symbolic dynamic models for highly varying power system loads

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CSI
= ∑( f
χ i
* fγi) /( f
χi
+ fγi)
where ƒ χi is the fractional occurrence of i th common word in χ dictionary, ƒ γi is the
fractional occurrence of i th common word in γ dictionary, and CSI max =0.5 (If the two
signals are exactly identical). The lower the value of CSI, the higher the degree of
dissimilarity between the two signals. Thus the value of CSI directly shows how much
the two signals are in agreement.
2.6 Forecasting a signal
Attention turns to the forecasting or prediction of future values of a signal. For
this purpose, a dictionary can be formed by taking the instantaneous values in a signal at
different points. Once the dictionary is formed, a word is selected randomly from the
dictionary and without looking at its past history, the subsequent word is identified. This
process continues until a long sequence of words is generated. Thereafter it is possible to
predict the future data from the sequence.
Model 1 (Single symbol checking)
1 Form a dictionary [D,F] from data, where D is the dictionary and F is the
fractional occurrence of words.
2 Set count = 1.
3 Load [D, F] into working dictionary [W D , W F ].
4 Form CW F column, which is the cumulative frequency of occurrence. Now the
dictionary is in the form of [W D , W F , CW F ].