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Memetic Type-2 Fuzzy System Learning for Load Forecasting

Lookup NU author(s): Professor Phil Taylor

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Abstract

This paper presents an automatic method to design interval type-2 fuzzy systems for load forecasting applications using a memetic algorithm. This hybridisation of a variable-length genetic algorithm and a gradient descent method allows for concurrent learning of the system's parameters and structure in a versatile fashion. Results are presented addressing chaotic system and market-level one-day-ahead load forecasting.


Publication metadata

Author(s): Leon IC, Taylor PC

Editor(s): José M. Alonso, Humberto Bustince, Marek Reformat

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology

Year of Conference: 2015

Pages: 909-916

Online publication date: 30/06/2015

Acceptance date: 01/01/1900

Publisher: Atlantis Press

URL: https://doi.org/10.2991/ifsa-eusflat-15.2015.128

DOI: 10.2991/ifsa-eusflat-15.2015.128

Library holdings: Search Newcastle University Library for this item

Series Title: Advances in Intelligent Systems Research

ISBN: 9789462520776


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