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Statements

Subject Item
n2:RIV%2F00216305%3A26210%2F01%3APU24464%21RIV%2F2002%2FMSM%2F262102%2FN
rdf:type
n16:Vysledek skos:Concept
dcterms:description
This paper describes the implementation of fuzzy logic model and fuzzy set theory for short-term load forecasting. The proposed technique utilizes fuzzy rules to incorporate historical weather and load data. The strengths of this powerful technique lie in its ability to forecast accurately on weekdays, weekends and public holidays. The use of fuzzy logic effectively handles the load variations due to special events. The fuzzy logic has been extensively tested on actual data obtained from the Czech Electtric Power Company (ČEZ) for 24-hour ahead prediction. Test results indicate that the fuzzy rule base can produce results better in accuracy than artificial neural networks (ANNs) method. This paper describes the implementation of fuzzy logic model and fuzzy set theory for short-term load forecasting. The proposed technique utilizes fuzzy rules to incorporate historical weather and load data. The strengths of this powerful technique lie in its ability to forecast accurately on weekdays, weekends and public holidays. The use of fuzzy logic effectively handles the load variations due to special events. The fuzzy logic has been extensively tested on actual data obtained from the Czech Electtric Power Company (ČEZ) for 24-hour ahead prediction. Test results indicate that the fuzzy rule base can produce results better in accuracy than artificial neural networks (ANNs) method.
dcterms:title
Fuzzy Logic Based Short_Term Electric Load Forecasting Fuzzy Logic Based Short_Term Electric Load Forecasting
skos:prefLabel
Fuzzy Logic Based Short_Term Electric Load Forecasting Fuzzy Logic Based Short_Term Electric Load Forecasting
skos:notation
RIV/00216305:26210/01:PU24464!RIV/2002/MSM/262102/N
n3:strany
19-24
n3:aktivita
n18:Z
n3:aktivity
Z(MSM 262200010)
n3:dodaniDat
n6:2002
n3:domaciTvurceVysledku
n8:9906525 n8:8866937
n3:druhVysledku
n9:D
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
680890
n3:idVysledku
RIV/00216305:26210/01:PU24464
n3:jazykVysledku
n10:eng
n3:klicovaSlova
Short-term average and seasonal load forecasting, artificial neural networks (ANN).
n3:klicoveSlovo
n14:artificial%20neural%20networks%20%28ANN%29. n14:Short-term%20average%20and%20seasonal%20load%20forecasting
n3:kontrolniKodProRIV
[0F2FBCE36FE0]
n3:mistoKonaniAkce
Žilina
n3:mistoVydani
Žilina, SR
n3:nazevZdroje
ELEKTRO 2001, 4th International Scientific Conference
n3:obor
n5:BB
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:pocetUcastnikuAkce
0
n3:pocetZahranicnichUcastnikuAkce
0
n3:rokUplatneniVysledku
n6:2001
n3:tvurceVysledku
Ondrůšek, Čestmír Žák, Libor
n3:typAkce
n19:WRD
n3:zahajeniAkce
2001-05-22+02:00
n3:zamer
n20:MSM%20262200010
s:numberOfPages
6
n11:hasPublisher
n
n7:isbn
80-7100-836-2
n21:organizacniJednotka
26210