DocumentCode :
3302780
Title :
Short-term multinodal load forecasting in distribution systems using general regression neural networks
Author :
Nose-Filho, Kenji ; Lotufo, A.D.P. ; Minussi, Carlos Roberto
Author_Institution :
Dept. of Electr. Eng., Ilha Solteira (UNESP), Ilha Solteira, Brazil
fYear :
2011
fDate :
19-23 June 2011
Firstpage :
1
Lastpage :
7
Abstract :
Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn´t necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature.
Keywords :
distribution networks; load forecasting; neural nets; power engineering computing; regression analysis; New Zealand distribution subsystem; bus load forecasting; distribution system; electrical network system; load participation factor; multinodal load forecasting; regression neural network; Artificial neural networks; Biological neural networks; Load forecasting; Load modeling; Neurons; Substations; Training; Bus Load Forecasting; General Regression Neural Network; Short-Term Load Forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
PowerTech, 2011 IEEE Trondheim
Conference_Location :
Trondheim
Print_ISBN :
978-1-4244-8419-5
Electronic_ISBN :
978-1-4244-8417-1
Type :
conf
DOI :
10.1109/PTC.2011.6019432
Filename :
6019432
Link To Document :
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