24-04-2012, 12:07 PM
Application of Chaos and Neural Network in Power Load Forecasting
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Introduction
Chaos theory is the important component of the nonlinear science 1. It is the random
phenomena which appeared in the deterministic nonlinear dynamic system. Chaos is not a
disorder but has a delicate inner structure. It reveals the order and regularity hidden behind
the disordered and complex phenomena. Since the 90s, chaos theory has been well developed.
Many subjects are infiltrated and promoted 2 under this tendency. So the research on chaos
gets an access to a breakthrough. At the meanwhile the application about chaos theory gets a
widely growing.
Chaos Theory
Power system loads are a set of time series. Chaos theory can analyze chaotic characteristics
of time series and reveal the sequence itself of the objective regularities to avoid the predicted
human subjectivity and improve the accuracy and credibility of load forecasting.
At present, phase-space delay coordinate reconstitution method is employed to
analyze chaotic characteristics of time series. Generally, the dimension is very great even
infinite. In fact, phase-space delay coordinate reconstitution method can expand the given
time series to three-dimensional and even higher-dimensional space, and the information
which exposed sufficiently from time series can be classified and extracted.
Improved GA Optimizes ANN
There are some deficiencies of BP neural network, such as a lower pace, being easy to local
minimum, and the uncertainty structure. But GA can overcome these and improve network
performance and convergence rate and optimize chaos neural network further.
The methods and steps to achieve genetic algorithm and optimize chaos neural
network are the following.
Conclusion
This paper employs the chaos theory into eclectic system load forecasting. From Lyapunov
exponents to phase-space reconstitution, it tells that power load time series are nonlinear and
chaotic and have all the characters of chaos. The chaotic phase diagram also shows there is
a chaos attractor. So chaos theory introduced into power load forecasting can describe the
nonlinear dynamic behavior of the system and get the accuracy and the precision improved
greatly.