skip to main content
10.1145/2832987.2833021acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicemisConference Proceedingsconference-collections
research-article

Estimating electrical energy consumption using Linear Genetic Programming

Authors Info & Claims
Published:24 September 2015Publication History

Editorial Notes

NOTICE OF CONCERN: ACM has received evidence that casts doubt on the integrity of the peer review process for the ICEMIS 2015 Conference. As a result, ACM is issuing a Notice of Concern for all papers published and strongly suggests that the papers from this Conference not be cited in the literature until ACM's investigation has concluded and final decisions have been made regarding the integrity of the peer review process for this Conference.

ABSTRACT

The electrical energy distribution subsystem is a component of the power delivery infrastructure that carries electricity from the high voltage transmission circuits to the customers. In order to prevent damage of the electrical energy distribution infrastructure, companies typically use transformers with digital meters that allow monitoring in real time, certain parameters like the amount of transformed energy. In several underdeveloped countries as Paraguay, the meters are installed only on a limited number of key transformers. Therefore, it is necessary to estimate the power consumption for the unmetered transformers using existing measurements. For this aim, this paper proposes the application of Linear Genetic Programming (LGP) to find good estimates of the power consumption of unmetered transformers. The proposal is compared with an analytical consumption estimation model proposed in a previous related work, being 13% better on average and 41% better in the best case. Dimensionality reduction proves to be useful to speed up calculation without losing much precision in the LGP estimations.

References

  1. Andre, D., Koza, J.R.: Advances in genetic programming. chap. Parallel Genetic Programming: A Scalable Implementation Using the Transputer Network Architecture, pp. 317--337. MIT Press, Cambridge, MA, USA (1996) Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Brameier, M., Banzhaf, W.: A comparison of linear genetic programming and neural networks in medical data mining. Evolutionary Computation, IEEE Transactions on 5(1), 17--26 (2001) Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Brameier, M., Banzhaf, W.: Linear genetic programming. Springer Science & Business Media (2007) Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Brameier, M., Hoffmann, F., Nordin, P., Banzhaf, W., Francone, F.D., Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V.: Parallel machine code genetic programming. In: GECCO. p. 1228 (1999)Google ScholarGoogle Scholar
  5. Dagum, L., Menon, R.: Openmp: an industry standard api for shared-memory programming. Computational Science & Engineering, IEEE 5(1), 46--55 (1998) Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Gardel, P., Baran, B., Estigarribia, H., Fernandez, U., Duarte, S.: Multiobjective reactive power compensation with an ant colony optimization algorithm. 8th IEE International Conference on AC and DC Power Transmission pp. 276--280 (2006)Google ScholarGoogle ScholarCross RefCross Ref
  7. Horn, R.A., Johnson, C.R.: Matrix analysis. Cambridge university press (2012) Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Jackson, D.: Phenotypic diversity in initial genetic programming populations. In: Genetic Programming, pp. 98--109. Springer (2010) Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Jolliffe, I.: Principal component analysis. Wiley Online Library (2002)Google ScholarGoogle Scholar
  10. Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection, vol. 1. MIT press (1992) Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Martınez Canillas, J., Sánchez, R., Barán, B.: Estimation models generation using linear genetic programming. CLEI ELECTRONIC JOURNAL 12(3) (2009)Google ScholarGoogle Scholar
  12. Oltean, M., Groşan, C., Oltean, M.: Encoding multiple solutions in a linear genetic programming chromosome. In: Computational Science-ICCS 2004, pp. 1281--1288. Springer (2004)Google ScholarGoogle ScholarCross RefCross Ref
  13. Pham, T.A., Nguyen, Q.U., Nguyen, X.H., O'Neill, M.: Examining the diversity property of semantic similarity based crossover. Springer (2013)Google ScholarGoogle Scholar
  14. Planás, G., Benítez, E.: Ubicación Óptima de Medidores Digitales en un Sistema de Distribución de Potencia Eléctrica. Tesis de la Universidad Nacional de Asunción, Facultad Politécnica (2014)Google ScholarGoogle Scholar
  15. Short, T.A.: Electric power distribution handbook. CRC press (2014)Google ScholarGoogle Scholar
  16. Suttasupa, Y., Rungraungsilp, S., Pinyopan, S., Wungchusunti, P., Chongstitvatana, P.: A comparative study of linear encoding in genetic programming. In: ICT and Knowledge Engineering (ICT & Knowledge Engineering), 2011 9th International Conference on. pp. 13--17. IEEE (2012)Google ScholarGoogle ScholarCross RefCross Ref
  17. Zito, T., Wilbert, N., Wiskott, L., Berkes, P.: Modular toolkit for data processing (mdp): a python data processing framework. Frontiers in neuroinformatics 2 (2008)Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    ICEMIS '15: Proceedings of the The International Conference on Engineering & MIS 2015
    September 2015
    429 pages
    ISBN:9781450334181
    DOI:10.1145/2832987

    Copyright © 2015 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 24 September 2015

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate215of605submissions,36%

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader