Publication detail

FUZZY-NEURAL NETWORK BASED SHORT-TERM SEASONAL AND AVERAGE LOAD FORECASTING

KHAN, M., ŽÁK, L., ONDRŮŠEK, Č.

Original Title

FUZZY-NEURAL NETWORK BASED SHORT-TERM SEASONAL AND AVERAGE LOAD FORECASTING

Type

conference paper

Language

English

Original Abstract

A hybrid approach utilizing a fuzzy system and artificial neural network for short-term average and seasonal load prediction is proposed for the Czech Electric Power Utility (ČEZ), Czech Republic in this paper. The FNN is trained on real data and evaluated for forecasting seasonal and average load profiles based on forecast weather data. The fuzzy membership values of the load and weather variables are the inputs to the hybrid fuzzy-neural network (FNN) and the output is the predicted load. The performance of this network has been compared with ANN technique in order to demonstrate the superiority of this approach.

Key words in English

Hybrid fuzzy-neural network (FNN), Short-term average and seasonal load forecasting, artificial neural networks (ANN).

Authors

KHAN, M., ŽÁK, L., ONDRŮŠEK, Č.

RIV year

2001

Released

25. 9. 2001

Publisher

n

Location

Zlín

ISBN

80-7318-030-8

Book

4th International Conference on Prediction and Nonlinear Dynamics, Nostradamus Prediction Conference

Pages from

13

Pages to

13

Pages count

1

BibTex

@inproceedings{BUT3902,
  author="Muhammad R {Khan} and Libor {Žák} and Čestmír {Ondrůšek}",
  title="FUZZY-NEURAL NETWORK BASED SHORT-TERM SEASONAL AND AVERAGE LOAD FORECASTING",
  booktitle="4th International Conference on Prediction and Nonlinear Dynamics, Nostradamus Prediction Conference",
  year="2001",
  pages="1",
  publisher="n",
  address="Zlín",
  isbn="80-7318-030-8"
}