Next Article in Journal
CFD Simulations of the Propagation of Free-Surface Waves Past Two Side-By-Side Fixed Squares with a Narrow Gap
Previous Article in Journal
The Impacts of Water Pricing and Non-Pricing Policies on Sustainable Water Resources Management: A Case of Ghorveh Plain at Kurdistan Province, Iran
Article Menu

Export Article

Open AccessArticle

OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering

State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Author to whom correspondence should be addressed.
Energies 2019, 12(14), 2668;
Received: 5 June 2019 / Revised: 7 July 2019 / Accepted: 9 July 2019 / Published: 11 July 2019
(This article belongs to the Section Smart Grids and Microgrids)
PDF [9261 KB, uploaded 11 July 2019]


Customers’ electricity consumption behavior can be studied from daily load data. Studying the daily load data for user behavior pattern analysis is an emerging research area in smart grid. Traditionally, the daily load data can be clustered into different clusters, to reveal the different categories of consumption. However, as user’s electricity consumption behavior changes over time, classical clustering algorithms are not suitable for tracing the changes, as they rebuild the clusters when clustering at any timestamp but never consider the relationship with the clusters in the previous state. To understand the changes of consumption behavior, we proposed an optimized evolutionary clustering (OPEC) algorithm, which optimized the existing evolutionary clustering algorithm by joining the Proper Restart (PR) Framework. OPEC relied on the basic fact that user’s energy consumption behavior would not abruptly change significantly, so the clusters would change progressively and remain similar in adjacent periods, except for an emergency. The newly added PR framework can deal with a situation where data changes dramatically in a short period of time, and where the former frameworks of evolutionary clustering do not work well. We evaluated the OPEC based on daily load data from Shanghai, China and the power load diagram data from UCI machine learning repository. We also carefully discussed the adjustment of the parameter in the optimized algorithm and gave an optimal value for reference. OPEC can be implemented to adapt to this situation and improve clustering quality. By understanding the changes of the users’ power consumption modes, we can detect abnormal power consumption behaviors, and also analyze the changing trend to improve the operations of the power system. This is significant for the regulation of peak load in the power grid. In addition, it can bring certain economic benefits to the operation of the power grid. View Full-Text
Keywords: smart grid; behavior pattern; optimized evolutionary clustering smart grid; behavior pattern; optimized evolutionary clustering

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).

Share & Cite This Article

MDPI and ACS Style

Lin, R.; Ye, Z.; Zhao, Y. OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering. Energies 2019, 12, 2668.

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



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