DocumentCode :
2095442
Title :
Input Vector Comparison for Hourly Load Forecast of Small Load Area Using Artificial Neural Network
Author :
Tasre, Mohan B. ; Ghate, Vilas N. ; Bedekar, Prashant P.
Author_Institution :
Electr. Eng. Dept., Gov. Coll. of Eng., Amravati, Amravati, India
fYear :
2012
fDate :
11-13 May 2012
Firstpage :
254
Lastpage :
258
Abstract :
This paper presents an hourly load forecast of small load area using Artificial Neural Network (ANN). For this case-study duration of February-2010 to Januray-2011 is considered. In this study ANN is trained and tested for by providing two different input vectors. In this paper the input vector design and the data is mainly focused. Also, suitable ANN topology is also discussed. Further the training and testing process for ANNs of these months are explained. Back-propagation algorithm is employed in this process. Finally by comparing network performances for these two input vectors for each of the considered month, optimum vector is selected.
Keywords :
load forecasting; neural nets; power engineering computing; ANN topology; artificial neural network; back-propagation; hourly load forecast; input vector comparison; input vector design; small load area; Artificial neural networks; Forecasting; Load forecasting; Neurons; Testing; Training; Vectors; Artificial Neural Network; Back Propagation algorithm; Input Vector; Momentum learning rule; Short-term Load Forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communication Systems and Network Technologies (CSNT), 2012 International Conference on
Conference_Location :
Rajkot
Print_ISBN :
978-1-4673-1538-8
Type :
conf
DOI :
10.1109/CSNT.2012.63
Filename :
6200643
Link To Document :
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