Electric load pattern classification for demand-side management planning: A hybrid approach

Ahmed Abdulaal, Jaime Buitrago, Shihab S Asfour

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

4 Citations (Scopus)

Abstract

Smart Grids require a clear understanding of consumer demand patterns. Classification of consumers according to their demand pattern is required for the effective planning of tariffs, eligibility for demand-side management (DSM) programs, energy production and transmission, as well as for security purposes. We propose a framework for classification of consumer load patterns using a hybrid system with a parameter estimation model, a clustering model and an artificial neural network (ANN). The proposed model provides an effective unbiased classification method. The process starts with generating a training data set from existing consumers without a priori classification. The raw load data is processed through a parameter estimation model and a clustering algorithm to generate a training data set with distinct impartial classification clusters. The training data is fed to an ANN for learning. Once the load patterns are learned, the model can be used to further classify new consumers into a demand pattern. The ANN provides fast and accurate clustering performance without underlying assumptions about shape or class. An analysis of the optimal number of clusters is presented. Results indicate that clusters with distinguishable characteristics are achieved and we demonstrate how regulators can make use of this method in demand curtailment planning.

Original languageEnglish (US)
Title of host publicationProceedings of the IASTED International Symposium on Advances in Power and Energy Systems, APES 2015
PublisherActa Press
Pages133-140
Number of pages8
ISBN (Electronic)9780889869776
DOIs
StatePublished - 2015
Event2015 IASTED International Symposium on Advances in Power and Energy Systems, APES 2015 - Marina del Rey, United States
Duration: Oct 26 2015Oct 27 2015

Other

Other2015 IASTED International Symposium on Advances in Power and Energy Systems, APES 2015
CountryUnited States
CityMarina del Rey
Period10/26/1510/27/15

Fingerprint

Electric loads
Pattern recognition
Planning
Neural networks
Parameter estimation
Hybrid systems
Clustering algorithms
Demand side management

Keywords

  • ANN
  • Clustering
  • Demand
  • Framework
  • Parameter estimation
  • Pattern recognition

ASJC Scopus subject areas

  • Fuel Technology
  • Energy Engineering and Power Technology

Cite this

Abdulaal, A., Buitrago, J., & Asfour, S. S. (2015). Electric load pattern classification for demand-side management planning: A hybrid approach. In Proceedings of the IASTED International Symposium on Advances in Power and Energy Systems, APES 2015 (pp. 133-140). Acta Press. https://doi.org/10.2316/P.2015.831-012

Electric load pattern classification for demand-side management planning : A hybrid approach. / Abdulaal, Ahmed; Buitrago, Jaime; Asfour, Shihab S.

Proceedings of the IASTED International Symposium on Advances in Power and Energy Systems, APES 2015. Acta Press, 2015. p. 133-140.

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

Abdulaal, A, Buitrago, J & Asfour, SS 2015, Electric load pattern classification for demand-side management planning: A hybrid approach. in Proceedings of the IASTED International Symposium on Advances in Power and Energy Systems, APES 2015. Acta Press, pp. 133-140, 2015 IASTED International Symposium on Advances in Power and Energy Systems, APES 2015, Marina del Rey, United States, 10/26/15. https://doi.org/10.2316/P.2015.831-012
Abdulaal A, Buitrago J, Asfour SS. Electric load pattern classification for demand-side management planning: A hybrid approach. In Proceedings of the IASTED International Symposium on Advances in Power and Energy Systems, APES 2015. Acta Press. 2015. p. 133-140 https://doi.org/10.2316/P.2015.831-012
Abdulaal, Ahmed ; Buitrago, Jaime ; Asfour, Shihab S. / Electric load pattern classification for demand-side management planning : A hybrid approach. Proceedings of the IASTED International Symposium on Advances in Power and Energy Systems, APES 2015. Acta Press, 2015. pp. 133-140
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