Memetic Type-2 Fuzzy System Learning for Load Forecasting

DOI

10.2991/ifsa-eusflat-15.2015.128How to use a DOI?

Keywords

Interval type-2 fuzzy systems, memetic learning, load forecasting.

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Iván Castro León
AU  - Philip C. Taylor
PY  - 2015/06
DA  - 2015/06
TI  - Memetic Type-2 Fuzzy System Learning for Load Forecasting
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 909
EP  - 916
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.128
DO  - 10.2991/ifsa-eusflat-15.2015.128
ID  - CastroLeón2015/06
ER  -