Title :
Performance of STLF model from the PSO, time series and regression perspectives
Author :
Hassnain, S. ; Asar, A. ; Mahmood, F.
Author_Institution :
Dept. of Electr. & Electron. Eng., NWFP UET, Peshawar, Pakistan
Abstract :
This paper presents a comparison of ANN based short term load forecasting technique using (PSO) particle swarm optimization with time series and regression techniques. The results indicate a remarkable difference among the performance of the above mentioned methods.
Keywords :
load forecasting; neural nets; particle swarm optimisation; power engineering computing; regression analysis; time series; artificial neural network; particle swarm optimization; regression technique; short term load forecasting model; time series; Artificial neural networks; Data mining; Economic forecasting; Load forecasting; Neural networks; Particle swarm optimization; Power industry; Power system modeling; Power system security; Predictive models; Short term load forecasting; artificial neural network; particle swarm optimization; regression; time series;
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2009.5179033
