Abstract: In order to improve the performance of power knowledge text mining,FP-Growth algorithm is used to mine the strong correlation factors that affect the power demand,and GRNN algorithm is used to realize the power demand forecasting.Firstly,the index of the power text to be mined is extracted and encoded to generate the initial FP-Tree.Then,FP-Growth algorithm traverses all FP-Tree generated frequent sets,filters out the items less than the minimum support,leaves the frequent items with higher frequency.And then according to the updated FP-Tree statistical correlation items,it selects variables with strong correlation with the growth rate of total electricity consumption to generate training samples.Finally,the GRNN algorithm is used to train the power demand text,input the power demand forecasting samples,set the smoothing factor,and obtain the power demand forecasting results through the output and weighted sum of the mode layer.Experimental results show that better power text mining performance can be obtained by setting the minimum support and the smoothing factor of GRNN.Compared with common mining algorithms,this algorithm can obtain higher accuracy of power demand forecasting.

Key words: FP-Growth algorithm, Frequent set, Generalized regression neural network, Power text mining, Smoothing factor