon University, Seongnam 13120, Korea
Jun Kyun Choi: Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea

Energies, 2018, vol. 11, issue 9, 1-20

Abstract: In this paper, a clustering method with proposed distance measurement to extract base load profiles from arbitrary data sets is studied. Recently, smart energy load metering devices are broadly deployed, and an immense volume of data is now collected. However, as this large amount of data has been explosively generated over such a short period of time, the collected data is hardly organized to be employed for study, applications, services, and systems. This paper provides a foundation method to extract base load profiles that can be utilized by power engineers, energy system operators, and researchers for deeper analysis and more advanced technologies. The base load profiles allow them to understand the patterns residing in the load data to discover the greater value. Up to this day, experts with domain knowledge often have done the base load profile realization manually. However, the volume of the data is growing too fast to handle it with the conventional approach. Accordingly, an automated yet precise method to recognize and extract the base power load profiles is studied in this paper. For base load profile extraction, this paper proposes Sample Pearson Correlation Coefficient (SPCC) distance measurement and applies it to Mean-Shift algorithm based nonparametric mode-seeking clustering. The superiority of SPCC distance over traditional Euclidean distance is validated by mathematical and numerical analysis.

Keywords: SPCC distance; mean-shift clustering; load data clustering; profile extraction; daily power profile; load profile; correlation coefficient; distance measurement (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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