Next Article in Journal
Influencing Factors and Scenario Forecasts of Carbon Emissions of the Chinese Power Industry: Based on a Generalized Divisia Index Model and Monte Carlo Simulation
Previous Article in Journal
A Novel Pilot Protection Principle Based on Modulus Traveling-Wave Currents for Voltage-Sourced Converter Based High Voltage Direct Current (VSC-HVDC) Transmission Lines
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessFeature PaperArticle
Energies 2018, 11(9), 2397; https://doi.org/10.3390/en11092397

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

1
Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
2
Department of Software, Gachon University, Seongnam 13120, Korea
*
Author to whom correspondence should be addressed.
Received: 15 July 2018 / Revised: 29 August 2018 / Accepted: 6 September 2018 / Published: 11 September 2018
(This article belongs to the Section Electrical Power and Energy System)
Full-Text   |   PDF [20180 KB, uploaded 11 September 2018]   |  

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. View Full-Text
Keywords: SPCC distance; mean-shift clustering; load data clustering; profile extraction; daily power profile; load profile; correlation coefficient; distance measurement SPCC distance; mean-shift clustering; load data clustering; profile extraction; daily power profile; load profile; correlation coefficient; distance measurement
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Kim, N.; Park, S.; Lee, J.; Choi, J.K. Load Profile Extraction by Mean-Shift Clustering with Sample Pearson Correlation Coefficient Distance. Energies 2018, 11, 2397.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top