FUZZY-NEURAL NETWORK BASED SHORT-TERM SEASONAL AND AVERAGE LOAD FORECASTING
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F01%3APU24471" target="_blank" >RIV/00216305:26210/01:PU24471 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
FUZZY-NEURAL NETWORK BASED SHORT-TERM SEASONAL AND AVERAGE LOAD FORECASTING
Original language description
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 performaance of this network has been compared with ANN technique in order to demonstrate the superiority of this approach.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2001
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
4th International Conference on Prediction and Nonlinear Dynamics, Nostradamus Prediction Conference
ISBN
80-7318-030-8
ISSN
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e-ISSN
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Number of pages
1
Pages from-to
13-13
Publisher name
n
Place of publication
Zlín
Event location
Zlín
Event date
Sep 25, 2001
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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