A fuzzy genetic algorithm classifier: The impact of time-series load data temporal dimension on classification performance

Ahmed Abdulaal, Shihab Asfour

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Utilization of machine learning algorithms in timeseries data analysis is crucial to effective decision making in today's dynamic and competitive environment. One data type of growing interest is the electricity consumer load profile (LP) data. Owing to advances in the smart grid, immense amount of LP data became available to policymakers as potential to improving the electricity sector. Due to the growing size and volatile nature of LP data, development and evaluation of clustering approaches has been of high demand in recent energy research, whereas the classification techniques receive less attention. This study is the first to address the effect of LP time-series data temporal dimension on the classification performance using the most popular classification algorithms in machine learning including decision trees, support vector machines (SVM), discriminant analysis, and ensemble methods. Results indicate a decline in the classification accuracy as the temporal dimension increases. Accordingly, this study proposes a fuzzy classification heuristicbased method inspired by the genetic algorithm (GA) which proves to maintain robustness against high temporal dimensions. The results are assessed using real data from industrial consumers with 420 daily LPs and 93 weekly LPs.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1050-1054
Number of pages5
ISBN (Electronic)9781509061662
DOIs
StatePublished - Jan 31 2017
Event15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 - Anaheim, United States
Duration: Dec 18 2016Dec 20 2016

Publication series

NameProceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016

Other

Other15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
CountryUnited States
CityAnaheim
Period12/18/1612/20/16

Keywords

  • Classification
  • Clustering
  • Data mining
  • Fuzzy logic
  • Genetic algorithm
  • Load profiling
  • Machine learning
  • Time-series

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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    Abdulaal, A., & Asfour, S. (2017). A fuzzy genetic algorithm classifier: The impact of time-series load data temporal dimension on classification performance. In Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 (pp. 1050-1054). [7838294] (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2016.15