Detail publikace

FORECASTING WEEKLY ELECTRIC LOAD USING A HYBRID FUZZY-NEURAL NETWORK APPROACH

KHAN, M. ŽÁK, L. ONDRŮŠEK, Č.

Anglický název

FORECASTING WEEKLY ELECTRIC LOAD USING A HYBRID FUZZY-NEURAL NETWORK APPROACH

Typ

Článek recenzovaný mimo WoS a Scopus

Jazyk

en

Originální abstrakt

A hybrid approach utilizing a fuzzy system and artificial neural network (ANN) for short-term load forecasting of the Czech Electric Power Company (ČEZ) is proposed in this paper. Expert knowledge represented by fuzzy rules is used for preprocessing input data fed to a neural network. For training the neural network for one-week ahead load forecasting, fuzzy ‘If-Then’ rules are used, in addition to historical load and temperature data that are usually employed in conventional supervised training algorithms. The fuzzy-neural network is trained on real data for the years 1994 through 1998 and evaluated on the data for the year 1999 for forecasting next-week load profiles. A very good prediction performance is attained as shown in the simulation results, which verify the effectiveness and superiority of the modeling technique.

Vydáno

2001-11-26

ISSN

1210-2717

Časopis

Inženýrská mechanika – Engineering Mechanics

Ročník

2001

Číslo

8

Strany od–do

44–

Počet stran

55

BIBTEX


@article{BUT40535,
  author="Muhammad R {Khan} and Libor {Žák} and Čestmír {Ondrůšek}",
  title="FORECASTING WEEKLY ELECTRIC LOAD USING A HYBRID FUZZY-NEURAL NETWORK APPROACH",
  journal="Inženýrská mechanika - Engineering Mechanics",
  year="2001",
  volume="2001",
  number="8",
  pages="55",
  issn="1210-2717"
}