IDEAS home Printed from https://ideas.repec.org/a/ids/ijisen/v17y2014i3p285-301.html
   My bibliography  Save this article

A greedy randomised adaptive search procedure - genetic algorithm for electricity consumption estimation and optimisation in agriculture sector with random variation

Author

Listed:
  • Ali Azadeh
  • Seyed Mohammad Asadzadeh
  • Rana Jalali
  • Samira Hemmati

Abstract

This study presents a flexible algorithm for electricity energy consumption estimation and optimisation in agriculture sector based on greedy randomised adaptive search procedure (GRASP) and genetic algorithm (GA) with variable parameters using stochastic procedures. The standard economic indicators used in this paper are price, value added, number of customers and electricity consumption in the previous period. The proposed algorithm may be used to estimate energy demand in the future by optimising parameter values. The proposed algorithm uses analysis of variance (ANOVA) to select from GA, GRASP or conventional regression for future demand estimation. Furthermore, if the null hypothesis in ANOVA F-test is rejected, the least significant difference (LSD) method is used to identify which model is closer to actual data at α level of significance. To show the applicability and superiority of the proposed algorithm the data for electricity consumption in Iranian agriculture sector from 1979 to 1999 is used and applied to the proposed algorithm. This is the first study that introduces and uses an integrated GRASP-GA-regression for electricity consumption estimation and optimisation in agriculture sector.

Suggested Citation

  • Ali Azadeh & Seyed Mohammad Asadzadeh & Rana Jalali & Samira Hemmati, 2014. "A greedy randomised adaptive search procedure - genetic algorithm for electricity consumption estimation and optimisation in agriculture sector with random variation," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 17(3), pages 285-301.
    • Handle: RePEc:ids:ijisen:v:17:y:2014:i:3:p:285-301
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=62539
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Khalid Abdulaziz Alnowibet & Shalini Shekhawat & Akash Saxena & Karam M. Sallam & Ali Wagdy Mohamed, 2022. "Development and Applications of Augmented Whale Optimization Algorithm," Mathematics, MDPI, vol. 10(12), pages 1-33, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijisen:v:17:y:2014:i:3:p:285-301. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=188 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.