Application of Chaos and Neural Network in Power Load Forecasting

<table class="table-group" width="800px"><tr class="fig-image" id="a"><td><img alt="597634.fig.004a" src="https://static.hindawi.com/articles/ddns/volume-2011/597634/figures/597634.fig.004a.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(a) </b>Calculate delay time by mutual information</td></tr></table></td></tr><tr class="fig-image" id="b"><td><img alt="597634.fig.004b" src="https://static.hindawi.com/articles/ddns/volume-2011/597634/figures/597634.fig.004b.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(b) </b>Calculating embedding dimension by Cao method</td></tr></table></td></tr><tr class="fig-image" id="c"><td><img alt="597634.fig.004c" src="https://static.hindawi.com/articles/ddns/volume-2011/597634/figures/597634.fig.004c.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(c) </b>Calculating the largest Lyapunov exponent by wolf method</td></tr></table></td></tr></table>

<table>The graphics of the three parameters.</table>

Discrete Dynamics in Nature and Society

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Figure 4

Figure 4: Application of Chaos and Neural Network in Power Load Forecasting 