symbolic dynamic models for highly varying power system loads

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To check the accuracy of the above algorithm (Model 1), synthetic tests were
performed. Results of these synthetic tests showed that Model 1 did not work well for
some cases. All the synthetic tests and results have been reported in Chapter 3.
The main drawback of Model 1, described above, was that the information from
the historical data set was not being utilized fully. The data were used only to form a
dictionary. The model was predicting new value by checking the appearance of one
symbol (one data point) only, in the past history. In other words the model was never
utilizing a sequence of symbols to predict the next (new) value.
2.7 Development of a model based on multiple symbol checking (Model 2)
The foregoing is a description of a ‘first-cut’ model for signal forecasting. The
main drawbacks identified above can be alleviated through relatively minor modification
of the algorithm of Model 1. The aim of the Model 2 is to overcome the drawbacks of
Model 1 and enable the new algorithm to identify a long sequence of symbols to predict
the next word.
Model 2
Input: historical current data in the form of a row vector.
1. Form a dictionary from the data set.
2. Scan through the dictionary and take out all the words in which the order of
symbols is the same as in the data set (starting from the last data and looking
backward). In the worst case a word may start with the last data point. In other
words, it catches a sequence length of one symbol only.