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Neuro-Fuzzy Approaches to Short-Term Electrical Load Forecasting
Como, Italy July 24-July 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.859401IEEE-INNS-ENNS International Joint Co ...
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Witold Bartkiewicz, "Neuro-Fuzzy Approaches to Short-Term Electrical Load Forecasting," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 6, pp. 6229, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6, 2000.
 
 
BibTex
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@article{ 10.1109/IJCNN.2000.859401,
author = {Witold Bartkiewicz},
title = {Neuro-Fuzzy Approaches to Short-Term Electrical Load Forecasting},
journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on},
volume = {6},
year = {2000},
issn = {1098-7576},
pages = {6229},
doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.859401},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
TI - Neuro-Fuzzy Approaches to Short-Term Electrical Load Forecasting
SN - 1098-7576
SP
EP
A1 - Witold Bartkiewicz,
PY - 2000
VL - 6
JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
ER -
 
Witold Bartkiewicz, University of L?
In this paper, we investigate the application of the Takagi-Sugeno fuzzy models to short-term electrical load forecasting problem. Several learning algorithms for these type fuzzy systems are discussed. For identification of the models with linear antecedents has been applied the combination of the cluster estimation and ordinary least squares method. For nonlinear antecedents modeling purposes has been used the fuzzy switched ensemble of feedforward neural networks. The performance of the models is compared for two-day ahead peak load prediction in the distribution network.
Citation:
Witold Bartkiewicz, "Neuro-Fuzzy Approaches to Short-Term Electrical Load Forecasting," ijcnn, vol. 6, pp.6229, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6, 2000
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