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  4. Load forecasting with neural nets: prediction of the hourly load with a time horizon of up to seven days-a common project of EOS, EPFL and ABB
 
research article

Load forecasting with neural nets: prediction of the hourly load with a time horizon of up to seven days-a common project of EOS, EPFL and ABB

Buchenel, B.
•
Germond, A.  
•
Piras, A.
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1996
Bulletin des schweizerischen elektrotechnischen vereins

This article discusses the forecasting of load for a period varying from an hour to a week. As usual for the modelling and prediction of nonlinear processes, the use of artificial neural nets appears very promising. In the project described, measured loads for the past seven days, those for the two preceding days, maxima and minima of average temperatures of the previous day, and predicted day temperatures were used as input values, with the day of the week and day of the year as indicators. The load and temperature are modelled separately. An automated method is used for online testing

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Type
research article
DOI
10.5169/seals-902377
Author(s)
Buchenel, B.
Germond, A.  
Piras, A.
Jaccard, Y.
Imhof, K.
Bernascon, J.
Dondi, P.
Date Issued

1996

Published in
Bulletin des schweizerischen elektrotechnischen vereins
Volume

87

Issue

21

Start page

11

End page

16

Subjects

load (electric)

•

load forecasting

•

neural nets

•

power system analysis computing

•

load forecasting

•

neural nets

•

hourly load prediction

•

EOS

•

EPFL

•

ABB

•

modelling

•

nonlinear processes

•

measured loads

•

seven days ahead prediction

•

average temperatures

•

predicted day temperatures

•

temperature modelling

•

load modelling

•

online testing

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LRE  
SCI-STI-FR  
Available on Infoscience
April 4, 2007
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/4411
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