Detail publikace

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

KHAN, M., ŽÁK, L., ONDRŮŠEK, Č.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova v angličtině

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

Autoři

KHAN, M., ŽÁK, L., ONDRŮŠEK, Č.

Rok RIV

2001

Vydáno

25. 9. 2001

Nakladatel

n

Místo

Zlín

ISBN

80-7318-030-8

Kniha

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

Strany od

13

Strany do

13

Strany počet

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"
}