Title :
A joint of adaptive predictors for electric load forecasting
Author :
Nastac, Dumitru I. ; Ulmeanu, Anatoli Paul ; Tuduce, Rodica ; Cristea, P.D.
Author_Institution :
Univ. Politeh. of Bucharest, Bucharest, Romania
Abstract :
This work describes a new approach of the adaptive retraining model for data forecasting. This time, six predictors are simultaneously employed in order to produce a better forecasting for electric load. By doing so, the new forecasting system eliminates iterative simulation. The set of predictors is regularly trained in order to be adjusted to the latest modifications of the input data. The new approach could be useful as a forecasting tool for a large variety of signals.
Keywords :
load forecasting; neural nets; power engineering computing; prediction theory; adaptive predictors; adaptive retraining model; artificial neural network; data forecasting; electric load forecasting system; forecasting tool; Adaptation models; Artificial neural networks; Forecasting; Load modeling; Predictive models; Training; Vectors; artificial neural networks; electric load; forecasting; retraining;
Conference_Titel :
Systems, Signals and Image Processing (IWSSIP), 2013 20th International Conference on
Conference_Location :
Bucharest
Print_ISBN :
978-1-4799-0941-4
DOI :
10.1109/IWSSIP.2013.6623447
