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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보통신학회JICCE Journal of information and communication convergence engineering Journal of information and communication convergence engineering 제9권 제4호
- 발행연도
- 2011.8
- 수록면
- 369 - 374 (6page)
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이 논문의 연구 히스토리 (2)
2011
Chaotic Predictability for Time Series Forecasts of Maximum Electrical Power using the Lyapunov Exponent
Jae-Hyeon Pa
,
k
,
Young-Il Kim
외 1명
Journal of information and communication convergence engineering
2011.08
학술저널
Time Series Forecasts of Maximum Electrical Power using the Lyapunov Exponent with Chaotic Predictability
Jae-Hyeon Pa
,
k
,
Yeon-Gyu Choo
외 1명
INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING
2011.06
학술대회자료
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Generally the neural network and the Fuzzy compensative algorithms are applied to forecast the time series for power demand with the characteristics of a nonlinear dynamic system, but, relatively, they have a few prediction errors. They also make long term forecasts difficult because of sensitivity to the initial conditions.
In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.
In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. THE CHAOTIC SIGNAL ANALYSIS
Ⅲ. SHORT-TERM PREDICTION USING L.E.
Ⅳ. PREDICABILITY OF A CHAOTIC SIGNAL
Ⅴ. SIMULATIONS
Ⅵ. CONCLUSIONS
REFERENCES
참고문헌 (11)
참고문헌 신청
1. [학술지(정기간행물)] - C.E.Asburt / 1975 / Weather load model for electric demand and energy forecasting / IEEE Trans. on Power Appr. and Sys PAS-94 : 1111 ~ 1116
2. [보고서] - M. T. Hagan / 1987 / The time series approach to shortterm load forecasting
3. [학술지(정기간행물)] - Gwan-Sik Yoo / 2000 / Choas Fuzzy Controller Design for Supplied Electrical Power Analyzer / Journal of KICS 25 (12) : 247 ~ 253
4. [단행본] - Heinz-Otto Peitgen / 1992 / Chaos and Fractals: New Frontiers of Science / Springer-Verlag : 745 ~ 753
5. [단행본] - Steven H. Strogatz / 1996 / Nonlinear Dynamics and Chaos / Addison Wesley : 320 ~ 323
2. [보고서] - M. T. Hagan / 1987 / The time series approach to shortterm load forecasting
3. [학술지(정기간행물)] - Gwan-Sik Yoo / 2000 / Choas Fuzzy Controller Design for Supplied Electrical Power Analyzer / Journal of KICS 25 (12) : 247 ~ 253
4. [단행본] - Heinz-Otto Peitgen / 1992 / Chaos and Fractals: New Frontiers of Science / Springer-Verlag : 745 ~ 753
5. [단행본] - Steven H. Strogatz / 1996 / Nonlinear Dynamics and Chaos / Addison Wesley : 320 ~ 323
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