DocumentCode :
2020312
Title :
ANN techniques for power consumption forecasting
Author :
Barbulescu, C. ; Kilyeni, St ; Simo, A. ; Pop, Oana ; Oros, C. ; Schiopu, Raluca ; Deacu, A.
Author_Institution :
Power Syst. Dept., Politeh. Univ. of Timisoara, Timisoara, Romania
fYear :
2013
fDate :
16-20 June 2013
Firstpage :
1
Lastpage :
6
Abstract :
The authors are focusing on developing a software tool designed for power consumption forecasting based on artificial neural networks (ANN). The hourly and peak consumed power and daily load curves are forecasted. The backpropagation algorithm is presented within the paper. Also, some practical considerations are highlighted, necessary to be known to develop a real application. The base propagation algorithm is completed with conjugated gradient and parabolic interpolation. A software tool has been developed in Matlab environment. The results are compared with the ones provided by classic forecasting methods. The analyses and the corresponding conclusions highlight the fact that the ANN based forecasting algorithm has a better behaviour.
Keywords :
backpropagation; conjugate gradient methods; interpolation; load forecasting; neural nets; power consumption; power engineering computing; software tools; ANN technique; Matlab environment; artificial neural network; backpropagation algorithm; base propagation algorithm; conjugated gradient method; daily load curve; parabolic interpolation; power consumption forecasting; software tool design; Artificial neural networks; Correlation; Forecasting; Load forecasting; Neurons; Software tools; Training; ANN; backpropagation; consumed power; daily load curve; forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
PowerTech (POWERTECH), 2013 IEEE Grenoble
Conference_Location :
Grenoble
Type :
conf
DOI :
10.1109/PTC.2013.6652260
Filename :
6652260
Link To Document :
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