Title :
A New Short Term Load Forecasting Using Multilayer Perceptron
Author :
Kazeminejad, M. ; Dehghan, M. ; Motamadinejad, M.B. ; Rastegar, H.
Author_Institution :
Azad Islamic Univ., Aliabad Katul
Abstract :
This paper presents a neuro-based short term load forecasting (STLF) method for Iran National Power System (INPS) and its regions. The architecture of the proposed network is a three-layer feed forward neural network whose parameters are tuned by Levenberg-Marquardt BP (LMBP) augmented by an early stopping (ES) method tried out for increasing the speed of convergence. Instead of seasonal training, an input as a month indicator is added to the input vectors. The short term load forecasting simulator developed so far presents satisfactory and better results for one hour up to a week prediction of INPS loads and region of INPS, Bakhtar Region Electric Co (BREC). This paper is compared with another one.
Keywords :
backpropagation; load forecasting; multilayer perceptrons; power engineering computing; Iran National Power System; Levenberg-Marquardt BP; early stopping; feedforward neural network; multilayer perceptron; neuro-based short term load forecasting; Feedforward neural networks; Feeds; Indium phosphide; Load forecasting; Multi-layer neural network; Multilayer perceptrons; Neural networks; Power system modeling; Predictive models; Temperature;
Conference_Titel :
Information and Automation, 2006. ICIA 2006. International Conference on
Conference_Location :
Shandong
Print_ISBN :
1-4244-0555-6
Electronic_ISBN :
1-4244-0555-6
DOI :
10.1109/ICINFA.2006.374131
