DocumentCode :
1701368
Title :
Short-term load forecasting with chaos time series analysis
Author :
Mori, Hiroyuki ; Urano, Shouichi
Author_Institution :
Dept. of Electr. Eng., Meiji Univ., Kawasaki, Japan
fYear :
1996
Firstpage :
133
Lastpage :
137
Abstract :
This paper presents a new approach to short-term load forecasting in power systems. The proposed method makes use of chaos time series analysis that is based on deterministic chaos to capture characteristics of complicated load behaviour. Deterministic chaos allows us to reconstruct a time series and determine the number of input variables. This paper describes chaos time series analysis of daily power system peak loads. The nonlinear mapping of deterministic chaos is identified by the multi-layer perceptron of an artificial neural network. The proposed approach is demonstrated in an example
Keywords :
chaos; load forecasting; multilayer perceptrons; power system analysis computing; time series; artificial neural network; chaos time series analysis; complicated load behaviour; daily power system peak loads; deterministic chaos; multi-layer perceptron; nonlinear mapping; power systems; short-term load forecasting; Artificial neural networks; Chaos; Input variables; Load forecasting; Nonlinear dynamical systems; Power system analysis computing; Power system dynamics; Power system modeling; Power system planning; Time series analysis;
fLanguage :
English
Publisher :
ieee
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
Type :
conf
DOI :
10.1109/ISAP.1996.501057
Filename :
501057
Link To Document :
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