Title :
A daily peak load forecasting system using a chaotic time series
Author :
Choi, Jae-Gyun ; Park, Jong-Keun ; Kim, Kwang-Ho ; Kim, Jae-Cheol
Author_Institution :
Dept. of Electr. Eng., Seoul Nat. Univ., South Korea
Abstract :
In this paper, a method for the daily peak load forecasting which uses a chaotic time series and an artificial neural network in a power system is presented. We find the chaotic characteristics of the power load curve and then determine an optimal embedding dimension and delay time. For the load forecast of one day ahead daily peak load, we use the time series load data obtained in the previous year. By using the embedding dimension and delay time, we construct a strange attractor in the pseudo phase plane and the artificial neural network model trained with the attractor mentioned above. The one day ahead forecast errors are about 1.4% for absolute percentage average error
Keywords :
chaos; learning (artificial intelligence); load forecasting; neural nets; power system analysis computing; time series; artificial neural network; chaotic time series; daily peak load forecasting system; delay time; neural network model training; optimal embedding dimension; power load curve; power system; pseudo phase plane; strange attractor; time series load data; Artificial neural networks; Chaos; Delay effects; Economic forecasting; Load forecasting; Neural networks; Power system economics; Power system modeling; Power system reliability; Predictive models;
Conference_Titel :
Intelligent Systems Applications to Power Systems, 1996. Proceedings, ISAP '96., International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-3115-X
DOI :
10.1109/ISAP.1996.501083
