H. Y. SHUAI ET AL. 99

distribution map, and further affects the insulation capa-

bility of power system.

The WNN combination forecasting model combined

with multivariable linear regression technique, BP neural

network and least squares support vector machines avoids

the limitations of linear combination model and single

forecasting model, so it can boost the forecasting accu-

racy, especially deduce maximum relative error, in other

words, it can decline predicting risk. The simulation re-

sults in Table 1 show in the five models, the prediction

accuracy of WNN combination model is the highest one,

namely, the values of WNN are closest to the observed

ones. The ESDD values produced by WNN combination

forecasting model can better meet the request of drawing

pollution distribution map of power network. The model

proposed by the paper is an effective and doable way for

ESDD forecasting and provides a new thinking for the

computerization of drawing pollution distribution map.

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