Mining load profile patterns for Australian electricity consumers

Vanh Khuyen Nguyen*, Wei Emma Zhang, Quan Z. Sheng, Jason Merefield

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

The transformation from centralized and fossil-based electricity generation to distributed and renewable energy sources is an inevitable trend in the energy industry. One of the prime challenges in this transformation is the task of load/battery management, especially at the residential level. In solving this task, it is critical that a good strategy for analyzing and grouping residential electricity consumption patterns is in place so that further optimization strategies can be devised for different groups of consumers. Based on the real data from an Australian electricity retailer, we propose a clustering process to determine typical customer load profiles. It can be served as a standard framework for dealing with real-world unsupervised problems. In addition, some statistical techniques, including cumulative sum and calculation of the most frequent value in dataset by using mode, are integrated into our data preprocessing and analysis. CUSUM chart is a graphical method to clearly visualize as well as detect changes in time-series data and then using mode values is to replace missing values in the dataset. Furthermore, in our framework, more practical Elbow method is conducted to determine appropriated number of clusters for k-centers algorithm. We then apply multiple state-of-the-art clustering methods for time series data and benchmark their respective performance. We found that k-centers clustering techniques produces better results compared to exemplar-based methods. Additionally, choosing appropriated number of clusters for k-means can improve performance of clustering model. For example, k-means++ with k=2 has significantly outperformed other methods in our experiment.

Original languageEnglish
Title of host publication13th International Conference on Advanced Data Mining and Applications (ADMA 2017)
Subtitle of host publicationproceedings
EditorsGao Cong, Wen-Chih Peng, Wei Emma Zhang, Chengliang Li, Aixin Sun
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages781-793
Number of pages13
Volume10604
ISBN (Electronic)9783319691794
ISBN (Print)9783319691787
DOIs
Publication statusPublished - 2017
Event13th International Conference on Advanced Data Mining and Applications, ADMA 2017 - Singapore, Singapore
Duration: 5 Nov 20176 Nov 2017

Publication series

NameLecture Notes in Computer Science
Volume10604
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Advanced Data Mining and Applications, ADMA 2017
CountrySingapore
CitySingapore
Period5/11/176/11/17

Keywords

  • Data mining
  • Residential electricity consumption
  • Time series clustering

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